2026-06-10T09:32:55Zhttps://escholarship.org/oaioai:escholarship.org:ark:/13030/qt6hx7q0fc2026-06-09T06:35:00Zqt6hx7q0fcThe Politics of Trade Protection: Evidence, Coalitions, and Rhetoric in International TradeLam, Shing HonPeters, Margaret E2026-06-05This dissertation examines how trade protection is justified and contested across three levels of contemporary politics: international institutions, domestic interest-group coalitions, and the mass public. Each chapter studies a different currency of justification––scientific evidence, organized coalitions, and political rhetoric––and asks how protectionist measures are sustained or disciplined once that justification is scrutinized. The first chapter asks how the World Trade Organization constrains regulatory protectionism despite the erosion of its adjudicatory enforcement. Analyzing 50,599 Technical Barriers to Trade and Sanitary and Phytosanitary notifications (2010–2024), I show that the WTO operates as an epistemic regime in which governments signal regulatory legitimacy through the volume of justificatory evidence they disclose. Regulatory credibility follows a non-linear pattern: both minimal and excessive evidence invite peer scrutiny and revision, while intermediate disclosure attracts the least. Peer comments, not litigation, drive modification. The second chapter turns to the domestic politics that produce trade-restrictive regulation. Linking 1,788 U.S. regulations notified to the WTO to 1.25 million public comments, I use large-language-model classification to map actor coalitions across rulemaking rounds and estimate their effects on three outcome margins: whether a rule is finalized, how its stringency moves, and how extensively the agency engages comments. Opposition from locally concentrated producers is the most robust veto force; the modal supportive coalition pairs advocacy groups with industry associations; and mass-mobilization campaigns systematically depress finalization. The third chapter examines reactions at the mass level. Across two survey experiments on tariff retaliation, I find that voters reward the rhetoric of toughness but punish the material costs of tariffs once those costs become salient: conservative-styled rhetoric raises support primarily among anti-trade and Republican respondents, yet escalating to costly tariffs mobilizes only a narrow base while alienating moderates, and trade preferences prove elastic, shifting with co-partisan leadership rather than fixed material interest. Together, the chapters trace one argument: contemporary trade protection succeeds or fails not through enforcement or material interest alone, but through the contested evidentiary, coalitional, and rhetorical justifications that surround it.International relationsLawPolitical scienceEconomicsNon-tariff barriersRegulatory politicsTrade protectionWorld Trade OrganizationpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6hx7q0fcetdoai:escholarship.org:ark:/13030/qt1qr6n9812026-06-09T06:34:55Zqt1qr6n981Characterizing molecular targets in cell division, cell cycle, and cell state transitionsRich, Kayla JoanTorres, Jorge2026-06-05Cell state transitions are useful concepts to approach investigating cellular processes and their dysregulation in disease. With this approach cellular processes can be further investigated based on their differences across molecular expression markers, epigenetic patterns, and phenotypic features. This opens the opportunity to identify novel targets important in the transitions and shifts between states to address progression and initiation of disease. The first process that is investigated the mechanism of microtubule severing by katanin in mitosis within non-neuronal cell types. The role of the individual katanin subunits on microtubule severing within human cell cycle function has yet to be fully elucidated. This study developed tools that investigate individual katanin subunits protein level perturbation on mitotic spindle morphology. This included cell lines that perturb protein level by knockdown or overexpression with A549 and RPE-1. Constitutively expressed all-in-one single guide RNA katanin subunit CRISPR-Cas9 RPE-1 cell lines were generated and assessed katanin protein level perturbation, which resulted in perturbed mitotic spindle shape and size. An additional approach to investigating cell state transitions, is within disease context. Retinal pigment epithelium (RPE) undergoes a process known as the epithelial-to-mesenchymal transition (EMT) which occurs in age-related macular degeneration (AMD). There is an increased need to identify and develop novel therapeutics to target the onset and progression of AMD by inhibiting RPE EMT. This research addresses the need by identifying novel target proteins and pathways for the onset and progression of AMD by inhibiting RPE EMT. This was achieved by developing a high-content cell painting imaging assay of the epithelial and mesenchymal cell states coupled with small molecule chemical modulator screens. Targets were identified by AI image analysis to extract features and reconfirmed by secondary dose-response screens. Targets were further classified by phenotypes that perturb EMT. This work has identified potential target proteins and pathways critical for inhibiting EMT in RPE for the treatment of AMD. Overall, this research details the design, implementation, and identification of a high-content cell-based imaging approach that identifies target molecular pathways and proteins of interest of RPE dysfunction during EMT, which impacts the development of AMD therapeutics.BiochemistryCellular biologyMolecular biologyage-related macular degenerationcell paintingcell stateEMThigh-throughputRPE-1publiceScholarship, University of Californiahttps://escholarship.org/uc/item/1qr6n981etdoai:escholarship.org:ark:/13030/qt70k1764q2026-06-09T06:34:50Zqt70k1764qTrans on Transit: The mobility and public transit experiences of transgender and nonbinary peopleGuy, Tam J.Loukaitou-Sideris, Anastasia2026-06-05Successful public transit requires understanding the full breadth of rider experiences to inform policies and procedures. Prior research shows that gender affects security and mobility, yet binary gender metrics erase whether and to what extent people with marginalized gender identities experience transit differently. This national mixed methods study addressed the US public transit experiences of transgender and nonbinary adults.I designed the survey and semi-structured interview instruments to answer the following questions. What are the public transit experiences of transgender and gender nonconforming people? How do these experiences influence/affect their use of public transit, as well as their mobility and travel more broadly? In what ways are these experiences influenced by other individual characteristics (e.g. age, race, appearance)? Which gender conformity aspects influence harassment on public transit? How do these experiences inform our understanding of social hierarchy enforcement in the public spaces of transit? And, what policies/strategies can help improve the transit experiences of transgender people?Based on quantitative and qualitative analyses of the data from 353 online surveys and interviews of 22 survey participants, this study showed that gender identity, particularly at the intersection of race and gender, results in different experiences for riders—notably a likely increased risk of harassment for trans and/or nonbinary riders, more so for trans and nonbinary riders of color. The surveys and interviews both demonstrated that infrastructure failures and social hostility limit mobility. The interviews revealed how trans and nonbinary riders conceptualize social norms of who deserves what treatment. The data was not clear on which gender conformity aspects influence harassment.Interventions to improve the transit experiences of transgender and nonbinary people include better transit service, attention to physical infrastructure, shifting social norms towards respect for all people, and research centering the knowledge of trans and nonbinary riders. Though these interventions would benefit all transit riders, improving the experiences of transgender and nonbinary people holds its own value.Urban planningTransportationGender studiesSocial psychologyGenderMobilityPublic spacePublic transportationTransgenderTransitapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/70k1764qhttps://escholarship.org/content/qt70k1764q/qt70k1764q.pdfetdoai:escholarship.org:ark:/13030/qt9x7333gx2026-06-09T05:03:32Zqt9x7333gxDeep Learning Methodology and Theoretical Analysis for Change Points, Spatiotemporal and Contaminated DataGENG, JIALIANGMichailidis, George GM2025-01-01Many modern statistical problems are inherently nonparametric, including a variety of problems in change point detection, graph models, and robust statistics. Previously, the literature is abundant and well studied in parametric and even linear cases in these problems. And for nonlinear cases, many problems remain open and unresolved. In my dissertation, I would like to bridge this gap by leveraging deep learning techniques to solve these nonparametric statistical problems. Moreover, with the rapid development of deep learning techniques and machine learning theory, we can now conduct asymptotic analysis in a variety of different neural network spaces. And this allows us to thoroughly analyze the convergence rate of the algorithm using these networks. My first project proposes an offline methodology in change point detection capable of both regression-type problems and multivariate time-evolving data, and provides rigorous theoretical analysis with guarantees for detecting change points and bounding the distance between true and estimated points, covering both independent and dependent sub-Gaussian data sets.My second project focuses on the graph model and proposes a novel methodology for modeling the time-evolving data, having the capability to predict both existing points with previous data known and new points with previous data unknown. The proposed model also included an asymptotic analysis of the model convergence rate. Two datasets are used to demonstrate the model performance.My third project proposes a novel two-step neural network-based model structure combining nuclear norm penalization and output truncation, which is robust to various data contamination mechanisms. The asymptotic analysis on the convergence rate of proposed algorithms is given as well, and the numerical experiments in both synthetic and real datasets are included to demonstrate the model capabilitiesIn my dissertation, I also include an additional project that I collaborate on with Scientists at the Mayo Clinic. We propose a novel redundancy removal methodology that works on the indexing, archiving, and searching of high-resolution medical images and saves storage and search costs significantly.StatisticsData ContaminationDeep learningGraph Neural NetworkMachine LearningMedical ImagingNonparametric Statisticsenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9x7333gxhttps://escholarship.org/content/qt9x7333gx/qt9x7333gx.pdfetdoai:escholarship.org:ark:/13030/qt5n1563r82026-06-09T05:02:26Zqt5n1563r8A Modified Expectation-Maximization Algorithm for Accelerating Item Response Theory Model Estimation with Large DatasetsFeng, TianyingWu, Yingnian2025-01-01The Expectation-Maximization (EM) algorithm is widely used for parameter estimation in item response theory (IRT) modeling. Despite its utility, when applied to datasets with large numbers of individuals and items, the standard EM algorithm can be slow to converge, with computationally expensive E-steps. This study proposes a modified EM algorithm with a two-stage structure that capitalizes on data subsets to accelerate estimation for unidimensional two-parameter logistic (2PL) IRT models. Two simulation studies evaluated the reduction in convergence time, bias and root mean squared error (RMSE) of parameter recovery, and bias and RMSE of standard error (SE) estimation under the proposed algorithm. The proposed algorithm was also compared to standard EM across varying subset sizes and item set lengths. Results showed that (a) parameter recovery differed between types of IRT parameters and between moderate and extreme parameter values; (b) recovery improved with larger subset sizes; (c) reduction in time was more pronounced with a larger item set; and (d) a small upward bias and RMSE in SE estimates were observed compared to standard EM, though both metrics remained modest in all conditions. Overall, the proposed algorithm improved computational efficiency while maintaining comparable estimation accuracy and precision under larger item sets and moderate-to-large subset sizes. The study concludes with future applications in large-scale operational contexts with large volumes of examinees and moderate to large item pools, and potential extensions to multidimensional IRT settings.StatisticsEducational tests & measurementsEM AlgorithmEstimationItem Response Theoryenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5n1563r8https://escholarship.org/content/qt5n1563r8/qt5n1563r8.pdfetdoai:escholarship.org:ark:/13030/qt5920q6gm2026-06-09T05:02:24Zqt5920q6gmCorrective Gendered Spaces: Testimonios from Young Women of Color in Group HomesChavez, JoanaCarpio, Genevieve2025-01-01Corrective Gendered Spaces examines the institutional gendered violence experienced by young women of color in the juvenile legal system, with a particular focus on group homes in Southern California’s Inland Empire and Los Angeles. Employing a mixed-methods approach, this study draws on testimonios—shared lived experiences from 20 young women placed in group homes, foster care, and juvenile detention—and archival records that trace the historical development of correctional institutions. I argue that while group homes claim to replicate familial environments, they in fact reproduce carceral logics and structures that fail to support young women’s mental, physical, spiritual, and emotional well-being. I conceptualize the framework of corrective gendered spaces: institutions that operate as alternatives to incarceration but function to discipline and regulate gendered behavior under the guise of care. These sites replicate punitive practices through geographic isolation, behavioral surveillance, and coercive “rehabilitation,” targeting the gender expression and autonomy of young women of color. Through an interdisciplinary feminist and abolitionist framework, this research theorizes group homes and other gendered carceral spaces as part of a prison continuum, where gendered incarceration is central to shaping how the state manages and punishes girlhood.This dissertation is guided by several key questions: How do group homes function as carceral spaces? What are the lived experiences of young women within these systems? What roles do surveillance, isolation, and resistance play in their experiences? And how do young women navigate and challenge the gendered expectations imposed on them by institutional care? Structured across four chapters—including a review of literature, theoretical framing, methodology, case studies, and analysis—this work centers the voices and resistance strategies of young women who have experienced these systems firsthand. In its conclusion, the dissertation reflects on the transformative wisdom these young women offer. It advocates for a shift from punitive responses to violence and trauma toward healing-centered, intersectional approaches rooted in collective care and love. Ultimately, Corrective Gendered Spaces contributes to broader conversations around institutional reform, abolitionist visions of justice, and the politics of care for criminalized girls and women.Ethnic studiesGender studiesHistoryabolitioncarceralitycorrective gendered spacesgendergroup homesjuvenile legal systemenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5920q6gmhttps://escholarship.org/content/qt5920q6gm/qt5920q6gm.pdfetdoai:escholarship.org:ark:/13030/qt7zr7p2b62026-06-08T06:35:35Zqt7zr7p2b6Probing the Interior Structure of Europa and Ganymede with Magnetic Induction: From Molecular-Scale Ocean Conductivity to Spacecraft ObservationsPsarakis, Catherine AnneKavner, Abby2026-06-04The Jovian moons Europa and Ganymede host subsurface oceans beneath their water-ice shells, and are therefore prime targets in the search for habitability beyond Earth. These oceans were discovered by meticulous analysis of the magnetometer data returned from the Galileo spacecraft. The interpretation of the magnetometer observations require a near-surface electrically conductive layer to react to Jupiter’s time varying magnetic field and produce the moons' induced secondary magnetic field. The recently launched European Space Agency’s (ESA) JUICE and NASA’s Europa Clipper missions will reach the Jovian system in the early 2030s with upgraded magnetometers. The aim of this dissertation is to prepare for the arrival of this finer-scale geophysical data, so it is ready to be interpreted in terms of the properties of the moons' interior, especially their ice-ocean hydrospheres. Here we present a series of studies addressing the properties of the moons’ interiors, in preparation for the magnetometer data return.Chapter 1 presents a molecular dynamics-based determination of the electrical conductivity of aqueous-salt solutions as a function of pressure, temperature, and NaCl concentrations that are relevant to the Jovian moons. Chapter 2 outlines a 1D planetary geophysical model, the “Icy Moons Interior Explorer”, that can predict the moons' induction response to Jupiter's magnetic field. This model use observed mass and moment of inertia to constrain possible density versus depth profiles. Thermal models are also included, which permit the creation of a radial electrical conductivity profile-- anchored in the oceans with our data from chapter 1, as well as with literature values of electrical conductivity of mantle and core materials. The generated families of conductivity models are then fed into a multi-shell magnetic induction model. This gives us a full pipeline in which we can trace how individual interior properties manifest in the induced field signal a spacecraft would measure. The goal is to determine which properties are magnetically distinguishable with current data, and whether future missions can resolve degeneracies in which multiple interior structures produce the same induced field.Using this framework, I derive lower bounds on ocean conductivity at both Ganymede and Europa from recent re-analyses of Galileo and Juno data and demonstrate that indeed a global conductive ocean near the surface is the only plausible interior model consistent with the observed amplitudes. I then show under what conditions the deep interior becomes visible, and what is needed to better constrain layer thicknesses. Finally, a global sensitivity analysis tests how variations in each modeled parameter propagates to changes in an observed induced field. With this tool, we show that the field is generally insensitive to properties of the mantle and core, but can be especially sensitive to hydrosphere variations for oceans with low conductivity. This analysis was also used to examine variations in thermal models, showing a lack of sensitivity to variations in thermal boundary layers, including stratified ocean models. Ultimately, I aim to demonstrate both the utility and the limitations of magnetic induction in probing the properties of icy moon interiors, thus preparing for accurate interpretation of future spacecraft data.GeochemistryGeophysicsPlanetologyAerospace engineeringEuropaGanymedeMagnetic InductionPlanetary Interiorsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7zr7p2b6https://escholarship.org/content/qt7zr7p2b6/qt7zr7p2b6.pdfetdoai:escholarship.org:ark:/13030/qt7x2930hj2026-06-08T06:35:31Zqt7x2930hjBlueprints: 11918Webster-Zuber, Kaitlin RoseKrouse, Ian2026-06-02Blueprints: 11918 is a collection of miniatures inspired by family and home – a sincere labor of love. As my parents designed and built our home, I had the opportunity to experience this ambitious undertaking firsthand! The title originates from the architectural concept of a blueprint: a technical drawing, illustrating DNA of a designed space. Having an architect as a father means space is always a relevant conversation. It's about how we occupy space – those who live, breathe, and create in the space – and continually questioning what turns a house into home.
Each movement offers glimpses into the unique personalities within my home, and each person’s motif was composed by applying his/her name to an “alphabet-to-musical-pitch” cypher I created. Further, there are seven letters in KAITLIN, and seven total possible combinations of the ensemble of this work: two violins and two pianos, solo violin, violin and piano, etc. This piece uses all combinations of this ensemble and, excluding the Prelude, the tonal center of each successive movement is based on the letters in KAITLIN. For example, using the pitch cypher: starting with movement 2, "K" = D, oscillating between variations of D (i.e. D Major and d minor); for movement 3, "A" = A and this movement flirts with a minor; so on and so forth. In the culminating movement, Blueprints: 11918, everyone’s themes are interwoven in conversation with one another.Musical compositionMusicMusic educationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7x2930hjhttps://escholarship.org/content/qt7x2930hj/qt7x2930hj.pdfetdoai:escholarship.org:ark:/13030/qt4cf662mg2026-06-08T06:35:25Zqt4cf662mgAn underappreciated role of stratospheric ozone depletion in recent Southwestern North America dryingWang, ZelinDong, Yue2026-06-03Southwestern North America (SWNA) has experienced pronounced drying since 1980s, yet the causes remain elusive. Using large-ensemble single-forcing simulations from the Community Earth System Model (CESM), we revisit the forced response of precipitation trends and their anthropogenic drivers. While previous studies have suggested a substantial contribution from anthropogenic aerosol forcing, we here demonstrate that stratospheric ozone depletion, part of the “Everything Else” (EE) forcing group, has also played an important role in driving the long-term drying trend. Although ozone depletion has occurred primarily over Antarctica, it exerts remote impacts on SWNA precipitation by driving a La Niña-like tropical sea-surface temperature (SST) trend pattern through teleconnections. This finding is robust across an EE ensemble and stratospheric ozone-only ensembles in two global climate models. Our results suggest that stratospheric ozone depletion represents an underappreciated anthropogenic driver of recent SWNA drying, with important implications for future hydroclimate projections as ozone is projected to recover in the coming decades.Climate changeGeophysicsAtmospheric sciencesMeteorologypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4cf662mgetdoai:escholarship.org:ark:/13030/qt3fs372gg2026-06-08T06:35:20Zqt3fs372ggMechanism-Based Alternating-Pressure Support Surfaces for Pressure Ulcer PreventionLanger Weida, James EdwardHopkins, Jonathan B2026-06-04Pressure injuries remain a major clinical, financial, and caregiving burden, particularly for patients with limited mobility, impaired sensation, advanced age, or extended exposure to bedrest. Although repositioning protocols and some pressure-redistributing support surfaces are widely used, pressure injuries remain prevalent, suggesting that current prevention strategies are insufficient for many high-risk populations. Alternating-pressure support surfaces are compelling because they address the time-dependent nature of tissue damage by periodically unloading different body regions without requiring dense pressure sensing or closed-loop control. However, existing pneumatic alternating-pressure systems have shown mixed clinical results and are often limited by slow actuation, noise, vibration, leakage, and dependence on pumps and air-cell architectures. This dissertation investigates mechanism-based alternating-pressure support surfaces as an alternative approach for pressure-ulcer prevention. Rather than using inflatable air cells, the developed prototypes impose alternating pressure profiles through mechanical motion of structured support surfaces. The research began with prior actuator-bed studies that identified pressure-pattern parameters capable of reducing persistent tissue loading. These findings informed a series of full-scale prototype mattresses designed to cyclically shift support between two complementary states while minimizing areas exposed to continuous pressure above the commonly cited 32 mmHg occlusion threshold.Five prototype concepts were developed and evaluated through iterative mechanical design, fabrication, and pressure-mapping experiments. Early prototypes demonstrated the feasibility of producing alternating-pressure patterns using compliant and linkage-based mechanisms, but also revealed limitations related to durability, safety, stability, and incomplete elimination of persistently loaded regions. Subsequent designs transitioned toward more robust mechanism architectures, including shuttle- and roller-based systems compatible with bedframe articulation and human testing. Prototype performance was evaluated against commercial alternating-pressure products using pressure-sensing mats.The results show that mechanism-based support surfaces can create clinically relevant alternating-pressure patterns without relying on conventional pneumatic architectures. This work also provides mechanical insight into the underperformance of current products by showing how pressure profiles, switching behavior, and support geometries can fail to fully interrupt sustained loading in high-risk tissue regions.Mechanical engineeringBiomedical engineeringBiomechanicsalternatingBedsoremattressocclusionpressurePressure UlcerpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3fs372ggetdoai:escholarship.org:ark:/13030/qt8sc4388b2026-06-08T06:35:15Zqt8sc4388bComputational Imaging Inspired by Photonics Time-StretchGunawan, WesleyJalali, Bahram BJ2026-06-04The evolution of computing has transitioned from specialized analog mechanisms to general-purpose digital systems, culminating in the current dominance of data-driven deep learning. While effective, these models often suffer from prohibitive computational costs, lack of interpretability, and heavy reliance on massive training datasets. This dissertation presents a paradigm shift toward physics-inspired computing, where established physical laws serve as structural priors for algorithmic design. Rooted in the principles of Photonic Time Stretch (PTS), this research develops PhyCV (Physics-inspired Computer Vision), a library of algorithms that translate optical phenomena into digital image processing tasks. The core contribution of this work is the development and optimization of VEViD (Vision Enhancement via Virtual Diffraction and Coherent Detection), a physics-inspired algorithm for low-light enhancement. VEViD reimagines digital images as spatially varying light fields subject to virtual diffraction and coherent detection, offering a high-performance alternative to empirical methods. To enable practical deployment, the thesis details the mathematical simplification of VEViD into a single-parameter model and its subsequent optimization via Look-Up Table (LUT) implementation. This approach eliminates computationally expensive trigonometric operations, achieving real-time processing speeds on resource-constrained edge devices. Benchmarks demonstrate 1080p video processing at 45 FPS on Raspberry Pi 4 and 4K video at 67 FPS on iPhone 14 Pro Max, significantly outperforming standard neural network approaches in latency and energy efficiency. Furthermore, this research quantifies the energy efficiency of computational enhancement versus physical illumination. It establishes that while physical lighting power requirements scale quadratically with distance, the computational cost of VEViD remains constant for a fixed pixel count, positioning it as a superior solution for energy-constrained environments. To enhance accessibility and usability, the dissertation proposes an integration framework using Large Language Models (LLMs) via the Model Context Protocol (MCP), enabling natural language control over vision pipelines. By combining theoretical rigor with practical optimization, this work demonstrates that physics-inspired algorithms can deliver robust, interpretable, and efficient solutions for modern computer vision challenges.Electrical engineeringComputer engineeringComputer scienceArtificial intelligenceEdge computingEnergy efficiencyInterpretable AILow-light enhancementPhysics-inspired computer visionapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8sc4388bhttps://escholarship.org/content/qt8sc4388b/qt8sc4388b.pdfetdoai:escholarship.org:ark:/13030/qt7cm1d4n22026-06-08T06:35:09Zqt7cm1d4n2Frequency-Programmable Liquid Acoustic Sensing Networks for Self-Powered Underwater SurveillanceZhang, JingyuanChen, JunYang, Lin2026-06-04Underwater acoustic environments span a wide and dynamic frequency spectrum, posing a fundamental challenge for conventional acoustic sensors with fixed and limited bandwidths. Here, we introduce spectrally programmable permanent fluidic magnets to develop a self-powered, frequency-programmable liquid acoustic sensory network that achieves broadband underwater sound sensing through physically encoded spectral programmability. Each liquid acoustic sensor is designed with a tunable frequency response, enabling selective sensitivity to a targeted acoustic band while operating without an external power supply. By integrating sensors with distinct programmed frequency responses to a non-symmetric network configuration, the network collectively covers a wide frequency range of up to 10 kHz with a minimum current response of 3.5 μA, overcoming the intrinsic bandwidth limitations of individual sensing elements and spatial array ambiguities. Combined with a multi-task convolutional neural network, the system achieves 98.5% accuracy in identifying five distinct underwater sound sources and a mean squared error below 2% in estimating source direction and distance. This self-powered, frequency-programmable sensing architecture provides a scalable and energy-efficient solution for underwater surveillance in complex marine environments.Electrical engineeringElectromagneticsAcousticspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7cm1d4n2etdoai:escholarship.org:ark:/13030/qt3829x9122026-06-08T06:35:04Zqt3829x912“Unity, Struggle, Victory!” The PAIGC, Amílcar Cabral, and the Organization of a Pan-African Revolution, 1945-1974Fonseca, DesmondKelley, Robin DG2026-06-04This dissertation reframes the African Party for the Independence of Guinea and Cabo Verde (PAIGC) as a pan-African revolutionary party in the Leninist tradition whose praxis was centered around unity on two dialectically connected fronts. The first was based on the core of its political program, Guinean-Caboverdean unity, which bound two distinct but related nations in a single struggle against Portuguese colonialism and NATO imperialism. The second was its organizational unity predicated on the Leninist conception of the vanguard party operationalized through a shifting balance of centralized and democratic leadership towards the fulfillment of a revolutionary program. The PAIGC expressed these principles through four constantly reiterated concepts: revolutionary democracy, democratic centralism, collective leadership, and criticism and self-criticism. I argue that these two forms of unity were not merely parallel but mutually constituting: programmatic unity required and reproduced organizational unity, and each referred back to the party’s revolutionary purpose. The party’s motto, “unity and struggle,” names this relationship: they saw unity as the means to struggle, and the struggle as a means towards unity. Cabo Verde was pivotal on both fronts. The liminal position of Cabo Verde and Caboverdeans within the Portuguese colonial order shaped the program of Guinean-Caboverdean unity and gave the party’s leadership its distinctive formation, making Marxism, Leninism, and pan-Africanism especially resonant. Yet the same Caboverdean centrality that anchored the PAIGC’s cohesion was also the fault line exploited by colonial and reactionary nationalist psychological warfare, culminating in the 1973 assassination of Amílcar Cabral which ruptured the very unity the party had been built to sustain. The majority of the scholarly literature on the PAIGC mimics — often implicitly — the position of the Portuguese and opposing nationalists, missing what made the party a uniquely successful organization on the African continent. Drawing on multilingual archival research supplemented by oral histories with surviving militants, the dissertation traces this dual unity from the PAIGC’s clandestine origins through the armed struggle, the plans to extend the revolution to Cabo Verde, and the decolonization of both territories. It concludes that the party’s organizational and programmatic unity secured its historically distinct unilateral victory over a colonial power, and that Cabo Verde was central to both that triumph and the party’s deepest tensions.HistoryAfrican studiesBlack historyAmilcar Cabralanti-imperialismdecolonizationLeninismpan-AfricanismrevolutionspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3829x912etdoai:escholarship.org:ark:/13030/qt3kb070ct2026-06-08T06:34:59Zqt3kb070ctExploring Previously left-behind Central American Immigrant and Refugee Students’ Perceptions of Parent-Child Relationships and Academic Experiences during the Separation and Upon Reunification in CaliforniaMagana Montoya, Karen BeatrizOmwami, Edith S2026-06-04Migration-related family separation has become a common yet understudied feature of transnational family life among Central American immigrant families. Although previous scholarship has documented the emotional consequences of family separation and reunification, less attention has examined how these experiences continue shaping youths’ educational experiences after reunification in the United States, particularly from youths’ own perspectives. This qualitative phenomenological study explores how previously left-behind Central American immigrant and refugee youth understand and make meaning of migration-related family separation, parent-child relationships, reunification, and academic experiences in Los Angeles County, California. Guided primarily by Latina/o Critical Race Theory (LatCrit), racist nativism, and Bronfenbrenner’s ecological systems theory, this study conceptualizes family separation as an ongoing structural condition rather than a singular event. Data were collected through pláticas with ten previously left-behind Central American immigrant and refugee youth between the ages of 12 and 19 who experienced prolonged separation from one or both parents before reunification in the United States. Findings revealed that migration-related family separation continued shaping participants’ emotional well-being, parent-child relationships, and educational experiences long after reunification occurred. Participants described unresolved grief, loneliness, emotional distance, and tension within family relationships while simultaneously recognizing migration as a sacrifice motivated by love and economic necessity. Findings also demonstrated that educational experiences were shaped by broader structural conditions, including immigration systems, language barriers, economic precarity, and institutional support. This study contributes to scholarship on migration and immigrant education by centering youths’ voices and highlighting the need for educational systems responsive to immigrant youths’ lived realities.Multicultural educationEducational sociologyIndividual & family studiesCentral American Immigrant YouthEducational ExperiencesFamily ReunificationFamily SeparationImmigration and EducationParent-Child RelationshipspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3kb070ctetdoai:escholarship.org:ark:/13030/qt9ms6q2gf2026-06-08T06:34:54Zqt9ms6q2gfThe Making and Unmaking of The Tale of GenjiLi, YueyingEmmerich, Michael2026-06-04This dissertation rereads The Tale of Genji, the eleventh-century Japanese classic, as a metafictional monogatari. I use monogatari not only as a generic term for the work, but also as a process through which persons and events become narratable within the court society depicted in Genji. This dissertation argues that Genji is fundamentally concerned with the processes of narrativization and the social dynamics through which stories are produced, transmitted, and contested.The dissertation follows this politics of storytelling from the making of the tale to the limits of its own narrative forms. The first two chapters examine Genji as a world structured by the circulation of information in varied media. Chapter 1 takes gossip as its focal point that reveals and sustains the communicative networks of the court. It also examines attempts to manipulate or contain these networks, showing how such efforts are repeatedly undone by the very circulation they seek to control. Chapter 2 extends this framework beyond speech to material and sensory media, including handwriting, gifts, scent, music, dreams, and spirits, through which characters shape or resist the narratives forming around them. Chapter 3 defines authorship in the context of the tale as the shifting relation between characters and the words or texts attributed to them, through which characters navigate their narrative agency and participate in the formation of the tale, even as their words become vulnerable to appropriation and reinterpretation. Chapter 4 follows the failure of Genji’s ambition to master the tale as a tale of his own. In the Uji chapters, this failure moves toward a more radical limit, as Ukifune’s withdrawal gestures toward what the narrative order of this tale may no longer be able to absorb.The dissertation concludes by considering the ending of the tale as the point at which the forms that have long sustained monogatari reveal their own limits. By reading Genji as a meta-monogatari, this dissertation shows that its psychological depth, courtly realism, and poetic refinement are inseparable from its inquiry into the power and limits of storytelling.Japanese literatureLiteratureEast Asian studiespubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9ms6q2gfetdoai:escholarship.org:ark:/13030/qt5nz5r4xh2026-06-08T06:34:49Zqt5nz5r4xhReading History, Reading Race: Historical Narrative, Racial Epistemology, and the Multiplicity of Interpretation in Nineteenth-Century U.S. TextsDriben, BrettLópez, Marissa K2026-06-04Reading History, Reading Race argues that, for nineteenth-century U.S. readers, the technologies of historical narrative and of race co-constitute each other: making sense of historical narrative is to simultaneously make sense of race and vice versa. In the dissertation’s first half, I argue that, for these readers, the coherency of U.S. historical narrative is the coherency of race (Chapter 1 on Walker’s Appeal), so that to dissolve the coherency of historical narrative is to dissolve the coherency of race (Chapter 2 on Life Among the Piutes). In the dissertation’s second half, I argue that the varying verisimilitude of historical narrative, which I call genre, is likewise inseparable from race. I explore how nineteenth-century readers’ divergent, racial convictions about whether or not a historical narrative is naturalistic are sometimes distributed across many readers (Chapter 3 on The Conjure Woman) and other times contained in conflicted, individual readers (Chapter 4 on “The Yellow Wall-paper”).These claims all take off from the historical reality that, even though nineteenth-century U.S. readers read relatively identical printed texts, they crafted multiple, radically different interpretations of those texts. Engaging this history of reading requires, I argue, conceptualizing textual meaning not as pre-set, but as coming into being through a performance, namely the duration of time during which people read, listen to, or otherwise engage a printed text. Studying historical acts of reading benefits from engaging not only the texts real readers read, but also evidence of their readings preserved in newspapers, magazines, and other printed materials. In this dissertation, I craft and mobilize an epistemological framework to engage both kinds of evidence.As a whole, this dissertation argues that nineteenth-century U.S. readers understood interpretative authority as an open question rather than a singular, perhaps shifting, answer, and, furthermore, that these readers’ understandings of and engagement with the diverse field of possible readings were critical to their interpretations and their attempts to cultivate interpretative authority. Chapters 1 and 2 focus on readers’ struggles for interpretative authority and dominance, while Chapters 3 and 4 focus on how readers’ conflicting interpretations necessarily made meaning in context of one another.American literatureAmerican historyPerforming artsEpistemologyGenderHistorical NarrativeHistory of ReadingNineteenth-Century LiteraturePerformanceRacepubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5nz5r4xhetdoai:escholarship.org:ark:/13030/qt8h86d36w2026-06-07T05:03:14Zqt8h86d36wMagnetic Field Based Quasistatic Indoor Position Sensing and Navigation: Enhancing Precision Tracking and Location Accuracy for First Responders in Emergency SituationsWilson, Tristan AlexanderWang, Yuanxun E2024-01-01First responders navigating compromised environments, such as burning buildings, often face obstructed fields of view, making navigation challenging. Alleviating these difficulties is crucial for providing essential aid to individuals at risk. Quasistatic electromagnetics serve as an effective tool for real-time position sensing and tracking within non-line-of-sight, steel-reinforced structures. The quasistatic regime is characterized by electromagnetic waves that vary slowly over time, allowing for deep penetration into structures with minimal signal loss. We propose a multiaxial system architecture that couples magnetoquasistatic and electroquasistatic waves radiating from collocated loop antennas and piezoelectric field emitters. Our research into the radiation characteristics of these devices revealed that piezoelectric emitters cannot produce fields strong enough for effective coverage in single-story buildings. Consequently, we adjusted to a magnetoquasistatic sensor fusion system which predicts positioning with meter-level accuracy. The efficacy and accuracy of our system operating within a single-story structure were verified and presented in a live demonstration.Electromagneticsenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8h86d36whttps://escholarship.org/content/qt8h86d36w/qt8h86d36w.pdfetdoai:escholarship.org:ark:/13030/qt3902m0tv2026-06-07T05:01:51Zqt3902m0tvTowards the Physical Origin of Flexible-to-Rigid Transition in GexSe1-x GlassZhao, ZhangjiTaciroglu, Ertugrul ET2024-01-01Based on their connectivity, network glasses can be classified as flexible, stressed–rigid, or isostatic, if the number of topological constraints is lower, larger, or equal to the number of atomic degrees of freedom, respectively. Thanks to the absence of any stoichiometric requirement, the rigidity of glasses can be continuously tuned (e.g., from flexible to stressed–rigid) by changing their chemical composition. Interestingly, optimally-constrained isostatic glasses have been noted to exhibit unusual properties (e.g., nearly-reversible glass transition, low relaxation, desirable mechanical properties, etc.). Especially, the non-aging intermediate state features an almost vanished endotherm between the first and second heat scan across glass transition, providing a pathway for phase change material optimization in the application of non-volatile rewriteable media. However, the physical origin of the unusual behaviors and properties of isostatic glasses remain unclear.This thesis begins with investigating how the flexible-to-rigid transition in network glasses is encoded in their energy landscape based on molecular dynamics simulations. To this end, we introduce a simplified, yet realistic model of network glasses with varying connectivity. We characterize the topography of these glasses by adopting the activation-relaxation technique (ART), which enables a systematic search of saddle points and transition pathways in the energy landscape surface. We then demonstrate that the flexible-to-rigid transition arises from an interplay between low-energy saddle points (in flexible glasses) and topological frustration (in stressed–rigid glasses). Also, by utilizing the ring structure, we expand the transition correlation with ring size distribution. Meanwhile, we highlight the local heterogeneity with all the energy landscape features by dicing the model into small cubes. Comparing within a single glass helps exclude the effect of different configurations, further consolidating our conclusion on the physical origin of rigidity transition.
Finally, to explore the role of chemistry effect in rigidity transition, we compare the behavior of the simple connectivity model with a realistic GexSe1-x model. With the similar shape of enthalpic differences, the realistic model could reveal the effect of glass-forming ability with experimental results where the simple model fails. Overall, we have a clear pathway towards understanding the physical origin of rigidity transition of GexSe1-x glass.
EngineeringGlassMD simulationRigidity transitionenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3902m0tvhttps://escholarship.org/content/qt3902m0tv/qt3902m0tv.pdfetdoai:escholarship.org:ark:/13030/qt96x024x12026-06-06T05:03:18Zqt96x024x1Interactive Tuning on Batch Content Generation TasksYang, ChunxuChen, Xiang2024-01-01Batch Content Generation (BCoG) tasks necessitate the simultaneous generation of a substantial volume of similarly formatted content, such as batches of scientific articles, storybooks, or font sets. Currently, this process is typically accomplished through user-initiated multi-session interactions with generative models. This approach requires users to re-prompt the generative AI multiple times and manually apply changes to each item, thereby increasing the cognitive load associated with fine-tuning across multiple targets. To address these limitations, we propose StubCog, a novel solution mechanism for BCoG tasks, which leverages pipeline management and a task scheduler. An evaluation study conducted with 18 users possessing varying levels of domain expertise demonstrated that (i) in comparison to multi-session operations with dialog-based chatbots, StubCog reduces the time required for batch content generation and fine-tuning by 50%; (ii) StubCog significantly reduces the cognitive load associated with the fine-tuning process. Our findings suggest that StubCog isa promising solution for BCoG tasks that require the generation of large volumes of content.Computer engineeringComputer scienceElectrical engineeringenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/96x024x1https://escholarship.org/content/qt96x024x1/qt96x024x1.pdfetdoai:escholarship.org:ark:/13030/qt62s662jw2026-06-06T05:02:37Zqt62s662jwTailoring and Characterizing Pore Size Distribution of Phase Inverted Porous Polymeric Membranes: What, Why, and How?Xiao, MinhaoHoek, Eric2024-01-01In this thesis, the methodology of tailoring and characterizing pore size distribution (PSD) of phase-inverted porous polymeric membranes is conducted with theoretical model simulations and experimental verification. The investigative journey begins with the revealing of the significance of pore size and pore size distribution for membrane performance with the CFD and other classical model analysis and culminates in the systematic fabrication matrix for membrane pore manipulation, which is strongly supported by precise characterizations and distinguished by the illustration of classical hydrodynamic models.Chapter 2 initiates the effects of PSD mean, shape, and scale on water permeance and solute rejection of membranes by using classical analytical models and computational fluid dynamics (CFD), which elaborates on the importance of precisely manipulating membrane pore size and pore size distribution from a theoretical perspective, emphasizing the need to avoid excessively large or small pores.
Chapter 3 systematically examines the influence of polymeric membrane sample preparation techniques on their morphologies and structures as revealed by scanning electron microscopy (SEM) to develop a sample characterization method that not only serves as a guide for accurate SEM characterization of polymeric membranes but also as a stepping stone towards standardizing protocols.
Chapter 4 involves the fabrication of porous membranes and integrates the surface porosity characterization via SEM and the data derived from contact angle measurements (with dry-wet adjustment ratio applied in wet conditions), establishing a novel method for quantifying the surface porosity of porous membranes through contact angle measurements.
Chapter 5 investigates the water permeability and solute rejection of microfiltration (MF) and ultrafiltration (UF) membranes in pressure-driven filtration by integrating the surface porosity, mean pore size, pore size distribution, and skin layer thickness of dry membrane samples into three classic hydrodynamic pore-flow models (Hagen-Poiseuille (HP), Kozeny-Carman (KC), and Happel’s cell (HC)). The dry-wet ratio, developed in Chapter 4, is conducted to correct and simulate the fully-wetted condition.
The culmination of these insights is found in Chapter 6, which presents a series of experimental methods for manipulating membrane pore size and pore size distribution. Here, the superiority of nano-bubble water as a pore former is highlighted, characterized by enhanced water permeance without trading off solute rejection, indicating an innovative approach for fabricating polymeric porous membranes.
In summary, this thesis offers accurate and standardized methods for membrane characterizations from a novel and comprehensive perspective. Additionally, it elucidates the significance and provides techniques for precisely tailoring membrane pore size and pore size distribution through simulation models and experimental validation, paving the way for the advancement of mesoporous membrane design for a wide range of academic and industrial applications.
Environmental engineeringenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/62s662jwhttps://escholarship.org/content/qt62s662jw/qt62s662jw.pdfetdoai:escholarship.org:ark:/13030/qt5bd0h3n52026-06-05T06:35:29Zqt5bd0h3n5Mechanical Metamaterials for Unique Deformations, Shape Morphing and Energy AbsorptionAgarwal, ShivamJin, Lihua2026-06-03Mechanical metamaterials are designed for engineered properties unavailable in regular materials. Moreover, metamaterials based on soft materials exhibit high stretchability, reusability, and ease of fabrication. We develop several metamaterial architectures and material modifications to achieve unique mechanical responses, diverse deformation modes, enhanced energy absorption, and superior shape-morphing capabilities. To investigate unique in-plane deformations, we create topological metamaterials for a multi-directional, two-step deformation response. Additionally, we create template-driven woven 3D meta-shells for compression-induced twisting and friction-enabled multistability. We also develop 3D-printed woven shells to realize disruptive geometries, such as wavy, asymmetric, combined, and closed designs. Moving from passive to active structures, we create shape-morphing polygonal frames with targeted actuation at internal angle sites, and discuss strategies for complex arrangements, such as tessellations, nesting, stacking, and 3D configurations. We then create techniques to enhance energy absorption by utilizing instability and viscoelastic dissipation. First, we investigate the effect of strain-softening and strain-stiffening in the buckling and post-buckling of columns, where we explain snap-through in slender columns via a continuum approach. Second, we exploit the viscoelastic properties of thermoplastic polyurethane to increase the energy absorption capacity of truss-based, bending- and stretching-dominant lattices. Overall, our work advances metamaterial development for potential applications in the aerospace, packaging, sports, personal protective equipment, furniture, marine, and automotive industries.Mechanical engineeringEnergyMaterials scienceArchitected LatticesBucklingEnergy absorptionMetamaterialsShape morphingWoven structurespubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5bd0h3n5etdoai:escholarship.org:ark:/13030/qt1vk4130r2026-06-05T06:35:24Zqt1vk4130rHybrid Spatiotemporal Modeling of Electric Vehicle Diffusion in the United States Using Sequential Gaussian SimulationXia, NingzeWu, Yingnian2026-06-03This thesis proposes a hybrid spatiotemporal modeling framework to address the limitations of traditional machine learning methods in capturing localized "neighbor effects" during the process of Electric Vehicle (EV) diffusion. Although the rate of EV adoption in the United States has accelerated significantly, passing 10% in 2023, growth remains highly heterogeneous across states. Utilizing a national dataset spanning all 50 U.S. states from 2018 to 2023, this study comparatively evaluates the predictive performance of the XGBoost model against a hybrid model incorporating Sequential Gaussian Simulation (SGS) techniques. The standalone XGBoost model achieved an R2 value of 0.70 on the 2023 test set and identified infrastructure density, socioeconomic fundamentals, policy and political environments, and environmental psychological characteristics as the primary drivers of EV adoption. Furthermore, a Global Moran’s I analysis revealed that, beginning in 2021, spatial autocorrelation within the model residuals became statistically significant, indicating that there exist non-random regional factors resulting in the adoption variance that typical covariates cannot capture. To address this issue, the study introduces an SGS-based correction layer designed to model the spatial structures within the residuals that were not explicitly captured by the standalone XGBoost model. The resulting hybrid ML-SGS model significantly enhanced predictive accuracy, boosting the R^2 score to 0.80. Beyond predictive metrics, this study produces a spatial mean surface alongside a probabilistic mapping of adoption uncertainty, identifying stable leading markets and unstable adoption frontiers. As the 2026 Middle East conflict and subsequent fuel price volatility expose the systemic risks of gasoline dependency, this thesis provides the guidance of infrastructure planning and confirms electric vehicles as a strategic necessity for national energy resilience.StatisticsEnergyElectrical engineeringapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1vk4130rhttps://escholarship.org/content/qt1vk4130r/qt1vk4130r.pdfetdoai:escholarship.org:ark:/13030/qt4c61q3cx2026-06-05T06:35:19Zqt4c61q3cxFood Security and Blood Lead, Cadmium, and Mercury Among U.S. Children and Adolescents 1-19: National Health and Nutrition Examination Survey 2017 to 2023Campa, Isabellavon Ehrenstein, Ondine S.2026-06-03Exposure to toxic metals during childhood can adversely affect neurodevelopment and long-term health. Food insecurity may serve as an indicator of broader structural and environmental inequities that influence patterns of metal exposure. This study examined associations between household food security and blood concentrations of lead (Pb), cadmium(Cd), total mercury (THg), and methyl mercury (MeHg) among U.S. children and adolescents ages 1 to 19 using National Health and Nutrition Examination Survey (NHANES) 2017 to 2023 data. Survey weighted regression models estimated adjusted associations, and dietary factors were explored as potential co-factors or exposure pathways. Blood lead concentrations were modestly higher among children from households with lower food security, although
associations were not statistically significant. Cadmium concentrations were similar across food security categories. In contrast, THg and MeHg concentrations were 10-15% lower among children experiencing lower food security. Differences in fish consumption partially explained this pattern. These findings may suggest different environmental and dietary pathways linking food insecurity to metal exposures.Public healthEnvironmental healthHealth sciencesNutritionAdolescent HealthChild HealthFood SecurityNHANESToxic Metalsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4c61q3cxhttps://escholarship.org/content/qt4c61q3cx/qt4c61q3cx.pdfetdoai:escholarship.org:ark:/13030/qt74k2b86h2026-06-05T06:35:13Zqt74k2b86hToward aTrustworthy MetaverseCai, KunlinTian, Yuan2026-06-03The Metaverse is widely understood as a persistent, shared, immersive virtual space that integrates physical and digital realities. As the Internet evolves toward this paradigm, the convergence of spatial computing and machine learning is reshaping human-computer interaction. The resulting ecosystem rests on two complementary capabilities: a spatial interaction layer that captures continuous, embodied user kinematics, and a cognitive intelligence layer that synthesizes this data through predictive and generative models. Together they enable immersive experiences but also introduce system-level risks. Without careful design, the Metaverse may evolve into a pervasive sensing and inference infrastructure with expanded attack surfaces, opaque data practices, and entrenched centralization across both the spatial and cognitive layers.
This dissertation reexamines the security and privacy foundations of Metaverse applications around this dual-pillar architecture. Rather than relying on reactive, point-wise defenses, it advocates for a proactive approach that prioritizes trustworthiness at the architectural level.
For the spatial interaction layer, we bridge vulnerability discovery with structural reform. We empirically uncover a novel class of side-channel threats showing that seemingly benign VR kinematics expose sensitive user intentions. To understand why such vulnerabilities persist, we conduct an in-depth interview study with 23 professional XR developers and surface ecosystem-level root causes. Finally, instead of treating decentralization as a binary switch between keeping the central server and removing it entirely, we redistribute each role the central operator plays—identity, shared state, behavior auditing—to system participants one role at a time. We ground this design in a 22-participant interview study of user-stated needs, and a 20-participant within-subjects double-blind comparison against a centralized baseline returns no evidence of a perceptual gap on the constructs participants themselves consider load-bearing for a usable collaborative experience.
For the cognitive intelligence layer, we establish rigorous privacy boundaries using model-centric attacks. We show that predictive spatial-temporal models leak precise user trajectories, and that fine-tuned zero-shot Text-to-Speech models expose biometric voice features at both speaker-level identity and record-level verbatim granularities, with attack performance grounded in an information-theoretic characterization of the optimal query.
Together, these contributions move the Metaverse toward an architecture that is secure, accountable, and user-sovereign by design.Computer engineeringArtificial intelligenceElectrical engineeringMetaversePrivacySecurityUser studyXRapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/74k2b86hhttps://escholarship.org/content/qt74k2b86h/qt74k2b86h.pdfetdoai:escholarship.org:ark:/13030/qt8176j13r2026-06-05T06:35:08Zqt8176j13rAmour de soi – the Stanchion of Sixties Soul Sovereigns Sam Cooke, Aretha Franklin, and Tina TurnerKilman, SandraFink, Robert W2026-06-01“Amour de soi – the Stanchion of Sixties Soul Sovereigns Sam Cooke, Aretha Franklin, and Tina Turner” argues that the career accomplishments of these musical artists exemplified Rousseau’s concept, amour de soi as they took a leap of faith and led the soul music genre. The mid-twentieth century music business was fraught with pitfalls, risks, as well as powerful, unscrupulous, and even some very dangerous participants. Attributes of courage, discipline, and faith were indispensable qualities for those artists who struggled to survive the exigencies of an industry with historical affiliations with sex work and organized crime. Female artists faced sexism, misogyny, and the danger of sexual assault in this field dominated by men. African American artists encountered additional obstacles presented by racist music moguls. This project examines the history of the music industry and the racism present in locations relevant to the lives and careers of Cooke, Franklin, and Turner. In relating their stories, this study argues that, in addition to the above referenced positive attributes, they exhibited an unquenchable amour de soi that powered their extraordinary achievements in the popular music genre, appropriately called, soul.Music historyAfrican American studiesMusicAretha FranklinMusic - African AmericanPopular musicSam CookeSoul musicTina Turnerapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8176j13rhttps://escholarship.org/content/qt8176j13r/qt8176j13r.pdfetdoai:escholarship.org:ark:/13030/qt3qq1117g2026-06-05T06:33:30Zqt3qq1117gThe Oversedation Zero Tool: A Quality Improvement Project Evaluating Nurses’ Sedation Knowledge, Attitudes, and Documentation in Mechanically Ventilated ICU PatientsOrosco, CinthiaPavlish, Carol2026-06-01Background: Oversedation in mechanically ventilated patients in the Intensive Care Unit (ICU) is associated with adverse outcomes, including delirium, prolonged mechanical ventilation, and longer length of stay. Despite guidelines recommending light, goal-directed sedation, typically defined as a Richmond Agitation-Sedation Scale (RASS) target of −2 to 0, sedation practices and documentation remain variable at the bedside. Objectives: To evaluate the impact of the Oversedation Zero tool, a structured, consensus-based bundle, on nurses’ sedation knowledge, attitudes, and documentation practices in a mixed medical-surgical ICU. Methods: A pre–post quality improvement (QI) project was conducted over an eight-week period in a mixed medical-surgical ICU. The intervention included brief huddle-based education, visual reinforcement tools, and ongoing support from the project lead. Outcomes were measured using a sedation knowledge questionnaire, the Nurse Sedation Practices Scale (NSPS), and chart audits assessing documentation of adherence to the Oversedation Zero tool elements, including documentation of RASS goals and scoring. Results: A total of 29 nurses completed the pre-intervention survey, and 11 completed the post-intervention survey. Mean knowledge scores increased from 4.24 (SD = 0.64) to 4.64 (SD = 0.50) (p = .05). No statistically significant differences were observed across NSPS subscales, indicating minimal change in nurses’ attitudes, perceived norms, and behavioral influences related to sedation practices (p > .05). Chart audit findings were associated with improvements in several clinical practice measures, including increased documentation of the Critical Care Pain Observation Tool (CPOT) (81% to 100%, p = .02), improved pain control (CPOT <3) (35% to 67%, p = .01), and a reduction in inappropriate sedation escalation before pain management (35% to 8%, p = .02). Documentation of sedation goals and maintenance within target RASS ranges remained consistently high across both time points. Conclusion: Implementation of the Oversedation Zero tool appeared to be associated with improvements in selected clinical practice behaviors, particularly analgesia-first sedation practices and pain assessment. Changes in knowledge and self-reported perceptions were minimal, likely reflecting high baseline knowledge among participating nurses. Interpretation of the findings should be cautious, given the small post-intervention survey sample size and concurrent organizational QI efforts during implementation. Brief, workflow-integrated interventions may support bedside clinical decision-making and reinforcement of evidence-based sedation practices in ICU settings.NursingHealth educationHealth care managementMechanical ventilationNursing educationOversedationQuality improvementSedationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3qq1117ghttps://escholarship.org/content/qt3qq1117g/qt3qq1117g.pdfetdoai:escholarship.org:ark:/13030/qt8ks0f30z2026-06-05T06:33:24Zqt8ks0f30zThe Sorcerer’s Apprentice: Colonial Settlement, Splintered Sovereignty, and the Making of an Injurious AllianceWeinger, Joseph Rafael KaplanLuft, Aliza R.2026-06-01This dissertation develops a sociological account of colonial settlement by examining the historical formation and contemporary operation of the Israeli settler–state compact in the Occupied Palestinian Territories. It asks how political projects that instrumentalize violence are carried out when the actors who advance them occupy disparate social and institutional locations, and how variable coordination shapes outcomes. Drawing on multi-sited ethnography conducted in the Occupied West Bank among Israeli settlers, soldiers, state and military officials, and Palestinians living in the rural frontiers of Areas B and C, alongside semi-structured interviews and extensive archival research, the study reconstructs the processes through which territorial expansion and coercive governance are produced through interaction. The dissertation advances a theory of colonial embeddedness to capture the historically produced entanglement of state institutions and settler networks in the joint exercise of rule. Rather than treating settler–state alignment as given, it shows how this relationship is assembled through contingent yet patterned processes, including path-dependent institutional repertoires, learning through diffusion and adaptation, institutional transformation, and the empowerment of extra-state actors. These processes redistribute coercive capacity across formal and informal actors, producing what the study terms splintered sovereignty: a governing arrangement in which domination is delegated and unevenly coordinated. Empirically, the dissertation traces the evolution of Zionist settlement from its late-Ottoman and Mandate-era foundations through the territorial reconfigurations of 1948 and 1967 and into the contemporary West Bank. It argues that war functions as a critical juncture that reorganizes the conditions under which colonization proceeds, opening space for actors to reconfigure territorial rule and set durable trajectories of expansion. Colonial violence emerges through relational mechanisms: settler actors deploy territorial and vigilante violence and, together with state actors, give rise to deprivation, predation, constraints on social reproduction, and manipulations of futurity. Their articulation is mediated through mechanisms such as social appropriation, entryism, bureaucratic forbearance, and role collapse, which blur the boundary between state and non-state authority. Palestinian contention, in turn, reshapes these dynamics by delaying, disrupting, exposing, and possibly entrenching practices of domination. By foregrounding process over teleology, the dissertation shows that dispossession and displacement arise from shifting configurations of embedded rule. In so doing, it contributes to sociological debates on state formation, the organization of political violence, and the nature of colonial rule.SociologyHistoryAsian historypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8ks0f30zetdoai:escholarship.org:ark:/13030/qt53m527p52026-06-05T06:33:18Zqt53m527p5Hopi Wicker Basketry: A look at the state of tangible and intangible preservationMoore, RachelWharton, Glenn2026-06-01Hopi wicker basketry is a living tradition integral to Hopi ways of life despite ongoing threats of environmental and social changes. This thesis explores the state of tangible and intangible preservation of wicker basketry through a condition survey of selected baskets from the Heard Museum, Museum of Northern Arizona, and Fralin Museum of Art at the University of Virginia, and semi-structured interviews with four Hopi weavers from Hotevilla village. This method of inquiry centers Indigenous perspectives as a part of developing collaborative conservation practice in future research. The survey provides insight into material condition issues with particular attention to structural damage, color fading, and pests/pesticide use. Interviews covered topics of intangible and tangible preservation. Taken together, the survey and interviews underscore the resilience of Hopi wicker basketry while pointing to difficulties in the preservation of the craft in the face of environmental, social, and cultural changes. This research considers changes in approaches to basketry conservation, works to illustrate a model to establish community collaboration as part of conservation inquiry, and contributes to a broader understanding of preservation needs for Hopi wicker basketry.ArchaeologyCultural resources managementNative studiesBasketryConservationHopiIndigenouspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/53m527p5etdoai:escholarship.org:ark:/13030/qt5wk302372026-06-05T06:33:13Zqt5wk30237A Longitudinal Analysis of Expressive Language Trajectories in Autistic Individuals and the Influence of Receptive LanguageByrne, KatherineLord, Catherine2026-06-01There is considerable heterogeneity in the expressive language development of autistic individuals across the lifespan. Some children score in the average (or even above average) range on language or vocabulary tests but exhibit more nuanced or subtle semantic and pragmatic language difficulties. At the other end of the continuum, a significant subset of the autistic population – estimated at around 30% – do not acquire functional expressive language, even after intensive interventions. Another approximately 40% of autistic individuals fall somewhere in between these endpoints. The heterogeneity in rate of language development and linguistic outcomes is not well understood. Furthermore, variable methods of expressive language measurement have complicated comparisons across autistic samples. The current study characterized trajectories of expressive language from ages 2 to 25 years and analyzed the influence of receptive language (as opposed to aspects of nonverbal cognition, such as nonverbal problem solving or fine motor skills, which have been widely studied) on these trajectories. Group based trajectory modeling revealed five distinct expressive language trajectory classes best characterized as: Stable No Words, Stable Single Words, Phrase Speech Never Complex, Early Catch-Up, and Stable High-Language. Multinomial logistic regression using the Stable High Language group as the reference group revealed that higher receptive language raw scores were associated with a significantly lower likelihood of belonging to all four alternative expressive language trajectories, even after controlling for age and symptom severity. Language growth slowed after age 5 years and plateaued around age 9 years. Results confirmed the immense heterogeneity in expressive language of autistic individuals across time. Furthermore, results point to the use of receptive language as a potentially important intervention target within language interventions. Finally, the use of a valid, well-established observational measure commonly used in autism research, the ADOS, to characterize the complexity of expressive language use in a semi-structured and naturalistic way allowed for better comparison of fine-grained language abilities across samples, thereby fostering increased consistency within autism research.PsychologyLanguageDisability studiesautismlanguagelongitudinaltrajectoriesapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5wk30237https://escholarship.org/content/qt5wk30237/qt5wk30237.pdfetdoai:escholarship.org:ark:/13030/qt0d96g4pb2026-06-05T06:33:08Zqt0d96g4pbVoltage-Controlled Magnetization Dynamics: From Memory Switching to Neuromorphic FunctionalitySHU, QINGYUANWang, Kang L.2026-06-01Magnetic random-access memory (MRAM) is a candidate for next-generation non-volatile memory due to its high speed, endurance, and compatibility with Complementary Metal-Oxide-Semiconductor (CMOS) technology. Among different MRAM writing approaches, voltage-controlled magnetic anisotropy (VCMA) enables low-energy operation by modulating the magnetic energy landscape of perpendicular magnetic tunnel junctions (pMTJs). However, VCMA-based switching faces reliability challenges, particularly elevated write error rates (WER) in the sub-nanosecond regime.
This dissertation investigates the dynamical behavior of VCMA-driven magnetization switching and explores approaches to improve device performance and functionality. First, a macrospin framework is introduced to understand VCMA and spin-orbit torque (SOT) dynamics under in-plane magnetic field. Based on the understanding, SOT-assisted VCMA switching is experimentally demonstrated. The results show that a moderate SOT bias can reduce WER by modifying the switching dynamics, leading to improved switching reliability and energy–delay characteristics.
We further report the observation of a VCMA dynamics in a bottom-pinned VCMA MTJ where the magnetization switching probability is independent from the applied pulse width, in contrast to conventional precessional dynamics. This dynamics originates from a finite barrier under sub-critical VCMA voltage that traps the switched magnetization. The results provide a potential pathway to deterministic VCMA write mechanism, which could be useful to solve the operation window challenge of VC-MRAM.
Finally, the dynamical properties of VC-MTJs are utilized to implement a nanoscale spintronic neuron without in-plane magnetic field. The proposed neuron device exhibits leaky integration and dendritic filtering within a single device. The device operates on nanosecond timescales with low energy consumption per spike. When evaluated in a spiking neural network for gesture recognition, the system achieves high inference accuracy.Electrical engineeringComputer engineeringComputer sciencepubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0d96g4pbetdoai:escholarship.org:ark:/13030/qt1ns8r6fv2026-06-05T06:33:03Zqt1ns8r6fvIn Pretrained Models We Trust: Effectiveness of XLNet and DistilBERT on Predicting User RatingsHalabi, AyahHandcock, Mark S2026-06-01This study evaluates the effectiveness of pretrained transformer-based language models, DistilBERT and XLNet, for predicting categorical Yelp review ratings from text. Using datasets derived from the Yelp Open Dataset, the study examines model performance under varying class imbalance conditions and experimental configurations, including raw baseline, class weighting, and binary classification. The results show that pretrained language models significantly outperform the Random Forest baseline across all tasks. XLNet consistently achieves higher performance than DistilBERT, particularly in multi-class classification. However, both models exhibit reduced effectiveness in imbalanced multi-class settings, especially for intermediate classes. Performance improves substantially when the task is simplified to binary classification, while class weighting provides limited benefit. Additionally, the models demonstrate stable performance across state-level datasets, indicating strong geographic generalization.StatisticsComputer scienceData ScienceMachine LearningNatural Language ProcessingPretrained ModelsReviewsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1ns8r6fvhttps://escholarship.org/content/qt1ns8r6fv/qt1ns8r6fv.pdfetdoai:escholarship.org:ark:/13030/qt9bz0m4nm2026-06-05T06:32:58Zqt9bz0m4nmWhat We Talk About When We Do: On Knowledge Found in ConversationSindhu, Sahiba KaurJulius, Alexander J2026-06-01What do I know when I know someone and how do I know it? This dissertation is an answer to this question. Whereas I do not need to talk to someone in order to know about them, knowing someone essentially involves talking to them. If having a conversation with someone is how I get to know them, then my knowledge of someone is not the knowledge I have about them but rather is my knowledge of what I can do with them. Following G.E.M. Anscombe, we know that practical knowledge is the knowledge I have of what I am doing. In this dissertation, I show how my practical knowledge of the conversation I am having with someone is my knowledge of that person insofar as this is how I am getting to know them.PhilosophyEthicsHistoryPolitical scienceconversationethicshistorypoliticsrelationshipsunderstandingapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9bz0m4nmhttps://escholarship.org/content/qt9bz0m4nm/qt9bz0m4nm.pdfetdoai:escholarship.org:ark:/13030/qt9vb6g1d22026-06-05T06:32:53Zqt9vb6g1d2Genetic contributions to risk of coccidioidomycosis disseminationJensen, Samantha LeeArboleda, Valerie A2026-06-01Coccidioidomycosis, also known as valley fever, is a fungal pathogen endemic to the Americas that kills thousands annually, yet the host factors that lead to increased risk of life-threatening dissemination of coccidioidomycosis, remain poorly understood. We assembled the largest, comprehensively sequenced coccidioidomycosis cohort to date, comprising 795 individuals with laboratory confirmed coccidioidomycosis and clinical disease severity phenotyping, many with paired whole blood genomic and transcriptomic data. Individuals with greater than 50% African genetic ancestry are significantly enriched in disseminated coccidioidomycosis (DCM) cases (OR=13.37, p=1.08×10⁻¹⁸), reflecting ancestry-associated differences in allele frequencies at immune loci. Transcriptomic profiling (n=267) revealed upregulation of interferon inducible genes IFI44 and IFI44L, the fungal recognition receptor CLEC4D, and pro-inflammatory protein S100A12, with sex specific expression differences in immune cell composition. Among a set of immune response genes, we assessed rare coding variants and identified a rare coding variant in IFI27, p.Ser63Leu, carried only by DCM individuals. Gene-burden testing identified NOD-like receptor NLRX1 as carrying significantly more damaging rare variants than expected by chance, including a missense variant carried by five patients with disseminated coccidioidomycosis and not seen in uncomplicated cases, implicating a role of LC3 associated phagocytosis as a mechanism driving dissemination risk. Together, these findings reveal both ancestry-linked immune dysregulation and rare-variant architectures underlying severe coccidioidomycosis, and identify new targets for risk stratification and treatment.GeneticsBioinformaticsMedicineMolecular biologyImmunologyCoccidioidomycosisGenetic ancestryGeneticsGWASInfectious diseaseRare diseaseapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9vb6g1d2https://escholarship.org/content/qt9vb6g1d2/qt9vb6g1d2.pdfetdoai:escholarship.org:ark:/13030/qt8t59q83r2026-06-04T06:46:16Zqt8t59q83rThe Preparedness of K-12 Charter School PrincipalsSmith, Virginia FrineFranke, Megan2026-06-01This qualitative study examined the preparedness of K-12 public charter school principal leaders in Georgia and how they understood their role in ensuring schools’ academic, fiscal, and operational success while navigating challenges that can contribute to school closure. Grounded in Maslow’s hierarchy of needs and systems theory, the study explored how principals’ lived experiences shaped their ability to fulfill required responsibilities and the supports available to sustain their work. The study examined leaders’ educational backgrounds, experiences in charter and traditional public schools, student populations, facilities, and whether schools operated as standalone or network charter schools. It further investigated principals’ preparedness across key operational domains, including financial management, human resources, curriculum, community engagement, special education, staff support, enrollment, and teacher professional development. Drawing on survey data from 20 participants and interview data from 10 participants, the study found that leaders in standalone and network charter schools were similar in education, experience, and reported preparedness. Across both groups, principals described playing an essential role in school success, yet they faced substantial challenges related to facilities, funding, enrollment, and limited resources. Although preparedness varied across domains and among leaders with different backgrounds, the findings showed that charter school leadership requires a broad set of capacities that extend beyond traditional principal preparation. Overall, the study concludes that charter principal leaders are central to school success but need stronger systems of support to navigate the fiscal, operational, and structural demands of their roles effectively.Educational leadershipEducational administrationEducationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8t59q83rhttps://escholarship.org/content/qt8t59q83r/qt8t59q83r.pdfetdoai:escholarship.org:ark:/13030/qt3943b6r62026-06-04T06:46:11Zqt3943b6r6Majorization-Minimization Algorithms and Their Applications in Neuroimaging, Clustering and Graph EmbeddingZhou, GaitingLange, KennethTward, Daniel2026-06-01This dissertation develops majorization–minimization (MM) algorithms for structured optimization models arising in image registration and sequential image reconstruction, clustering, graph embedding, and matrix equations with applications in computational biology and biomedical data analysis. Neuroimage registration and histological reconstruction are essential for building three-dimensional brain volumes, aligning time-series images, and mapping biological data into common coordinate systems. However, these tasks are complicated by high dimensionality, nonconvexity, image artifacts, and the need for accurate and robust optimization. To address pairwise neuroimage registration, we propose a gradient-independent rigid-motion algorithm that avoids step-length selection and improves performance over gradient descent on simulated and mouse brain microscopy images. To address sequential neuroimage alignment and reconstruction, we extend the MM framework to estimate 3D reconstructed volumes together diffeomorphic transformation parameters, using a robust loss function that downweights corrupted observations and accommodates heterogeneous microscopy data. We test our algorithms on mouse brain microscopy data and simulated phantoms under various imaging artifacts, and compare the results with other established methods. Beyond imaging, many biological data-analysis problems also require scalable solvers for structured matrix equations, especially in graph-based models where spatial, regional, or network relationships are encoded algebraically. We therefore derive MM algorithms for Sylvester equations AX + XB = C and transposed Sylvester equations AX + XTB = C, focusing on the common setting where A is large and sparse and B is small and dense. These solvers avoid large Hessian computations, line searches, and repeated large-matrix inversions, and are applied to spatial transcriptomics clustering in the mouse brain and joint graph embedding of brain regional networks. Together, these contributions show that carefully designed MM surrogates can convert difficult high-dimensional optimization problems into simpler, stable, and computationally efficient subproblems, providing reusable tools for analyzing biomedical imaging, spatial transcriptomics, and graph-structured biological data.Medical imagingApplied mathematicsBiomedical engineeringNeurosciencesGraph embeddingImage RegistrationMM AlgorithmNeuroimagingSpatial TranscriptomicsSylvester Equationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3943b6r6https://escholarship.org/content/qt3943b6r6/qt3943b6r6.pdfetdoai:escholarship.org:ark:/13030/qt17v4k2p22026-06-04T06:46:06Zqt17v4k2p2When Homes Overheat: Examining the Role of Housing in Heat-Health Inequities in a Warming Los AngelesCaballero-Gomez, HasibeCushing, Lara2026-06-01In the United States, climate change projections estimate increases in average temperatures, amidst existing thermal inequities, presenting increasingly uneven risks to public health and a critical need for interventions. In this context, this dissertation aims to explore heat risk, heat-health outcomes, and ethno-racial disparities in Los Angeles County, and assess housing as a potential point of intervention to reduce health disparities. In Chapter 2, I present findings from a longitudinal cohort study measuring bedroom air temperature, humidity, and sleep health over three weeks, supplemented with outdoor temperature and humidity measurements from a nearby weather station. To assess the role of housing, I conducted a visual inspection and documented modifiable household characteristics common to climate change adaptation programs. I found evidence that rising summertime temperatures reduced sleep health in a disproportionately heat-burdened community, suggesting that climate change will worsen existing sleep health disparities if left unaddressed. I also identified double-paned windows and operable bedroom air conditioners as housing features that improved indoor-outdoor temperature differences and sleep efficiency, respectively.In Chapter 3, I link 2015 - 2019 Los Angeles County administrative birth records to 2022 tax parcel data to estimate disparities in heat exposure via the following heat-risk related factors: individual-level housing thermal performance, occupation in high-heat-risk professions, tract-level urban-heat-island (UHI) intensity, and zip-code-level extreme ambient temperature exposures. Further, I conduct a survival analysis to evaluate whether ambient extreme heat exposure has a synergistic effect with housing, occupation, or neighborhood built environments on preterm birth. As a result, I found housing, occupation, and land cover compound heat-risk exposures among ethno-racially marginalized and socio-economically disadvantaged birth parents in Los Angeles, contributing to preterm birth. In Chapter 4, I use my linked birth-tax parcel records and develop a novel heat metric capturing the frequency of extreme temperatures above historical thresholds by gestational period to assess its association with fetal growth, and how housing thermal performance modifies this association. I found repeated exposure to extreme heat during pregnancy was associated with a higher likelihood of term, low birthweight, and small for gestational age infants, particularly in later trimesters and among parents in thermally inefficient housing. In sum, I argue that indoor thermal environments are a critical driver of heat risk and health inequities. I urge future research to further evaluate the role of housing in heat-health outcomes and inequities. My dissertation findings highlight the need for investigating chronic heat exposures among heat-vulnerable populations and multi-sited, dynamic heat exposure measurements for a more holistic understanding of heat risk and sites of intervention. Finally, I suggest routes for improving housing thermal performance by restructuring funding priorities within existing weatherization programs, adapting prenatal care visits to include visual housing assessments, and broadly greater investment in communities and households already facing thermal inequities as a way towards reducing unsafe indoor temperatures and heat-health disparities.Environmental healthEpidemiologyPublic healthClimate changebirth outcomesclimate changeextreme heathousingsleep healthpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/17v4k2p2etdoai:escholarship.org:ark:/13030/qt4v91n27t2026-06-04T06:46:02Zqt4v91n27tIn Search of the Black Star Line: Preservation and Digitization of the 20th-Century Pan-African Shipping CompanyHumphrey, Jewell Ruth-EllaDunnavant, Justin P.Acabado, Stephen2026-06-01This project explores how digital archaeological methods can reshape interpretations of the spatial and cultural legacy of the 20th-century Pan-African shipping company, the Black Star Line. This study contends that Black digital humanities methodologies can benefit archaeological methods by enabling the archiving and documentation through a community-engaged practice. Drawing on material culture related to the Universal Negro Improvement Association (UNIA), this project argues that the impact of the BSL extends beyond the usual interpretation of failure and pushes for the digital preservation of material culture. Black Digital Humanities and the Black Radical Tradition, in conjunction with archaeological research, are useful for interpreting material related to the African diaspora. Theoretically founded in Kim Gallon’s definition of “Black Digital Humanities” and specifically geospatial technologies such as ArcGIS or remote sensing, the project combines frameworks utilized in anthropology, Africana studies, and archaeology. The leading questions for analysis include; How can digital archival archaeological methodologies be deployed to document and exhibit the scattered legacy of the UNIA? and How can the Black Radical Tradition, as articulated by Dr. Greg Carr, extend the possibilities of digital humanities towards a more community-engaged Black digital heritage recovery praxis? The results will be intentionally made public, and continuously shifting to encourage digital heritage preservation.Black studiesArchaeologyCaribbean studiespubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4v91n27tetdoai:escholarship.org:ark:/13030/qt1z07z8ht2026-06-04T06:45:57Zqt1z07z8htSymbolic Language, Embodied Worlds: Multimodal Intelligence in Humans and MachinesJiang, YanruDale, Richard Alan Clarke2026-06-01Language and perception operate over fundamentally different representational regimes: language (especially in its textual form used to train models) tends to promote more discrete, symbolic processing, whereas perception and action are more continuous, gradient, and grounded in embodied experience. This distinction shapes how both humans and machines learn, generate, and evaluate information. This dissertation advances a unified account of how these representational differences influence model behavior and human–AI interaction, integrating theoretical analysis, computational modeling, and human experiments.
Chapter 1 synthesizes prior work in cognitive science, language models, and multimodal AI to argue that, while language encodes substantial information about embodied experience through statistical regularities and compositional structure, it remains constrained by its reliance on representations of prior experience. Incorporating multimodality provides access to continuous signals that support the construction of novel shared experiences, while also enhancing communicative clarity in human–AI interactions.
Chapter 2 introduces a model explainability approach that characterizes the learning trajectories of recurrent neural networks processing linguistic versus behavioral signals. Through computational modeling, this chapter demonstrates that sequential models encode linguistic and gestural inputs in distinct ways, highlighting differences in how discrete and continuous signals support information processing and learning.
Chapter 3 examines human evaluations of AI-generated news image captions. Despite strong performance in language generation, models exhibit weaker perceptual and cross-modal capabilities. This chapter shows that cross-modal context mitigates linguistic heuristics that would otherwise bias judgments under text-only evaluation. Chapter 4 introduces Visual Narrative Freedom, a property emerging from loosely coupled image–text relationships in real-world multimodal communication. By systematically varying textual constraints, this chapter illustrates that higher degrees of freedom increase the diversity of generated images and can shift user preferences—sometimes favoring AI-generated images over human-selected ones in high-stakes civic domains.
Overall, these contributions establish a theoretical and empirical framework linking representational structure to model behavior and human perception. This dissertation demonstrates that multimodal intelligence is not merely an additive interface, but is fundamentally shaped by the structure of the representations through which information is encoded, influencing both system performance and human–AI interaction.CommunicationArtificial intelligenceInformation technologyCognitive psychologyCognitive ScienceHuman-AI InteractionMultimodal AIMultimodal CommunicationVision-Language Modelsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1z07z8hthttps://escholarship.org/content/qt1z07z8ht/qt1z07z8ht.pdfetdoai:escholarship.org:ark:/13030/qt3099h15f2026-06-04T06:45:51Zqt3099h15fBiomechanically Constrained Quantitative MRI: Enhancing the Fidelity of Perfusion, Morphometric, and Radiomic Markers in Obstetric and Oncologic ApplicationsRamirez, Raymi OdalysSung, Kyunghyun2026-06-01This dissertation develops and applies advanced quantitative magnetic resonance imaging pipelines to investigate functional and structural tissue adaptations in two complex clinical applications; obstetrics and oncology. The overarching objective is to bridge the gap between raw, high-dimensional radiological data and reliable clinical diagnostic accuracy through investigating the feasibility of spatially and biomechanically constrained imaging markers.In the obstetric application (Aims 1 and 2), multiparametric MRI, morphometrics, and radiomic analysis were utilized to evaluate early-gestation placental function and predict infant growth trajectories. Results demonstrated that early deviations in placental volume and spatial perfusion heterogeneity independently shape birth percentiles. Furthermore, specific morphometric features, such as elongation and sphericity, and habitat-wise radiomic texture features significantly outperformed clinical and mean multiparametric MRI features in classifying complex postnatal growth outcomes, such as early downward growth and Small for Gestational Age trajectories. These findings highlight structural and vascular adaptations that precede clinical detection.In the oncologic clinical application (Aim 3), the study evaluated the impact of rigid versus deformable image registration on the extraction of pharmacokinetic (PK) parameters (?!"#$%, ?&', ?&) from qDCE-MRI. While deformable registration improved global image alignment, as shown by higher Mutual Information (MI) scores, validation revealed a critical "Mutual Information Paradox" For highgrade, biomechanically stiff cancers (PI-RADS: 4–5), the uniform elasticity assumptions of the deformable algorithm failed catastrophically. This introduced severe local spatial warping, which artificially shifted the ROIs into healthy tissue. This corrupted downstream pharmacokinetic parameters and produced falsely significant statistical correlations.Ultimately, this work establishes that extracting feasibly reliable predictive imaging markers requires a rigorous balance of localized spatial heterogeneity and anatomically specific biomechanical constraints. Relying solely on global similarity metrics is insufficient; local biomechanical integrity must be preserved to translate pharmacokinetic data into diagnostically accurate clinical outcomes.Medical imagingBiomedical engineeringMedicineBiomechanicsMachine LearningModelsMRIPlacentaProstateRadiomicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3099h15fhttps://escholarship.org/content/qt3099h15f/qt3099h15f.pdfetdoai:escholarship.org:ark:/13030/qt65m1j2wd2026-06-04T06:45:46Zqt65m1j2wdDevelopment of Isothermal, Additively Manufactured, Infrared Aluminum Mirrors for Space ApplicationsLohser, Julian RydaleYang, Jenn-Ming2026-06-01The successful development of future space-based infrared mirrors requires agile manufacture of high-quality mirrors. Aluminum has been identified as an ideal mirror material for these applications due to the speed of manufacturing and resulting mirror quality. However, traditionally manufactured aluminum mirrors suffer from low mirror efficiency and thermal distortion. Laser-powder bed fusion (L-PBF) of aluminum mirrors can improve performance through stiffness-optimized structures, integrated iso-thermalization, and novel alloys. This dissertation investigates the mechanisms controlling the diamond turning performance of L-PBF modified aluminum alloys and identifies critical factors affecting the capillary performance to produce the next generation of highly reflective, isothermal aluminum mirrors.The first part of this work focuses on understanding the critical factors controlling mirror performance in nucleation-modified, additively manufactured (AM) 7xxx-series aluminum alloys. Several candidate alloys with different grain-refining strategies were identified. Grain-refining agents successfully eliminate hot-cracking in these high-strength alloys, but the high-melting point of the inoculants results in frequent, incomplete dissolution. The mechanical performance is not impacted by the un-dissolved particles. However, the diamond turning performance is severely degraded by large, hard inclusions. This led to the down-selection of a TiC-modified 7xxx-series alloy due to its reduced grain-refining agent size, which minimized the size of the inclusion in the microstructure and maximized the surface quality.The TiC-modified alloy was then characterized and developed to understand the critical mechanisms affecting the final surface quality. A dual-scanning strategy was optimized that reduced grain size and orientation. Chemistry changes due to printing parameters were analyzed with computational modeling and thermal analysis, revealing changes in phase stability that affected the critical homogenizing heat treatment range. Nano-scratch analysis revealed the influence of print orientation, precipitation state, and cutting depth on the micro-machining characteristics of the material. Single-point diamond turning confirmed the applicability of the nano-scratch methodology as a screening tool and further revealed the sensitivity of the technique to spatter contamination. Elimination of spatter contamination combined with the findings from the nano-scratch testing resulted in a process to produce mirrors with extremely high surface quality.Finally, the influence of print processing and post-processing parameters was evaluated to produce high capillary performance wicks for thermal gradient control in heat pipes and vapor chambers. Surface energy of the TiC-modified alloy was found to depend on print orientation and heat treatment parameter, which modified surface chemistry. A two-step chemical process was developed to maximize surface energy by varying concentrations and exposure times. The developed treatment was applied to additively manufactured wicks to correlate the changes in surface energy with capillary performance. The optimized treatment resulted in a simultaneous increase in capillary performance and wick efficiency. This treatment enables high performance thermal gradient control in future additively manufactured aluminum mirrors to eliminate thermal distortion.Overall, this dissertation demonstrates that detailed consideration of processing parameters is critical to ensuring the surface quality of the material as well as the capillary performance. These results represent the building blocks for modified-7xxx series aluminum alloys to be well positioned to excel as the next generation of space-based infrared mirrors.Materials scienceCondensed matter physicsEngineeringThermodynamicsadditive manufacturingaluminummirrorsvapor chamberapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/65m1j2wdhttps://escholarship.org/content/qt65m1j2wd/qt65m1j2wd.pdfetdoai:escholarship.org:ark:/13030/qt22t781fr2026-06-04T06:45:41Zqt22t781frBrokers of the Humanitarian Interface: Humanitarian Entrepreneurship, Gendered Brokerage, and the Politics of Aid in LebanonGhaddar, SimaKligman, GailHarris, Kevan2026-06-01Lebanon has long hosted one of the world’s most concentrated and institutionally layered humanitarian aid apparatus. Over the past two decades, multilateral institutions and aid agencies have built administrative infrastructures, social protection systems, and coordination architectures within otherwise collapsing and hollowed out state institutions— simultaneously distrusting the institutions they partnered with and deepening their entanglement with them. The result is not a humanitarian system operating parallel to or in place of the state but one that inhabits its institutional shell while also interpenetrating civil society organizations, local governance structures, and the everyday lives of crisis-affected populations. This dissertation examines how the humanitarian apparatus in Lebanon produces institutional capacity and local governance effects—and for whom—at the point of encounter with communities in crisis. It draws on eighteen months of ethnographic fieldwork in the popular quarters of Wadi Al-Nahleh- Mankoubin in Tripoli city, Lebanon’s second-largest and most impoverished Sunni-majority city. These quarters constitute a Sunni Muslim community in which tribal authority, kinship obligations, and Islamic moral order structure everyday social and political life. My research combines participant observation within community spaces, aid distribution spaces, households, community-led NGOs with fifty-seven in-depth interviews with humanitarian professionals, managers and volunteers of community-led NGOs, local administrators, tribal elites, aid recipients and participants in aid projects. I triangulate these methods with a longitudinal tracing of global aid policies between 2005 and 2025 and an analysis of Lebanon’s successive humanitarian policies and social protection frameworks.Lebanon’s compounding crises created an incentive structure that made the NGO form an aspirational vehicle for local actors embedded in the communities aid is meant to serve. Male tribal members in Wadi al Nahleh—with pre-existing kinship authority and patronage networks— fashion themselves into what I term “humanitarian entrepreneurs:” self-styled brokers accumulating resources, institutional recognition, and maneuvering room across multiple domains of transnational aid. Women navigate the same interface from a structurally different position—as household welfare administrators, primary cash recipients, outreach volunteers, and the core care work on which humanitarian programming runs. This gendered aid architecture becomes a defining feature of institutional effectiveness. Designated as the preferred node between the humanitarian system and the household, women perform the flexible and care labor on which the system depends at its most local scale. The brokerage practices they occupy lie at the institutional seams of local state bureaucracies, localized humanitarian programming, and kinship dynamics. Their labor is less visible yet indispensable, and more extractive of care and relational labor. Oscillating between aid beneficiary and care broker, women become the capillary infrastructure through which global aid logics permeate households and community. Humanitarian policy frameworks reframe this as empowerment, self-entrepreneurialism, and local ownership; in practice, it materializes as precarious labor that builds women’s soft capacities while offloading institutional disruptions, public scrutiny, and the management of patriarchal anxieties onto them.SociologyPolitical scienceMiddle Eastern studiesWomens studiespubliceScholarship, University of Californiahttps://escholarship.org/uc/item/22t781fretdoai:escholarship.org:ark:/13030/qt6s57d0fk2026-06-04T06:45:36Zqt6s57d0fkThe Impact of Threat and Anxiety on Latino Political BehaviorAlegre, ClaudiaPérez, Efrén O2026-06-01Why do Latinos who face no personal risk of deportation mobilize politically in response to anti-Latino rhetoric and immigration enforcement? This dissertation argues the answer lies in a distinction existing frameworks have overlooked: the difference between anxiety experienced as an individual and anxiety experienced as a member of a group. Group-based anxiety, appraised as a collective threat to one's racial and ethnic community rather than a personal vulnerability, generates fundamentally different behavioral consequences than individually experienced anxiety, producing action tendencies oriented toward collective engagement rather than individual avoidance.Drawing on appraisal theory and intergroup emotions theory, I develop the Racialized Group Anxiety framework, which specifies a causal pathway through which racialized political threat produces collective political mobilization among racially marginalized groups, tested here among Latino adults in the United States. The framework makes three core claims: group-based anxiety mobilizes pro-group political participation in ways individually experienced anxiety does not; this mobilizing effect is moderated by pan-ethnic identification; and group-based anxiety can strengthen pan-ethnic identity among lower-identifying Latinos. I test these claims across three studies. Using observational data from the 2020 Collaborative Multiracial Post-Election Survey, I show that anxiety among Latino adults is positively associated with collective and expressive forms of political participation but not voting. Because this measure cannot distinguish individual from group-based anxiety, findings are suggestive rather than causal, motivating the experiments that follow. Two survey experiments with Latino adults in October 2024 and April 2026 provide causal evidence that group-based anxiety increases pro-group political participation where individually experienced anxiety does not. Both reveal a pre-treatment effect: the mobilizing effect is concentrated among Latinos with lower and moderate levels of pan-ethnic identification, attenuating among high identifiers already oriented toward collective engagement. Suggestive evidence from the second experiment indicates group-based anxiety may strengthen pan-ethnic identity, pointing toward an identity strengthening pathway. These findings contribute to scholarship on emotions and political behavior, Latino politics, and minority political mobilization, demonstrating that emotional mechanisms of political engagement are not racially uniform and that anxiety felt on behalf of one's group can function as a catalyst between lower pan-ethnic identification and collective political participation.Political sciencePsychologySociologyEthnic studiesEmotions and politicsGroup-based anxietyLatino politicsPan-ethnic identificationPolitical mobilizationRacialized threatapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6s57d0fkhttps://escholarship.org/content/qt6s57d0fk/qt6s57d0fk.pdfetdoai:escholarship.org:ark:/13030/qt9z0877p92026-06-04T06:45:31Zqt9z0877p9Quantifying the Complexity of Multilevel Confirmatory Factor Analysis ModelsKim, Yun KyungCai, Li2026-06-01In multilevel modeling, as in any statistical modeling, model selection requires balancing goodness-of-fit (GOF) against model complexity, yet the complexity of multilevel models and how it operates differently across levels have received little systematic attention. This study quantified and decomposed the fitting propensity (FP) of multilevel confirmatory factor analysis (ML-CFA) models, a class of models widely used to validate multilevel constructs. The 12 ML-CFA models under scrutiny had comparable numbers of freely estimated parameters but differed in specification, including the number of latent factors, relationships among the latent factors, or the presence of cross-level measurement invariance constraints. The FP of each model was decomposed into level-specific components that represent the capacity of the model to fit any data at the within- and between-group levels, respectively.Two new mechanisms for generating multilevel data were developed: the covariance decomposition (CD) and marginal composition (MC) approaches, where the former was a constrained instance of the latter. Using the more flexible MC approach, multilevel data were generated across 18 conditions defined by the number of groups, expected intraclass correlation coefficient (ICC) level, and per-group sample size. In each data condition, level-specific FPs of ML-CFA models were examined by two complementary approaches: comparing the distributions of level-specific GOF indices and visualizing the data space occupied by each model. Three findings were central. First, cross-level measurement invariance constraints disproportionately reduced between-group level FP while leaving within-group level FP largely unaffected, which is a substantial asymmetry that parameter counts alone do not anticipate. Second, parameter nesting did not guarantee data-space nesting. For instance, configural models that are parametrically nested within their unconstrained counterparts were capable of representing some data better than their unconstrained counterparts. Third, models with identical parameter counts differed substantially in their level-specific FPs, and their relative ordering could vary depending on data conditions. These results contribute to the literature on multilevel measurement invariance, the relationship between parameter nesting and covariance matrix nesting, and the customization of GOF index cutoff thresholds to candidate models as well as data conditions.Quantitative psychologyEducational tests & measurementsStatisticsData space nestingFitting propensityMeasurement invarianceModel complexityModel fit evaluationMultilevel factor analysispubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9z0877p9etdoai:escholarship.org:ark:/13030/qt3cp0q1hj2026-06-04T06:45:26Zqt3cp0q1hjHow Artificial Intelligence is Affecting the Teaching of Teaching: Examining the Changing Practices of Teacher Education Program FacultyCarroll, Ian MichaelFranke, Megan2026-06-01Artificial intelligence has the potential to dramatically reshape how we teach, learn, and generate knowledge, and indeed is already doing so. As teachers, designers of academic programs and standards, and educational administrators seek to grapple with the novel challenges and opportunities posed by this technology, a thorough understanding of the changing practices of teacher education program (TEP) faculty can provide a roadmap to intentional program design. This is a mixed-methods study with a convergent design, using UTAUT2 as a lens, wherein TEP faculty at a local 4-year university were surveyed and interviewed to investigate their perceptions, changing practices, and support needs.The data show this population being characterized by general belief that lessons on AI would be beneficial for their students, and high intent on doing so, but lagging follow-through into actual implementation, which is primarily an issue of establishing new pedagogical habits. Those faculty who have taken the leap into implementation point to AI forcing a critical reevaluation of their practices, offering opportunities for the development of new, more inclusive and dialogic pedagogies. For these educators, teaching is an essentially human-relational endeavor, for which AI cannot be a replacement, but a tool or a lens to help enable greater educational opportunities. Furthermore, they voice their concerns about the need for critical AI literacy skills to be built into the curriculum at all levels, not only as part of their teacher education classes. Finally, AI represents a new type of technological development, the harnessing of which requires support systems which blend functional technical assistance with content-specific pedagogical development, placing unforeseen needs on university support systems. A multitude of specific examples and experiences are shared in support of these core claims.Teacher educationPedagogyArtificial intelligenceCurriculum developmentArtificial intelligenceCurriculumFormative assessmentPedagogyTeacher educationUTAUT2application/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3cp0q1hjhttps://escholarship.org/content/qt3cp0q1hj/qt3cp0q1hj.pdfetdoai:escholarship.org:ark:/13030/qt4fn6w88f2026-06-04T06:45:21Zqt4fn6w88fLongitudinal functional connectivity during risky decision making in youth across the anxiety spectrumSmith, CharlesUddin, Lucina Q2026-06-01Adolescence is a critical period of development during which the brain undergoes massive reorganization. Excess synapses are pruned away, and key synapses are refined resulting in improved cognitive control, increased reward seeking behavior, and changes in decision making circuitry. Activation in the ventral striatum (VS) is known to peak during adolescence while the ventromedial prefrontal cortex (vmPFC) displays a delay in maturation in relation to subcortical regions. However, little is known about the functional connectivity (FC) among these key brain regions and how their coupling with other brain regions changes across time. Additionally, adolescence is also a time during which the emergence of anxiety disorders becomes increasingly prevalent and can have a negative impact on the development of regions such as the VS and vmPFC and their connectivity. Thus, this dissertation aimed to understand VS and vmPFC whole brain FC during cautious and risky decision making longitudinally and uncover how anxiety may impact the development of this neural circuitry. To achieve this, data from the Development of Anxiety in Youth Study (DAYS), an accelerated longitudinal study aimed to examine the changes in brain activation and connectivity over time in youth across the anxiety spectrum, was utilized. To examine FC and activation related to risky decision-making, the driving task was used. The aim of this fMRI task is to try and reach the finish line as quickly as possible. Throughout the task participants approach a yellow light at which they must choose to either stop (i.e., cautious), adding a 3-second delay or go (i.e., risky). If they choose to go, there is a 50% chance that they crash, adding a 6-second delay. In Chapter 2 of this dissertation, we observed that anxiety was associated with faster cautious responses across time, and while cautious trial engagement was shown to increase, this engagement wasn’t moderated by anxiety. Consistent engagement across timepoints was also seen for cautious and risky trials and, notably, an effect of anxiety was shown at year 3 in the lateral PFC. Expanding beyond functional activation, in Chapter 3 we then explored whole brain FC for cautious and risky trials using psychophysiologic interactions both cross-sectionally and longitudinally. Positive VS-vmPFC coupling for the cautious condition was observed, which was significantly more strongly connected compared to the risky condition. Additionally, cautious trial engagement was shown to be correlated with VS-vmPFC coupling. Finally, to elucidate the effect of anxiety on adolescent brain development, in Chapter 4, we implored a latent modeling technique (latent transition analysis, LTA) to identify latent anxiety trajectories and classify differences in VS and vmPFC FC between trajectories. This LTA identified three latent states which we then classified into four anxiety trajectories (i.e., low, high, improved, worsen). Analysis revealed that cautious VS connectivity with the caudate and pregenual anterior cingulate cortex (pgACC)/vmPFC for the low anxiety trajectory (AT) group, vmPFC and PCC for the high AT group, and vmPFC and sgACC for the improved AT group. Connectivity for the vmPFC during cautious trials was consistent across all groups, with connectivity of vmPFC-IPL and vmPFC-PCC coupling observed. Overall, this dissertation has shown that anxiety may influence the rate at which adolescents make decisions, but not their level of engagement in cautious nor risky conditions. Additionally, different anxiety trajectories display unique, albeit nonsignificant, differences in FC at baseline, suggesting a potential marker for persistent versus improved anxiety, which may help to inform early interventions of adolescent anxiety disorders and aid in the reduction of anxiety on adolescent development.NanosciencePsychologyNeurosciencesBehavioral psychologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4fn6w88fhttps://escholarship.org/content/qt4fn6w88f/qt4fn6w88f.pdfetdoai:escholarship.org:ark:/13030/qt1zg1c7102026-06-04T06:45:16Zqt1zg1c710Describing the sequence-structure-function relationship of pathogenic New World Hemorrhagic Fever Mammarenavirus glycoproteinsTaylor, Lily JaneRodriguez, Jose A2026-05-29Clade B New World Mammarenaviruses (NWMs) are endemic to South America; they include human pathogens: Junin, Machupo, Guanarito, Chapare, and Sabia. These viruses can cause lethal hemorrhagic fevers resulting from zoonotic transmission from rodents to humans. A tripartite glycoprotein complex (GPC) decorates the surface of all Clade B NWM particles. Despite low sequence similarity across glycoproteins in this Clade, most bind a single conserved site on a cell surface receptor (human transferrin receptor, hTfR1) to mediate cellular entry and initiate infection. The natural role of hTfR1 in facilitating the internalization of the iron-rich transferrin from the cell surface into acidic compartments provides an efficient route for NWM cellular entry. Though, the specificity of infection is largely determined by the NWM receptor binding domain, GP1. The low sequence identity across NWM GP1s (25-48%) curtails efforts to develop broadly neutralizing therapeutics and limits our ability to identify new variants that pose a serious risk to human health.The work presented in this dissertation aims to first describe a computational, biochemical, and structural workflow to investigate and predict cryptic Machupo/Junin-derived GP1 sequence variants capable of internalizing through hTfR1 and eliciting cross-neutralizing immune responses, giving rise to promising avenues for the development pan-acting NWM vaccine candidates. Then, this work details the in situ structural determination of the Junin glycoprotein complex bound by neutralizing antibodies, describing the molecular architecture of the natively embedded viral glycoprotein and its neutralization by receptor-mimicking therapies. Lastly, this work describes the molecular and structural basis for fusion inhibition of the NWM glycoprotein complex by small-molecule and antibody-based therapeutics, detailing routes of therapeutic escape and the structural basis for fusion function.Together these results aim to illuminate specific routes of sequence adaptation that may give rise to new pathogenic NWM variants, provide a better understanding of mechanisms and factors that contribute to viral entry and infectivity, and detail important avenues for broad-based therapeutic action against these lethal pathogens.BiochemistryCellular biologyPathologyMolecular biologypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1zg1c710etdoai:escholarship.org:ark:/13030/qt9405r7n62026-06-04T06:45:10Zqt9405r7n6Arguments for RAM Programs: Doubly Efficient, Black-Box, and Zero-KnowledgeShah, Akash AmritlalOstrovsky, Rafail2026-06-01Proving statements about small subsets of a large database, and in particular proving the correct execution of RAM programs on committed databases when the running time is sublinear in the size of the database, is a problem of substantial practical interest. Indeed, most real-world computations are naturally expressed as RAM algorithms in high-level programming languages such as C/C++, Python, and Rust. Efficient solutions to this problem have applications in searching data structures, sequence matching, such as for genomic data or blockchains, statistical analysis, secure inference, and proofs of training.
Motivated by this goal, we introduce and study the notion of projection codes. A standard error-correcting code allows one to encode a message x into a codeword X, such that even if a constant fraction of X is corrupted, the message x can still be recovered. A projection code extends this guarantee to any subset of the bits of x. Concretely, for every projection of x to a subset s of its coordinates, there is a subset S of comparable size such that the projected encoding X|_S forms a robust encoding of the projected message x|_s.
Our first result constructs projection codes with near-optimal overhead in both codeword length and projected-subset size. We then use them to obtain doubly efficient arguments for RAM programs on large committed databases, with near-optimal communication and prover and verifier computation time even when the computation is much smaller than the database. Our construction makes only a black-box use of collision-resistant hash functions, giving the first black-box alternative to prior non-black-box constructions with comparable asymptotic efficiency. Finally, we extend this result to support both zero knowledge and database updates, obtaining the first doubly efficient zero-knowledge implementation of RAM programs that relies only on a black-box use of symmetric cryptography.Computer scienceInformation scienceapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9405r7n6https://escholarship.org/content/qt9405r7n6/qt9405r7n6.pdfetdoai:escholarship.org:ark:/13030/qt9f57j5jh2026-06-04T06:45:05Zqt9f57j5jhPathways to Equity in Travel Demand Modeling: An Evaluation of Auto Ownership ModelSrisan, TatBills, Tierra Suzan2026-06-01Transportation is fundamentally the means through which individuals access essential socioeconomic opportunities, yet existing travel demand forecasting tools may inadvertently overlook the experiences of transportation-disadvantaged populations. This dissertation addresses a critical gap in contemporary auto ownership modeling: the systemic failure to formally account for the structural, financial, and psychological constraints that dictate the transportation outcomes for disadvantaged populations. Across four interrelated essays, I demonstrate that conventional models, which assume universal, unconstrained choice, fail to capture the realities of low-income, minority, and spatially isolated households.The first essay establishes the foundational empirical case by examining racial residential clustering and micro-regional spatial effects in Los Angeles County, demonstrating that composite models incorporating explicit racial and spatial indicators significantly outperform standard race-neutral specifications. The second essay introduces an auto insurance burden index as a supply-side constraint, revealing that insurance affordability functions as a financial gatekeeper that disproportionately restricts vehicle access in low-income and majority-minority communities. The third essay implements Manski's two-stage probabilistic choice set formation framework, showing that incorporating explicit constraint indicators into the choice set generation stage significantly improves predictive accuracy for Black households and those with lower educational attainment. The fourth essay synthesizes these insights into a novel Hybrid Choice Set Formation model that integrates latent psychological constructs—Financial Strain and Car Dependency—directly into the choice set formation stage, formally distinguishing between preference-driven and structurally captive auto ownership outcomes. The empirical results from these studies suggest that structural constraints are central determinants of auto ownership. Specifically, race-neutral models systematically overestimate built-environment effects and misrepresent mobility barriers for Black and Hispanic households. On the supply side, incorporating the Auto Burden Index sharpens the identification of households facing binding ownership constraints. The choice set formation framework reveals that standard models perform poorly for constrained subgroups, yielding prediction error rates exceeding 30\% for Black households. Furthermore, the Hybrid CSF model demonstrates that latent Financial Stain functions as a strict filter removing auto ownership from consideration sets. Moreover, the Hybrid CSF model consistently outperforms standard compensatory models in goodness-of-fit and predictive power, particularly for disadvantaged groups. Consequently, these captive populations are substantially more inelastic to policy interventions than standard models predict.These findings carry immediate implications for policy and practice. Standard models overpredict the effectiveness of price-based or land-use interventions for disadvantaged populations, risking misdirected long-range planning and infrastructure investments. Effective transportation planning requires forecasting tools that distinguish between households that choose not to own a vehicle and those structurally excluded from doing so. By bridging the divide between sociological understandings of transportation disadvantage and the formal econometric theory of travel demand modeling, this dissertation provides the analytical foundation to design targeted policies, accurately evaluate infrastructure investments, and ensure mobility access for all segments of the population.TransportationManagementCivil engineeringAuto ownershipDiscrete choiceTravel demandpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9f57j5jhetdoai:escholarship.org:ark:/13030/qt4rb0g9jn2026-06-04T06:45:00Zqt4rb0g9jnMachine Learning for Materials Modeling: End-to-End Differentiable and Batched Atomistic SimulationGangan, AbhijeetTaciroglu, ErtugrulMarian, Jaime2026-06-01Atomistic simulations like molecular dynamics are essential tools for studying materials at the scale of individual atoms. Their usefulness depends on two things: the quality of the interatomic potential used to compute properties, and the efficiency with which simulations can be run. As machine-learned interatomic potentials become more common, simulation workflows increasingly need to connect model development with downstream material properties, many of which are not used as training labels and require running simulations. A second challenge is computational throughput: existing simulation workflows often run these systems one at a time, leaving modern accelerator hardware underused.This dissertation addresses these needs through differentiable and batched atomistic simulation. The first part develops methods for differentiating simulation outputs with respect to interatomic-potential parameters. This makes it possible to train potentials using material properties beyond energies and forces, including elastic constants, vibrational properties, and radial distribution functions.The second part focuses on running many atomistic systems together. It develops data representations and algorithms for heterogeneous systems and implements them in TorchSim, a PyTorch-based simulation engine for machine-learned interatomic potentials. The resulting workflow connects physical observables to parameter fitting while making large collections of simulations practical on accelerator hardware.Computational chemistryComputational physicsMaterials scienceArtificial intelligenceCivil engineeringapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4rb0g9jnhttps://escholarship.org/content/qt4rb0g9jn/qt4rb0g9jn.pdfetdoai:escholarship.org:ark:/13030/qt3br9x5xx2026-06-04T06:44:54Zqt3br9x5xxDevelopment of Novel Immunotherapeutic Strategies for Generating Effective T Cell Responses against Pancreatic Ductal AdenocarcinomaLee, Hailey RoseRadu, Caius G2026-06-01Pancreatic ductal carcinoma (PDAC) remains a deadly solid tumor type with 90% of cases resulting in death within 5 years of diagnosis. The arid immune landscape of PDAC is intensely immunosuppressive, leading to a paucity of CD8+ T cell responses against the tumor. As such, immunotherapeutic strategies aimed at stimulating proinflammatory responses in the patient could yield potential in overturning the immunologically cold setting and facilitate the generation of anti-tumor CD8+ T cell responses. Essential inflammatory mechanisms act through key pattern recognition receptors, many of which are based in sensing of RNA or DNA species as a readout of pathogen presence or cellular distress. Next generation agonists of stimulator of interferon genes (STING), a dsDNA-sensing pathway, induce potent systemic immune response but require a pharmacodynamic biomarker for monitoring the amplitude and duration of such responses. We found that systemic STING agonists induced T and B cell metabolic alterations commensurate with STING agonist-induced activation, which could be monitored through 18F-fluorodeoxyglucose PET imaging. To address how RNA-sensing mechanisms could be critical for immunotherapeutic intervention, we developed a novel N1-methylpseudouridine-modified dsRNA construct complexed in lipid nanoparticles (m¹Ψ dsRNA-LNP). m¹Ψ dsRNA-LNP enhanced innate immune activation compared to that of unmodified dsRNA-LNP sensing and elicited inflammation through canonical dsRNA-sensing pathways of Toll-like receptor 3 (TLR3) and mitochondrial antiviral signaling protein (MAVS). Novel multi-component development of an RNA-LNP-based cancer vaccine platform, consisting of m¹Ψ dsRNA-LNP and an antigen-encoding m¹Ψ mRNA-LNP, elicited robust CD8+ T cell activation and memory formation in the setting of targeting tumor-associated antigen glycoprotein 100 (gp100). Further, the dual component RNA-LNP cancer vaccine platform suppressed tumor growth in mice bearing orthotopic gp100-expressing PDAC tumors. The optimization of the dual component RNA-LNP cancer vaccine platform was further evaluated in the setting of highly relevant and prevalent tumor neoantigen mutant KRAS. Immunizations utilizing a mutant KRASG12V-encoding m¹Ψ mRNA-LNP and the m¹Ψ dsRNA-LNP elicited CD8+ T cell responses against known nonamer and decamer epitopes of mutant KRASG12V. Overall, these results reveal next generation immunotherapies for enhancing engagement of nucleic acid sensors as a strategy to rationally design vaccines aimed at boosting T cell responses in PDAC.ImmunologyPharmacologyOncologyCellular biologyGeneticsnucleic acid-sensingpancreatic cancerT cellsvaccinepubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3br9x5xxetdoai:escholarship.org:ark:/13030/qt6hv4n9b42026-06-04T06:44:49Zqt6hv4n9b4Common Ground: Exegetical Methods in Origen’s and Proclus’ CommentariesGram, Zakarias DimitriBlank, David L2026-06-01The imperial and late antique periods saw a proliferation of commentary-writing in various communities, including among the early Christians and the Neoplatonists. While these commentaries have been studied independently of each other, the present investigation identifies and explores methodological similarities between the two traditions. In particular, it analyzes Origen’s Commentary on John and Proclus’ Commentary on Plato’s Timaeus as representative and innovative exegetical works with the aim of identifying common interpretative methodologies. Both authors use a multi-layered allegorical approach to their texts, in which the literal or historical meaning of the text is generally set aside in favor of a deeper theological meaning. The use of allegory and the claim to divine inspiration allow both commentators to exercise control over the meaning of the text (Chapter 1). Furthermore, both authors conceive of similar goals for their work, including preserving the supposed original meaning of the text, educating their reader in theology, and correcting heterodox interpretations (Chapter 2). Finally, the study considers how Origen and Proclus use the Hebrew Bible and Homeric epic in their commentaries, respectively, as additional sources of evidence and authority to back up their innovative and at times controversial interpretations (Chapter 3). As a result, we are better able to understand how scholars and philosophers in the imperial and late antique periods approached authoritative texts in their traditions, regardless of institutional affiliation.Classical studiesPhilosophyReligionPhilosophy of religionTheologyAllegoryCommentariesOrigenProclusText interpretationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6hv4n9b4https://escholarship.org/content/qt6hv4n9b4/qt6hv4n9b4.pdfetdoai:escholarship.org:ark:/13030/qt6b82r5b32026-06-04T06:44:44Zqt6b82r5b3Addressing Shape and Phase Heterogeneity in EEG Signals: A Bayesian PerspectiveLandry, Emma Valentine MarieTelesca, Donatello2026-06-01Electroencephalography (EEG) recordings arise as a noninvasive and low-cost clinical imaging technology for monitoring brain development, facilitating earlier and more effective interventions. Their analysis, however, presents a rich set of statistical challenges due to the complex structure of the data, requiring methodological tools that preserve the underlying geometry of the observations. Motivated by the study of autism, this dissertation develops novel Bayesian methodology to address shape and phase heterogeneity in EEG power spectral densities. The proposed models focus on functional and distributional data analysis techniques aimed at identifying and quantifying biomarkers associated with autism. Chapter 2 introduces warped functional mixed membership models. Existing approaches for handling misalignment in functional observations typically assume that the data is governed either by a single common shape or by a finite mixture of population-level shapes. We instead propose a more flexible framework based on mixed membership models, allowing subject-specific variation across multiple latent features. A Bayesian hierarchical model is constructed to jointly estimate the underlying shape functions, individual time-transformation functions, and subject-specific membership weights. Chapter 3 adopts a distributional data analysis perspective by modeling EEG power spectra as density functions. We develop a Bayesian Fréchet framework for distributional data under the Wasserstein metric that leverages a Hilbert space representation of the observations through quantile functions. This representation enables the use of standard functional techniques within a coherent Bayesian framework, including function-on-scalar regression and functional principal component analysis, while preserving the Wasserstein geometry. Finally, Chapter 4 explores extensions of the preceding methodology to the multichannel setting. We first consider a mixed membership framework that accounts for cross-electrode dependence through a hierarchical model leveraging repulsive priors to improve identifiability of the latent membership structure. We also propose a distributional modeling framework in which electrode-specific deviations are represented through optimal transport maps relative to a subject-level latent Wasserstein barycenter. These two directions are presented as preliminary methodological proposals, with full theoretical and computational development left as future work.BiostatisticsBioinformaticsMedical imagingapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6b82r5b3https://escholarship.org/content/qt6b82r5b3/qt6b82r5b3.pdfetdoai:escholarship.org:ark:/13030/qt9hq8c8qh2026-06-04T06:44:39Zqt9hq8c8qhAntimicrobial Polymer Delivery via Bacteriophages for Improved Biocompatibility and Reduced Resistance PotentialVexler, ShelbyChen, Irene A2026-06-01The rise of antibiotic resistance necessitates developing alternative treatments with minimal potential for bacterial adaptation. Two promising solutions include membrane-disruptive polymers and phage therapy, which utilizes viruses specific to bacteria, but they face opposite shortcomings; many polymers lack specificity for bacterial cells, resulting in poor biocompatibility, while phages are restricted to a very narrow host range. In this dissertation, we aim to circumvent each agent’s individual shortcomings through the synergistic approach of engineering the non-lytic M13 bacteriophage into a polymer delivery platform. Our first goal was to modify M13 bacteriophage, which normally infects Escherichia coli, to target the pathogenic bacteria Staphylococcus aureus. We expressed an antibody fragment specific to a conserved surface polysaccharide, poly-N-acetylglucosamine, on the receptor-binding protein. Successful binding of the anti-PNAG phage was quantified through enzyme-linked immunosorbent assays (ELISAs) and visualized by transmission electron microscopy (TEM). Next, we assessed the antimicrobial activity of three separate libraries of bio-inspired polymers with either methacrylate, hydroxybenzoate-co-lactide, or peptide backbones. We explored the differing trends in activity for each library with respect to degrees of polymerization, hydrophobicity, and charge. Our results highlight the diversity of antimicrobial polymers while demonstrating the combined utility of synthetic biology and chemistry for designing them. Lastly, antimicrobial polylysines varying by degrees of polymerization (48, 90, or 170) were loaded onto the anti-PNAG phage through electrostatic interactions or EDC crosslinking. Characterization determined higher drug-to-phage ratios for electrostatic loading compared to crosslinking, yet both attachment strategies improved potency, reducing the minimum inhibitory concentration (MIC) five-fold towards S. aureus. Critically, all bioconjugates demonstrated improved biocompatibility compared to polylysine alone; mammalian cells exposed to covalent polylysine-phages with DP ≤ 90 exhibited the highest viability over a five-day period. Furthermore, no resistance was induced in S. aureus after thirteen rounds of selection with covalent polylysine-phages, confirming bioconjugates retain the “resistance-resistant” nature of antimicrobial polymers. In summary, the findings of this dissertation present phage-based delivery of antimicrobial polymers as an effective strategy to target pathogenic bacteria while minimizing harm to human cells and potential for resistance.ChemistryPolymer chemistryBiochemistryAntibiotic ResistanceAntimicrobial PolymerBacteriophageapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9hq8c8qhhttps://escholarship.org/content/qt9hq8c8qh/qt9hq8c8qh.pdfetdoai:escholarship.org:ark:/13030/qt88m0p4tg2026-06-04T06:44:34Zqt88m0p4tgIon-Induced Electron Emission from Facility Walls during High-Power Electric Thruster TestingFERNANDEZ-COPPEL VELASCO, JORGEMarian, Jaime2026-06-01High-power electric propulsion systems are increasingly used for spacecraft station-keeping, orbital transfer, and deep-space missions, yet their ground-based qualification relies on measurements performed inside vacuum facilities. In these facilities, energetic plume species interact with chamber walls and diagnostic surfaces, producing electron emission that can modify plasma sheaths, alter local charge balance, and contribute to facility effects. While electron-induced secondary electron emission has been widely studied for relevant materials and energy ranges, ion-induced electron emission from low-energy propellant ions incident on facility-relevant surfaces remains comparatively under-characterized, particularly in the energy regime relevant to electric propulsion ground testing.
This dissertation investigates ion-induced electron emission from facility wall materials during electric thruster testing. A physics-based computational framework is developed to describe electron emission from ion-surface interactions through potential and kinetic electron-emission mechanisms. Potential emission is modeled through Auger neutralization as the incoming ion approaches the surface, while kinetic emission is described through electron excitation processes associated with ion transport in the solid. These include direct excitations from binary collisions between the ion and quasi-free conduction electrons, as well as Firsov-type inelastic energy transfer during atomic collisions. A Monte Carlo ray-tracing algorithm is also developed to model electron transport, tracking excited electrons as they undergo elastic and inelastic scattering before either thermalizing in the target or escaping through the surface barrier.
The resulting framework is applied to xenon, krypton, and argon ions incident on molybdenum, copper, tungsten, gold, silver, nickel, iron, stainless-steel, and amorphous-carbon surfaces over low-to-intermediate impact energies. The calculations provide predicted electron yields, emitted-energy distributions, angular distributions, excitation-depth distributions, and inelastic energy-loss spectra. These results clarify the relative importance of kinetic and potential emission mechanisms, the role of material properties in controlling emission probability, and the extent to which low-energy ion impacts can contribute to facility-induced perturbations in electric propulsion experiments.
Overall, this work provides a computational basis for estimating ion-induced electron emission under electric propulsion ground-test conditions and supplies material and energy-dependent emission data for use in predictive engineering models of facility effects, or other fields were IIEE may be relevant.Applied physicsAerospace engineeringMaterials scienceEngineeringPlasma physicsElectric propulsionElectron emissionFacility effectsion-induced electron emissionPlasma physicsSolid-state physicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/88m0p4tghttps://escholarship.org/content/qt88m0p4tg/qt88m0p4tg.pdfetdoai:escholarship.org:ark:/13030/qt0qm0g0pj2026-06-04T06:44:29Zqt0qm0g0pjFor Justice to Last: A Stability-Based Argument for a Duty of StewardshipPensler, Samuel LevinHerman, BarbaraShiffrin, Seana2026-06-01In politics, questions of “feasibility” often concern whether proposed political change, such as the expansion of justice, can be achieved. But there is a second dimension of feasibility: whether just institutions and practices can be sustained over time. An acceptable theory of justice must therefore be feasible in the sense of being sustainable—or, in Rawls’s terminology, stable. This dissertation examines whether liberal democratic theories of justice are stable in this way. Taking Justice as Fairness as a central test case, I reconstruct and assess Rawls’s powerful argument for its stability. While I argue that Rawls succeeds in showing that Justice as Fairness can stably regulate a liberal democratic society, his account also reveals the inadequacy of coercion-centered explanations of political stability. The enduring maintenance of liberal democratic justice must depend very little upon coercive enforcement and primarily on citizens’ shared sense of justice, expressed through their active democratic support for just institutions.In this vein, I argue that liberal democratic justice such as Justice as Fairness cannot be sustained by a citizenry whose civic life may only manifest in intermittent and often private acts such as voting and taxpaying. Rather, stability requires that citizens relate to one another and their institutions as “civic stewards.” Civic stewards take advancing the justice of their polity to be among the organizing principles of their lives. Practically, this means being personally involved in civic or civic-minded institutions, engaging with some regularity in civic deliberation and conversation, tracking the trajectory of justice within their polity, and remaining prepared to participate in collective democratic action when necessary to establish and sustain their just institutions.PhilosophyLawPolitical sciencePublic administrationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0qm0g0pjhttps://escholarship.org/content/qt0qm0g0pj/qt0qm0g0pj.pdfetdoai:escholarship.org:ark:/13030/qt7qc4j8ch2026-06-04T06:44:24Zqt7qc4j8chMultimodal Sensing and Robot Manipulation for Automated Needle Access in Deformable TissueForoutani, YasaminTsao, Tsu ChinBarzelay, Aya2026-06-01Needle-based ophthalmic procedures require precise interaction with soft, deformable tissues under limited visualization, depth perception, and tactile feedback. In applications such as vascular cannulation and subretinal injection, success depends not only on accurate positioning, but also on reliable tissue penetration, confirmation of access, and safe response to failure. These challenges motivate robotic systems that can combine precise motion with intraoperative sensing and feedback at the tool--tissue interface.
This dissertation presents robotic methods for safe, reliable, and repeatable needle access in ophthalmic procedures. First, a multimodal robotic framework is developed for vascular cannulation using microscope imaging, optical coherence tomography (OCT), and pressure sensing. The system localizes the vessel, aligns the needle, confirms successful access, detects failure modes, and enables reset-and-retry behavior. Bidirectional axial needle rotation is introduced to improve needle--tissue interaction and increase cannulation repeatability in ex vivo vessel experiments. Second, the same rotational insertion concept is extended to robotic subretinal injection, where the required precision is substantially higher. To prevent unintended tooltip motion during rotation, a dual iterative learning control strategy is developed to compensate kinematic and dynamic errors, enabling high-precision rotational insertion and improving subretinal access in ex vivo porcine eyes. Finally, an intraocular OCT A-scan probe is developed to extend sensing beyond the limitations of microscope and transpupillary OCT imaging. Through calibration and learning-based interpretation of A-scan signals, the probe supports local distance measurement, peripheral tissue mapping, tissue recognition, and deformation sensing. Together, these contributions advance tissue-aware robotic eye surgery by integrating improved manipulation, multimodal sensing, and feedback-driven decision making for delicate intraocular procedures.RoboticsOphthalmologyHealth care managementOphthalmologySurgical Robotsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7qc4j8chhttps://escholarship.org/content/qt7qc4j8ch/qt7qc4j8ch.pdfetdoai:escholarship.org:ark:/13030/qt6b0538wt2026-06-04T06:44:18Zqt6b0538wtAnisotropic and Crystalline Area-preserving Mean Curvature Flow in the PlaneKim, EricKim, Inwon2026-06-01This thesis investigates the dynamics of area-preserving anisotropic mean curvature flow in the plane, with a particular emphasis on long-time behavior. The evolution is a gradient flow of the anisotropic surface energy and is thus expected to converge to a critical point. We study the flat flow solution in two regimes: smooth elliptic and crystalline anisotropies. In both regimes, we establish the unconditional exponential convergence of bounded initial data to a disjoint union of Wulff shapes. In the smooth elliptic setting, we also prove that certain reflection comparison symmetries are preserved under the flow, allowing us to specify further constraints on the convergent profile depending on the initial data. In the crystalline setting, we show that the flat flow solution coincides with a classical ODE evolution for regular initial data, which further implies the eventual regularity of the flat flow for arbitrary initial data. Our analysis mainly adopts variational perspectives, employing techniques such as energy dissipation estimates and geometric barrier arguments.MathematicsTheoretical mathematicsApplied mathematicsAnalysis of PDEsCalculus of variationsInterfacial evolutionsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6b0538wthttps://escholarship.org/content/qt6b0538wt/qt6b0538wt.pdfetdoai:escholarship.org:ark:/13030/qt3mg258g72026-06-04T06:44:13Zqt3mg258g7Structured Group Mentorship Program for New-Graduate PMHNPs Transitioning into PracticeSanchez, AlexanderChoi, Kristen2026-06-01Background: New-graduate psychiatric-mental health nurse practitioners (PMHNPs) often begin practice without structured mentorship, contributing to role stress, reduced clinical confidence, and early turnover.Objectives: To evaluate a structured, virtual group mentorship program for new-graduate PMHNPs and examine feasibility, acceptability, and preliminary changes in self-rated competence, job stress, job satisfaction, and intent to stay.Methods: This single-group, pretest-posttest professional development program enrolled 10 PMHNPs with fewer than 12 months of experience who practiced in Southern California. Participants completed an 8-week virtual mentorship program delivered via Kajabi. Weekly 60-minute sessions led by an experienced PMHNP integrated case-based clinical reasoning and facilitated peer support. Outcomes measured at baseline and week 8 included perceived role competence, job satisfaction, perceived stress, retention intent, and program feedback. Non-parametric bivariate tests were used for pre/post comparisons.Results: Median perceived role competence increased significantly from 16 (range, 13-35) at pretest to 29.5 (range, 15-37) at posttest (p = .022). Median overall job satisfaction increased from 179 to 188.5 and approached statistical significance (p = .059), while perceived stress did not change significantly (p = .44). Exploratory analyses showed significant improvements in selected job satisfaction subscales and multiple competence items. Retention indicators and posttest feedback were favorable: 90% had not considered leaving their current NP position, 100% had not left their position since program start, and mean program feedback ratings ranged from 4.5 to 4.7 on a 5-point scale.Conclusion: Structured virtual group mentorship was acceptable and associated with improved self-rated role competence among new-graduate PMHNPs. Larger-scale evaluations are warranted to examine sustained competence, stress, satisfaction, retention, and patient or organizational outcomes, and efforts are needed to implement PMHNP mentorship models in practice.NursingOccupational psychologyHealth sciencesMental healthHealth care managementMentorshipNursingapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3mg258g7https://escholarship.org/content/qt3mg258g7/qt3mg258g7.pdfetdoai:escholarship.org:ark:/13030/qt9ch8s24n2026-06-04T06:44:08Zqt9ch8s24nUltra-low Frequency Waves at Mars: Exploring the Connection Between Surface and Space Through InSight and MAVEN ObservationsWebster, KyleCao, HaoMa, Yingjuan2026-06-01The Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) lander is a stationary lander located on Elysium Planitia that returned the first magnetometer data from the Martian surface. Arriving in November 2018, InSight observed the repeated occurrence of ultra-low frequency (ULF) electromagnetic waves during the nighttime. While ULF waves had been observed extensively in planetary magnetospheres throughout the solar system, including Mars, their common existence on the surface of Mars was not expected. In this dissertation, we embark on a journey to connect the observations of ULF waves on the surface with their origins throughout the Martian space environment.We catalog 444 nighttime ULF wave events throughout the InSight mission duration. The characteristic frequencies range from 1 mHz to 16 mHz, with a median frequency of about 7 mHz. ULF wave activity is found across all nighttime hours, but the activity is more commonly observed after midnight and before dawn local times. The ULF wave events show a ubiquitous signature of decreased power in the vertical component, suggesting that currents flowing through the Martian subsurface are responsible for this reduction. We suggest that ULF waves are likely produced in the Martian magnetosphere by at least two different mechanisms and that the occurrence is sensitive to the solar wind interaction with the Martian crustal fields. Next, we statistically investigate how the ULF wave power differs between the north-south, east-west, and up-down components. The median east by north and horizontal by vertical power ratios remain consistent throughout the entire InSight mission dataset, not just during InSight ULF wave events. Preliminary results from MAVEN observations show that we cannot rule out the magnetotail as a source of the east by north power anisotropy; however, the conducting subsurface remains the most consistent hypothesis for the regulation of directional ULF wave power.Two observational case studies are presented with evidence for ULF wave source mechanisms. The first event occurred on October 04, 2019, when the Mars Atmosphere and Volatile Evolution (MAVEN) spacecraft transited through the Martian magnetotail. MAVEN observed wave-like oscillations, which exhibited characteristics of both types of magnetotail flapping waves, large-scale steady flapping of the magnetotail current sheet and propagating kink-like flapping. At the same time, InSight also records a ULF wave event on the surface of Mars, with a similar characteristic frequency. Using a simplified Martian ionosphere model, we determine that the nightside ionosphere is too weak to significantly shield the surface from waves in the ULF band. Therefore, we interpret the simultaneous magnetotail flapping waves and ULF waves observed at the Martian surface as having the same origin. The second event occurred during an interplanetary coronal mass ejection (ICME) on December 10, 2021. MAVEN and Tianwen-1 observed a near-simultaneous magnetic field reversal in the magnetosheath. The reversal is well explained by a large current sheet, possibly the heliospheric current sheet at the leading edge of the ICME. At the arrival of the ICME, InSight observed a ULF wave packet (for about 1 hour) with high coherence (> 0.85) and a consistent phase response. The source mechanism for this event is possibly due to the draped current sheet structure, which makes it a unique space weather driven ULF wave source.Plasma physicsGeophysicsAstrophysicsMagnetic FieldMagnetosphereMarsULF Wavesapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9ch8s24nhttps://escholarship.org/content/qt9ch8s24n/qt9ch8s24n.pdfetdoai:escholarship.org:ark:/13030/qt7t74j65q2026-06-04T06:44:03Zqt7t74j65qThe Space of Letters: Correspondence and Narrative in Kagerō NikkiPrice, Deborah AlexisDuthie, TorquilEmmerich, Michael2026-06-01Like many vernacular kana texts of the Heian period (794–1185), the writing of Kagerō nikki (ca. 972-974) is predicated on correspondence as the essential mode of informal writing for men and women. The records of poetry composition and exchange throughout Michitsuna’s Mother’s (935–995) diary emphasize how correspondence between figures in the aristocratic social world of the Imperial capital and across geographies in the material world of the Heian realm utilized epistolary and poetic conventions to negotiate for real-world benefits, play with literary convention, and experiment with vernacular forms of individual expression. Moreover, Kagerō’s autobiographical narrative of these exchanges reveals how the abstract, discursive spaces in which the social, political, and literary intersect: Letters, poems, and the messengers who carry them traverse the geographic, social, gendered, and ritual spaces of the Heian capital and beyond, and the poetic content of these missives takes advantage of poets’ knowledge of their environment, even though this knowledge must be tacitly recognized by readers. As such, geographic and material spaces are implicated in certain social and cosmological discourses of movement and access, which are then implicated in sociopolitical discourses unique to the Heian capital. Because correspondence is a written/literary expression of movement, moments of epistolary exchange in Kagerō illuminate the elements of space and provide for the dedicated analysis of place, rather than merely time, in the narrative. This dissertation considers space as a lens for bringing into focus a narratological approach in considering the literary techniques that Michitsuna’s Mother inscribes and the interpersonal contexts she ascribes to imagined, poetic geographies, which also have social and material significance to herself. Through her narrativization of these correspondences in her diary, the author and other contributors advocate and negotiate for social and material outcomes through a poetic logic that expands with Michitsuna’s Mother’s literary experience into a more individualized vernacular prose style. Ultimately, my examination of space in Kagerō nikki considers both the material and cultural conditions of space and place through the social practice of written poetic correspondence that constructs the unique literary space of the diary.Japanese literatureLanguageEast Asian studiesdiary literatureKagerō Nikkinarratologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7t74j65qhttps://escholarship.org/content/qt7t74j65q/qt7t74j65q.pdfetdoai:escholarship.org:ark:/13030/qt3f83m5jb2026-06-04T06:43:58Zqt3f83m5jbSearching for Support: A quantitative exploration of the role of online, digital and interpersonal information sources in the dynamics of mental health information seeking and help-seeking among Black emerging adults in the United States.Ofei, LeonaPrelip, Michael L2026-06-01Poor mental health is a significant contributor to morbidity and Black young people are reporting accelerated suicide rates, yet treatment use for this group is minimal. Prior work also indicates that they encounter mental health stigma in their communities and interpersonal networks and may be turning to online and digital sources for mental health information. These sources can be beneficial for mental health support, but they can facilitate exposure to further stigma and misinformation. Yet, mental health information seeking is an understudied area of research for this population.This dissertation focuses on a sample of Black emerging adults (18-29 years old) in the US and examines mental health information exposure and professional help-seeking patterns, with a focus on online, digital and interpersonal sources. Participants were recruited via convenience sampling from online research panels and data were collected via an online survey in 2024.The first study (N=1,193) examined socio-demographic and psychological factors as predictors of past year mental health information exposure from: social media, a Google search, health websites, mental health apps, a health provider, family, in-person friends. Findings show some variation in mental health information exposure based on health insurance access, LGBTQ+ identity, and multiracial identity. In addition, those who meet study criteria for a mental health need and report current treatment use are most likely to be exposed to mental health information from all sources, with the highest odds reported for social media. Further, among those who do not meet this study’s mental health need criteria, a self-assessed perceived need for mental health support is related to increased exposure to information from social media, mental health apps and family.The second study (N=1,193) identified three latent class groups based on the information sources in the first study. The first group (~20% of sample) is classified as the “saturated exposure group” with a high probability of mental health information exposure across all sources. The second group is the “fragmented exposure group” (50% of sample) reflecting a high probability of mental health information exposure from social media, a moderate probability of exposure from family, in-person friends and Google, and a lower but considerable exposure from a health provider, mental health apps and health websites. The last group is the “mostly social media exposure group” (30% of sample), with lower but considerable exposure to mental health information from social media and negligible exposure from all other sources. Findings show that age, education and health insurance access predict latent class group membership. In addition, having a criteria-based need also predicts latent class group membership but this depends on treatment status and the type of mental health need. Further, both higher stigma agreement and higher mental health literacy are associated with greater exposure to a wider variety of mental health information sources (saturated exposure).The third study examined the association between past year mental health information exposure and current professional treatment use and the role of mental health literacy and stigma as mediators. The analysis focused on the most popular platforms for the sample with a criteria-based mental health need (N=746) i.e. social media, a Google search, mental health apps, family, and in-person friends. Results indicate that past year exposure to mental health information is positively and directly associated with current treatment use for social media, mental health apps, and family. Moreover, mental health literacy mediates the association between past year mental health information exposure on Google and current treatment use, and stigma and literacy jointly (but not independently) mediate this association for mental health apps.Overall, findings indicate that Black emerging adults with poor mental health are exposed to mental health information from online and digital sources, particularly those who are currently in treatment. The findings also suggest that exposure to mental health information from social media, mental health apps and family is related to improved professional help-seeking although underlying mechanisms need to be explored further. These findings indicate the need for mental health help-seeking interventions that leverage the benefits of online platforms while minimizing harms. Findings also point to the value of family-oriented help-seeking interventions.This study’s limitations include the cross-sectional nature of the data, the convenience sampling approach and limitations with the mental health information exposure measure. However, the study utilizes recent data and analyzes a large sample of a very specific population, to investigate an understudied but important area of research. Overall, understanding mental health information seeking and professional help-seeking patterns among Black emerging adults is valuable. As Viswanath et al. (2007) argue, “communication could offer one potent explanation for inequalities in health” and “may play a role in linking SES, resources, and final health outcomes” (p. 286).Mental healthHealth educationWeb studiesAmerican studiesdigital healthhelp-seekinginformation seekinginternetmental healthsocial mediapubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3f83m5jbetdoai:escholarship.org:ark:/13030/qt8ww5378g2026-06-04T06:43:54Zqt8ww5378gIntegrating Multiple Sources of Validity Evidence Using Cross-Classified Item Response Theory ModelingFeng, TianyingCai, Li2026-06-01In technology-based and data-rich research, integrating outcome data with process data is increasingly common. Outcome data, such as item-level responses on traditional assessments, capture what individuals know or can do. Process data, generated as individuals engage with computerized tasks, games, and simulations, capture how they engage with the content. Existing methods are limited in their ability to integrate these different data sources, accommodate explanatory predictors, and account for dependencies introduced by study design within a flexible and unified framework. This dissertation proposes a three-level cross-classified item response theory model and develops a variant of the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm for its estimation, in which the Metropolis-adjusted Langevin sampler is used to draw the missing data. The model jointly analyzes data from different sources (e.g., assessment and game-based measures), design and background variables, and nesting from study design. The Langevin-based MH-RM algorithm uses gradient information to guide proposals, motivated by the need for sampler efficiency in higher-dimensional settings. Two simulation studies were conducted to evaluate parameter recovery and computational performance under data conditions plausible for technology- and game-based evaluation research. The model and algorithm were applied to a multisite evaluation dataset to examine intervention effects and the interplay among assessment, gameplay, and design features. By combining process and outcome data within a coherent analytic framework, this dissertation shows how process data can serve as a valuable source of evidence for hard-to-measure behavioral and cognitive processes and for informing decisions about evaluation and improvement.Educational tests & measurementsStatisticsEducational technologyEducational psychologyCross-classified modellingEstimationGame-based evaluationItem response theoryMultilevel modellingTechnology-based evaluationpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8ww5378getdoai:escholarship.org:ark:/13030/qt7158c7932026-06-04T06:43:49Zqt7158c793Probing the Physicochemical Determinants of Self-Assembly of Plant Virus-Like Particles for RNA DeliveryHarpell, Nina MirandaGelbart, William MRodriguez, Jose A2026-06-01Plant-derived virus-like particles (VLPs) can self-assemble and package non-native, therapeutic RNAs, making them promising platforms for RNA delivery. However, the rational design of VLPs for therapeutic use depends on understanding the physical and chemical principles that govern self- assembly across different viral platforms. This thesis investigates the limits of viral self-assembly across three plant virus platforms: Cowpea Chlorotic Mottle Virus (CCMV), Brome Mosaic Virus (BMV), and Tobacco Mosaic Virus (TMV). Chapter 2 investigates the effect of a single surface mutation of alanine to cysteine on CCMV capsid assembly. While the mutation is useful for site-specific conjugation of targeting ligands, it impairs assembly efficiency, with most of the mutant capsid protein forming incomplete, aggregated structures. A gel electrophoresis framework is developed to quantify mutant subunit incorporation into mixed wild type/mutant VLPs, demonstrating that the number of incorporated mutant capsid proteins into the capsid is linearly related to the number of mutant capsid proteins in the assembly mixture. Chapter 3 maps the structural phase diagram of BMV capsid protein across a broad range of pH and ionic strength, revealing that the predominant assembly states are in thermodynamic equilibrium, rather than kinetically controlled. Additionally, the phase diagram work is used to guide optimization of the two-step in vitro assembly protocol of BMV to produce a higher yield of complete particles. Chapter 4 demonstrates that CCMV and BMV can package precursor microRNA. Because this cargo is shorter than the ~3000 nt RNAs these capsids evolved to package, both platforms assemble non-canonical structures. CCMV produces uniform T=1 icosahedral particles, while BMV yields a heterogeneous population dominated by pseudo-T=2 particles and a minority of T=1 particles. Chapters 5 and 6 investigate growing tobacco for TMV production and the structure of TMV VLPs by packaging a truncated TMV genome (10% of the native length) to produce “mini” TMV rods 30 nm in length. These short particles provide a novel platform for interrogating the TMV capsid ends, which are implicated in co-translational disassembly of RNA and remain structurally unresolved. Finally, chapter 7 investigates methods to conjugate gold nanoparticles for RNA end modification. Taken together, this thesis maps the solution conditions, RNA cargos, and surface modifications that yield intact, functional VLPs. These findings establish a set of physicochemical rules that govern the tolerance of plant virus capsids to perturbation and reveal where that tolerance is most usefully harnessed for biomedical design, advancing the foundation for plant-virus-based RNA delivery vehicles.BiochemistryMolecular biologyGeneticsNanoscienceVirologyBiochemistryChemical Biologyplant virusesRNAStructural Biologyvirus-like particlesapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7158c793https://escholarship.org/content/qt7158c793/qt7158c793.pdfetdoai:escholarship.org:ark:/13030/qt7kj3n9mk2026-06-04T06:43:43Zqt7kj3n9mkHolding Contradiction Together: Countering Relational Abandonment in the Figueroa CorridorFuentes, KimberlyRitterbusch, Amy E2026-06-01Despite decades of sex work research, minimal scholarship has directly addressed the relationship of sex workers, specifically street-based sex workers, to the places and communities they inhabit. Meanwhile, communities facing the brunt of organized abandonment consolidate their political agency on eradicating the sex trade. To address this gap, I organized five cohorts of three art-based workshops within the Figueroa Corridor with sex workers, motel owners, residents, and youth (n=41) in the Summer of 2025. This dissertation centers on the Figueroa Corridor, a contemporary site of urban warfare, where gentrification, human trafficking task forces, and a nonprofit-led moral contagion campaign to “Free the Land” mobilizes safety discourses towards carceral logics. Our methodological approach utilized collage, writing exercises, the construction of collective timelines, and mapping. The workshops provided insight into the remembered, the imagined, and the unsaid of the participant’s embodied geographies. Methodologically, this study demonstrates the utility of art-based, collective methods for accessing embodied and contested geographies.Drawing on Gilmore’s organized abandonment (2007), I conceptualize the relational organization of abandonment: the process through which we come to sort each other and (re)produce broader conditions of fragmentation through everyday social relationships. Principal findings offer three interrelated mechanisms of relational abandonment: the silencing of dissent as boundary enforcement, the fabrication of moral contagion narratives that produce material fortress logics, and the nonprofit gaze as the subjectivation of marginalized communities into nonprofit subjects. These findings contribute to scholarship on sex work, placemaking, and abolition geographies by tracing the symbiotic relationship between systems-level abandonment and its grassroots accomplice that is produced and maintained in multiply-marginalized communities. I contribute to critiques of the nonprofit-industrial complex (Rodríguez, 2007) by elucidating how the proliferation of nonprofits in marginalized neighborhoods creates emotional displacement, the consequence of being both resource-dense and community-poor. In contending with how to interrupt fragmentation, I offer sites of insurgent pedagogy as a method for civil society to organize against their abandonment and generate liberatory geographies through collective knowledge production. These insights can inform efforts to disrupt carceral safety regimes and reimagine responses to harm that do not rely on carceral intervention.Social workGeographyCriminologyabolition geographiessex workpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7kj3n9mketdoai:escholarship.org:ark:/13030/qt7b92r8kh2026-06-04T06:43:38Zqt7b92r8khStrategies for broad pathogen neutralization through benign antibody-mediated targeting of human transferrin receptor 1Munoz, Aldo JavierRodriguez, Jose A2026-06-01Transferrin receptor 1 (TfR1) is at the intersection of cellular iron internalization, infectious disease, oncology, and neurological delivery. Its main role is to regulate iron uptake into cells by binding the iron carrier, transferrin (Tf), and internalizing it into acidic endosomes for iron release. However, it has been observed that TfR1 is a target of a variety of pathogens. Many of which target the apical domain, a subdomain of TfR1. This creates an opportunity to target TfR1 as a novel strategy to develop broadly neutralizing therapeutics. In this regard, chapter 2 discusses the use of apical domain binding antibodies to prevent the cellular entry of pathogenic New World Mammarenaviruses (NWM). Additionally, high-resolution structures of the antibody fragments bound to TfR1 were determined to explain the mechanisms of pathogen neutralization and pH dependency of the antibodies. In chapter 3, we then discuss the humanization of the murine antibodies to engineer them into better therapeutics. In chapter 4, we shift focus to elucidating the yet to be described structural conformational changes that occur to the TfR1-Tf complex as it cycles into acidic endosomes for iron release. We reveal the highest resolution structure of TfR1-Tf at physiological pH to date, and a novel high-resolution structure at pH ~5, near the pH seen in the acidic endosomes. This work gives a structural explanation on the mechanisms of pathogen neutralization of deadly pathogenic NWMs by antibodies that can be used to neutralize the many other pathogens that bind TfR1. The engineering of the antibodies means that they would not only be useful for pathogen neutralization but also for cancer targeting or delivery across the blood-brain barrier. Both of which are areas of observed increased TfR1 expression. Lastly, this work structurally illuminates an essential biochemical mechanism that underlies nearly every fundamental category of metabolism.BiochemistryMolecular biologyImmunologyAntibodycryo-EMIronStructure-based humanizationTransferrinTransferrin Receptor 1publiceScholarship, University of Californiahttps://escholarship.org/uc/item/7b92r8khetdoai:escholarship.org:ark:/13030/qt2x31b6vs2026-06-04T06:43:33Zqt2x31b6vsMechanisms of efficacy and toxicity in enhanced CAR-T cell therapy for liquid and solid tumorsShih, RyanChen, Yvonne Y.2026-06-01Chimeric antigen receptor (CAR)-T cell therapy has led to impressive clinical responses in patients with advanced hematological malignancies, encouraging efforts to translate those benefits to patients with solid tumors. However, both liquid and solid tumors pose unique challenges to cell therapies, including propensity for antigen escape, immunosuppressive signaling, antigen heterogeneity, and polarization of endogenous immune cells toward pro-tumoral phenotypes. Additionally, engineering synthetic biology circuits into T cells can lead to diverse pathological processes of immune activation that, when severe, can be fatal. Achieving sufficient anti-tumor efficacy in the face of multiple resistance mechanisms while simultaneously avoiding severe immune toxicities is therefore a primary objective. Accordingly, diverse functional enhancements to improve the efficacy and/or toxicity profile of CAR-T cell therapy have been explored, and understanding the mechanisms that drive those improvements are of paramount importance to inform rational combinatorial approaches and provide foundational understanding about expected effects in clinical translation. Here, we present mechanistic investigations of three enhanced CAR-T cell therapies designed to improve efficacy or toxicity:(1) To prevent the common toxicity of cytokine release syndrome (CRS) in treatment of leukemia, our group engineered CD19-targeted CAR-T cells to secrete immunomodulatory proteins to disrupt inflammatory signaling cascades. We find that secretion of a tocilizumab-derived single-chain variable fragment improves both efficacy and toxicity through a transcriptional balance of effector and memory function.(2) To oppose immunosuppression mediated by transforming growth factor (TGF)-β in glioblastoma, our group engineered IL-13Rα2–targeted CAR-T cells that sequester TGF-β and rewire its signaling to stimulate, rather than suppress, CAR-T cells. We find that these enhanced CAR-T cells safely improve control of orthotopic glioma by remodeling endogenous immune compartments in the brain without negative systemic effects.(3) To oppose antigen heterogeneity and immunosuppressive myeloid cells in glioblastoma, our group engineered IL-13Rα2-targeted CAR-T cells that inducibly secrete cytokine armors to repolarize endogenous immune cells and stimulate native anti-tumor immunity. We find that this “armoring” strategy profoundly alters intrinsic CAR-T cell function, leading to marked pro-inflammatory changes in diverse immune lineages. While toxicity was observed, our investigations identify the Bhlhe40/GM-CSF axis as a potential ablation target for toxicity mitigation.ImmunologyCellular biologyOncologyMolecular biologypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2x31b6vsetdoai:escholarship.org:ark:/13030/qt2t1731jh2026-06-04T06:43:26Zqt2t1731jhMicrobial Regulation of Serotonin in Gut Neuroimmune Interactions and Colorectal Canceryu, lewis wHsiao, Elaine Y2026-06-01The gut microbiome is increasingly recognized as a central regulator of host physiology, influencing immune, neural, and metabolic systems through complex host–microbe interactions. Emerging evidence demonstrates that the microbiome actively shapes the neuroimmune landscape, impacting the development and function of immune and nervous system networks in both the intestine and distal organs, including the central nervous system. Dysregulation of microbiome–neuroimmune interactions has been implicated in a wide range of inflammatory, neurological, and neoplastic diseases, highlighting the importance of understanding the molecular mechanisms underlying these interactions.Recent studies suggest that classical neurochemicals serve as key mediators of communication between microbial, immune, and neural systems. Among these, serotonin (5-hydroxytryptamine; 5-HT), a tryptophan-derived signaling molecule produced predominantly by intestinal enterochromaffin cells (ECs), has emerged as an important regulator of microbiome–neuroimmune interactions. The gut microbiome modulates serotonin bioavailability through microbial metabolites and signaling pathways, leading to downstream effects on immune responses, neuronal activity, and microbial fitness. However, how microbiome-responsive serotonin signaling shapes local immune networks and contributes to disease pathogenesis remains poorly understood.In this dissertation, I demonstrate that microbial induction of EC-derived serotonin promotes immune tolerance and increases susceptibility to colorectal cancer. These findings identify EC-derived serotonin as a microbiome-regulated instructive signal that links epithelial sensing of the microbiota to dendritic cell programming, immune tolerance, and colorectal tumorigenesis. Collectively, this work establishes serotonin as a mediator of gut neuroimmune homeostasis and reveals a novel microbiome–epithelial–immune axis that can be subverted to promote cancer.ImmunologyNeurosciencesMicrobiologyCellular biologyOncologyColorectal cancerImmuneMicrobiomeNeuroimmuneSerotoninpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2t1731jhetdoai:escholarship.org:ark:/13030/qt63t1d6b62026-06-04T06:43:22Zqt63t1d6b6Genomic and Epigenomic Analysis for Forensic ApplicationsFlores, MariaPellegrini, Matteo2026-06-01Forensic investigations increasingly rely on genomic and epigenomic analyses to extract identifying information from complex biological evidence. Advances in forensic DNA technologies have enabled the interpretation of mixed DNA profiles from multiple contributors and the estimation of chronological age from trace biological samples. However, these approaches depend on statistical frameworks that must account for substantial biological and technical variability. A central challenge is that forensic models may exhibit systematic inaccuracies or biases when underlying assumptions fail to capture the complexity of human genetic and epigenetic variation. Understanding these limitations is critical for improving the reliability and equity of forensic methodologies. In chapter 2, we investigate the accuracy of forensic DNA mixture interpretation across human genetic variation. Using simulated DNA mixtures generated from allele frequency distributions representing 83 human groups, we evaluate false inclusion rates across varying numbers of contributors and reference population specifications. We demonstrate that false inclusion rates increase with mixture complexity and are elevated in groups with lower genetic diversity, particularly when inappropriate reference populations are applied during likelihood ratio calculations. These findings highlight the importance of population genetic diversity in forensic DNA interpretation and support the need for more conservative and carefully validated approaches to complex mixture analysis. In chapter 3, I examine forensic age estimation using DNA methylation-based epigenetic clocks. Many forensic age prediction models rely on linear regression approaches despite methylation changes exhibiting non-linear dynamics across the lifespan. To address the limitation, I compare multivariable linear regression, random forest regression, and a LOESS-based maximum likelihood framework using publicly available droplet digital PCR methylation datasets. I show that the non-linear maximum likelihood framework consistently reduces age-associated residual bias relative to traditional approaches while maintaining competitive predictive accuracy. These results demonstrate the value of non-linear modeling techniques for improving the reliability and interpretability of forensic epigenetic age estimation. Together these studies emphasize the importance of statistical rigor, bias evaluation, and biological variability in the development of forensic genomic and epigenomic tools for forensic investigations.BioinformaticsForensic sciencesForensic anthropologyGeneticsDNA methylationDNA mixture interpretationepigenetic age estimationforensic genomicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/63t1d6b6https://escholarship.org/content/qt63t1d6b6/qt63t1d6b6.pdfetdoai:escholarship.org:ark:/13030/qt6sp2d3ds2026-06-04T06:43:17Zqt6sp2d3dsPost-school Engagement Outcomes and Predictors for High School Leavers with DisabilitiesKim, Eunice KunyoungKasari, Connie L2026-06-01Transitioning from high school to adulthood presents unique challenges for students with disabilities, particularly given the abrupt end of school-based services upon graduation or aging out. Despite federal mandates under the Individuals with Disabilities Education Act (IDEA, 2004) to support postsecondary transition, students with disabilities continue to lag behind their peers without disabilities in postsecondary engagement, college enrollment, and employment. This study examined demographic characteristics and in-school experiences as predictors of three postsecondary outcomes: postsecondary engagement, college enrollment, and competitive employment among high school leavers with disabilities in a large, diverse urban school district in Southern California. Using special education administrative records spanning 2008 to 2019, this cross-sectional study analyzed data from 13,823 students with documented postsecondary outcomes within one year of exiting high school. Multilevel logistic regression models were estimated to account for clustering within five geographic regions. Predictors included disability type, race/ethnicity, sex, free and reduced lunch status, English language learner status, inclusion in general education, diploma attainment, school type, and parental participation in the Individualized Education Plan (IEP). Results revealed persistent inequities shaped by both demographic factors and school-based experiences. Students with Emotional Disturbance faced the highest rates of disengagement, with 35.93% having no postsecondary engagement. Hispanic and African American students had significantly lower odds of postsecondary engagement and college enrollment compared to White peers. Students with Intellectual Disability had higher odds of engagement but substantially lower odds of college enrollment, often being routed toward adult programs. Inclusion in general education and diploma attainment were consistently associated with higher odds of postsecondary engagement and college enrollment across all models. In contrast, employment outcomes followed a distinct pattern, with students not graduating with a diploma and those attending continuation or alternative schools showing higher odds of competitive employment, suggesting divergent postsecondary pathways. Findings underscore the importance of equitable access to inclusive education, rigorous transition planning, and family engagement in supporting diverse postsecondary trajectories for students with disabilities. Limitations include the use of secondary administrative data, a one-year follow-up window, and the absence of school-level identifiers. Implications for policy and practice are discussed.Special educationDisability studiesBehavioral psychologycompetitive employmentdisabilitiesinclusionpostsecondary outcomesspecial educationtransitionsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6sp2d3dshttps://escholarship.org/content/qt6sp2d3ds/qt6sp2d3ds.pdfetdoai:escholarship.org:ark:/13030/qt1jz310z12026-06-04T06:43:12Zqt1jz310z1Backlash from the Streets: Public Demonstrations and their Impact on the Trade Agreement Ratification Process in Latin AmericaTrejo, AlfredoPeters, Margaret E2026-06-01Do public demonstrations by anti-trade activists affect the ratification process of preferential trade agreements in Latin America? Past research on the trade agreement process has largely focused on the composition of the trade partners and the domestic political actors with veto power. Yet, neoliberal market-driven reforms, like trade agreements, have been the catalyst for significant protest campaigns for the past 50 years. This project examines the relationship between protests against trade agreements in Latin America and the duration of the ratification process using event history modeling and an original data set. I find that public demonstrations can delay the ratification process, but that those effects can be mitigated by other factors, such as power asymmetries between the partner countries. The project examines the events that led to the creation of the Dominican Republic–Central American Free Trade Agreement (DR-CAFTA) with the United States, and the effect of public demonstrations during the ratification process of the treaty in Costa Rica. Drawing on semi-structured interviews with Costa Rican experts on DR-CAFTA, this study offers insights into why the “No” campaign started and what they accomplished.Political scienceLatin American studiesInternational relationsPublic administrationCAFTACosta RicaDominican Republic--Central American Free Trade AgreementpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1jz310z1etdoai:escholarship.org:ark:/13030/qt67c390x52026-06-04T06:43:07Zqt67c390x5Next-Generation Innovations for Liver Transplant Diagnostics and Tissue EngineeringTang, Rui ChianDi Carlo, Dino2026-06-01Innovations in diagnostic tools and tissue engineering technologies have been indispensable in advancing healthcare and biomedical research. In the context of liver transplantation, these technologies can facilitate clinical decision-making before and after transplantation, improve post-transplant patient care, and contribute to future regenerative strategies that may reduce reliance on donor organs.This dissertation develops point-of-care diagnostic and biomaterial platforms to address key bottlenecks across the transplant continuum, including rapid pre-transplant graft evaluation, accessible post-transplant drug monitoring, and scaffold designs for future regenerative and organ-replacement strategies. For the diagnostic components, this work uses a vertical flow assay (VFA) platform, a paper-based testing format in which liquid samples flow through stacked assay layers to generate visible signals that can be captured by a portable reader or smartphone. This approach enables rapid and accessible measurement of multiple biomarkers or drug levels outside of centralized laboratory settings. For the biomaterial component, this work also develops a microporous annealed particle scaffold that supports three-dimensional cell organization and tissue ingrowth for future regenerative applications.In Chapter 1, we present a multiplexed diagnostic test for evaluating inflammatory markers in donor livers during normothermic machine perfusion. Although inflammatory cytokines such as IL-6, TNF-α, and IL-8 have been associated with transplant outcomes, they are not routinely screened because conventional laboratory assays are time-consuming, instrument-dependent, and difficult to implement during time-sensitive graft assessment. By adapting these biomarkers to the vertical flow assay platform, this work enables their simultaneous detection through a sandwich immunoassay format. Colorimetric signals generated on the assay membrane are captured with portable imaging and interpreted using a machine learning-based decision model to classify perfusate inflammatory status, correctly identifying elevated inflammatory status in a perfusate sample from a clinically discarded donor liver. This diagnostic tool may support more timely liver graft assessment, improve organ utilization, and reduce post-transplant complications.In Chapter 2, we develop a hydrophobic drug extraction workflow and a point-of-care diagnostic for detecting an immunosuppressant drug, tacrolimus, in post-transplant patient-blood samples. Proper control of tacrolimus levels in post-transplant patients is key to preventing nephrotoxicity from overexposure and enhanced risk of organ rejection from underexposure. However, the current LC-MS-based monitoring relies on centralized laboratory workflows, which can prolong result turnaround and delay timely dose adjustment. To address this limitation, we present a competitive vertical flow assay for tacrolimus, integrated with a liquid-liquid extraction method that isolates tacrolimus from the blood matrix into an aqueous phase that is compatible with the paper-based immunoassay. Together, the assay and extraction workflow provide a foundation for rapid therapeutic drug monitoring and may be adaptable to other hydrophobic small-molecule drugs that are difficult to detect directly in complex biological matrices.While Chapters 1 and 2 focus on diagnostic bottlenecks in transplantation, Chapter 3 addresses the longer-term engineering challenge of engineering scaffold architectures that may support tissue formation and future organ-replacement strategies. Specifically, we manufacture hydrogel microparticles that assemble into microporous granular architectures to facilitate cell migration and proliferation. By engineering individual particles into a crescent shape, we enhance scaffold porosity and pore interconnectivity. These geometric cues enhance cell infiltration and proliferation both in vitro and in vivo without the addition of chemoattractant or biochemical cues. This chapter highlights the critical role of three-dimensional geometry in cellular recruitment and its potential for next-generation regenerative scaffolds and future organ-replacement strategies.Overall, this dissertation introduces platform technologies to address the clinical need for organ transplantation, including pre-transplant graft assessment, post-transplant therapeutic monitoring, and regenerative scaffold design. These technologies have the potential to be applied to other biofluid-based diagnostic applications, small-molecule drug extraction workflows, and biomaterial systems for three-dimensional cell expansion. By expanding on these innovative technologies, we can advance the forefront of diagnostics and tissue engineering, eventually improving patient care and biomedical translation.BioengineeringBiomedical engineeringHealth care managementDiagnosticsLiver TransplantMicrofluidicsPoint of CareTissue EngineeringpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/67c390x5etdoai:escholarship.org:ark:/13030/qt6wx946c72026-06-04T06:43:03Zqt6wx946c7Label-Efficient Learning for Medical Imaging and Multimodal Healthcare AIVepa, Arvind MurariSun, Yizhou2026-06-01Deep learning has seen remarkable advancements in machine learning, yet it often demands extensive annotated data. In healthcare, vast clinical datasets are available, yet several barriers limit their use for training medical deep learning models, including privacy constraints, noisy or missing clinical annotations, and imperfect label extraction methods. These challenges slow both research progress and real-world deployment. In my PhD, I explore active learning and various forms of weak supervision to reduce annotation cost while preserving model performance. I then apply what we learned to emerging problems in medical AI, demonstrating strong performance relative to clinicians and highlighting the feasibility of developing low-cost, clinically useful medical foundation models.Computer scienceArtificial intelligenceHealth care managementArtificial IntelligenceDataHealthcareMultimodalSegmentationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6wx946c7https://escholarship.org/content/qt6wx946c7/qt6wx946c7.pdfetdoai:escholarship.org:ark:/13030/qt6hp234bz2026-06-04T06:42:57Zqt6hp234bzAugmented Causal Diagrams and Estimands for Causal Inference in Non-Communicable and Infectious Disease EpidemiologyCoates, Matthew MajerusArah, Onyebuchi A2026-06-01Causal questions in public health research are often posed about exposures and outcomes that are situated within complex systems. The studies used to answer these questions are often observational, and data can be messy, containing mismeasured or misclassified variables, omitting information about key confounders, and reflecting only partially observed causal processes. In these contexts, careful causal inference – including defining a meaningful and clear causal estimand and specifying a causal model that describes the causal processes underlying the relationships between the exposure and the outcome – is necessary to produce valid, policy-relevant analysis. This dissertation focuses on causal diagrams and estimands. The first study proposes an algorithm for constructing causal diagrams for interventionist causal inference using published causal loop diagrams and illustrates its use in cancer screening and COVID-19 vaccination applications. Automated software that accepts user inputs about the exposure(s) and outcome, as well as additional assumptions, could facilitate easier translation of CLDs to DAGs, which can be arduous for large, complex CLDs. The second study uses augmented causal diagrams, causal estimands, and simulation to understand lead-time bias and co-occurring biases in cancer screening studies. Lead-time bias can be classified as information bias arising from measurement error in the outcome (survival from diagnosis rather than from some other fixed time point) that is differential with respect to screening. It generally co-occurs with selection bias resulting from restricting analysis to people who have been diagnosed. The study also demonstrates that specification of a clear causal estimand is necessary to understand the nature and magnitude of the bias and explores challenges with target populations related to cancer status, which is only known after the intervention of interest. The third study uses augmented causal diagrams to inform an empirical study examining whether pregnant and postpartum women remain at continued risk of more severe COVID-19-related outcomes than non-pregnant women using commercial health insurance claims data. Concerns about measurement and uncontrolled confounding led to analytical choices regarding outcomes and to sensitivity analyses to strengthen the validity of the study findings, which indicate that pregnant women continue to be at increased risk of severe COVID-19 outcomes in the post-pandemic period.EpidemiologyPublic healthHealth sciencescausal diagramCausal infereceCOVID-19lead-time biaspregnancypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6hp234bzetdoai:escholarship.org:ark:/13030/qt3d25478n2026-06-04T06:42:52Zqt3d25478nCounterinsurgent Care: Social Work, Policing, and the Management of Black LiberationMurrray, Bethany JoTurner, David CRoy, Ananya2026-06-01This dissertation, Counterinsurgent Care: Social Work, Policing, and the Management of Black Liberation, examines how social work has been mobilized as a bureaucratic technology to maintain state legitimacy by absorbing Black rebellion and critiques of law enforcement into palatable reforms that maintain racial domination. Focusing on Los Angeles between the Watts Rebellion (1965) and the Tax Revolt (1978), this study situates this era of law enforcement reform within the long durée of neoliberalism, an era characterized by fiscal austerity and the reorganization of welfare and social care. Across these decades, social work was deployed not only as a “caring” response to police violence amidst racial unrest but as an instrument of bureaucratic violence, translating demands for structural change into administrative reforms that stabilized white supremacy and state authority.Drawing on Antonio Gramsci’s theorization of conjuncture and Stuart Hall’s method of conjunctural analysis, the dissertation conceptualizes this period as a care conjuncture: a moment in which the meaning of care, safety, and reform was rearticulated through struggle. The Watts Rebellion and Proposition 13 Tax Revolt emerge as two “overdetermined events” that bookend this conjuncture. The first organic crisis (police violence) revealed a hegemonic struggle over who is deemed worthy of care and safety, and exposed how anti-Blackness underpins the state’s social order. The second crisis (fiscal responsibility) arose in backlash to investment in care and social services, reasserting white dominance through austerity, privatization, and propertied wealth. Together, these crises illuminate how neoliberal carceral governance transformed racialized fear and resentment into fiscal discipline, institutionalizing scarcity as a mode of social care.Through archival research, oral histories, and policy analysis, this study traces how social work was repeatedly reconfigured at the nexus of care and coercion, expanding through post-Watts welfare reforms and contracting amid the white suburban tax revolt and austerity politics of the late 1970s. Later, echoes of this unaddressed care conjuncture would reemerge in twenty-first-century “community safety” and “trauma-informed” frameworks following the 2020 uprisings. In each instance, social work’s ostensibly benevolent reforms operated as what Gramsci terms passive revolutions, absorbing elements of oppositional critique (such as calls for accountability or community control) to reconstitute hegemony without dismantling the racial state.Social workHistoryLaw enforcementPolitical scienceBlack studiesAbolitionCarceral StudiesCritical theoryOral historyPolice reformSocial work historypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3d25478netdoai:escholarship.org:ark:/13030/qt6wc341zp2026-06-04T06:42:47Zqt6wc341zpInvestigating Mitochondrial Heat Shock Stress in Non-Small Cell Lung CancerRodriguez, Jocelyn DianeShackelford, David B2026-06-01Non-Small Cell Lung Cancer (NSCLC) is a leading cause of cancer-related deaths worldwide. The 5-year survival rate for advanced NSCLC is 6%, highlighting the need for targeted therapies. While recent advances have improved outcomes, many patients develop resistance to therapies. Limited studies have mapped the mitochondrial heat shock protein (mHSPs) and the regulation of mitochondrial stress in NSCLC. mHSPs are crucial signaling molecules for protein folding and maintaining mitochondrial homeostasis. In this study, we dissect the metabolic vulnerabilities regulated by HSP75, a mHSP in NSCLC. HSP75 prevents apoptosis, protects cells from cellular stress and is selectively expressed in multiple cancers. Here, we show that HSP75 localizes to the mitochondrial matrix in both lung adenocarcinoma (LUAD) and squamous (LUSC) carcinomas. Genetic and pharmacological inhibition of HSP75 exhibits differential sensitivity in lung cancer cell viability. Notably, pharmacological inhibition of HSP75 in vivo significantly attenuated tumor growth, suggesting therapeutic vulnerability. Our study highlights targeted HSP75 inhibition can potentially be used as a therapeutic regiment for NSCLC.Molecular biologyCellular biologyOncologyBiochemistryImmunologyHeat Shock proteinsHSP75Lung CancerMitochondriaMitochondria StressTRAP1publiceScholarship, University of Californiahttps://escholarship.org/uc/item/6wc341zpetdoai:escholarship.org:ark:/13030/qt1dn7v3xx2026-06-04T06:42:42Zqt1dn7v3xxQuantum Averaging Theory: Effective Hamiltonians Beyond the RWA for Multi-Timescale Driven Quantum SystemsBarajas, KristianCampbell, Wesley C2026-06-01Driven quantum systems are central to quantum computation and quantum control across every major hardware platform. In the simplest setting, a qubit driven near resonance is well described by the rotating-wave approximation, which eliminates rapidly oscillating counterrotating terms and retains the slowly-varying resonant interaction. This picture breaks down when drives span multiple frequencies at incommensurate values, when near-resonant beat notes arise from higher-order interactions, or when corrections beyond leading order cannot be neglected. The existing toolkit handles each of these regimes separately: Floquet–Magnus and Van Vleck high-frequency expansions preserve unitarity for periodic drives but fail at resonance, while adiabatic elimination and Schrieffer–Wolff transformations break down as the detuning approaches the coupling strength. No single method treats the general multi-timescale case within a systematic, unitarity-preserving framework.We present a quantum averaging theory (QAT) that resolves this by integrating the Magnus expansion with the classical method of averaging on multiple timescales. QAT factorizes the unitary evolution into a slow propagator governed by a Hermitian effective Hamiltonian and a dynamical phase operator encoding the fast oscillatory dynamics, with unitarity preserved at every order.In the far-detuned regime, QAT recovers and generalizes Magnus-based high-frequency expansions to almost-periodic multi-modal drives, with explicit error bounds. The central result is the multi-timescale extension, where near-resonant drives are not averaged away and the effective Hamiltonian retains a slow time-dependence. Two complementary derivations are given: a deductive two-timescale derivative expansion and an inductive Partitioned Expansion by Timescale Separation (PETS), shown to be equivalent and extendable to arbitrarily many timescales through an iterative renormalization procedure.We further extend QAT to open quantum systems by replacing the Schrödinger equation with the Lindblad master equation, yielding an effective Lindbladian that captures the coarse-grained dynamics and a dissipative dynamical phase encoding fast decoherence at off-resonant frequencies.Together, these results constitute a unified framework for driven quantum systems, demonstrated on touchstone problems in quantum optics including Rabi oscillations and Raman transitions with higher-order corrections, with broad applicability to any driven quantum system wherever multiple timescales compete.Quantum physicsAtomic physicsPhysicsTheoretical physicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1dn7v3xxhttps://escholarship.org/content/qt1dn7v3xx/qt1dn7v3xx.pdfetdoai:escholarship.org:ark:/13030/qt3s41v0pm2026-06-04T06:42:37Zqt3s41v0pm(Des)Madres: Motherhood in Contemporary Latin American LiteratureJaramillo, Brenda SaraíMarturano, Jorge2026-06-01This doctoral thesis links three different authors writing about motherhood in Argentina, Brazil, and Colombia to analyze how women writers throughout Latin America are representing the institution of motherhood. By focusing on the dissonance between institutional motherhood and individual mothering, this dissertation explores how women produce and reproduce knowledge about motherhood, an institution that interpellates them as gendered subjects. Specifically, this dissertation explores how women strategically appropriate, reject, and subvert patriarchal motherhood, paying close attention to their unexamined expectations and the moments of negotiation that unsettle them.The principal objects of analysis include Clarice Lispector’s Laços de Família, Pilar Quintana’s La perra, and Camila Sosa Villada’s Las malas. These texts use motherhood as a site of discursive identification wherein larger institutions are navigated, subverted, or reinforced. To ground the texts in their proper thematic and cultural context, this dissertation also engages a variety of other authors and cultural productions, including novels, short stories, memoirs, and translations that illustrate by comparison the principal text’s significance.The analytical arc of this dissertation begins by studying the moment of epiphany in which a mother becomes aware of the extent of her own complicity in the institution of motherhood. From there, it moves on to a marginalized woman’s attempt to secure social significance via a strategic appropriation of institutional motherhood. Finally, it ends with a study of a queer mother’s defiant care work that directly challenges the state’s insistence on binary identification. In all three texts, institutional motherhood is revealed as a tool of control, imposed by an internalized biopolitics of gender that demands obedience and submission to another’s needs. This results in a living death: the Claricean walking slumber from which mothers awaken, terrified; a vampiric motherhood rooted in extractive logics of social death; a celebration of a dead mother’s mythic breast.By combining the interdisciplinary framework of maternal theory with the protagonism of mothers in contemporary literature written by Latin American women, this dissertation advances Guadalupe Nettel’s call to “de-sacralize” biological motherhood. This dissertation thus bridges the divide between Latin American literary studies and motherhood studies. Esta tesis doctoral vincula a tres autoras que escriben sobre la maternidad en Argentina, Brasil y Colombia, con el fin de analizar cómo las escritoras de América Latina representan la institución de la maternidad. Al centrarse en la disonancia entre la maternidad institucional y el ejercicio individual de la maternidad, esta disertación explora cómo las mujeres producen y reproducen conocimientos sobre la maternidad, una institución que las interpela como sujetos generizados. Específicamente, este trabajo estudia cómo las mujeres se apropian estratégicamente de la maternidad patriarcal —así como la rechazan y la subvierten—, prestando especial atención a sus expectativas inexploradas y a los momentos de negociación que las desestabilizan.Los principales objetos de análisis incluyen Laços de Família, de Clarice Lispector; La perra, de Pilar Quintana; y Las malas, de Camila Sosa Villada. Estos textos utilizan la maternidad como un espacio de identificación discursiva en el que se transitan, subvierten o refuerzan instituciones de mayor envergadura. Para situar estos textos en su contexto temático y cultural adecuado, la disertación dialoga también con una variedad de otras autoras y producciones culturales —incluyendo novelas, cuentos, memorias y traducciones— que ilustran, por vía comparativa, la relevancia de las obras principales.El arco analítico de esta disertación comienza con el estudio del momento epifánico en el que una madre toma conciencia del alcance de su propia complicidad con la institución de la maternidad. A partir de ahí, avanza hacia el intento de una mujer marginada por asegurar su relevancia social mediante una apropiación estratégica de la maternidad institucional. Finalmente, concluye con un análisis de la maternidad desafiante de una madre travesti cuya adopción de un bebé abandonado directamente reta la lógica binaria del Estado. En los tres textos, la maternidad institucional se revela como una herramienta de control, impuesta por una biopolítica de género interiorizada que exige obediencia y sumisión a las necesidades ajenas. El resultado es una muerte en vida: ese «sueño despierto» de estirpe clariceana del que las madres despiertan aterrorizadas; una maternidad vampírica, arraigada en lógicas extractivas de muerte social; una celebración del pecho mítico de una madre muerta.Al combinar el marco interdisciplinario de la teoría de la maternidad con el protagonismo de las madres en la literatura contemporánea escrita por mujeres latinoamericanas, esta disertación da continuidad al llamado de Guadalupe Nettel a «desacralizar» la maternidad biológica. De este modo, el presente trabajo tiende un puente entre los estudios literarios latinoamericanos y los estudios sobre la maternidad.Latin American literatureGender studiesLatin American studiesWomens studiesLatin American literatureLatin American motherhoodapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3s41v0pmhttps://escholarship.org/content/qt3s41v0pm/qt3s41v0pm.pdfetdoai:escholarship.org:ark:/13030/qt0tm701mz2026-06-04T06:42:33Zqt0tm701mzAutomation and AI for Data-Driven Radiotherapy Planning, Plan Quality Assessment, and Large-Scale Data IntegrationAbdulkadir, YasinLamb, James M2026-06-01Radiotherapy treatment planning relies on labor-intensive contouring, iterative optimization, and fragmented data systems, which contribute to inefficiency, inter-planner variability, and barriers to large-scale quality improvement. This dissertation develops and evaluates automation and artificial intelligence tools to improve contouring, data integration, dose prediction, and plan quality assessment in radiation oncology.First, deep learning models were developed for automated segmentation across multiple radiotherapy settings. For 0.35T MRI-guided pelvic radiotherapy, a UNet++ workflow achieved median Dice coefficients of 0.92, 0.92, 0.93, and 0.87 for the right and left femoral heads, bladder, and rectum, respectively, with 59% to 96% of contour slices accepted without modification in clinical use. For CT-guided high-dose-rate brachytherapy, models achieved median Dice coefficients of 0.95 for bladder and 0.87 for rectum on clinically relevant target-containing slices. This work also developed a multi-modal framework for head-and-neck gross tumor volume auto-contouring using planning CT, T1- and T2-weighted MRI, and PET, extending automation from organs at risk to tumor delineation.Second, a generalized software framework was developed to consolidate radiotherapy planning and delivery data from heterogeneous clinical systems. Starting from treatment records and recursively resolving DICOM relationships, the framework reconstructed complete treatment datasets across institutions. In a two-clinic deployment spanning eleven years, it retrieved and linked 13,871 radiotherapy plans from 6,164 patients with a 99.76% success rate.Third, a MedNeXt-based 3D dose prediction model was developed for heterogeneous multi-cohort radiotherapy planning. On the GDP-HMM validation set, the model achieved a mean absolute error of 2.04 Gy, and an ensemble achieved 2.071 Gy on the held-out test set, corresponding to first-place overall performance. Direct application to independent institutional head-and-neck data showed substantial domain shift, but fine-tuning reduced mean absolute error from 7.387 to 3.676 Gy. Building on this work, a plan quality assessment framework was developed using predicted 3D dose as a patient-specific benchmark compared with clinical plans through dose-volume histogram-based and spatial metrics.Together, these studies show that clinically useful radiotherapy automation requires accurate segmentation, scalable data infrastructure, adaptable dose prediction, and objective plan evaluation. The resulting framework supports efficient planning, reduced variability, and patient-specific quality assessment in a learning radiotherapy system.PhysicsBiomedical engineeringBiologyArtificial intelligenceArtifical IntelligenceAutomated SegmentationDose PredictionRadiation OncologyRadiotherapyTreatment PlanningpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0tm701mzetdoai:escholarship.org:ark:/13030/qt9p4160zz2026-06-04T06:42:28Zqt9p4160zzA Steenrod Square for Link Floer HomologyTao, YanSarkar, Sucharit2026-06-01Link Floer homology is an invariant of knots and links by Ozsv´ath-Szab´o with many powerful applications. Recently, Manolescu-Sarkar constructed a stable homotopy type for the “hat version” of link Floer homology, which is equipped with stable homotopy invariants which are conjectured to contain additional information compared to link Floer homology.In this dissertation, we give an algorithm to compute second the Steenrod square Sq2 for link Floer homology. In order to do so, we use the grid homology formulation of link Floer homology due to Manolescu-Ozsv´ath-Sarkar to explicitly compute the framings of the lowdimensional moduli spaces used in the Manolescu-Sarkar construction.In the meantime, we also extend the Manolescu-Sarkar construction to the more full link Floer complex CFK + , though for this “plus version” the stable homotopy type is not known. In the plus version, we obtain a framed 1-flow category, which is a construction by LobbOrson-Sch¨utz which contains enough information to find the second Steenrod square. Finally, we show some computations of Sq2 for the link Floer homology of certain knots.MathematicsApplied mathematicsFloer homologyKnot theoryLow-dimensional topologyStable homotopyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9p4160zzhttps://escholarship.org/content/qt9p4160zz/qt9p4160zz.pdfetdoai:escholarship.org:ark:/13030/qt5s40333s2026-06-04T06:42:23Zqt5s40333sEssays on Dynamic Games and Mechanism DesignMagganas, AristotleBoard, Simon A.2026-06-01This dissertation consists of two essays on dynamic game theory and mechanism design.The first chapter studies the credibility of optimal deadlines when a principal and an agent jointly contribute to a project of uncertain duration. The agent is privately informed about how quickly the project is likely to succeed and is more eager than the principal to start and continue. Deadlines can screen out projects unlikely to pay of soon, which the principal would rather not undertake. But deadlines may fail to be credible: once one expires without success, the principal may prefer to extend it, and the agent may anticipate this when deciding whether to start, harming the principal. I study when optimal deadlines are credible and when the ability to commit makes a difference. If the project’s hazard rate is always decreasing, as in a Poisson good-news environment, commitment has no value. With more general unimodal hazard rates, optimal deadlines may fail to be credible; lack of commitment can then lead either to complete unraveling or to relaxed deadlines that induce the agent to start even when success is likely to take a long time.The second chapter studies advance sales by a capacity-constrained, profit-maximizing seller. Consumers enter over two periods and learn their valuations in the second period, when they may consume the service. Early entrants can invest to affect the distribution of their eventual valuations. The seller cannot observe entry times, investment choices, or valuations, but can choose any selling mechanism. Under certain conditions, an optimal mechanism is an advance contract giving early entrants the option to consume or receive a refund, paired with a posted price for late entrants. Because early entrants can mimic late entrants, the terms offered to late entrants determine consumer surplus for both cohorts. To limit this surplus, the seller under-provides the service to late entrants relative to the first best. When capacity binds, this distortion leads to over-provision to early entrants and induces socially excessive investment. The chapter also shows how the endogeneity of early entrants’ valuation distribution, together with their ability to mimic late entrants, affects optimal pricing.Economic theoryEconomicsHome economicsFinanceadvance salescredible deadlinesdynamic gameseconomic theorymechanism designapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5s40333shttps://escholarship.org/content/qt5s40333s/qt5s40333s.pdfetdoai:escholarship.org:ark:/13030/qt53w7q39v2026-06-04T06:42:19Zqt53w7q39vA Holistic Approach to Develop the Next Generation of Aqueous Zinc-Ion Energy Storage SystemsUemura, Sophia KananiKaner, Richard R.B.K.2026-06-01Lithium-ion batteries have become the dominant energy storage technology for portable electronics and electric vehicles, thanks to their high energy density and long cycle life, but growing global demand for sustainable, large-scale storage has revealed their limitations. Key challenges include lithium scarcity, rising costs, safety risks from flammable organic electrolytes, and environmental concerns. Aqueous zinc-based batteries have emerged as promising candidates for safe and cost-effective energy storage, benefiting from the intrinsic nonflammability of water-based electrolytes, the natural abundance of zinc, and the high theoretical capacity of Zn metal. Despite their great potential, this technology remains underdeveloped for practical use. To address the limitations, the development and improvement of all facets of a Zn-based energy storage systems is discussed in this dissertation.First, a 3D-printed flooded electrochemical test cell is presented as an alternative to conventional open beaker configurations for aqueous energy storage research. A sealed, customizable cell design using Computer-Aided Design (CAD) and Fused Deposition Modeling (FDM) 3D printing with polycarbonate filament is proposed to address the issues of electrolyte evaporation, inconsistent electrode placements, and poor reproducibility. As a result, this custom-designed, modular 3D-printed test cell achieves minimal standard errors and maintains electrolyte integrity for over a month. Additionally, this set-up in a full-cell configuration enabled stable long-term cycling, further reinforcing the practicality of the design for realistic electrochemical device architectures.In Chapter 3, a interphase was composed of Ag nanoparticles embedded in a laser-scribed reduced graphene oxide framework (Ag-rGO@Zn) is established to mitigate dendrite formation and corrosion in zinc metal anodes. The conductive rGO framework facilitates charge transport while Ag nanoparticles act as zincophilic nucleation sites that guide uniform Zn deposition, together suppressing side reactions and stabilizing cycling tests. Furthermore, time-of-flight secondary ion mass spectrometry (TOF-SIMS) analysis confirms the Ag-guided Zn deposition behavior. This approach demonstrated significantly improved cycling stability and reduced polarization, with the practical viability further confirmed in a flexible pouch cell, offering a scalable strategy to enable an ultra-stable zinc metal anode for next-generation zinc-ion energy storage.In Chapter 4, a zinc-coordinated interphase is introduced to regulate dynamics of the desolvation step during Zn2+ electrodeposition to mitigate the irreversible loss of Zn and electrolyte. A histidine-rich zinc-coordinated carbon nanodot (H-ZnCND) is introduced to alleviate electrolyte consumption during Zn plating/stripping process. Experiments and simulations confirm that the interphase regulates dendrite-free electrodeposition and reduces parasitic side reactions. This approach demonstrated significantly improved cycling stability and Coulombic efficiency in full cells, even under lean-electrolyte conditions, offering a promising strategy for low-electrolyte, high-energy-density batteries.In Chapter 5, an amine-functionalized polybenzimidazole membrane (APBI) was fabricated using non-solvent induced phase separation to optimize the surface chemistry and pore morphology, respectively. The functionalized amine groups can actively scavenge hydroxyl ions and suppress free water activity, whereas the nano morphological surface can suppress Zn dendrite propagation, mitigate corrosive side reactions, and facilitate fast Zn2+ transport. Post-mortem analysis was done by topological SEM analysis which reveals an uniform, planar deposition on the electrode with APBI. Full cells using APBI separator had an extended cycling lifetime, supporting its potential for industrial applications. The superior performance of APBI separators demonstrate their potential as a viable alternative to conventional glass fiber separators in practical applications.In Chapter 6, a freestanding, binder-free 3D carbon lattice (3DC) integrated with vanadium oxide (VOx) is proposed as a high-performance cathode for zinc-ion supercapacitors. Using stereolithography 3D-printing and electrodeposition, this architecture enables ultra-high mass loading (38 mg cm-2 ) and surface area and areal capacitance greater than commercially available activated carbon. Our findings highlight the synergistic benefits of combining VOx with SLA-enabled 3D carbon scaffolds, offering a scalable and robust strategy for next-generation ZISC cathodes.ChemistryEnergyNanotechnologyAnalytical chemistryElectrical engineeringenergy storagezinc ion batteriesapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/53w7q39vhttps://escholarship.org/content/qt53w7q39v/qt53w7q39v.pdfetdoai:escholarship.org:ark:/13030/qt96d0f2r62026-06-04T06:42:14Zqt96d0f2r6Locating “Home”: Constructing West Indian American Identity Through Caribbean Popular MusicRhodd-Lee, HollandJohnson, Jenny2026-06-01This dissertation examines how Caribbean popular music (CPM) facilitates pluralistic identity construction in New York City's West Indian diaspora. Utilizing narrative ethnography, census data, and a multidisciplinary literature review, I argue that Afro-West Indian communities use CPM styles—specifically Jamaican dancehall and Trinibagonian soca—as musical contact zones for transcultural identity construction. Despite substantial scholarship on Caribbean American migration, musical taste formation, and music’s role in identity construction, no critical analysis addresses music-making as a site for transcultural engagement in U.S.-based Afro-West Indian communities. Drawing on diaspora, postcolonial, and transcultural studies, my work addresses this gap by situating Afro-West Indian musico-cultural practices within transcultural identity construction discourse.My work employs the Pluralized Identity model, an anti-essentialist framework I developed to conceptualize the imaginary as a site for diasporic identity formation. It draws on Premdas, Bhabha, Glissant, Rushdie, and Smith to examine how hybridity operates alongside structural inequality, recognizing that identities remain fluid yet crystallize around shared marginalization. Integrating Mann’s embodied cognition (2015), Butterfield’s selective acculturation (2003), Hulme’s extended-Caribbean (1986), and Meehan's decolonizing contact zones (2008), my analysis reveals how CPM operates as a space wherein Afro-West Indian Americans navigate these inequalities while constructing transcultural identities.My dissertation contributes to Musicology and Caribbean Studies by 1) addressing a literature gap, 2) illustrating how Afro-West Indian Americans interact with CPM, and 3) examining how cultural artifacts facilitate pluralistic identity formation.Caribbean studiesMusicCultural anthropologyCaribbean Popular MusicDancehallDiaspora StudiesIdentity PoliticsSocaWest Indian Cultureapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/96d0f2r6https://escholarship.org/content/qt96d0f2r6/qt96d0f2r6.pdfetdoai:escholarship.org:ark:/13030/qt8q04g03w2026-06-04T06:42:08Zqt8q04g03wLinking the Gap Between Traumatic Brain Injuries and Cognitive Impairments Through the Creation of Machine Learning-Based Diagnostic and Prognostic Clinical ToolsAshikyan, SonyaMonti, Martin MKnowlton, Barbara J2026-06-01A traumatic brain injury (TBI) is a condition in which normal brain function is disrupted because of an external force which impacts the head. There are three main types of TBI: mild, moderate, and severe, with sports-related concussions (SRC) being a subcategory of mild cases. Mild TBI and sports-related concussions often involve slowed processing speed, attention deficits, difficulty recalling memories, trouble sleeping, and mood swings. On the other hand, moderate-to-severe cases involve more pronounced brain damage leading to challenges with language, executive function, memory loss, and in many cases, death. In the United States alone, TBI is the cause of over 5.3 million individuals living with a disability and each year an additional 1.7 million Americans also suffer across their lifespan, especially vulnerable groups, such as youth and older adults. Moderate-to-severe TBI leads to about 52,000 deaths each year in the United States alone, making it the main result of injury-induced death and disability with about 86% of these deaths being caused by the withdrawal of life-sustaining therapy. The great majority of those who do survive such an injury typically do not have a full recovery and are forced to live with life-long impairments.This dissertation offers and tests various solutions for the clinical and research settings discussed in the first chapter, which presents a brief literature review of traumatic brain injuries. In the second chapter, the relationship between clinical assessments and structural neural consequences are investigated at the single mild TBI level. In the third chapter, the interaction between sleep quality and structural and functional neural correlates are investigated in sports-related-concussions (SRC). The fourth chapter investigates the relationship between outcome measures and the structural neural consequences of patients with moderate-to-severe TBI experiencing disorders of consciousness (DOC). Lastly, the fifth chapter presents a tool that automates this process by creating a machine learning based clinical tool which serves diagnostic and prognostic purposes using the mild TBI dataset. Overall, this dissertation presents a series of studies suggested to better understand the relationship between cognitive impairments and neural states after a single mild TBI, sports-related concussions, and moderate-to-severe TBI, and offers a solution to incorporate neuropsychological assessments in the real world setting through the creation of a deep learning model.NeurosciencesCognitive psychologyArtificial intelligencePsychologyCognitive ImpairmentCognitive NeuroscienceMachine LearningMultimodal NeuroimagingSports-Related ConcussionTraumatic Brain Injuryapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8q04g03whttps://escholarship.org/content/qt8q04g03w/qt8q04g03w.pdfetdoai:escholarship.org:ark:/13030/qt8hf3q7n32026-06-04T06:42:03Zqt8hf3q7n3Electrically Small Antennas with High Bandwidth-Efficiency Product Leveraging on Ferromagnetic Resonance and Direct Antenna ModulationHuang, Shih-MingWang, Yuanxun2026-06-01The design of electrically small antenna (ESA) is attractive to wireless communication systems, where physical dimensions often bottleneck the miniaturization of low-profile electronics like bioelectronics, avionics, and Internet of Things (IoT) devices. However, the primary challenge of ESA is achieving a high bandwidth-efficiency product (BWEP). According to Chu limit, as the electrical size (ka) drops below unity, the radiation quality factor (Q) increases inversely with the cube of the electrical size. This high radiation Q, in turn, severely constrains the achievable BWEP of matching circuits per Bode-Fano limit. Consequently, conventional highly miniaturized ESAs are either efficient but narrowband or wideband but inefficient. The BWEP limitation of ESA impedes low-profile wireless electronics from establishing wideband links for high-speed wireless communications.This dissertation addresses BWEP bottleneck by proposing a new class of ferromagnetic resonance (FMR) enhanced ESA, termed the “E-Spin ESA”, integrated with direct antenna modulation (DAM). FMR of microwave ferrites can effectively reduce the size of E-Spin ESA, because the resonant frequency of FMR is fundamentally determined by the magnetic bias field but not the physical dimension. It also enhances radiation efficiency due to its low radiation Q compared to conventional ESAs based on structure resonance and a higher material Q compared to typical lumped components. Furthermore, by integrating DAM into the E-Spin ESA, the antenna's resonant frequency is dynamically modulated to synchronize with the instantaneous frequency of wideband Minimum-Shift-Keying (MSK) signals. The DAM integration converts the E-Spin ESA from a linear-time-invariant (LTI) system to a non-LTI system, thereby decoupling the stored energy of the resonator from its resonant frequency. The non-LTI conversion can allow E-Spin ESA to achieve a BWEP that transcends the classical bounds confined by the Chu and Bode-Fano limits.Experimental results for the DAM-enabled E-Spin ESA show a 708% (8.5 dB) improvement on BWEP over the state-of-the-art commercial GPS ESA, despite a smaller electrical size. This work demonstrates the effectiveness of using DAM and FMR to enable ESA with a much higher BWEP compared to existing techniques.Electrical engineeringElectromagneticsCommunicationInformation technologyDirect Antenna ModulationElectrically Small AntennaFerromagnetic ResonancepubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8hf3q7n3etdoai:escholarship.org:ark:/13030/qt3gj1d7jk2026-06-04T06:41:58Zqt3gj1d7jkBeyond Clinical Toxicity: The Financial and Time Burden of Immune-Based Combination Therapy in Metastatic Renal Cell CarcinomaBaclig, Nikita VashiGanz, Patricia2026-06-01Background: Immune-based combination therapies (IBCTs), which are combinations of immune checkpoint inhibitors (ICIs) and tyrosine kinase inhibitors (TKIs), have transformed the treatment of metastatic renal cell carcinoma (mRCC) and have substantially improved survival outcomes. As survival for patients with metastatic cancer continues to increase, there is growing recognition that treatment-related burdens extend beyond traditional physical toxicities. In particular, the financial burden and healthcare time demands associated with modern mRCC therapies remain poorly understood.Methods: This dissertation used a retrospective cohort design utilizing the Merative MarketScan Commercial Claims databases to evaluate adults initiating systemic therapy for newly diagnosed mRCC between 2014 and 2023. Three aims were addressed: (1) to characterize the uptake and dissemination of IBCT over time and identify factors associated with treatment receipt, (2) to evaluate the association between treatment type and financial burden in the first year following treatment initiation, including total and out-of-pocket healthcare costs, and (3) to evaluate the association between treatment type and time toxicity, defined as days with healthcare contact during the first year following treatment initiation. Multivariable generalized linear models were used to evaluate associations between treatment type and study outcomes while adjusting for demographic, geographic, clinical, employment, and insurance-related factors.Results: IBCT uptake increased rapidly following regulatory approval and became the predominant first-line treatment approach for mRCC over the study period. Treatment dissemination varied largely by patient demographic, geographic, and insurance characteristics, suggesting disparities in access to novel therapies. Compared with prior standards of care, IBCT was associated with higher total healthcare expenditures and higher patient out-of-pocket costs during the first year after treatment initiation. IBCT was also associated with greater time toxicity, driven primarily by infusion-related healthcare utilization. Across treatment groups, healthcare utilization represented a substantial component of patients lived experience during the first year of treatment. Conclusions: While IBCT has altered the prognosis of mRCC, these therapies are associated with meaningful financial and time burdens that are not captured by traditional efficacy or safety endpoints. These findings underscore the importance of incorporating patient-centered outcomes into the evaluation of modern cancer therapies and highlight the broader survivorship challenges faced by patients living longer with metastatic cancer.Public healthMedicineEconomicsOncologyImmunologyDissemination and UptakeFinancial ToxicityImmune Based Combination TherapyMetastatic Renal Cell CarcinomaSurvivorshipTime Toxicityapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3gj1d7jkhttps://escholarship.org/content/qt3gj1d7jk/qt3gj1d7jk.pdfetdoai:escholarship.org:ark:/13030/qt5zv048z42026-06-04T06:41:52Zqt5zv048z4CRISPR Screening Reveals Cullin-RING Ligase Complexes as Regulators of Durable T-cell FunctionSaco, JustinRibas, Antoni2026-06-01Adoptive cell therapy (ACT) has emerged as a powerful approach for cancer immunotherapy; however, durable responses in solid tumors remain limited by progressive T-cell dysfunction induced by chronic antigen exposure. Persistent stimulation within the tumor microenvironment drives T-cell exhaustion, characterized by impaired proliferation, diminished cytokine production, sustained inhibitory receptor expression, and loss of long-term effector function. Improving the persistence and durability of engineered T cells therefore represents a major challenge for adoptive cell therapies.This dissertation describes the development and application of functional genomic screening approaches designed to identify pathways regulating durable T-cell function during chronic antigen stimulation. Genome-wide pooled CRISPR-Cas9 screening workflows were established in primary human T cells together with repetitive antigen challenge (RAC) assays modeling prolonged solid tumor antigen exposure through serial melanoma co-culture. Optimization of pooled CRISPR screening workflows and repetitive antigen challenge assays established a physiologically relevant co-culture system capable of imposing sustained antigen-driven selective pressure on engineered T cells, enabling identification of genetic and synthetic engineering strategies that preserve durable effector function.Using integrated genome-wide CRISPR screening and repetitive antigen challenge assays, this work identified Cullin-RING E3 ligase pathways as central regulators of durable T-cell function. Notably, disruption of Cullin5-RING ligase (CRL5) complex components including CUL5, RNF7, and ARIH2 enhanced long-term cytotoxicity, cytokine production, and polyfunctionality in chronically stimulated NY-ESO-1-specific human CD8+ T cells. CRL5-deficient T cells maintained effector-associated transcriptional programs while suppressing exhaustion-related gene signatures during repetitive tumor challenge. Pharmacologic inhibition of cullin neddylation using the NEDD8-activating enzyme inhibitor MLN4924 partially recapitulated these effects and improved T-cell effector function during chronic stimulation. In vivo adoptive transfer studies further demonstrated enhanced tumor control mediated by CRL5-deficient T cells in human melanoma xenograft models.Together, these studies establish physiologically relevant chronic stimulation screening systems for functional genomic discovery in primary human T cells and identify CRL5-associated ubiquitin signaling pathways as negative regulators of durable antitumor response. This work provides a framework for the rational engineering of T cells with sustained effector function and highlights functional genomic approaches as a strategy for improving ACT durability in solid tumors.ImmunologyCellular biologyOncologyPharmacologypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5zv048z4multimediaoai:escholarship.org:ark:/13030/qt7kk4x9n02026-06-04T06:41:46Zqt7kk4x9n0Light-Scattering Spectroscopic Characterization of Quasi-One-Dimensional Bi4I4 Topological van der Waals MaterialThiruthukkal Puthenveettil, NidhishBalandin, Alexander A.2026-06-01his thesis presents a Raman spectroscopy study of Bi4I4, a quasi-one-dimensional van der Waals material that exhibits two crystallographically similar polymorphs with distinct topological insulating phases. These α and β phases are separated by a first-order structural transition near room temperature, which occurs without a change in space group and is associated with a subtle rearrangement of chain stacking registry. The Raman-active phonon modes were investigated using polarization- and angle-resolved micro-Raman spectroscopy with 633 nm and 488 nm excitation sources. Rotation of the crystal relative to the incident polarization enabled the identification of the dominant ?? and ?? vibrational symmetries through their characteristic angular intensity modulation arising from Raman tensor selection rules. A complex Raman tensor formalism was used to describe deviations from the ideal angular response, accounting for absorption-induced phase effects in this anisotropic material. Temperature-dependent Raman measurements across the α–β transition revealed abrupt and hysteretic changes in selected phonon frequencies, linewidths, and relative intensities. These results demonstrate that polarization-resolved Raman spectroscopy can detect subtle stacking-driven structural rearrangements that underlie topological band character, even when global crystallographic symmetry remains unchanged. The obtained results provide valuable insights into the interplay among lattice dynamics, structural distortions, and topological properties in this class of low-dimensional materials, with strong potential for unique functionalities.Materials scienceCondensed matter physicsAnalytical chemistryInterfacesOne-dimensional materialsPhase transitionsPhononsTopological insulatorsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7kk4x9n0https://escholarship.org/content/qt7kk4x9n0/qt7kk4x9n0.pdfetdoai:escholarship.org:ark:/13030/qt0d10d7vv2026-06-04T06:41:42Zqt0d10d7vvThe Material of Mortality: Tree Death and Wooden Objects in Homeric PoetryMoat, Collin JamesPurves, Alex2026-06-01When seeking early Greek conceptions of human-environment relationships, scholars have often turned to the Homeric similes that compare human life and death to the lifecycles of trees. Consideration of the tree similes in isolation, however, provides only a limited view of the relationship between hero and tree. This dissertation analyzes the figurative and non-figurative instances of tree death in Homeric poetry to offer new insights into how tree felling and the production of wood are entangled in human mortality. I argue that, as an important component of Homer’s material poetics, trees and wood reveal the nuances of human mortality that are only fully comprehensible when death is framed in a more-than-human world, where the end of life does not mean the end of material existence. While death stands as an important inflection point, each chapter shows that the long “lifespan” of the tree is bound up with heroic life in many unexpected and inextricable ways.In Chapter 1, I read the Homeric funeral alongside the tree similes, environmental history, and the archaeological evidence for Early Iron Age cremation burials. I show that the funeral creates a mounting pattern of conflation between the wood harvested for the pyre and the corpses of the fallen that culminates in the cremation process, which redraws the distinction between human and nonhuman material and results in a mixture of wood and human ash on the one hand and preserved bones on the other. In a very real way, wood becomes assimilated to the life and body exchanged for heroic fame. As the matter sacrificed in the transformative process of cremation, it thereby acquires a lasting connotation as the material of mortality.Chapters 2 and 3 focus on the ways that Achilles and Odysseus interact with the immanent memories of dead trees in the Iliad and Odyssey, respectively. Drawing on the theories of human-thing entanglement and new materialism, I examine how Achilles’ heightened awareness of the arboreal origins of wooden objects enables his exclusive and sympathetic relationship with the Pelian spear, a weapon which stands out as particularly earthly and mortal among the rest of his divine armament. I then turn to analyze how Odysseus is well known for transforming trees into useful objects to facilitate his homecoming, but his skill in carpentry is at odds with his desire to set down roots and tend to the trees in his orchard on Ithaca. I bring this tension to bear on Odysseus’s act of fixing a wooden oar in the ground and argue that this gesture, which marks the conclusion of the hero’s homeward journey, can be interpreted as a dead tree replanted and thus a reminder of the irrecuperable loss of spending decades away from home.The final chapter examines the Trojan hero Aeneas’s relationship with the tree nymphs of Mount Ida to understand how the lives of humans and nonhumans are overlap in the mythological Troad. To illuminate the lives of these nymphs and their companion trees, I read the description of their lifecycles in the Homeric Hymn to Aphrodite alongside recent scholarship on plant ontology, phenomenology, and language. What emerges is that, despite the radical differences in manner of existence and lifespan, humans and trees intersect and entangle at the significant moments of birth and death, epitomizing the wider ecosystem around Troy in which the human, nonhuman, and divine inhabitants are connected through kinship and care.Classical literatureEnvironmental studiesAncient historyHomerIliadnew materialismOdysseytreewoodpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0d10d7vvetdoai:escholarship.org:ark:/13030/qt8mr315z42026-06-04T05:03:13Zqt8mr315z4Relationship of Prehospital Clinical Biomarkers and New York Heart Association Classification in Management of Heart Failure Patients Infected with COVID-19 and long COVIDBakir, MaralDeVon, HolliRezk-Hanna, Mary2024-01-01AbstractBackground: Clinical management of coronavirus disease 2019 (COVID-19) requires understanding of the virus. Heart failure (HF) patients infected with COVID-19 have a higher risk for worse outcomes compared to those with no HF diagnosis. There is a critical gap in the scientific knowledge on COVID-19 outcomes and clinical management among HF patients. The physiological effects of COVID-19 among HF patients are still largely unknown. There is also a gap in the identification and clinical management of long COVID among HF patients.
Aims. The following research questions were explored: What are the outcomes seen from COVID-19 among HF patients? Is there a difference in prehospital clinical biomarker, NYHA classification and clinical outcome (mortality, hospital length of stay (LOS), and readmission) among HF patients with and without COVID-19? Is there a difference in prehospital clinical biomarkers, NYHA classification, and clinical outcome (mortality, hospital LOS, and readmission) among HF patients with and without long COVID-19? Can prehospital clinical biomarkers and NYHA classification predict the development of long COVID among HF patients? Aim 1 is a review of the literature on the effects of the COVID-19 virus among HF population. Aim 2 compared the prehospital clinical biomarker, NYHA classification, and clinical outcomes (mortality, hospital LOS, and readmission) among HF patients with and without COVID-19. Aim 3 compared the prehospital clinical biomarker, NYHA classification, and clinical outcome among HF patients with and without long COVID and explored any predictive variables.
Methods. A retrospective electronic health record (EHR) review was conducted to answer the research questions. Only HF patients were included in the study and prehospital clinical biomarker as well as NYHA classification were utilized to assess differences among those with and without COVID-19 and to make predictions on the development of long COVID. Data were analyzed using bivariate and multivariate analyses.
Results. Two hundred adult HF patients with hospitalization between January 2020 and December 2023 (n=100) and without COVID-19 (n=100) were included in the study. Prehospital BNP (p=0.006) and mortality (p=0.03) showed significantly higher rates in the HF group without COVID-19. Prehospital BNP was the only marker predictive of mortality (p=0.029). There were no statistically significant differences in NYHA class data. Among HF patients with and without long COVID, there was no statistically significant difference in clinical biomarkers, NYHA classification, and clinical outcome data. No variable was predictive of developing long COVID.
Conclusions. Prehospital biomarkers and NYHA data do little to explain the risk of patients infected with COVID-19. There was no association with long COVID.
Nursingenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8mr315z4https://escholarship.org/content/qt8mr315z4/qt8mr315z4.pdfetdoai:escholarship.org:ark:/13030/qt4qn2881z2026-06-04T05:02:21Zqt4qn2881zStructure-Induced Positive Magnetoelastic Effect in Soft Systems for Voice RecognitionYin, JunyiChen, Jun J.C.2024-01-01The conventional magnetoelastic effect describes the variation in magnetic flux density of certain materials under mechanical pressure within the megapascal range. In this study, we introduce porous microstructures into a soft magnetic system, resulting in an enhanced magnetoelasticity, characterized by a counterintuitive increase in magnetic flux density with the application of subtle mechanical pressure. This phenomenon, termed the structure-induced positive magnetoelastic effect, is attributed to the stress-induced redistribution of micromagnets within the porous, soft system, which exhibits a low Young’s modulus and near-zero Poisson’s ratio behavior. The technological impact of this discovery is demonstrated in its application to acoustic sensing and voice recognition, overcoming the low-pressure detection limits of current platform technologies. This breakthrough holds substantial potential for ultrasensitive pressure detection, with wide-ranging applications in extracting subtle physiological information.Bioengineeringenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4qn2881zhttps://escholarship.org/content/qt4qn2881z/qt4qn2881z.pdfetdoai:escholarship.org:ark:/13030/qt36j7v1gm2026-06-04T05:02:00Zqt36j7v1gmESSAYS ON BEING AND PREDICATION IN ARISTOTLEGUGUMCU, SEVCANCRAGER, ADAM DAVID2024-01-01This dissertation is made up three chapters which are conceived as independent essays. Each chapter is concerned with some aspect of Aristotle’s notion of katēgoria (in the sense of both predication and ontological category). Chapter I, “Predicates and Katēgoriai,” traces the emergence of the notion of katēgoria. Chapter II, “Being Per Se and Categorical Predication,” examines the role of this notion in Aristotle’s conception of being per se. Chapter III, “Be-true-of,” explores true-of that is related to our notion of predication in first-order logic.Philosophyenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/36j7v1gmhttps://escholarship.org/content/qt36j7v1gm/qt36j7v1gm.pdfetdoai:escholarship.org:ark:/13030/qt2058j6hm2026-06-03T06:42:45Zqt2058j6hmAn Examination of Place-Based Structural and Social Determinants of Breastfeeding Among Low-Income Women in Los Angeles CountyShodahl, SkyePrelip, Michael L2026-05-29Background: Despite ongoing public health efforts, racial/ethnic disparities in breastfeeding remain a major barrier to maternal and child health equity. Minority women (e.g., Black and Hispanic) continue to experience lower breastfeeding rates than White women. Place-based SDOH, such as neighborhood conditions, are well-established determinants of health and may also be relevant to breastfeeding. Although research on their role in breastfeeding remains limited, existing studies have indicated an association that warrants further investigation. This dissertation attempts to address this gap by examining the association between structural and social determinants of health and breastfeeding (initiation, duration, and exclusivity) among low-income women, specifically participants in the Supplemental Nutrition Assistance Program for Woman, Infant, and Children (WIC) living in Los Angeles County. Three interconnected studies were conducted, each focusing on a distinct and understudied aspect of the neighborhood environment. Study 1 provides the most comprehensive assessment by examining how a multidimensional measure of place-based SDOH may help explain individual-level differences in breastfeeding practices. This measure reflects overall neighborhood quality as well as conditions across eight domains: economic, education, social, housing, transportation, neighborhood-built environment, clean environment, and healthcare access. Study 2 is an exploratory examination of how a broad measure of neighborhood racial/ethnic concentration, operationalized as the percentage of Hispanic residents in a neighborhood, may help explain individual-level differences in breastfeeding practices. Lastly, Study 3 examines the association between a neighborhood’s food environment, specifically access to healthy foods, and individual-level breastfeeding practices. Methods: Multilevel data were used throughout each study. The primary datasets include individual-level data from the Los Angeles County WIC Survey (Studies 1-3) and neighborhood-level data from the Healthy Places Index (Study 1), United States Department of Agriculture’s Food Access Research Atlas (Study 3), and the United States Census Bureau’s Decennial Census and Geographic Relationship files (Study 2 and 3, respectively). These datasets were merged across the three studies, in different combinations, to create the multilevel datasets used to examine neighborhood effects on breastfeeding. Given the multilevel nature of the data, all three studies employed multilevel analyses, using mixed models, to account for the nesting of individuals within ZIP codes. Results: Study 1 found that overall neighborhood quality is not associated with breastfeeding outcomes, however domain-specific analyses of neighborhood conditions revealed that the education domain is positively and independently associated with increased initiation, duration (at 3 and 6 months) and exclusivity (at 3 and 6 months) beyond maternal covariates. Additionally, exclusive breastfeeding is the only outcome independently associated with multiple domains (i.e., education, income, and healthcare), with better neighborhood conditions linked to increased initiation, duration (at 3 and 6 months), and exclusivity (at 3 and 6 months) even after adjusting for maternal covariates. Study 2 found that higher neighborhood Hispanic concentration is associated with lower odds of exclusive breastfeeding at 3 and 6 months, even after adjusting for maternal covariates. Study 3 found that the neighborhood food environment, as measured by the percentage of residents in a ZIP-code living in a food desert tract, is not independently associated with breastfeeding beyond maternal covariates. Conclusion: Overall, the findings of this dissertation provide evidence that breastfeeding behaviors are shaped not only by individual-level factors but also by some broader neighborhood contexts. By adopting a place-based approach, this dissertation integrated a contextual dimension to the understanding of breastfeeding inequities that has been largely overlooked in the existing body of research. The dominant individualistic framing of breastfeeding as a personal choice has narrowly placed responsibility on mothers while ignoring the structural inequities that limit their choice to breastfeed. Overlooking these structural forces allows systems that fail mothers to persist and reinforce inequities. Therefore, continuing to grow this body of research is critical to advancing maternal and child health equity by shifting the framing of breastfeeding from an individual choice to one shaped by broader structural conditions.Public healthHealth sciencesWomens studiesPediatricsBreastfeedingLactaitionLow-IncomeNeighborhoodsSocial Determinants of HealthWomans Healthapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2058j6hmhttps://escholarship.org/content/qt2058j6hm/qt2058j6hm.pdfetdoai:escholarship.org:ark:/13030/qt4jp9q4x32026-06-03T06:42:40Zqt4jp9q4x3Essays in Information EconomicsZhai, JiayinMeyer-ter-Vehn, Moritz2026-05-31This dissertation comprises three chapters that study how learning is shaped by attention constraints, peer pressure in social networks, and the information provider's strategic incentives.The first chapter studies how the incentive to prolong engagement affects the quality of information provided to a learning agent. In many advisory and digital environments, information providers benefit from keeping consumers engaged rather than from the actions consumers ultimately take. We develop a continuous-time principal-agent model in which the principal chooses the precision of a drift-diffusion signal process, and the agent decides when to stop learning and take an action. The main result is that optimal precision is U-shaped in the agent's prior belief, reaching its minimum when the agent is most uncertain. The principal provides the least precise information to the agent with the highest underlying demand for information, because that agent's willingness to remain engaged is greatest, giving the principal the most scope to dilute signal quality without triggering early exit. Despite receiving the worst information, the most uncertain agent also captures the largest consumer surplus. We develop two screening extensions that deliver a sharp contrast: when the agent's prior is private information, all types rank contracts identically and any menu collapses to a single pooling contract; when the agent's attention cost is private, single-crossing holds and the principal can implement a fully separating menu with standard second-degree price discrimination features.The second chapter studies how information-production costs shape persuasion in a dynamic setting. We study a continuous-time model in which the sender chooses the precision of a drift-diffusion signal process and the receiver decides when to stop learning and take an action. The sender directly benefits when the receiver takes a particular action. When information production is costless, the sender chooses maximum precision, and in the high-precision limit the receiver's payoff converges to the full-information benchmark. When information involves a fixed cost, the sender's decision is distorted on the extensive margin: she provides information only when the prior is sufficiently close to the receiver's indifference belief. When the marginal cost of precision is increasing, the distortion extends to the intensive margin: near the indifference belief, the receiver is relatively easy to persuade, so the sender reduces precision to save on production costs. Together, the two chapters show that the same continuous-time framework with the same instrument: signal precision, generates qualitatively different information distortions depending on the principal's objective. In both chapters, precision is low near the agent's indifference belief, but for opposite reasons: in the consultation model, the most uncertain agent is the most valuable to keep engaged, so the principal withholds precision to prolong learning; in the costly persuasion model, the most uncertain receiver is the easiest to persuade, so the sender economizes on precision to save on production costs. The two objectives also reverse the welfare implications: in the consultation model, the most uncertain agent captures the largest surplus despite receiving the worst information; in the persuasion model, the receiver near indifference may obtain a payoff strictly below the full-information benchmark precisely because the sender reduces information quality. The shape of optimal precision further reflects this contrast: U-shaped and symmetric around indifference in the consultation model, and decreasing toward indifference within the persuasion region in the costly persuasion model.The third chapter studies how peer pressure in social networks shapes the evolution of social norms. We develop a network model in which agents repeatedly observe the behavior of their neighbors and update their own actions based on both private preferences and locally perceived social norms. The chapter highlights a discontinuity in the role of private preferences. When agents place any positive weight on their own preferred action, initial actions have no effect on long-run behavior: equilibrium actions become weighted averages of agents' true preferences, and consensus generally cannot be reached. In contrast, when agents place zero weight on private preferences and simply copy their perceived social norms, the model becomes a standard DeGroot process, and actions converge to a uniform social norm determined by initial actions and network centrality, while true preferences no longer enter the long-run outcome. This discontinuity shows that even a small degree of attachment to private preferences can prevent complete social conformity, while pure peer-pressure updating can generate stable norms that diverge from what agents privately prefer.Economic theoryIndustrial engineeringMarketingEconomicsInformation EconomicsSocial Learningapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4jp9q4x3https://escholarship.org/content/qt4jp9q4x3/qt4jp9q4x3.pdfetdoai:escholarship.org:ark:/13030/qt0926j27p2026-06-03T06:42:35Zqt0926j27pHeterogeneous Agent Macroeconomics and Fiscal PolicyKwicklis, Noah DuncanOhanian, LeeWeill, Pierre-Olivier2026-05-31This dissertation studies Heterogeneous Agent New Keynesian (HANK) models and their application to studying the effects of fiscal and monetary policy, with an emphasis on the former. In particular, I show the implications of HANK models under scenarios where the central bank does not raise interest rates in response to inflation and where the government does not (or cannot) commit to paying off its debt with new taxation for every path of the price level, but also does not explicitly default. I additionally show how this setting can be used in conjunction with information frictions to better understand the dynamics of macroeconomic variables.In Chapter 1, I show that when fiscal policy is active and monetary policy is passive in a HANK model, deficit-financed transfers to low-asset households lead to similar cumulative inflation but greater increases in real output than transfers to wealthier households. I use the inverse of the ``Phillips multiplier,'' the price level sacrifice ratio, to quantify this dynamic. Household heterogeneity and targeted policy change the timing of output gaps, making this consistent with the Phillips Curve and rendering conventional sacrifice ratio intuition misleading for assessing the inflation/output trade-off between policies.In Chapter 2, I show how linearized full information rational expectations HANK models can be easily converted into a sticky expectations environment in state-space form. The technique recycles the Jacobians of the full information model with only a few modifications. The process is greatly simplified by working in continuous time, which facilitates the use of natural boundary conditions to ensure agents do not violate idiosyncratic borrowing constraints and the measure of updating agents at any given moment is zero. Using this technique to solve a canonical HANK model, I show that if both households and firms have sticky expectations, then stimulus checks generate more output and much less inflation relative to a full information benchmark.I use the new numerical technique of chapter 2 to to study the effects of the post-COVID fiscal stimulus in Chapter 3. First, I note that during the inflationary surge of 2021-2023, inflation expectations were slow to rise -- but when they did, expected real interest rates fell as nominal rate expectations remained roughly constant. I estimate a medium-scale sticky-expectations heterogeneous agent New Keynesian (HANK) model and study the effect of fiscal transfers under a policy configuration that matches this expectations pattern. In particular, I simulate the deficit-financed transfers disbursed by the CARES Act of 2020 and the Consolidated Appropriations Act (CAA) and the American Rescue Plan Act (ARPA) of 2021 in a scenario wherein the central bank does not systematically respond to inflation with higher interest rates and the government does not commit to retire past deficits with future taxation. The simulated payments boost real annual GDP relative to trend by an average of 0.6% per quarter over 2020Q1-2023Q4 with an overall transfer multiplier of 2.0 and reduces unemployment by an average of 1.5 percentage points in each quarter. However, they also induce a 4.2% surge in the price level over the same period, with a peak annual inflation rate in 2022 of 2.8% above trend (slightly less than half of the post-pandemic peak observed in the data). This contrasts with the highly muted response of unemployment and inflation to similar fiscal shocks in an environment wherein all agents believe the central bank will raise interest rates in response to inflation. Unlike the first setting, this latter environment yields an expectations pattern that differs strongly from the post-2020 expectations data.EconomicsFinancePublic policyActive Fiscal PolicyHANKHeterogeneous AgentsMacroeconomicsSticky ExpectationsTransfersapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0926j27phttps://escholarship.org/content/qt0926j27p/qt0926j27p.pdfetdoai:escholarship.org:ark:/13030/qt98h2n6sg2026-06-03T06:41:22Zqt98h2n6sgIntegrative Three-Dimensional Genome Structure Modeling of the Human Nucleome: from Development to DiseaseWang, YeAlber, FrankZhou, Xianghong J.2026-05-29The spatial organization of eukaryotic genomes within the cell nucleus is linked to gene transcription, DNA replication, cell differentiation, and disease. Each genomic and imaging assay probes a different aspect of nuclear architecture at a different resolution, and no single method captures all features of chromosome folding. Population-based 3D genome structure modeling provides a unifying physical framework that integrates these complementary measurements and gives quantitative access to the nuclear microenvironment of every genomic region. This dissertation develops and applies that framework along four lines of work. Within the 4D Nucleome consortium, I show that cell-type-specific nuclear microenvironments in H1 human embryonic stem cells and HFFc6 fibroblasts align with cell-type-specific gene expression. Across a five-stage human embryonic differentiation trajectory, cell-to-cell folding variability decreases progressively, intra- and inter-chromosomal contacts drive this reorganization, and the most variably folded regions in pluripotent cells reposition toward the nuclear lamina or nuclear speckles in a manner that correlates with stage-specific gene expression. In platinum-based chemotherapy, sub-nuclear localization of chromatin correlates with DNA damage susceptibility in vivo, and oxaliplatin-induced damage redistributes spatially during the development of chemoresistance. Finally, I introduce scIGM, a platform that reconstructs single-cell 3D genome structures from sparse single-cell data combined with population-level reference. Together, this work establishes the nuclear microenvironment as a quantitative, structurally encoded axis of gene regulation, development, and disease.BioinformaticsGeneticsCellular biologyMolecular biology3D genome organizationcell differentiationcisplatin DNA damageintegrative genome modelingnuclear microenvironmentsingle-cell Hi-CpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/98h2n6sgetdoai:escholarship.org:ark:/13030/qt14b2b9xk2026-06-03T06:41:17Zqt14b2b9xkWriting with the Fly: Transcultural Histories of Race from Modern Hygiene to Cybernetic FuturesTrüper, Lena SophiePresner, ToddKim, David D2026-05-27Writing with the Fly examines the housefly as a central figure of science communication in the 20th century that contributed to the transfer of racist ideologies from hygiene into digital network cultures. Situated at the intersection of media studies, medical humanities, and visual culture studies, this project demonstrates that racism relies on medial forms that contradict their own ideologies of exclusion. Questioning the contradicting paradigms of cleanliness, balance and efficiency, my goal is to understand how and why “black bodies” of houseflies and their unruly behaviors were racialized throughout the 20th century. In what contexts were houseflies staged in science and popular science communication? What prejudices were associated with their bodies in their function as disease carriers? How did racist undercurrents in medical knowledge integrate into popular conceptions of computer systems through the image of the fly? To answer these questions, this project explores the depiction of houseflies and their moves in visual hygiene propaganda and cybernetic research between 1900 and 1970. It shows how houseflies as figures of transgression take on a double role as mobile bodies and mobile signifiers, which activate and provoke the boundaries of biopolitical regimes of enclosure as much as in the regulations of computational systems in which they interfere as noisy bits. In Writing with the Fly, I propose that the housefly's transgressive movements can serve as a model for developing unconventional writing strategies. These strategies can open up new transcultural avenues for historiography, potentially recuperating lost connections in fragmented, postcolonial contexts. By examining such unruly forms of writing, I demonstrate how libertarian ideologies of cybernetics did not overcome modern racializations of bodies, as broadly claimed, but revived them as behavioral classifications that linger on in the data biases embedded in contemporary algorithmic systems, digital infrastructures, and networked technologies. My dissertation contributes to the historical understanding of how modern modes of exclusion are maintained and reinforced by networked systems, contributing to increasing social polarization and racial stratification. It also interrogates the fields of Medical Humanities and Science and Technology Studies for their racist undercurrents.German literatureEuropean studiesFilm studiesHistorycyberneticshouseflyhygiene propagandainsectracismtransculturalapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/14b2b9xkhttps://escholarship.org/content/qt14b2b9xk/qt14b2b9xk.pdfetdoai:escholarship.org:ark:/13030/qt90h9268p2026-06-03T06:41:12Zqt90h9268pDynamics of Topological TexturesDao, Chau NguyenTserkovnyak, Yaroslav2026-05-27The general theme of this thesis is the exploration of topological hydrodynamics across different subfields of condensed matter physics, focusing on new proposals for topological charge manipulation and next-generation technologies.In materials with short-range order, the value of the order parameter (which characterizes phases of matter) can spatially vary, so the homotopic properties of the order parameter space dictate the topological excitations that the system can host. The excitations of the order parameter field generically exhibit topological hydrodynamics, meaning transport and flows of topological charges adhere to a conservation law. Different from conservation laws stemming from a symmetry of the Lagrangian, topological conservation laws are rooted in the homotopic properties of the order parameter space, and so they are immune to any structural imperfections or anisotropies. The robustness of the topological conservation law to disorder and thermal fluctuations have spurred efforts in spintronics to leverage topological textures for technological applications, such as long range signal transport, next-generation memory storage and computing elements, or energy storage devices. Moreover, these themes extend beyond solid state systems, with topological textures underlying some of the most striking physical phenomena observed in biological and soft condensed matter systems.We introduce the historical context for the study of topological hydrodynamics in Chapter 1, starting from the hydrodynamic theories of Hemholtz and culminating in the insights of Skyrme on topological solitons. We formulate the mathematical language to define order parameters and classify topological textures, and present Poisson bracket formalism to derive the equations of motion governing topological hydrodynamics.The following 3 chapters investigate aspects of abelian topological hydrodynamics in liquid crystal, magnetic, and superconducting systems. Chapter 2 investigates how spintronics techniques can be transplanted to the liquid crystal community. To this end, we study a novel reciprocal coupling between nematic dynamics and ionic currents within nematic electrolytes. Motivated by previous works on vortex transport in flat geometries, Chapter 3 delves into how geometry may be leveraged to control vortex transport in curved magnetic membranes. We formulate topological hydrodynamics of vortices on curved geometries and propose the vortexoelectric torque, which leverages the system’s spatial structure to energetically bias vorticity injection. To illustrate the consequences of this physics, we devise an experimentally feasible energy storage device. Chapter 4 explores the topological hydro-dynamics of magnetic skyrmions in spin-triplet superconductors. We find that a bulk-edge correspondence between the supercurrent and magnetic skyrmion density can be utilized to induce nonsingular phase-slips in a spin-triplet SQUID device.In Chapter 5, we pivot to explore how nonabelian charges can be nucleated and transported. Nonabelian topological charges have a richer internal structure compared to their abelian counterpart, exhibiting a higher capacity for encoding information. We leverage this advantage of nonabelian charges to devise a nonvolatile memory storage concept. Condensed matter physicsPhysicsThermodynamicsTheoretical physicscondensed matter physicsmagnetismspintronicssuperconductivitytopological defectstopological texturesapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/90h9268phttps://escholarship.org/content/qt90h9268p/qt90h9268p.pdfetdoai:escholarship.org:ark:/13030/qt1606x9g12026-06-03T06:41:07Zqt1606x9g1The Impact of Outpatient Exercise on Cognitive Function: An Intervention for Moderate to Severe Traumatic Brain Injury PopulationsPAN, SUWENRobbins, Wendie2026-05-27Background: Moderate to severe traumatic brain injury (msTBI) is a risk factor for ongoing cognitive decline, and cognitive impairment may progressively worsen into advanced dementia. Objectives: The Doctor of Nursing Practice (DNP) Scholarly Project aims to assess whether an 8-week moderate-intensity cycling exercise program can enhance neuropsychological and cognitive quality of life (QOL) scores in patients with TBI. Methods: A quasi-experimental pre- and post-intervention design was used. Seven msTBI participants with cognitive impairment, aged 22-65 and at least 6 months post-injury, completed eight weeks of supervised cycling exercise, twice weekly for 40 minutes per session, at a hospital wellness center. Three cognitive function tests [Modified Mini-Mental Status Exam (3MS), Trail Making Test Part A (TMT-A), Trail Making Test Part B (TMT-B)] and two Quality of Life measures [Traumatic Brain Injury Quality of Life (TBI-QOL) Cognition-General Concerns, and Executive Function] were administered before and after the exercise program. Pre- and post-intervention Wilcoxon signed-rank tests were performed to evaluate the effects of the exercise on the participants. Results: The mean scores on the 3MS and TMT-B tests showed significant improvement (α level = 0.05) compared to pre-intervention scores (Δ = 5.14, ρ = 0.027; Δ = -57.71, ρ = 0.046), while mean score changes for TMT-A, TBI General Concern, and TBI Executive Function were not statistically significantly different (Δ = 4.57, ρ = 1.000; Δ = 0.26, ρ = 0.753; and Δ = 2.56, ρ = 0.499, respectively). Hedges’ correction for 3MS and TMT-B pre- and post-intervention changes indicates a large effect size (g = -1.25 and 1.07, respectively). Conclusion: The designed cycling exercise program may improve cognitive functions in patients with msTBI, as evidenced by significantly higher scores on the 3MS and TMT-B. Both tools are effective for evaluating cognitive function in msTBI participants. The project provides insights into designing standardized long-term aerobic exercise for future initiatives to promote cognitive health and prevent dementia among patients with TBI.NursingNeurosciencesKinesiologyCognitive impairmentCognitive improvementExerciseQuality of lifeTraumatic brain injuryapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1606x9g1https://escholarship.org/content/qt1606x9g1/qt1606x9g1.pdfetdoai:escholarship.org:ark:/13030/qt9tc744v52026-06-03T06:41:02Zqt9tc744v5Applications of Radiopharmaceutical Therapy in Prostate CancerElls, Zachary IsaiahDahlbom, MagnusCalais, Jeremie2026-05-27The nuclear medicine paradigm is to inject patients with a strategically selected radio-pharmaceutical. There are three main parts of the radiopharmaceutical: the radioisotope, the chelator/linker, and the targeting molecule. Depending on the selected radioisotope, the radiopharmaceutical will be used for diagnostic or therapeutic purposes. The chelator will bind to the radionuclide keeping it in a stable state, while the linker will create a bond between the radioisotope and the targeting molecule. The targeting molecule looks to take advantage of the properties of the molecular phenotype of the cell. In the case of cancer typically this is a protein in which is overexpressed compared to other healthy tissue.Primarily by intravenous injection (the dose is termed injection activity) the radiopharmaceutical will be internalized by the tumor cell leading to the deposition of energy from the decay of the radioisotope. Regarding therapeutic treatment, this method has proven to be clinically effective already in a variety of diseases like neuroendocrine, prostate, and thyroid cancer respectively to name a few. This is done by taking advantage of somatostain receptor protein, prostate specific membrane antigen, and the thyroids iodine absorption respectively.The most prominently used radioisotope to date in the treatment of the neuroendocrine and prostate cancers is 177Lu. During the radioactive decay of 177Lu, gamma photons are ii emitted and can be detected using Single Photon Emission Computed Tomography (SPECT). This imaging allows probing of the biodistribution of the radiopharamaceutical while also being able to determine the radiation dose deposition.Broadly stated the nuclear medicine paradigm therefore can be investigated through treatment selection (e.g. radiopharmaceutical, injection activities, etc.), imaging (e.g. accurate quantification, image quality, etc.), and response assessment (e.g. progression free survival, overall survival, dose-response relationships, etc.). Overall this method of treatment has proven to be vital in disease management but inevitably some patients do not respond to the treatment and relapse. One hypothesis for treatment failure is that sufficient radiation to kill the entire tumor was not delivered.Regardless of the extent of disease, or patient characteristics (e.g. weight, comorbidities, past therapies) the current paradigm is to treat with fixed injection activities for the same number of cycles over a fixed time period. This methodology neglects personalization of the treatment and does not account for radiation delivery to tumors. By monitoring for adverse events and toxicities in patients treated would be the gold standard to maximize the energy deposition to tumors ultimately treating the disease.This dissertation focuses on the overarching methodology in treatment with radioactive therapies. The first aim is to explore of how imaging protocols can affect overall image interpretation and quantification by physicians and physicists; secondly determine radiation absorbed doses in 177Lu cancer therapy to tumors and organs at risk and corresponding dose-response relationship; and lastly, investigate RPT in combination with other modalities like external beam radiation therapy and surgical intervention.Medical imagingNuclear physicsOncologyMedicinePharmaceutical sciencesMedical ImagingNuclear MedicineProstate CancerRadiopharmaceutical ThearpySPECTpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9tc744v5etdoai:escholarship.org:ark:/13030/qt45j0g8t52026-06-03T06:40:56Zqt45j0g8t5Efficient Learning at Scale: From Dataset Distillation to Streaming Long Video GenerationCui, JiaxingHsieh, Cho-Jui CH2026-05-27Modern deep learning is powered by large-scale data and large pretrained models, yet deploying them efficiently remains a fundamental challenge. Training on massive datasets is prohibitively expensive, and powerful generative models are too slow and too limited in duration for real-time interactive applications. This thesis addresses both challenges under a unified theme of efficient learning at scale. I first study dataset distillation, where the goal is to compress a large dataset into a compact synthetic substitute. I show that progress in this area has been hampered by inconsistent evaluation, memory bottlenecks that prevent scaling, and a previously unrecognized tendency for compression to amplify data biases, and I propose solutions to each. I then turn to the problem of transferring knowledge from large pretrained video models into lightweight models capable of real-time, long-form streaming. I show that the key obstacles preventing real-time long-form generation can be identified and resolved, enabling continuous video streaming from seconds to hours. Together, these contributions advance efficient learning across both data compression and real-time generation.Computer scienceEngineeringArtificial intelligenceInformation technologydiffusion modelinteractive long video generationlong video generationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/45j0g8t5https://escholarship.org/content/qt45j0g8t5/qt45j0g8t5.pdfetdoai:escholarship.org:ark:/13030/qt98c1t8w02026-06-03T06:40:50Zqt98c1t8w0Organ-wise Abdominal CT Radiomics for Biological Age Modeling and Pre-diagnostic Disease Risk StratificationDeng, ZengtianLi, Debiao2026-05-27Chronological age is widely used as a standard measure of health status in clinical studies. However, individuals may age at different rates due to variations in genetics, lifestyle, and environmental exposures. Therefore, chronological age often fails to capture the underlying tissue-level biological aging process. This limitation motivates the development of biological age (BA) as a more informative measure of physiological aging.
Prior studies have estimated biological age using a range of biomarkers, including proteomic, physiological, medical imaging measurements etc. Although these studies suggest that biological age may better reflect abnormal aging than chronological age alone, several important limitations remain. First, many existing models are trained on large cohorts that either span a limited age range or lack rigorously defined inclusion criteria, particularly with respect to restricting the training population to individuals who represent normative aging without major abnormal phenotypes. Such cohort design limitations may reduce model generalizability and weaken the ability to accurately characterize healthy aging trajectories. Second, most existing biological age models estimate system-level age from whole-body or global biomarkers, which may obscure organ-specific aging patterns and reduce sensitivity to disease processes arising in specific tissues. Third, many imaging-based biological age models rely on deep learning approaches that often provide limited interpretability and unclear physiological relevance.
This dissertation addresses these limitations by developing a radiomics-based, organ-specific biological age framework using abdominal computed tomography (CT). By leveraging a large, carefully curated healthy cohort to model normative aging, this work aims to generate biologically meaningful organ-wise age estimates and evaluate their utility for early disease and mortality risk stratification. The specific aims of this dissertation are as follows:
Aim 1. Develop an organ-wise abdominal CT biological age model using a carefully curated healthy control cohort to characterize normative aging patterns across abdominal organs.
Aim 2. Evaluate whether ensemble and organ-specific biological age gaps provide incremental risk stratification for pre-diagnostic disease and mortality beyond chronological age, demographic covariates, and routine blood biomarkers.
Aim 3. Establish the hierarchical and biological relevance of CT-derived abdominal aging by determining when ensemble biological age provides broad risk stratification and when organ-specific biological age adds disease-selective prognostic information beyond the ensemble.BioengineeringComputer scienceArtificial intelligenceBiomedical engineeringAbdominal computed tomographyFederated learningOrgan-specific agingPancreatic ductal adenocarcinomaRadiomicsSegmentationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/98c1t8w0https://escholarship.org/content/qt98c1t8w0/qt98c1t8w0.pdfetdoai:escholarship.org:ark:/13030/qt5z23k3xr2026-06-03T06:40:45Zqt5z23k3xrHorn ConcertinoHarrison, JacobDanielpour, Richard D2026-05-28Horn Concertino is the result of two intersecting academic pursuits: recent research into the works of Aaron Copland, Wolfgang Amadeus Mozart, Andrew Norman, and Esa-Pekka Salonen, as well as a growing interest in contemporary compositional procedures. In this work, a motif from Mozart’s Symphony No. 41 is distorted through time-stretching and timbral procedures into material that becomes nearly unrecognizable from its original form. The pitch material of the motif is then transformed through various expansion processes to generate the core material of the third movement. These ideas are structured within formal frameworks influenced by the works of Copland and Salonen.Musical compositionMusicapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5z23k3xrhttps://escholarship.org/content/qt5z23k3xr/qt5z23k3xr.pdfetdoai:escholarship.org:ark:/13030/qt2j3697sz2026-06-03T06:40:40Zqt2j3697szLifecourse Financial Capital and Later-Life Cognitive Outcomes: Causal Effects, Psychosocial Pathways, and Effect HeterogeneityHabib, Mina HanyNianogo, Roch A2026-05-27Financial capital is an economic determinant of health that encompasses both objective and subjective dimensions of financial status, each of which has been associated with later-life cognitive outcomes, including cognitive function and dementia risk. The objective of this dissertation is to develop and validate an adulthood financial capital index and to use this index to: 1) examine the lifecourse relationship of childhood and midlife financial capital with cognitive function and dementia risk; 2) evaluate anxiety and depression as potential mediators of the association between midlife financial capital and cognitive decline and dementia risk; and 3) identify potential effect modifiers of the association between midlife financial capital and dementia risk.The first study investigates the association of childhood and midlife financial capital with cognitive decline and dementia risk. We used g-computation marginal structural models with inverse probability of treatment and censoring weights to estimate the effects of combinations of low, average, and high childhood and midlife financial capital on 6-, 8-, and 10-year incident dementia risk and cognitive function. Across follow-up periods, participants exposed to low financial capital in both childhood and midlife had the highest dementia risk and greatest cognitive decline. Findings also suggested that low midlife financial capital may be more strongly associated with adverse cognitive outcomes than low childhood financial capital. The second study investigated anxiety and depression as hypothesized psychosocial mediators of the association between midlife financial capital and 6-, 8-, and 10-year cognitive decline and dementia risk. We conducted longitudinal causal mediation analyses using g-computation and marginal structural models to estimate the total effect and total indirect effect of midlife financial capital on these outcomes. Consistent with the first study, higher midlife financial capital was associated with lower dementia risk and less cognitive decline over follow-up. Anxiety and depression consistently mediated a substantial proportion of the association between midlife financial capital and dementia risk. However, findings for incident dementia were more precise and may be more clinically meaningful than those for cognitive function. The third study examined potential effect modifiers of the association between midlife financial capital and dementia risk. Potential effect modifiers and subgroups were identified using a data-adaptive, g-computation-based heterogeneous treatment effect approach. Candidate modifiers included sociodemographic, health, and psychosocial factors. Consistent with the first two studies, higher midlife financial capital was associated with lower dementia risk over follow-up. Depression, perceived constraints, and anxiety were identified as potential effect modifiers, although evidence was strongest for anxiety. Individuals with low depressive symptoms, low perceived constraints, and low anxiety appeared more likely to benefit from interventions targeting midlife financial capital to reduce dementia risk. Taken together, this dissertation provides evidence that financial capital is an important economic determinant of later-life cognitive health. Through the development and validation of a multidimensional adulthood financial capital index and its application across three complementary studies, this work advances understanding of the lifecourse, psychosocial, and heterogeneous pathways through which financial capital may influence cognitive decline and dementia risk. These findings contribute to a growing body of literature supporting the causal effects of both objective and subjective financial conditions on cognitive aging and dementia. They also underscore the potential importance of mental health as an intervention target for reducing the substantial and unequal human and economic burden of later-life cognitive decline and dementia.EpidemiologyPsychologyMedicineCognitive psychologyFinanceCausal InferenceCausal Mediation AnalysisCognitive AgingFinancial CapitalHeterogenous Treatment EffectsLifecoursepubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2j3697szetdoai:escholarship.org:ark:/13030/qt1ds7c5rr2026-06-03T06:40:35Zqt1ds7c5rrColonial Uprooting: Reframing Carnets d’Orient through Land & FoodSackett-Hassan, AmberBrozgal, Lia2026-05-28This dissertation examines Jacques Ferrandez’s ten-volume graphic novel series Carnets d’Orient (1994–2009) as a contested site of postcolonial memory for French Algeria, arguing that its cultural power cannot be captured by a simple opposition between nostalgic apology and critical denunciation. Chapter 1 maps the series’ fragmented reception among scholars, prize committees, the press, Algerian media, and state institutions. It shows how Carnets d’Orient is alternately canonized as neutral historical fresco and condemned as pied-noir nostalgia. Chapters 2 and 3 then offer a sustained ecocritical and food-centered rereading of the decalogy. Chapter 2 foregrounds land, agriculture, and non-human actors, tracing the Barthélemy family farms as lieux de mémoire that both expose and reproduce settler-colonial logics of dispossession, labor exploitation, and environmental “improvement.” Chapter 3 analyzes scenes of food, drink, and commensality in dialogue with pied-noir cookbooks and (post)colonial food studies, showing how seemingly mundane acts of eating and drinking structure inclusion, exclusion, and colonial violence. Bringing comics studies into conversation with environmental humanities and food studies, the project argues that visual narratives of land and consumption are crucial to understanding how colonialism is remembered, naturalized, and unevenly contested in contemporary French and Francophone culture.French literatureFine artsCreative writingAlgeriabande dessinéeEcocriticismFood StudiesGraphic novel studiesPostcolonial studiesapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1ds7c5rrhttps://escholarship.org/content/qt1ds7c5rr/qt1ds7c5rr.pdfetdoai:escholarship.org:ark:/13030/qt8g68z4mb2026-06-03T06:40:30Zqt8g68z4mbMulti-omic characterization of the epigenetic and transcriptional consequences of pathogenic BRPF1 variants in human cellsThaker, ManeeshaArboleda, Valerie ADivakaruni, Ajit S2026-05-28Germline variants in the epigenetic reader BRPF1 result in a rare neurodevelopmental disorder known as Intellectual Developmental Disorder with Dysmorphic Facies and Ptosis (IDDDFP). BRPF1 is part of a tetrameric complex with the lysine acetyltransferase KAT6A and two accessory proteins, ING5 and MEAF6. Together, this complex facilitates histone H3 acetylation and is essential during early development. While pathogenic BRPF1 variants are known to cause IDDDFP, the molecular pathways disrupted by these variants remain uncharacterized. No study has modeled these variants endogenously in a human induced pluripotent stem cell (hiPSC) system, in part due to the lack of patient-derived hiPSC lines and the absence of a high-quality BRPF1 antibody. To address these challenges, I first established a pilot study in an immortalized Schwann cell line, demonstrating that endogenous tagging of BRPF1 with a reporter, followed by introduction of pathogenic BRPF1 variants, can meaningfully capture disease biology. Next, I leveraged this framework to build isogenic hiPSC models with both heterozygous and homozygous variants in BRPF1, enabling dosage-dependent analysis of the effects of BRPF1 loss at the epigenome and transcriptome. Although variant hiPSC lines maintain pluripotency and appear morphologically normal, they exhibit underlying transcriptional and epigenetic dysregulation. These effects become more pronounced at the cellular level during neural induction, when migration defects emerge. Across multi-omic profiling through RNA-seq, ChIP-seq, and scRNA-seq, Wnt signaling consistently emerged as a core dysregulated pathway. In summary, this work establishes the first endogenous, isogenic hiPSC model of pathogenic BRPF1 variants and provides insight into the molecular mechanisms underlying the disease biology of IDDDFP.GeneticsMolecular biologyDevelopmental biologyBRPF1chromatinopathyhistone acetylationhuman induced pluripotent stem cellsneurodevelopmental disorderWnt signalingpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8g68z4mbetdoai:escholarship.org:ark:/13030/qt0mk3h2hs2026-06-03T06:40:25Zqt0mk3h2hsEnhancing Precision in Pancreatic Cancer Risk Prediction: Integrating Clinical, Epidemiologic, and Machine Learning ApproachesLumsdaine, Cory TylerHashibe, MiaHuang, Brian Z2026-05-28BackgroundPancreatic cancer is the third leading cause of cancer-related death in the United States, with a five-year survival rate of approximately 13%. Late-stage diagnosis—driven by absent population-wide screening and nonspecific early presentation—underlies this poor prognosis. This dissertation advances risk prediction through three complementary studies integrating clinical, epidemiologic, and genomic approaches across diverse populations.Objectives & Specific AimsThree specific aims were pursued: (1) to develop and compare time-to-event prediction models for PDAC risk within 36 months of insulin initiation in a VA diabetes cohort; (2) to identify prediagnostic multimorbidity phenotypes and their associations with pancreatic cancer risk; and (3) to assess whether comorbidity typologies are reproducible across race/ethnicity and sex strata and independent of polygenic risk scores (PRS).MethodsAim 1 applied a retrospective cohort of 449,685 veterans with type 2 diabetes from the VA Corporate Data Warehouse, comparing Cox-LASSO, Cox-ridge, Random Survival Forests (RSF), and XGBoost. Aims 2 and 3 used a nested case-control design within the Multiethnic Cohort (MEC; 730 cases, 2,920 matched controls) linked to Medicare claims. Aim 2 identified multimorbidity phenotypes via k-prototypes clustering. Aim 3 derived stratified solutions across five race/ethnicity groups and two sex strata, evaluating cluster × PRS interactions in a GWAS-eligible subsample (307 cases, 1,228 controls).ResultsRSF achieved the highest discrimination in Aim 1 (C-index = 0.750, 95% CI: 0.727–0.772); Cox models demonstrated superior calibration. Key predictors included percent weight change, HbA1c change, bilirubin change, jaundice, and abdominal pain. In Aim 2, three multimorbidity phenotypes were identified: a low-burden reference cluster, an intermediate cardiometabolic cluster (OR 1.36, 95% CI: 1.12–1.66), and a high-risk metabolic-gastrointestinal cluster (OR 2.21, 95% CI: 1.71–2.85). Three-cluster typologies were recoverable in all seven Aim 3 strata with significant risk associations (highest-risk ORs: 1.85–2.77); no cluster × PRS interaction was detected (all LRT p > 0.10).ConclusionsThese findings support multidimensional pancreatic cancer risk stratification integrating EHR-based clinical predictors, comorbidity phenotypes, and genomic data, with particular utility for diverse, underserved populations. Metabolic-comorbidity pathways and inherited genetic susceptibility represent independent, additive risk dimensions whose integration could improve risk stratification beyond either approach alone.EpidemiologyBiostatisticsBiomedical engineeringOncologyClinical FactorsEpidemiologyMachine LearningPancreatic CancerPDACRisk PredictionpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0mk3h2hsetdoai:escholarship.org:ark:/13030/qt4k34942m2026-06-03T06:40:10Zqt4k34942mDevelopmental remodeling of emotion circuits in response to early life adversityGoodpaster, Caitlin MWilke, Laura A2026-05-26Early life adversity (ELA) is a major risk factor for the development of mood and anxiety disorders, which are often characterized by maladaptive threat avoidance. Despite this, the developmental origins and circuit mechanisms underlying altered threat processing remain poorly understood. The prefrontal cortex (PFC) plays a central role in regulating threat-related behaviors through its interactions with limbic structures, yet how these circuits emerge across development and are shaped by early experience is not well defined.This dissertation investigates the development and plasticity of prefrontal–limbic circuits that govern threat avoidance, with a particular focus on corticotropin-releasing hormone (CRH)-expressing neuronal populations. First, I synthesize existing literature to establish a framework for how PFC maturation contributes to vulnerability for psychiatric disease. I then demonstrate that top–down pathways originating in the medial PFC exhibit developmentally distinct architectures, revealing that circuits controlling avoidance behavior are dynamically reorganized across the lifespan. Building on this, I show that ELA selectively enhances avoidance behavior during adolescence by strengthening communication between the ventromedial PFC and basolateral amygdala (BLA) through CRH⁺ glutamatergic projection neurons. These findings identify a cell-type–specific mechanism through which early experience biases circuit function toward heightened threat responding. Finally, I examine how sex and stress interact to shape CRH/GABAergic projections from the BLA to the nucleus accumbens, demonstrating that stress exposure differentially alters circuit function and innervation patterns in males and females.Together, these studies define how early experience, developmental stage, and biological sex converge to shape CRH-associated prefrontal–limbic circuits. This work provides a mechanistic framework linking ELA to persistent alterations in threat processing and identifies specific cell types and circuit pathways that may serve as targets for intervention in stress-related psychiatric disorders.NeurosciencesPsychobiologyMedical imagingDevelopmental psychologyadolescenceamygdalaavoidancecorticotrophin-releasing hormoneearly life adversityprefrontal cortexpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4k34942metdoai:escholarship.org:ark:/13030/qt84t012vx2026-06-03T06:40:05Zqt84t012vxLoving the Monster: Musical Reimaginings of Race, Gender, Queerness, and Disability in Indie Video GamesPark, HyeonjinTaylor, Timothy D.2026-05-26In this dissertation, I focus on marginalized and intersecting identities—oft-perceived as monstrous by the dominant culture—and argue for alternative ways to understand identity formation through the lens of monstrosity by primarily analyzing the music and sound from four indie video games: Undertale (2015), Night in the Woods (2017), Celeste (2018), and Hades (2020). I turn to the monster as a site of possibility and a way of being for marginalized peoples, who, in response to being Othered, have created something more for themselves that humans and the human category cannot offer. I blend interpretive analysis and ethnography to propose alternative ways in which the player/listener can hear and listen to the queer, gendered, racialized, and/or disabled monster, all the while embracing the complexities of their monstrousness. I am in dialogue with queer theorist Eve Kosovsky Sedgwick’s theorization of reparative reading and argue that repair has its limitations. Instead, I inquire why the monster needs to be repaired and who actually benefits from repair. In this dissertation, I am not calling for an empathetic player/listener who will “humanize” the monster but, rather, I demonstrate how, when musically animated, monsters can refuse to follow the normative conventions by which the human category has been historically defined.Chapter 1 explores how identity has been discussed in both academic and non-academic spaces, as well as how music and sound can be tied to identity, particularly in video games. Chapter 2 investigates the relationship between horror and the monster. Chapter 3 turns from horror to failure framed through the lens of different temporalities. Last but not least, Chapter 4 considers how, in spite of their associations with horror and failure, monsters make worlds for themselves that let them be, which I argue is frequently done through a disidentificatory practice.MusicMusic historyFilm studiesindie video gamesludomusicologymonstrosityapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/84t012vxhttps://escholarship.org/content/qt84t012vx/qt84t012vx.pdfetdoai:escholarship.org:ark:/13030/qt1sz7r6js2026-06-03T05:22:26Zqt1sz7r6jsSounds We Saw, Visions We Heard: The History of Jazz Fandom and the Politics of Imagining the Past and Envisaging a Future through Jazz ListeningYamada, MasayoshiKelley, Robin DG2024-01-01This dissertation explores the social history of jazz through its various constituencies of fandom and listenership in the United States. Utilizing archival materials produced by jazz enthusiasts—such as fan mail, diaries, scrapbooks, fan club bulletins, photographs, legal and financial records, and oral history interviews—I offer three historical case studies of jazz fandom’s sociality. These include the Hot Music Society of San Francisco, the brainchild of etymologist-turned-record collector Peter C. Tamony; the production of The Cry of Jazz, a 1959 short film by a group of African American filmmakers in post-World War II Chicago; and the reciprocal relationships pianist Mary Lou Williams built with her adoring fans. Together, these critical inquiries into the cultural and political dynamics of jazz listening demonstrate how enthusiasts from diverse backgrounds used music to generate meanings, define values, construct identities, and make sense of a changing world in the mid-twentieth century. I argue that from the Great Depression to the Second Reconstruction, ordinary people with extraordinary enthusiasm for jazz transformed the social space of music listening into not only a democratic discursive arena but also a vehicle for confronting structural racism across different times and places. By foregrounding the everyday experiences of ordinary people, my study of jazz fandom’s history significantly departs from the dominant, musician-centered narrative. The institutionalization of jazz, while fostering interest in the music, has also formalized an official narrative that focuses on the lineage of isolated geniuses, thereby marginalizing the role of audiences. This dissertation on the history of jazz fandom suggests an alternative, bottom-up narrative of jazz history. Thus, my research contributes to a deeper understanding of the role of culture in everyday life and its political potential for social change, both in the past and the present. HistoryAfrican American studiesMusic historyAfrican American HistoryCultural HistoryFandomJazzMusicUS HistoryenpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1sz7r6jsetdoai:escholarship.org:ark:/13030/qt8b2202kq2026-06-03T05:03:36Zqt8b2202kqSkin Pulled Back: Race and Literary Abstractions in the 21st CenturyMeng, DandiMcMillan, Uri GNersessian, Anahid J2024-01-01This dissertation argues that abstraction is an important and undertheorized axis through which to think about race and aesthetics in contemporary American literature. The project seeks to work against two critical tendencies: 1) the overwhelming focus on representation, in both quantitative and qualitative terms, when analyzing and interpreting works of art by non-white artists, and 2) the primary way that abstraction, when it is addressed at all, has been characterized in such analyses—namely, as a non-figural visual style that can only be read either as post-racial or else as bluntly referential of some racial content. Against both of these approaches, I suggest that thinking about abstraction as a productive, multifaceted set of aesthetic and social tools can produce an alternate metric by which to describe and evaluate literature’s engagement with race, one which can ultimately move us through the impasse of prescriptive approaches to racial representation.I take as my case studies four works of experimental poetry by 21st century American writers of color. Chapter One focuses on Simone White’s 2016 book, Dear Angel of Death—in particular, the titular essay that makes up the second half of the book—and its engagement with philosophical and theoretical abstractions. I take up philosopher Charles Mills’ distinction between ideal and non-ideal theorizing as the basis of reading White’s intervention on contemporary Black thought. Chapter Two identifies metadata as a key preoccupation in the genre-bending work of Tan Lin. I focus on his novella, Insomnia and the Aunt, the most conventionally narrative of his works, which ostensibly recounts childhood visits made by a Lin-esque narrator to his Chinese aunt’s motel in rural Washington state. Chapter 3 takes a comparative approach, and examines the visual poetics of Don Mee Choi and Renee Gladman, two writers whose works transmute the pressure put on literary abstraction by racialized visuality into formal transformation.American literatureEthnic studiesabstractionaestheticsavant-gardeexperimentalismpoetryraceenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8b2202kqhttps://escholarship.org/content/qt8b2202kq/qt8b2202kq.pdfetdoai:escholarship.org:ark:/13030/qt7bf133772026-06-03T05:03:29Zqt7bf13377Prohibido Olvidar: Central American Communists during the Rise of Twentieth Century Fascism, 1920-1940Carcamo, JenniferDerby, RobinPerez-Montesinos, Fernando2024-01-01My dissertation explores the historical and ideological origins of Central America’s communist parties, specifically in El Salvador, Guatemala, and Costa Rica, from 1920-1940. I chose this period precisely because it preludes the revolutionary war period of the late twentieth century in Central America (1960-1996), which has received an exceptional amount of attention from contemporary scholars. Through archival research, my dissertation makes three central arguments. First, I argue that both communism and fascism were ideological and political responses to the dual crisis of agrarian capitalism and liberal nation-building in Central America, albeit on opposing sides of the political spectrum. While Central American communists sought to carry out an agrarian socialist revolution, fascist sympathizers sought to consolidate state power for a growing predominantly Ladino and Mestizo upper- and middle-class capitalist elite by utilizing state terror and military violence. Second, I argue that Central America’s first communist parties began developing in the 1920s and 1930s as a direct response to the imminent threat and rise of Central American fascism, which scapegoated communists and working-class people, specifically Indigenous and Black communities, as well as women and migrant laborers, as enemies of the state, and manifested in the form of state-sponsored mass extrajudicial killings, including, but not limited to genocide and ethnocide. Third, I argue that Central American communist parties were inherently transnational, meaning that they functioned beyond the scope of each independent Central American nation-state and worked closely with other Latin American revolutionary movements, especially in Mexico and Cuba, thus upholding a practice of internationalism in their struggle against fascism, capitalism, and imperialism.Latin American studiesAfroindigenousCentral AmericaCommunismFascismSocialist FeminismWomenenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7bf13377https://escholarship.org/content/qt7bf13377/qt7bf13377.pdfetdoai:escholarship.org:ark:/13030/qt6gg864hq2026-06-03T05:03:24Zqt6gg864hqNetizen A11y: Engaging Internet Users in Making Visual Media AccessibleWang, RuolinChen, Xiang2024-01-01Visual media without alternative text descriptions are inaccessible to blind and low vision (BLV) people. While mainstream automated solutions often focus on achieving acceptable results based solely on visual media, they neglect the essential engagement of internet users in contributing to accessibility. With an explosion of data, diverse media formats, and constant updates, the responsibility of ensuring accessibility and equity cannot rest solely on the shoulders of a select few. The sheer volume of digital contents demands a community effort where each individual becomes an advocate for accessibility. With the central goal of effecting this community-level change, I design and develop intelligent user interfaces that engage internet users in making visual media accessible at three progressive levels: i) extracting visual information from human-authored texts in metadata to generate in-context descriptions; ii) nudging people to use visual descriptive language in online activities; iii) creating symbiotic information seeking experiences that connect sighted and BLV people of the same community. Three practical application scenarios, online shopping, online video watching and commenting, online research paper reading, are explored to demonstrate the implementation of each engagement level. Moving beyond paid crowdsourcing, this dissertation envisions a future where user advocacy plays a crucial role in creating a more unified and inclusive internet.Computer scienceenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6gg864hqhttps://escholarship.org/content/qt6gg864hq/qt6gg864hq.pdfetdoai:escholarship.org:ark:/13030/qt65k2j2gk2026-06-03T05:03:21Zqt65k2j2gkLongitudinal Microanalysis of the Vineland Adaptive Behavior Scales in Autism Spectrum Disorder: Implications for Adult OutcomesClarke, Elaine BethanyLord, Catherine2024-01-01Many studies have documented adaptive behavior deficits in individuals with autism spectrum disorder (ASD). A considerable body of work has also catalogued challenges in adult outcome attainment amongst autistic individuals; notably, relatively strong adaptive function is associated with a higher likelihood of attaining some adult outcomes in this population. To date, little work has examined whether competency in specific adaptive behaviors increases the likelihood of attaining adult outcomes. This study sought to address this gap via item set and item-level analyses of scores from a common adaptive behavior measure, the Vineland Adaptative Behavior Scales, from ages 5, 9, 14, and 18 in a well-characterized longitudinal cohort. Item sets related to cooking and personal hygiene predicted adult employment and subjective well-being at multiple ages. Individual items related to community skills (i.e., money management, rules, rights, and safety, restaurant skills, etc.) emerged as significant predictors of adult employment, social relationships, living status, and subjective well-being. Implications for adaptive behavior interventions are discussed. Educational psychologyenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/65k2j2gkhttps://escholarship.org/content/qt65k2j2gk/qt65k2j2gk.pdfetdoai:escholarship.org:ark:/13030/qt5jc4h20p2026-06-03T05:03:18Zqt5jc4h20pDignity and Rebellion in the Barrio: Housing Justice Organizing in Los Angeles’ EastsideMiranda, Kimberly IvetteJohnson, Gaye T.Avila, Eric R.2024-01-01The rise of evictions, houselessness, and criminalization for severely rent burdened tenants has necessitated sustainable and transformative justice-centered discourse and praxis that is accountable to the public. In recent years, the eviction epidemic, prompted by predatory gentrification processes, has exacerbated, creating a tenant crisis occurring in, but not limited to, major cities like Los Angeles. Undocumented, queer, and multi-generational tenants, populations most affected by this crisis, have been and continue to unite in confronting and resisting the ongoing disruption of the ways in which property is increasingly valued over people. They do so through grassroots frameworks that enact direct actions like rent strikes, boycotts, and popular political education that provoke local officials and housing policies to recognize and uphold the dire need of pro-tenant protections that restrict mechanisms of harm within the scope of housing injustices. Such examples of predatory housing financialization are landlords illegally overcharging rent, illegally evicting tenants, and enacting harassment upon tenants. This project highlights such moments in three interconnected ways. I first examine how housing organizers critique the limitations of local governments who influence tenant policies. I also analyze the ways that state-funded housing organizations, like the Los Angeles Center for Community Law & Action (LACCLA), face systemic barriers as a non-profit, due to its contingency of state funding through grants and protocols that inhibit autonomous decision making, while also working towards tenant-centered housing justice initiatives with grassroots housing organizations. Additionally, I discuss the role of the local and transnational housing brigade to Venezuela that brought Los Angeles housing organizers together with Venezuelan communes to discuss and envision liberated housing futures. This project ultimately examines tenant organizing in Los Angeles as a unique and transformative place where many unite across diverse diasporas, yet find common ground in social injustice, community organizing, and fostering global solidarity. Ethnic studiesUrban planningenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5jc4h20phttps://escholarship.org/content/qt5jc4h20p/qt5jc4h20p.pdfetdoai:escholarship.org:ark:/13030/qt5cg6701n2026-06-03T05:03:15Zqt5cg6701nUncertain Practices: Healthcare Provision and Access for Syrian Refugees in TurkeyKayali, NihalKligman, Gail2024-01-01Sociological research has examined how temporary legal statuses suspend migrants’ lives between inclusion and exclusion in a polity. Yet scholarship analyzing the effects of temporary policy on the organizational terrain of migrant reception is scant. This dissertation addresses this gap by asking: How do explicitly temporary migration policies shape service provision for refugees? Specifically, how do forced migrants themselves—both as consumers and purveyors of services—shape these organizations? And what are the implications of these activities for refugees’ access to services? I answer these questions through a qualitative, longitudinal study of one organizational field in a case of forced migrant reception: healthcare provision for Syrians in Istanbul. In 2014, Turkey established the Temporary Protection Regulation, granting registered Syrians free access to state healthcare services. Yet, despite the extension of free state healthcare, Syrian doctors mobilized to provide care through informal, nonstate providers and have gradually institutionalized their services. Drawing on over 175 interviews with Syrian patients, Syrian doctors, Turkish doctors, NGO administrators, and government officials, the dissertation examines the persistence of these clinics, the factors driving refugees to seek out nonstate services, and the efforts of refugee doctors to formalize their work within a complex legal environment that both accepts Syrians and forestalls their full integration.
Integrating scholarship on organizations, refugee reception, and immigrant entrepreneurship, the study traces how Syrian healthcare providers institutionalize in Istanbul’s urban fabric. The dissertation argues that when national policies are ambivalent about integration, immigrant organizations embed themselves in the host community by leveraging local and transnational networks for legitimacy and resources. First, it analyzes how informal immigrant organizations survive in “humanitarian niches” that emerge in host states shocked by large refugee populations. Next, it examines how providers adapt their work within shifting opportunity structures, as humanitarianism recedes as an organizing principle. Finally, it demonstrates how immigrant organizations gradually re-position themselves as facilitators of transnational economic exchange and essential providers in their localities. Over time, humanitarian organizations privatize, serving diverse groups of local immigrant beneficiaries and international tourists, even as national policies hinder their full legalization—a phenomenon I refer to as “cosmopolitan embeddedness”.
Sociologyhealthcareimmigrant organizationsmigrationrefugeesSyriaTurkeyenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5cg6701nhttps://escholarship.org/content/qt5cg6701n/qt5cg6701n.pdfetdoai:escholarship.org:ark:/13030/qt43h3r8ft2026-06-03T05:02:38Zqt43h3r8ftPredicting Future Technology Development Paths for Multi-Locational Firms in China: A Collaborative Filtering Approach and Case StudyZhang, YiouHandcock, Mark2024-01-01In the dynamic landscape of global business, the strategic decisions of multi-locational firms play a critical role in shaping their future trajectories. This thesis investigates the intricate relationship between technological innovation and the strategic decisions of multi-locational firms within the context of China, utilizing patent data as a primary source of analysis.The study begins by examining the patent data of multi-locational firms to uncover potential trajectories of technological development. Employing a recommender system analysis, the research aims to reveal underlying patterns and trends guiding the future technological classes that multi-locational firms are likely to pursue within the ever-evolving Chinese market.
Furthermore, the thesis delves into a comprehensive case study, exploring the strategic imperatives and decision-making processes involved in the pursuit of technological innovation by multi-locational firms. Drawing insights from the analysis of patent data, the case study provides valuable insights into the strategies employed by these firms to navigate the complexities of the Chinese innovation landscape and position themselves for future success.
Through this comprehensive investigation, the research seeks to offer actionable insights and strategic guidance for multi-locational firms striving to optimize their technological innovation efforts within the dynamic context of China’s innovation environment. By integrating rigorous data analysis with real-world case study findings, this thesis aims to contribute to the understanding of the strategic decisions driving technological innovation in multi-locational firms.
StatisticsCollaborative filteringMulti-locational firmsRecommender systemenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/43h3r8fthttps://escholarship.org/content/qt43h3r8ft/qt43h3r8ft.pdfetdoai:escholarship.org:ark:/13030/qt3vk8t6d22026-06-03T05:02:38Zqt3vk8t6d2Traditions in Practice: Utilizing Traditional Knowledge for Sustainable Pest Management in the Preventive Conservation of Cultural HeritageSalmon, Elizabeth CarolinePearlstein, Ellen J2024-01-01The word traditional calls to mind something immutable, unbroken, or even stagnant. However, traditional methods of caring for cultural heritage should instead be considered as culturally conscious, sustainable, and practical conservation tools. Plants that naturally kill or repel insects, or botanical pesticides, have been used by communities throughout the world for centuries to protect valued belongings, including cultural items, from insect damage. Time-tested tools for pest management that utilize locally available plants are part of the shared, intergenerational wisdom, or traditional knowledge, of communities. Today, staff at museums in India have adapted the traditional practice of storing dried neem leaves with cultural items, primarily textiles, to keep insects that can feed on and damage these materials safely at bay.This dissertation examines a traditional method of pest management, neem, that is presently used in museums in Rajasthan, a state in Northwest India. The neem tree (Azadirachta indica) is indigenous to the Indian subcontinent, where its pesticidal properties have been understood and utilized for centuries. All parts of the neem tree contain the active ingredient Azadirachtin, an insect repellent and insect growth regulator. Neem leaves are collected from abundant local trees and prepared on-site to be stored with collections, making it a sustainable and resource-efficient pest management tool. This research begins with conversations with staff at museums in India where neem is used to protect textile collections from insect damage. These conversations inform further research to assess the effectiveness of neem on museum pests and the effects of neem on collections, including experiments to assess how neem affects the eating habits of varied carpet beetle larvae, Oddy Testing, and artificial aging experiments.This doctoral research is a direct response to the need for increased accessibility and sustainable practice in conservation that can be met by promoting preventive conservation and giving due consideration to traditional methods of caring for cultural heritage. Looking to traditional knowledge for pest management strategies that are locally available and culturally relevant meets the needs of stewards and contributes to a shift in the field of conservation toward more inclusive and sustainable practice. Art criticismIntegrated Pest ManagementMuseum Pest ManagementPreventive ConservationTraditional Knowledgeenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3vk8t6d2https://escholarship.org/content/qt3vk8t6d2/qt3vk8t6d2.pdfetdoai:escholarship.org:ark:/13030/qt3px400vb2026-06-03T05:02:22Zqt3px400vbManaging the Distressed in an Age of Anxiety: Psychiatric Knowledge and Practice in South Korea, 1945-2000Chi, HyunjungLee, Namhee2024-01-01In this dissertation, I examine the development of psychiatry in the social and political context of South Korea. Although psychiatry was a product of the West that was introduced to Korea, the history of post-1945 Korea that I present reveals a multi-directional flow of ideas across regions. Drawing on extensive archival research, I explore the conditions of Korean psychiatry’s evolution and how psychiatrists used their professional platform to address the problems of a newly liberated nation—problems that included an authoritarian dictatorship and the social disruption that accompanied a drastic form of economic development and modernization. Previous historiography has tended to connect understandings of mental illness and mental health in Korea tightly to the indifference of the state and the therapeutic community. But the archives show psychiatrists competing for authority with various groups at the same time as they were developing their understanding of politics, illness, and normality, both within Korea and in a transnational context. Korean psychiatrists established themselves as therapeutic experts equipped to understand many aspects of Korean psychopathology and Korean manifestations of mental illness. I bring together the history of psychiatry, medicine, epidemiology, and international health, asking how Korean psychiatrists attended to the mental health of Koreans. This is one of the ways my dissertation integrates Korean history and the global history of psychiatry and demonstrates that Korean psychiatry was transnational in nature.
Asian studiesAsian historyEpidemiologyGlobal historyHistory of psychiatryKorean historyMental healthModern Koreaenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3px400vbhttps://escholarship.org/content/qt3px400vb/qt3px400vb.pdfetdoai:escholarship.org:ark:/13030/qt34v633s02026-06-03T05:02:17Zqt34v633s0Chinese Photojournalism 1937–1952: Materiality and the Institutionalization of Culture via a Computer Vision ApproachDu, LinBerry, Michael S.2024-01-01This dissertation presents a computationally-aided analysis of the Chinese Communist Party’s (CCP) use of photojournalism to forge a distinctive socialist media culture from the tumult of the Second Sino-Japanese War (1937–1945), through the Chinese Civil War (1945–1949), into the early years of the People’s Republic of China (1949–1952). It explores how CCP photojournalists, leveraging diverse media platforms, transformed photographic documentation into potent instruments of Party ideology. The dissertation introduces “digital historical forensics,” a novel digital humanities methodology that combines artificial intelligence technologies—specifically machine learning and computer vision—with traditional humanities approaches, including close reading and contextual analysis. This innovative method sheds light on previously unexamined textual and visual evidence, revealing patterns and strategies in ideological materials. These include the strategic emphasis on human subjects and textual elements, and the deployment of misinformation, influenced by the constraints of print technology and material conditions. The findings uncover unrecorded photographic, editorial, and curatorial decisions, offering new insights into the interplay between textual and visual materiality, power dynamics, and media convergence. It identifies key factors that shaped the CCP’s visual culture, including limited material supplies, military hierarchies, social interactions among cultural workers, and the drive to foster active participation among soldiers and local peasants. The study also highlights how wartime CCP photojournalism not only unveiled issues of social justice but also played a pivotal role in community-building efforts in 20th century China. This dissertation further examines how socialist culture prioritized ethical interactions between photographers and the local populace, enlightening them about their roles and responsibilities within their emerging political identities. Through an analysis of the institutionalization of photojournalism within the Party, this work reveals the genesis of the CCP’s propaganda branch and the development of visual paradigms by newly recruited photographers, which were disseminated among communities in the rural areas of northern China. The fostering of photographic literacy catalyzed the formation of a new generation of photojournalists, engendering a community bound by shared emotions, and facilitating dialogues on empathy and sympathy. By examining the aesthetics, content, production, and circulation of CCP photojournalism, alongside its interaction with various cultural forms, the dissertation elucidates the intermedial and institutional dimensions of state propaganda during a critical period in China’s history.Asian studiesComputer scienceArt historycomputer visioncontextual analysiscultural institutionalizationdigital humanitiesphotojournalismpropagandaenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/34v633s0https://escholarship.org/content/qt34v633s0/qt34v633s0.pdfetdoai:escholarship.org:ark:/13030/qt2cs3f25p2026-06-03T05:01:48Zqt2cs3f25pThree Essays in EconometricsZhang, WanMatzkin, Rosa L.Sheng, Shuyang2024-01-01The dissertation consists of three chapters. The first chapter studies the identification and estimation of binary choices with hidden information diffusion. It investigates the hidden information diffusion and consumer preferences for a new product, where only informed individuals who have acquired product information can make adoption decisions. Due to unobserved information acquisition/diffusion, we cannot distinguish informed but unwilling-to-adopt individuals from uninformed ones. In addition to a random utility model, we introduce a binary threshold crossing model to characterize information diffusion: an individual becomes informed if the exposure to the information, which depends on the fraction of already informed neighbors and personal attributes, exceeds some idiosyncratic threshold. We show the identification of diffusion and utility parameters through two subsets of observations: (i) adoption behaviors of seeds (first-informed individuals) over time; (ii) adoption behaviors of seeds' neighbors at time two. We estimate diffusion and utility parameters using the subset of observations that yields the identification. With regularity conditions, we establish consistency and asymptotic normality of the estimator.The second chapter applies the econometric framework developed in the first chapter to the diffusion of a microfinance program in rural India. We study the parametric estimation and have two major findings. First, communication with informed neighbors is crucial in acquiring the information. Second, the participation rate of neighbors has a non-monotone impact on the household's incentive to participate. We conduct a counterfactual analysis based on model estimates and evaluate the performance of many seeding strategies. The results suggest the benefits of exploiting available information in selecting seeds. The third chapter studies the nonparametric identification in generalized separable models. It provides new sufficient conditions for nonparametrically identifying structural models with generalized additive or multiplicative separability. The conditions generalize the normalizations used in the literature, such as differentiability of link and component functions, functional form normalization, and large support of an explanatory variable. We show that when a generalized separable model has two component functions, the identification only requires three location normalizations and weak support conditions. The new results can be applied to various influential econometric models after transformation. EconomicsNonparametric IdentificationSocial Networksenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2cs3f25phttps://escholarship.org/content/qt2cs3f25p/qt2cs3f25p.pdfetdoai:escholarship.org:ark:/13030/qt0s66j6vs2026-06-03T05:01:28Zqt0s66j6vsCorporatism, International Conflict, and Labor Politics in IranKalb, ZepWalker, EdwardHarris, Kevan2024-01-01This dissertation advances the literature on corporatism through a detailed case study of Iran. The dissertation’s main argument is that international conflict can provide opportunities for authoritarian state-building and state-labor integration. Combining process tracing methods with unique archival, interview, and quantitative analysis, I present a historical sociology of the development of labor market institutions in modern Iran, with a special focus on the 1940s-2010s period. I describe three waves of institution-building in the wake of international conflict and warfare, including the more recent period of severe economic sanctions. For each period, the dissertation pays close attention to causal sequences, showing how authoritarian politics and workers’ movements interacted to create institutions that shaped subsequent historical trajectories. The dissertation makes three main contributions. First, I find that international conflict can present non-zero-sum opportunities to both labor movements and autocratic leaders. This finding is especially relevant given intensifying interstate rivalries and the rise of economic sanctions in the 21st century. Second, I contribute to theories of corporatism by highlighting how oil rents can support labor organizing. Third, I show how labor politics in Iran has not just been shaped by control, neoliberalism, or resource abundance, but also by powerful bargaining institutions forged out of waves of war- and state-making. SociologyMiddle Eastern studiesPolitical scienceauthoritarianismcorporatismIranlaborenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0s66j6vshttps://escholarship.org/content/qt0s66j6vs/qt0s66j6vs.pdfetdoai:escholarship.org:ark:/13030/qt0dm7m62b2026-06-03T05:01:15Zqt0dm7m62bAssessing Impact of the COVID-19 Pandemic on Healthcare Access and Care Quality Measures in Children and Children with Intellectual and Developmental Disabilities (IDD)Mudnal, Purnima S.Chuang, Emmeline CNeedleman, Jack2024-01-01Improving access and care quality for children has received much research attention. However, substantial gaps still exist in knowledge about the effects of the COVID-19 pandemic on crucial aspects of children’s healthcare, such as delaying needed care or accessing preventive care. In this three-paper dissertation, we study the impact of the COVID-19 pandemic on these critical measures and examine disparities in accessing care. We stratify our analyses by pre- and post-COVID survey years using a nationally representative dataset as well as by neurotypical children and children with neurodevelopmental disabilities. In the first study, we assessed the role of effective care coordination and having a provider who understands a family’s cultural value systems on unmet healthcare needs for children with intellectual and developmental disabilities (IDD). The association between foregoing care and not receiving care coordination was large in pre- and post-COVID years. Similarly, the odds of children’s families forgoing healthcare when the provider sometimes or never showed cultural sensitivity were significantly higher compared to when the child’s provider was always or usually culturally sensitive.
In the second study, we continued to examine the role of a culturally sensitive provider in the uptake of developmental screening and provider monitoring for neurotypical children compared to children with IDD. Study findings demonstrated that parents of neurotypical children had 53% higher odds of completing a developmental screening questionnaire or receiving monitoring when the healthcare provider understood their cultural norms. These results were amplified for children with IDD when the provider was always/usually culturally sensitive.
Results from our third study showed that post-COVID, 35.8% of children were identified as overweight/obese compared to 32.8% in pre-COVID years, and this trend was similar in children with IDD. Obesity prevalence was largest in the youngest age group of 10-12 years old. There was a sharp increase in the proportion of children spending more than 4 hours of screen time during COVID-19. Few studies have examined how family resiliency might be a protective factor in obesity prevention. We found a lower proportion of overweight children in families with higher resiliency, although family resiliency was not statistically significant in post-COVID models.Public healthPublic policyHealth scienceschildrenCOVID-19culturally sensitive providerhealthcare accessneurodevelopmental disabilitiespediatric care quality measuresenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0dm7m62bhttps://escholarship.org/content/qt0dm7m62b/qt0dm7m62b.pdfetdoai:escholarship.org:ark:/13030/qt0d26w87q2026-06-03T05:01:14Zqt0d26w87qAttention in Typical Development and in the Psychosis SpectrumChang, SarahBearden, Carrie ENarr, Katherine L2024-01-01Psychotic disorders are severe psychiatric disorders, characterized by hallucinations and delusions, which occur in about 1-2% of the general population. While these disorders are highly disabling, increasing evidence suggests that psychotic experiences occur on a spectrum. Around 10% of children and adolescents from community samples endorse psychotic experiences, which are associated with greater risk of developing a full-blown psychotic disorder in adulthood. Psychotic disorders are often preceded by a prodromal period, with declines in work, school, and in social functioning, years before the onset of psychosis. Moreover, reducing the duration of untreated psychosis is associated with better response to medication, lower suicide rate, and better functional outcomes. As such, increasing research on psychosis focuses on understanding its neural precursors, with the goals of understanding its etiology and early intervention. Cognitive deficits are a core feature of schizophrenia. Against a backdrop of generalized cognitive deficit, attention is one of the most significantly impaired domains in schizophrenia. In individuals who later develop schizophrenia, attention deficits are detected as early as they can be assessed, long before psychotic symptoms typically emerge (ages 18-25) in those who later develop schizophrenia. Moreover, attentional impairments in schizophrenia likely have genetic underpinnings, as they are also observed first-degree relatives. Notably, current medications for schizophrenia do not address generalized cognitive or attention deficits, which are associated with worse psychosocial functioning and ability to live independently. Thus, understanding the neurobiology of attention deficits in the psychosis spectrum may ultimately identify novel avenues for early intervention for psychosis. Further introduction can be found in Chapter 1.
Yet, relationships between the brain and everyday attention problems in typically developing youth are not fully understood. Building on highly cited work establishing links between anticorrelated functional networks and attention in adults, Chapter 1 replicates and extends these findings in typically developing youth aged 9-10 in a manuscript published in Biological Psychiatry: Global Open Science. In Chapter 2, we evaluate whether the same anticorrelated functional networks, lab-based assessments of attention, and broad genetic liability related to attention are associated with psychotic-like experiences in early adolescence, in the general population. Lastly, in Chapter 3, we leverage a longitudinal, multi-site study of youth at clinical high risk for psychosis and typically developing youth and find that alterations in neural oscillations that support attentional control precede the onset of a full-blown psychotic disorder. In sum, the dissertation work herein focuses on attention in typical development and in the psychosis spectrum via a multi-modal investigation across genetic, neural, and behavioral levels of study, and in a risk period for psychosis and mental illness broadly.NeurosciencesAttentionDevelopmentPsychosisenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0d26w87qhttps://escholarship.org/content/qt0d26w87q/qt0d26w87q.pdfetdoai:escholarship.org:ark:/13030/qt05c5q8mq2026-06-03T05:01:09Zqt05c5q8mqTargeting Mitochondrial Bioenergetics in LiposarcomaKlingbeil, Kyle DShackelford, David B.2024-01-01Well-differentiated/dedifferentiated liposarcoma (WD/DD LPS) is a common subtype of soft tissuesarcoma in adults. Our understanding of this disease is lacking and treatment options are limited. First, by
investigating tumor metabolomics, we identify asparagine (Asn) as a fundamental purpose for mitochondrial
respiration in WD/DD LPS. Asn promotes mTORC1 activity to support tumor growth. Depleting Asn by
combining mitochondrial complex I inhibition with asparaginase holds therapeutic potential. Next, we
characterize WD/DD LPS beyond histological classification by defining mechanisms of dysregulation in
mitochondrial dynamics. WD LPS displays low MAPK signaling activity, elongated mitochondrial
morphology, and high mitochondrial respiration. Whereas DD LPS displays high activation of MAPK
signaling, which promotes activation of DRP1, fragmentation of mitochondria and a glycolytic phenotype.
This shift in mitochondrial bioenergetics promotes tumor growth. Trametinib represents a novel therapeutic
approach to target this adaptation. Moreover, Trametinib synergizes with chemotherapy, including agents
Pazopanib and Palbociclib, further expanding its clinical potential. DRP1 expression may also serve as a
prognostic biomarker, associating with worse survival. Altogether, these findings reveal several adaptations in
mitochondrial bioenergetics that provide opportunities for targeted therapy in patients with WD/DD LPS.Cellular biologyMolecular biologyMedicineBioenergeticsBiomarkerLiposarcomaMetabolismMitochondriaMulti-omicsenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/05c5q8mqhttps://escholarship.org/content/qt05c5q8mq/qt05c5q8mq.pdfetdoai:escholarship.org:ark:/13030/qt425919372026-06-02T06:37:35Zqt42591937Developmental desynchronization reorganizes cortical population geometry through parvalbumin interneuron maturationWu, MichellePortera-Cailliau, Carlos2026-05-29During the second postnatal week of development in rodents, cortical activity evolves from highly synchronized, low-dimensional dynamics to less synchronous activity spanning a greater number of dimensions, a process thought to underlie the emergence of sparse, flexible cortical representations. Previous studies have focused on the phenomenological characterization of this transition and on the role of sensory experience. However, how the geometric structure of population activity changes across this transition, what circuit mechanisms drive this reorganization, and the implications for information encoding have not been established. Using in vivo calcium imaging in early postnatal mice, we show that cortical desynchronization reflects a redistribution of population variance from dominant, low-dimensional modes into many weaker dimensions, fundamentally reorganizing the geometric structure of spontaneous cortical activity. All-optical circuit interrogation revealed that optogenetic activation of parvalbumin interneurons (PV-INs) is sufficient to drive this redistribution. In contrast, activation of somatostatin (SST) INs does not strongly alter the structure of population dynamics. Strikingly, a computational model that imposed known connectivity constraints between different IN classes and excitatory neurons was able to reproduce the developmental desynchronization and to predict the optogenetic findings. To understand the molecular basis of these differences, we leveraged single nucleus RNA-sequencing to analyze the maturation of PV and SST INs at the transcriptomic level. We find that both cell types undergo rapid and coordinated changes in gene expression during a narrow window in the early second postnatal week. Although they engage similar molecular pathways, they differ in their regulation of the specific genes, such that PV-INs preferentially enrich for fast synaptic transmission components while SST-INs are biased towards modulatory signaling. Furthermore, we are testing whether ablating a subset of PV-INs, a key component of cortical desynchronization, via cell type-specific caspase-3 expression at the beginning of the second postnatal week alters stimulus encoding. Together, these results demonstrate that PV-INs are critical for developing cortical networks to achieve a high-dimensional state that can support flexible and efficient representations of sensory information.NeurosciencesDevelopmental psychologyCognitive psychologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/42591937https://escholarship.org/content/qt42591937/qt42591937.pdfetdoai:escholarship.org:ark:/13030/qt93q2p5ts2026-06-02T06:37:30Zqt93q2p5tsStatistical Methods for Genetic Analysis of Survival TraitsKim, Do HyunZhou, HuaLi, Gang2026-05-29As modern biobanks and large-scale cohorts gather extensive genotype data alongside rich clinical information, there is a pressing need for statistical methods that can perform genetic analysis of survival traits such as disease onset or progression at scale while accommodating diverse genetic architectures, and leverage summary-level data. We propose three frameworks to meet these challenges. First, we introduce the censored multiple variance component model (CVC), an extension of linear mixed models designed for right-censored data. CVC estimates the narrow-sense heritability of survival traits using synthetic variables for unbiased variance component estimation, achieving computational scalability with up to one million subjects. Second, we propose AFT-SV, a semi-parametric accelerated failure time model leveraging synthetic variables that directly models time-to-event outcomes. By employing Wald-type test with a saddle-point approximation, this method is computationally efficient, can control the Type I error rate for testing rare variants, while providing more interpretable genetic effect size estimates. Finally, building on CVC and AFT-SV, we propose SumstatCVC, which can estimate the heritability of survival traits using summary statistics from AFT-SV. This approach facilitates efficient heritability enrichment analysis of functional genomic elements without requiring individual-level genotypes. Collectively, these methods address the unmet need for scalable and robust genetic analysis of time-to-event outcomes in large biobank datasets, providing insights into the genetic architecture of survival traits.BiostatisticsBioinformaticsGeneticsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/93q2p5tshttps://escholarship.org/content/qt93q2p5ts/qt93q2p5ts.pdfetdoai:escholarship.org:ark:/13030/qt6rd0j8xb2026-06-02T06:37:24Zqt6rd0j8xbMachine Learning Approaches for Leveraging Large-Scale Data Resources to Enhance Epigenomic and Genomic AnalysesFu, JingyuanErnst, JasonEskin, Eleazar2026-05-29Compendiums of genomic, transcriptomic, and epigenomic data have become valuable resources for large-scale biological studies. These data can be leveraged by machine learning methods to generate information not directly collected from experiments, yet challenges remain in bridging the gap between existing data and additional information of interest. In this dissertation, I propose three machine-learning-based methods to address some previously under-investigated areas in epigenomics and genomics. To predict chromatin state annotations in samples with RNA-seq data but lacking epigenetic marks, I propose a multiclass logistic regression-based model, GECSI. I then investigate, using large collections of chromatin state annotations, how to select a diverse and representative set of biological samples, and I propose ChromSelect. Finally, to address the gap between whole-genome sequencing–based reference panels and lower-density genotyping platforms, I propose ImputeMLP, a deep-learning-based method that imputes a higher-density of genetic variants using a limited set of genetic variants by incorporating nearby regions' variants, and show potential improvement compared to Hidden-Markov Model-based methods.Computer scienceBioinformaticsArtificial intelligenceChromatin StatesEpigenomicsGene ExpressionGenomicsImputationMachine learningpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6rd0j8xbetdoai:escholarship.org:ark:/13030/qt9gc451zc2026-06-02T06:37:19Zqt9gc451zcPathways to Self: Identity Development in Gender-Diverse Autistic PeopleGohari, DenaLord, Catherine2026-05-29Research on the intersection of autism and gender diversity remains limited, particularly in how identity development is understood and described by this population. This mixed-methods study with a qualitative focus explored identity development in 20 gender-diverse, predominantly late-diagnosed autistic adults ages 19 to 47 years old (M = 30.1, SD = 8.01). Interviewees were asked to narrate their identity development journeys and identify factors that shaped their development. Questionnaire measures were also collected; this sample completed the Hospital Anxiety and Depression Scale (HADS), the Social Responsiveness Scale Second Edition (SRS-2), and an abbreviated version of the Gender Diversity and Autism Questionnaire (GDAQ). Three-quarters of the sample met the clinical threshold for anxiety and nearly half met the threshold for depression. Participants reported greater engagement in queer communities compared to autism-related communities, with all participants endorsing recognition of initial signs of gender difference during childhood. Thematic analysis of semi-structured interviews produced four themes and 12 codes, with participants describing their identity journeys as multifaceted, evolving, and developed through ongoing interaction with social expectations. Participants commonly described autism as facilitating recognition of gender diversity (rather than causing it), and emphasized the role of external responses, including both invalidation and support, in shaping their development. Affirming relationships and community connections were consistently identified as critical sources of support amidst identity-related distress. Importantly, participants also offered practical recommendations for strengthening clinical and community spaces. Together, these findings provide a developmentally grounded account of identity formation at this intersection and highlight the need for more informed and responsive clinical and community-based supports.PsychologyClinical psychologyDevelopmental psychologyMental healthautismgendergender diverseidentity developmentqualitativeapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9gc451zchttps://escholarship.org/content/qt9gc451zc/qt9gc451zc.pdfetdoai:escholarship.org:ark:/13030/qt7j7558tn2026-06-02T06:37:13Zqt7j7558tnAn Audio-visual Approach to Spanish Trill Acquisition in Adult Learners: A Longitudinal Multilevel Study of Phonetic DevelopmentMelendez-Ballesteros, NancyKim, Ji Young2026-05-29Imitation, the ability to copy another person’s gestures, acoustic features, or speech style, is a cognitive skill present throughout life and plays an important role in language learning and acquisition. Evidence from first language research shows that infants begin imitating speech patterns early in development. This capacity continues into adulthood, particularly during conversational interaction or when individuals are explicitly instructed to imitate a model. The present study investigates a method for teaching second language (L2) phonology to adult English-speaking learners of Spanish through explicit focused-attention imitation training. The method builds on a pilot study (Meléndez-Ballesteros, 2012) with students enrolled in a second-semester elementary Spanish course. Participants were divided into four groups and performed specific listening and speaking exercises. The current study focuses on the audio-visual exercise of that study. Students were randomly assigned to either a training or non-training group and completed the same audio-visual activities following two separate sets of instructions. The training group received explicit focused-attention imitation instructions, whereas the non-training group repeated the model’s speech without explicit imitation instructions. The phonetic training lasted sixteen weeks. The effectiveness of the training program and developmental trends in students’ pronunciation were evaluated through acoustic analysis of the segmental duration of the Spanish trill [r], one of the most difficult sounds for L2 learners to acquire. Its duration was measured as a function of time in word-initial and word-medial positions, including intervocalic contexts, where these phones exhibit a phonemic contrast. The patterns of change over time in students’ productions of the Spanish trill were measured acoustically at four different time points throughout the semester. A growth-curve model within an HLM framework was used to examine individual learning trajectories over time, revealing variability in learners’ development. Although statistically significant group-level differences between the training and the non-training groups were not consistently observed, the longitudinal analyses revealed variability in individual trajectories, with some learners exhibiting changes in production and differing rates of change from baseline. The present study aims to contribute to the body of research on the explicit teaching of L2 speech sounds, focusing on adult learners of a second language. Unlike most studies of this type, it examines underlying individual differences that may contribute to the effectiveness of the training program by measuring students’ performance at four different time points rather than relying solely on the conventional two (i.e., pre- and post-measurements).LinguisticsEducationStatisticsLanguage artsAdult educationAdult L2 learners & Heritage SpeakersAudio-visual pronunciation trainingHierarchical Linear Modeling (HLM)longitudinal phonological developmentSecond Language PhonologySpanish Trill AcquisitionSpeech Imitationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7j7558tnhttps://escholarship.org/content/qt7j7558tn/qt7j7558tn.pdfetdoai:escholarship.org:ark:/13030/qt65r653982026-06-02T06:37:08Zqt65r65398Mechanistic analysis of polynucleotide phosphorylase (PNPase) dependent mitochondrial double stranded RNA export and its role in Non-Small Cell Lung Cancer (NSCLC)Krieger, MatthewKoehler, Carla M2026-05-29Previous studies show that inactivation of the mitochondrial protein polynucleotide phosphorylase (PNPase) results in mtdsRNA export, which upregulates a Type-I interferon response. Mutations in PNPase have been implicated in multiple neurodegenerative diseases with chronic inflammation, such as Aicardi-Goutieres disease and Leigh syndrome. Understanding the mechanism of export could serve as a framework for ameliorating mtdsRNA-induced chronic inflammation in neurodegenerative diseases. In this work, I dissected the mtdsRNA export pathway from the mitochondrial matrix to the cytosol (Chapter 2). I first validated the role of BAK and BAX in mtdsRNA export in the outer mitochondrial membrane. I then showed for the first time VDAC 1/2 as an essential component of outer mitochondrial membrane export. Then, I determined that PHB 1/2 are essential for mtdsRNA export through the inner mitochondrial membrane. To understand the relevance of mtdsRNA in other pathophysiological conditions, I explored mtdsRNA in Non-Small Cell Lung Cancer (NSCLC). Previous studies indicate that NSCLC cell lines downregulate proteins responsible for mitochondrial DNA sensing, and reintroduction of those proteins significantly arrests cell growth. Using genetic modulation of PNPase depletion, I observed significant mtdsRNA accumulation in a subset of NSCLC cell lines. Here, I aim to understand the prevalence and role of mtdsRNA in NSCLC to provide a framework for understanding the biological relevance of mtdsRNA in NSCLC. I generated a predictive model that stratifies NSCLC cell lines based on relative abundance of light strand RNA transcripts, which suggest mtdsRNA accumulation (Chapter 3). Here, I confirmed model’s accuracy in predicting mtdsRNA accumulation and relative abundance across nine NSCLC cell lines. I also showed that despite mtdsRNA export, there were varying levels of Type-I interferon response, a distinct difference to when PNPase has been depleted. Since inactivation of PNPase generates mtdsRNA, small molecule inhibition of PNPase would allow further study of the role of mtdsRNA in NSCLC. Thus, I tested a wide variety of citrate analogs in HeLa cells and determined two molecules effectively bind to PNPase and generate mtdsRNA (Chapter 4). I confirmed that the citrate analogs perform their inhibitory function in NSCLC cell line models, which allowed for their use in preclinical studies in NSCLC mice models (Appendix A). All in all, the work presented here highlights both mechanistic and therapeutic insight into PNPase inhibition.BiochemistryCellular biologyOncologyMolecular biologypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/65r65398etdoai:escholarship.org:ark:/13030/qt81j4864c2026-06-02T06:37:03Zqt81j4864cAttachment of lower-limb prostheses using magnetic attraction across a closed skin envelope.Flanagan, William JosephClites, Tyler R.2026-05-29Current socket-based methods of prosthetic limb attachment are responsible for many of the dominant problems reported by persons with amputation because these systems rely on soft tissues for load transfer. In light of these shortcomings, I present a novel magnetic prosthetic limb attachment system. My proposed system is comprised of a ferromagnetic implant that is fixed to the distal end of the residual bone and fully enclosed within the skin envelope, and an external electromagnet that is housed at the bottom of a socket. For suspension of the prosthesis, these components allow the transfer of tensile forces from the prosthesis directly to the bone using magnetic attraction instead of relying on soft tissues. During weight bearing, the large distal end of the implant provides an improved loading surface for transferring compressive forces.
In this dissertation, I detail the design, development, and evaluation of a magnetic attachment system for cases of above-knee amputation. Feasibility of the system is first demonstrated on the benchtop in a static case, then under full 6 degree-of-freedom walking dynamics using a custom robotic gait simulator. Finally, I describe a new class of adjustable permanent magnet arrays as an alternative to electromagnets that are particularly well-suited for low-power magnetic attachment systems. Results of these experiments show that magnetic attachment of a knee-ankle-foot prosthesis is not only feasible but also reduces pistoning compared to a conventional pin-lock socket system.Mechanical engineeringElectromagneticsBiomechanicsRoboticsmagnetic attachmentprosthetic socketsrobotic gait simulatorpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/81j4864cetdoai:escholarship.org:ark:/13030/qt2993x58h2026-06-02T06:36:57Zqt2993x58hEssays in Labor and Health EconomicsRighi, GiovanniBailey, Martha J2026-05-29This dissertation comprises three essays in labor and health economics that examine the role of social networks, firms, and public mental health infrastructure in shaping inequality in the United States.
The first chapter studies how racial segregation in coworker networks affects the transmission of labor market opportunities and contributes to persistent Black-White earnings gaps. Using matched employer-employee data covering workers across three U.S. states from 2002-2019, I find that same-race former coworkers transmit outside job opportunities more effectively than cross-race contacts. Job-to-job transitions are approximately five times more responsive to shocks of the same-race network than the cross-race network. I develop a sequential-auction labor market model with homophilous network transmission in which minority workers receive fewer effective outside offers because opportunities diffuse primarily through same-race contacts. Together, the model and empirical findings show how social networks propagate wage and employment gaps across racial lines even in the absence of discrimination.
The second chapter, joint work, studies how firms contribute to racial and ethnic wage gaps by distinguishing between sorting of workers across firms with different wage premiums and differential pay-setting within the same firm. Using linked employer-employee data from the LEHD covering nine states, we find that for Black men, firm-level inequality arises primarily through sorting into lower-premium firms, while for Black and Hispanic women, within-firm relative pay-setting is consistently important. Hispanic workers experience the largest overall firm-level contributions to wage gaps, especially in the West. The analysis highlights regional differences in the firm share of the earnings gap tied to differing institutional and demographic histories.
The third chapter evaluates the Community Mental Health Center (CMHC) program, a federal community mental health infrastructure initiative that began in 1963. Using the staggered rollout of facilities across U.S. counties, I find that CMHC establishment reduced age-adjusted mortality among adults ages 20-49 by roughly 10-12 deaths per 100,000 within a decade, with effects concentrated in external causes of death. I also find suggestive long-run benefits for children exposed to CMHCs during childhood, including higher earnings and lower institutionalization rates later in life.Labor economicsEconomicsMental healthapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2993x58hhttps://escholarship.org/content/qt2993x58h/qt2993x58h.pdfetdoai:escholarship.org:ark:/13030/qt7cp5b5gg2026-06-02T06:36:52Zqt7cp5b5gg“We have a responsibility as white people”: The Development of White Youth’s Critical Race Consciousness During Late AdolescenceKinnard, Lauren AnnMistry, Rashmita S.Wray-Lake, Laura2026-05-29Throughout U.S. history, some white individuals have acted in solidarity with People of Color to fight racism. Often these individuals began developing critical race consciousness—capacities necessary for disrupting racism—during adolescence. Examining the development of critical race consciousness during adolescence is critical because this developmental period is marked by key increases in relevant cognitive capacities (e.g., abstract thinking) and opportunities to engage with social issues. Drawing on the integrative model of critical race consciousness (Bañales et al., 2023), this dissertation includes two studies that examined the development and socialization of the sociocognitive, reflective, and behavioral capacities necessary for disrupting racism. Study samples are comprised of monoracial white adolescents who participated in a large nationwide study of youth civic engagement. Study 1 utilized survey data from 265 white participants (Mage = 17.3 years), and Study 2 utilized interview data from a subsample of 20 Study 1 participants (Mage = 19.4 years).Study 1 quantitatively examined the relations between family, peer, and teacher antiracism socialization and changes in adolescents’ racial beliefs and antiracism action one year later. Findings indicated that family antiracism socialization related to changes in beliefs and behavior over time. Findings also indicated that teacher antiracism socialization may play a preventative role in the development of race evasive beliefs when youth have few opportunities to engage civically.Study 2 qualitatively examined white adolescents’ racial reflexivity—an ongoing practice of reflecting on one’s positionality within racism—to explore how racial reflexivity may support youth in connecting their understanding of racism to their actions. Racial reflexivity varied across the sample, and three profiles of racial reflexivity emerged. Findings support the integrative model of critical race consciousness’s construct of racial reflexivity demonstrating that white youth who exhibited all dimensions of racial reflexivity reported more antiracism action.Altogether, this dissertation provides novel evidence about white adolescents’ development of critical race consciousness and offers insights into how to support white adolescents’ active engagement in building an antiracist society.EducationMulticultural educationDevelopmental psychologyEthnic studiesapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7cp5b5gghttps://escholarship.org/content/qt7cp5b5gg/qt7cp5b5gg.pdfetdoai:escholarship.org:ark:/13030/qt29f9w8c42026-06-02T06:36:47Zqt29f9w8c4cala and noob: A Streaming Framework for Scalable Calcium Imaging Analysis and Real-Time Neural ComputationChang, Raymond WoochanAharoni, DanielArisaka, Katsushi2026-05-29The rapid growth of experimental data and increasing analytical complexity in calcium imaging demand a new generation of computational tools. Modern experiments extend traditional limits by enabling continuous recordings over days or weeks and incorporating closed-loop systems that respond to neural activity in real time. These advances require analysis pipelines capable of processing terabytes of data, supporting real-time computation, and integrating with adaptive experimental systems.However, most existing analysis pipelines are not designed to meet these demands or require substantial software engineering expertise to adapt. Additionally, there is a lack of tools that enable scientists to easily develop, share, and reuse novel analysis software. As a result, creating and maintaining pipelines remains perpetually labor-intensive, and few solutions achieve the reach and longevity required to become a part of shared infrastructure.This dissertation addresses these challenges through two main contributions. First, it introduces cala, a streaming calcium imaging analysis pipeline designed for modern experimental needs. cala processes data frame-by-frame, enabling real-time analysis and seamless integration with closed-loop systems. It can handle arbitrarily long recordings without compromising speed or accuracy. To achieve this, cala incorporates novel algorithmic features such as self-correction and selective learning. Its modular design allows researchers to customize and adapt the pipeline to their specific experimental requirements.The second contribution is noob, a streaming graph processing library for scientists. noob provides a flexible framework for building and sharing complex analysis pipelines while abstracting away engineering overhead. In return, it emphasizes domain logic, enabling both authors and users to focus on scientific insights rather than implementation details, while maintaining compatibility with pure Python to maximize scientific expressions and minimize adoption barriers. It is also the engineering backbone of cala.Together, cala and noob have been able to produce breakthrough results, including the ability to analyze a 12-hour dataset on a modest personal computer without sacrificing speed or quality. This combined framework lays the foundation for future end-to-end streaming analysis systems, potentially enabling direct integration from imaging devices to real-time data visualization.PhysicsNeurosciencesArtificial intelligenceapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/29f9w8c4https://escholarship.org/content/qt29f9w8c4/qt29f9w8c4.pdfetdoai:escholarship.org:ark:/13030/qt51z1p6zb2026-06-02T06:36:41Zqt51z1p6zbFernando Alegría y las formas de arraigo colectivo: libertad, biculturalidad y chilenidadCuevas-Collante, PedroCortínez, Verónica2026-05-29Within the recent scholarly reappraisal of Chile’s Generation of 1938, this dissertation examines the work of Fernando Alegría (1918–2005) as an integrated intellectual, political, and narrative system organized around the problem of Chilean collective experience. Although Alegría has been recognized as a novelist, poet, intellectual, and cultural figure, his work has often been approached through fragmented readings that isolate genres, periods, or dimensions of his trajectory. The disparity between his historical significance and his critical reception stems in part from this fragmentation. I argue that Alegría’s writing develops a sustained reflection on forms of collective rootedness marked by classism, exclusion, violence, marginality, and social fracture, and that his oeuvre achieves coherence through three articulating principles: freedom, chilenidad as a collective sense of belonging, and the bicultural relation between Chile and the United States.Drawing on Roberto Hozven’s concept of negative symbolic cohesions, and combining literary and discourse analysis with archival research, my study reads Alegría’s essays, literary histories, political interventions, historical novels, poetry, archival practices, and narrative fiction as interconnected modes of critique and symbolic reconfiguration, integrating published and unpublished sources as well as testimonial material. The dissertation is organized in three chapters. The first examines Alegría’s intellectual trajectory from the Generation of 1938 to his bicultural position in California, focusing on his readings of Walt Whitman, Thomas Mann, Andrés Bello, Chilean poetry, Jorge Luis Borges, and the Hispanic heritage of California. The second analyzes his political action through the figure of the popular hero, anti-fascism, anti-imperialism, President Salvador Allende, exile, human rights, and political poetry. The third studies Alegría’s narrative fiction as a literary space of belonging, displacement, memory, and return, with particular attention to Camaleón (1950), Los días contados (1968), Caballo de copas (1957), and La rebelión de los placeres (1990). The dissertation argues that Alegría’s work does not resolve Chile’s historical fractures but transforms them into a dynamic archive through which alternative forms of community, freedom, bicultural belonging, and a positive reimagining of Chilean collective experience become imaginable.Latin American literatureSpanish literatureModern literatureLatin American studiesFernando AlegríaHistoria de la literaturaLiteratura chilenaapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/51z1p6zbhttps://escholarship.org/content/qt51z1p6zb/qt51z1p6zb.pdfetdoai:escholarship.org:ark:/13030/qt1zf327gt2026-06-02T06:36:36Zqt1zf327gtConditional Conformal Prediction for In-Context Learning: Architecture Scaling, Task Complexity, and Signal-to-Noise EffectsLin, JinruiDai, Xiaowu2026-05-29Transformer-based in-context learning (ICL) has demonstrated remarkable ability to perform regression tasks by conditioning on demonstration examples within a single forward pass, without parameter updates. While ICL predictions improve with more demonstrations, quantifying the uncertainty of these predictions with formal coverage guarantees remains an open challenge. In this work, we conduct a systematic empirical study of conditional conformal prediction methods— specifically CondConf and its computationally efficient variant SpeedCP—applied to ICL regression across multiple experimental axes. We investigate how model architecture (width, depth, and standard presets), task complexity (linear, quadratic, neural network, and decision tree regression), signal-to-noise ratio, and input dimensionality jointly influence both point prediction quality and the resulting conformal prediction intervals. Across 44 experimental configurations encompassing 12 architectures (S13–S24), 16 task–noise combinations (S52–S67), and 16 dimensionality configurations (S69–S84, 4 tasks × 4 dimensions), we find that SpeedCP maintains empirical coverage consistently near the nominal 95% level (93.1%–96.1%). However, the efficiency of conformal intervals—measured by average width—varies dramatically: from a 84% width reduction over the ICL trajectory for large-capacity models on low-noise linear tasks, to essentially no improvement for under-capacity models or high-noise regimes where irreducible noise dominates. At high input dimensions (d = 100), interval widths increase sharply despite separately training each model for its target dimensionality. These findings provide practical guidance for deploying conformal prediction in ICL systems and highlight the intimate coupling between model capacity, task learnability, and uncertainty quantification quality.StatisticsTheoretical mathematicsComputer scienceArtificial intelligenceapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1zf327gthttps://escholarship.org/content/qt1zf327gt/qt1zf327gt.pdfetdoai:escholarship.org:ark:/13030/qt7q66291w2026-06-02T06:36:31Zqt7q66291wOne-Dimensional Climate: Los Angeles at the Crossroads of Climate HistoryWeinger, Benjamin Asher KaplanAgnew, John A2026-05-29The City of Los Angeles has attempted to become one of the world’s most visible laboratories of urban climate governance. Its municipal climate plans have promised a transition away from fossil fuels through electrification, renewable energy, green infrastructure, and emissions reduction. Yet these promises have unfolded in a paradigmatic megalopolis of inequality, shaped by histories and geographies of oil extraction, racialized development, automobilization, infrastructural expansion, and deeply uneven exposure to environmental harm. Based on two years of ethnographic fieldwork with municipal climate planners and local social movements, this dissertation traces these contradictions through the everyday spaces where climate policy is made. It moves through planning meetings, technical documents, implementation debates, activist campaigns, and the material landscapes of Los Angeles’s energy transition. Across these sites, climate governance appears as a contested field of coordination, constraint, compromise, and political struggle. The dissertation situates Los Angeles as a climate core city: a post-industrial metropolis whose climate leadership has been organized through plans, targets, institutional networks, financial capacity, and consumption power. The result is a central contradiction of urban climate governance: the city is asked to manage a planetary crisis through municipal tools that are fragmented, territorial, and often unable to confront the wider geographic systems of accumulation that sustain urban life. Localized decarbonization is never only local. Bringing together urban climate governance, fossil urbanization, ecologically unequal exchange, and planetary urbanization, the dissertation traces how climate action is made through municipal practice and planetary extraction. Moving beyond climate governance as a technical project of carbon reduction, I ask how cities built through extraction can confront the climate crisis without reproducing the inequalities that made it possible.GeographyHistoryUrban planningClimate changePublic policyclimate changeclimate governancepolitical geographypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7q66291wetdoai:escholarship.org:ark:/13030/qt5pq786n82026-06-02T06:36:26Zqt5pq786n8Synaptic, Cellular, and Circuit Mechanisms Shaping Frontolimbic Control of Emotional Behavior: from Therapeutic Neuromodulation to Developmental Circuit RemodelingGongwer, MichaelDeNardo, Laura A2026-05-29Neuropsychiatric disorders frequently involve dysfunction in frontolimbic circuits that regulate emotion, motivation, and threat response, but the synaptic, cellular, and circuit mechanisms governing these behaviors remain incompletely understood. In this dissertation, I investigate how defined cell types and synapses within prefrontal and limbic networks shape emotional behavior in the context of therapeutic neuromodulation and development. First, I developed a preclinical mouse model of repetitive transcranial magnetic stimulation (rTMS) with strong clinical face validity and used it to identify circuit mechanisms underlying the rapid antidepressant effects of accelerated intermittent theta burst stimulation (aiTBS). I found that aiTBS induces cell type-specific plasticity in dorsomedial prefrontal cortex (dmPFC), selectively enhancing activity and dendritic spine density in intratelencephalic projection neurons, while parallel studies of inhibitory microcircuits revealed a key role for somatostatin interneurons in mediating antidepressant-like behavioral effects. Together, these findings identify excitatory and inhibitory circuit elements through which therapeutic neuromodulation reshapes prefrontal function. I then examined how distinct prefrontal neuronal classes contribute to adaptive threat avoidance, combining brain-wide anatomical mapping, activity measurements, and targeted circuit analysis to define projection-specific organization and encoding of learned and innate avoidance behaviors. Finally, I investigated developmental mechanisms shaping frontolimbic circuitry. I identified synaptic changes associated with maturation of prefrontal-limbic pathways, including developmental trajectories in prefrontal projections to the basolateral amygdala and nucleus accumbens, as well as modulation of these pathways by early life adversity. Extending this work to non-neuronal mechanisms, I found that microglia are required for normal excitatory synapse development in the nucleus accumbens and for the proper emergence of circuit function and threat avoidance behavior later in life. Collectively, this work demonstrates that emotional behavior is shaped by coordinated plasticity across projection-defined neurons, interneuron microcircuits, long-range frontolimbic pathways, and glial mechanisms, and provides a multiscale framework for understanding how these systems can be modified in both disease and treatment.NeurosciencesCellular biologyBehavioral psychologyDevelopmental psychologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5pq786n8https://escholarship.org/content/qt5pq786n8/qt5pq786n8.pdfetdoai:escholarship.org:ark:/13030/qt3j06w3t02026-06-02T06:36:17Zqt3j06w3t0Advancing Tsunami Source Imaging and Illuminating Drivers of Seismicity Before and After Large EarthquakesMohanna, Saeed YahyaMeng, Lingsen2026-05-29Understanding the physical drivers of large earthquake sequences and source characteristics of tsunamis is essential for comprehensive hazard assessment. Great strides in seismic sensor deployment have led to the discovery of numerous physical mechanisms that explain seismicity before and after large earthquakes. Additionally, detailed tsunami source imaging with minimal assumptions is facilitated by increased networks of deep-ocean and near-shore tsunami sensors. However, the interplay between different mechanisms of seismicity and their spatiotemporal evolution can vary significantly across dissimilar tectonic environments. Furthermore, tsunami source imaging has yet to be adequately tested for less common but important tsunami-generating scenarios such as submarine landslides and longer-duration, lower-magnitude (Mw 7-8) earthquakes. In this thesis, I present the research studies I conducted that fell into two themes.
Theme A highlights the interaction and spatial distribution of physical drivers of events during four notable earthquake sequences: the 2022 Mw 6.4 Ferndale, 2023 Mw 7.8-7.7 Türkiye-Syria, 2024 Mw 7.6 Noto Peninsula, and the 2024 Mw 7.0 Offshore Cape Mendocino earthquake sequences. These studies relied on the generation of high-resolution seismic catalogs. Our results highlight the complexity of hazards in these diverse settings and include evidence for swarm cessation following a large mainshock-aftershock-type sequence, migrating afterslip near creeping zones at fault junctions, post-seismic slip on an oceanic fault, and crustal permeability changes related to groundwater flow.
Theme B demonstrates the valuable capabilities of the adjoint tsunami source inversion method using observations of the 2024 Mw 7.6 Noto Peninsula tsunami and synthetic data of tsunamis occurring at the Japan Trench and the Cascadia Subduction Zone. Our work showed that the adjoint inversion method detected a submarine landslide during the 2024 Noto Peninsula event, yielding key insight into tsunami sources when applied alongside a more common source inversion technique. We also conducted a proof-of-concept study that tested an advancement to the adjoint method that enables imaging multiple temporal snapshots of the tsunami source. The findings suggest that the method’s applicability may be extended to often-overlooked but hazardous tsunamigenic earthquakes known as tsunami earthquakes.GeophysicsPlate tectonicsGeologyEarthquake SequencesPhysical Drivers of AftershocksTsunami Source ImagingTsunami WarningspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3j06w3t0etdoai:escholarship.org:ark:/13030/qt04b7z4z02026-06-02T06:36:06Zqt04b7z4z0A common terpene engages the endocannabinoid system to alleviate chronic painAlayoubi, MyraCahill, Catherine Marie2026-05-29Leveraging analgesic constituents of the cannabis plant, which has been used for centuries, has considerable potential in addressing the unmet need for effective analgesics. Understanding the effects of terpene isolates, such as myrcene, in pain response will advance this goal. Chronic pain is a condition that impairs people’s quality of life and affects approximately 1/5th of the population. Myrcene is a cannabis terpene that has demonstrated analgesic potential but a gap in the literature exists on pharmacological and behavioral experiments to characterize myrcene’s mechanism of action and understanding how its properties relate to the endocannabinoid (eCB) system. My dissertation work characterized the therapeutic potential and pharmacology of myrcene, using four distinct experimental approaches. I used behavioral pharmacology in a mouse model of neuropathic pain, TRUPATH binding assays with HEK293 cells, molecular structural elucidation with Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS), and in vivo fiber photometry. I aimed to support or refute the following two hypotheses: 1) Myrcene produces anti-allodynic effects via engagement of the endocannabinoid system. 2) Myrcene modulates vlPAG neuronal activity in response to a painful aversive stimulus but not a non-painful stimulus. Secondary hypotheses: Myrcene anti-allodynic effects will be prevented by blocking synthesis of endocannabinoid production and will cause endocannabinoids to increase in the vlPAG at time points consistent with peak anti-allodynic effects. My work confirmed that myrcene produces anti-allodynic effects to attenuate mechanical hypersensitivity in male mice with a chronic constriction injury (CCI) and demonstrate a dose-dependent shift to the left in female CCI mice without modulating antinociception in either sex in sham control groups. I established myrcene requires the endocannabinoid (eCB) 2-arachidonoylglycerol (2-AG) to produce these effects in both male and female CCI mice. I also demonstrated that myrcene does not directly activate CB1 receptors via G1alpha using a TRUPATH assay in vitro. However, myrcene increased 2-AG levels as quantified by LC-MS/MS at times consistent with peak anti-allodynic effects. Although capable of increasing eCBs, myrcene did not modulate neuronal activity in the same way as a CB1 agonist in the ventrolateral periaqueductal gray (vlPAG), an integral area in the descending modulation of pain. These findings provide clarification of how myrcene alleviates neuropathic pain as well as specific circuitry that appear not to be involved in this modulation of sensory pain hypersensitivity. Overall, this work supports that myrcene is engaging the eCB system to produce anti-allodynic effects.NeurosciencesPharmacologyBiochemistryBehavioral pharmacologyChronic painEndocannabinoidsPreclinicalTerpenespubliceScholarship, University of Californiahttps://escholarship.org/uc/item/04b7z4z0etdoai:escholarship.org:ark:/13030/qt0sg5t1kq2026-06-02T06:36:01Zqt0sg5t1kqAsset Tracking During WildfiresHateley, John Michael ThomasNarasimhan, Sriram2026-05-29During wildfires, hundreds to potentially thousands of firefighters and scores of firefighting equipment are deployed to contain the fire. It is critical to accurately track such deployed assets to coordinate resources, protect firefighters, and enhance situational awareness. Currently, most assets are tracked through radios or rudimentary techniques, such as visual light communication, which often results in poor situational awareness in active fire situations. RFID technology has been utilized in a wide array of tracking applications both indoors and outdoors, and has been shown to achieve high accuracy. However, there has been limited exploration of its use in wildfire applications.This thesis proposes the incorporation of RFID technology into wildfire asset tracking through the use of a mobile autonomous robots, leveraging Gaussian Processes for environmental analysis and range prediction. This research address challenges in environmental selection, range prediction, camera RFID association, and persistent monitoring of mobile assets. The key contributions of this research include a novel application of a Gaussian Process model, the ability to identify environments based off of RF signal without any spatial information, fusion of camera and RFID tags under challenging conditions, and algorithms to persistent monitor mobile assets that move in and out of detection range. This study aims to add a new application of RFID technology and enhance wildfire situational awareness through the incorporation of a low cost tracking system.A general list of the contributions is presented below, with a detailed list of contributions to be found at the start of each chapter:1) Contribution # 1 Assessing the viablity of Gaussian Process Models in Radio Frequency Identification (RFID) Tag localization in trained and untrained environments.2) Contribution #2 Develop and assessing an weighted log likelihood approach to model selection through only Radio Signal propagation and without geospatial references.3) Contribution #3 Developing and assessing an approach to associate n tags and visually detected objects at various density levels and tag counts using a combination of Uncertain Fréchet Distance, Mahalanobis Distance and minimum cost matching4) Contribution #4 Developing and assessing an optimization algorithm in conjunction with quadratic Bézier Functions for robot goal point generation to aid in the persistent monitoring of unmonitored mobile RFID tags across a variety of testbeds.RoboticsComputer scienceMechanical engineeringCamera/RFID FusionGaussian ProcessPersistent MonitoringRFID TrackingSensor FusionTrajectory PlanningpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0sg5t1kqetdoai:escholarship.org:ark:/13030/qt5vp0n56m2026-06-02T06:35:56Zqt5vp0n56mDeoxyribonucleoside salvage is required for disease in murine models of inflammatory bowel disease and optic neuritis.Ryan, AidenClark, Peter M.2026-05-29Autoimmune disease affects over 15 million individuals in the United States, roughly 4.6% of the population. Autoimmune diseases disproportionally affect women more than men, with a relative ratio of 2:1. Little is known about what causes autoimmune diseases, but their prevalence world-wide rises each year. Many factors such as genetic predisposition, environmental factors, and others have been thought to be involved in the loss of immunologic tolerance that gives rise to autoimmune disease. Patients diagnosed with autoimmune diseases have limited therapeutic options, often being prescribed broad immunosuppressive drugs which leave them susceptible to infections, or corticosteroids which cannot be taken long-term due to their detrimental side effects. Additionally, some autoimmune patients are unable to maintain long-term disease remission on their prescribed drug regimen. Thus, investigation into alternative, improved therapeutic options for autoimmune diseases is greatly needed.This dissertation will focus on inflammatory bowel disease and optic neuritis, and will investigate the involvement of the deoxyribonucleoside salvage pathway in each respective disease. The deoxyribonucleoside salvage pathway is one of two pathways involved in the production of deoxyribonucleotides, which are a crucial component of biomass needed for cells to proliferate. Given that autoimmune diseases are driven primarily by rapidly proliferating, auto-reactive immune cells, investigation into novel therapeutic avenues targeting cellular proliferation hold the potential to offer alternative therapeutic treatments for autoimmune patients.Chapter 1 of this dissertation will give an overview of autoimmune disease, deoxyribonucleoside metabolism, how the salvage pathway can be monitored non-invasively with PET/CT imaging, and how the salvage pathway can be pharmacologically inhibited. Chapter 2 of this dissertation will investigate the involvement and potential therapeutic benefit of targeting the salvage pathway for the treatment of inflammatory bowel disease. Chapter 3 of this dissertation will investigate the potential therapeutic benefit of treating optic neuritis with a novel salvage pathway inhibitor, will further the mechanism of how salvage pathway inhibition improves disease prognosis in a murine model of optic neuritis and multiple sclerosis, building on work previously published by our lab, and describes the potential value of using [18F]FAC PET imaging as a prognostic biomarker for salvage pathway inhibition-treatment response. Chapter 4 will draw conclusions regarding the involvement of the salvage pathway across inflammatory bowel disease and optic neuritis, and will provide an outlook for the potential benefit of pharmacologic inhibition the salvage pathway can have for other autoimmune diseases.PharmacologyMedical imagingMolecular biologyImmunologyAutoimmuneDeoxycytidine KinaseDeoxyribonucleoside SalvageInflammatory Bowel DiseaseMolecular ImagingOptic NeuritispubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5vp0n56metdoai:escholarship.org:ark:/13030/qt39b037tg2026-06-02T06:35:51Zqt39b037tgVarieties and Pairs of Small CompleityEnwright, Joshua LeeTotaro, BurtMoraga, Joaquin2026-05-29The complexity of a Calabi–Yau pair pX, Bq is an invariant that relates the dimension of X, the rank of the group of divisors, and the coefficients of B. If the complexity is less than one, then X is a toric variety. We study other geometric consequences of small complexity. We give a characterization of log Calabi–Yau pairs of complexity zero and arbitrary index. As an application, we show that a log Calabi–Yau pair of birational complexity zero admits a crepant birational model which is a generalized Bott tower. We prove that if the complexity is less than two, then X is a Fano type variety. Furthermore, if the complexity is less than 3/2, then X admits a Calabi–Yau structure of complexity one and index at most two, and it admits a finite cover Y Ñ X of degree at most 2, where Y is a cluster type variety. In particular, if the complexity is one and the index is one, pX, Bq is cluster type. Finally, we establish a connection with the theory of T-varieties. We prove that a variety of T-complexity one admits a similar finite cover from a cluster type variety.MathematicsTheoretical mathematicsApplied mathematicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/39b037tghttps://escholarship.org/content/qt39b037tg/qt39b037tg.pdfetdoai:escholarship.org:ark:/13030/qt6fp5734f2026-06-02T06:35:46Zqt6fp5734fParametric Mixing for Frequency-Tunable and Phase-Sensitive Dynamic RF FilteringChen, Sean Ching ChuanWang, Yuanxun2026-05-29Modern RF front ends must operate in increasingly crowded and dynamic spectral environments, where signals and interferers can vary rapidly in frequency, bandwidth, and phase. Conventional linear time-invariant filters provide strong selectivity, but are constrained by tradeoffs among bandwidth, quality factor, insertion loss, and temporal response. These limitations make it difficult to realize filters that are simultaneously selective, continuously tunable, efficient, and capable of symbol-level adaptation.
This dissertation investigates parametric mixing as a unified framework for tunable and dynamic RF filtering. By modulating reactive elements with external pump signals, energy can be controllably exchanged among signal, pump, and idler frequencies. This allows filtering to be viewed not only as static impedance shaping, but as programmable energy redistribution across frequency and time.
The first part of this work develops the theoretical foundation of parametric RF filtering, including the conditions under which time-varying capacitance produces effective negative resistance or positive conductance. These principles are applied to a cascaded parametric bandpass filter, where an RF signal is translated to a fixed idler resonance and then reconstructed at its original frequency. By separating the tuning mechanism from the resonant element, this architecture enables continuous frequency tunability while preserving narrowband selectivity.
The second part examines the complementary absorption regime of parametric mixing. Under an alternate frequency relationship, the signal-pump-idler interaction produces an effective positive conductance at the signal frequency, enabling frequency-controlled energy absorption and tunable notch filtering. This mechanism is extended through a double-pump parametric notch filter, where a primary pump generates the absorption notch and a secondary pump introduces negative resistance at the idler frequency to compensate resonator loss. This Q-enhanced architecture improves notch depth and selectivity in an integrated RF implementation.
The final part extends parametric filtering into the time domain through phase-sensitive dynamic filtering and correlation-based RF signal processing. By treating the pump as a time-varying reference, the filter selectively accumulates waveform components coherent with that reference while suppressing uncorrelated components. This enables symbol-level filtering behavior beyond the conventional bandwidth-settling-time tradeoff, with potential applications in spread-spectrum processing, co-site interference mitigation, and simultaneous transmit-and-receive leakage suppression. Together, these contributions establish parametric mixing as a general approach to RF filtering in which bandpass selection, notch rejection, Q-enhancement, and dynamic waveform selectivity arise from the same underlying energy-exchange mechanism. By using time variation as a design resource, this dissertation provides a foundation for dynamic RF front ends capable of frequency-selective, energy-selective, and waveform-selective operation.Electrical engineeringElectromagneticsEngineeringComputer engineeringDynamic FiteringParametric AmplifiersParametric MixingTime-varyingTunable FilteringpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6fp5734fetdoai:escholarship.org:ark:/13030/qt9xb8j0n82026-06-02T06:35:41Zqt9xb8j0n8Improving Power and Uncertainty Quantification in Spatiotemporal Climate ApplicationsMcEvoy, Kyle RobertsMcKinnon, Karen A2026-05-29Performing statistical analyses with climate data requires accounting for many types of variability. This variability is complex and spatiotemporal in nature, and the variability often integrates overlapping processes at multiple spatial and temporal scales. While this makes climate a challenging setting for applying statistical methods, we can use scientific expertise to properly account for and leverage these spatiotemporal structures in our analyses. This dissertation develops statistical methods for performing large scale multiple hypothesis testing and uncertainty quantification in the climate setting. Chapter 1 introduces the climate setting and some of the particular challenges that methods face in this setting. Chapter 2 focuses on multiple hypothesis testing in a common regression setting in climate, where a univariate response variable is regressed against a spatial grid of covariates. The false discovery rate (FDR) control framework provides a practical method for controlling for multiple hypothesis testing, but popular FDR controlling methods, such as Benjamini-Hochberg, exhibit weak power when applied to data that is highly spatially correlated, as is often the case with climate data. This limitation motivates a proposed spatial smoothing method using learned local covariance structures to smooth covariate data as pre-processing step before the analysis. We demonstrate that our method still controls the FDR rate empirically, while substantially increasing power in simulation studies. Then, we apply the method on regressions of central US temperatures against sea surface temperatures. In Chapter 3, we consider the problem of quantifying uncertainty in estimates of precipitation extremes over the conterminous United States (CONUS). Due to the inherently rare nature of extreme events, extreme quantiles are difficult to estimate, and this process is made more challenging by the temporal structure of precipitation data, which is influenced by modes of variability. We expand upon observational large ensemble methods in the literature to generate synthetic spatiotemporal fields of precipitation over CONUS. These synthetic fields incorporate variability related to large scale modes of climate variability as well as unrelated short term variability to produce fields with realistic precipitation variability. This method is validated using data from climate model initial condition large ensembles, and we use these ensembles to estimate uncertainty in estimates of monthly extreme quantiles of the precipitation distribution across CONUS. In Chapter 4, we apply this observational large ensemble approach with regression models to better quantify variability in Colorado River streamflows at Lees Ferry. Streamflows of the Colorado River are heavily influenced by temperature and precipitation over the Upper Colorado River Basin. In order to understand how the variability in temperature and precipitation can influence the variability in streamflows, we apply the observational large ensemble to generate synthetic precipitation and temperature records for the Upper Colorado River Basin. And, using these records with the regression models, we produce a synthetic ensemble of streamflows. This synthetic ensemble is used to estimate annual variability for streamflows that properly integrate the variability of temperature and precipitation records. Then, we use estimates of future warming to explore how streamflows might change by 2050. Finally, Chapter 5 concludes the thesis with a discussion of these methods and future work.StatisticsAtmospheric sciencesClimate changeClimate ApplicationsMultiple Hypothesis TestingSpatiotemporal DataStatisticsUncertainty Quantificationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9xb8j0n8https://escholarship.org/content/qt9xb8j0n8/qt9xb8j0n8.pdfetdoai:escholarship.org:ark:/13030/qt7z86q1822026-06-02T06:35:36Zqt7z86q182Identifying the Molecular Drivers of Cellular Senescence in MacrophagesSalladay-Perez, Ivan AlexanderCovarrubias, Anthony J2026-05-29Aging is characterized by chronic low-grade inflammation, termed inflammaging, which contributes to the development of metabolic and degenerative diseases. Cellular senescence is a key driver of this process; however, the identity and molecular features of senescent immune cells remain poorly defined. This dissertation investigates macrophage senescence as a central mechanism underlying immune aging. First, we establish that macrophages can undergo bona fide cellular senescence in response to DNA damage and metabolic stress, acquiring stable cell-cycle arrest and a senescence-associated secretory phenotype (SASP). Through multi-omic profiling, we identify a distinct transcriptional and metabolic signature of senescent macrophages characterized by p21 expression, type-I interferon signaling, and mitochondrial DNA–driven inflammation
We further demonstrate that excess cholesterol loading via acetylated low-density lipoprotein is sufficient to induce a stable, lipid-laden senescent macrophage phenotype, providing a reproducible experimental model for studying lipid-induced senescence. Importantly, we identify p21⁺TREM2⁺ macrophages as a major source of inflammaging in vivo, accumulating in aged and diseased tissues and contributing to metabolic dysfunction. Targeting these cells with senolytic therapies reduces inflammation and disease burden. Together, this work establishes macrophage senescence as a key driver of immune aging and highlights new therapeutic strategies to combat age-associated disease.BiologyImmunologyCellular biologyMolecular biologyGeneticsAgingCellular SenescenceCholesterolInflammationMacrophageapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7z86q182https://escholarship.org/content/qt7z86q182/qt7z86q182.pdfetdoai:escholarship.org:ark:/13030/qt6jb614j72026-06-02T06:35:31Zqt6jb614j7Strategic Communication in International Security: Alliance Management, Arms Control, and Emerging TechnologiesLuca, Laura MargaretaO'Neill, Barry2026-05-29Amid renewed great-power competition, eroding arms control regimes, and emerging military technologies, what bounds what states can credibly communicate to allies and adversaries? Existing scholarship treats credibility as the central problem, studying how officials signal resolve and capability through costly signals, audience costs, and treaty design. I show that credibility depends on more than what officials say or commit to. Two undertheorized structural features of the communicative environment bound strategic communication: audience interdependence in alliance management, where one audience’s response becomes information for another, and strategic substitutability in arms control, where alternative capabilities determine which treaties form and which credibly hold.
In multi-audience settings, communication across audiences produces downstream consequences that can convey what states did not intend. In alliance management, patrons routinely combine public commitments with private signals (both pressure for burden-sharing and reassurance against abandonment) to deter adversaries and induce burden-sharing. A three-player signaling model proves a structural trilemma: patrons cannot simultaneously deter, induce higher allied effort, and conceal a private hedge. A burden-sharing paradox follows: the ally spends as the patron demands and is still attacked, because the spending itself reveals the patron’s qualified commitment to the adversary. Cold War evidence from U.S.–Taiwan in the 1950s and U.S.–South Korea in 1950 traces the mechanism.
Conventional accounts treat arms control as a verification problem: making unobservable compliance behavior observable enough for signatories to trust each other. I argue the prior condition is strategic substitutability: whether signing states retain alternative capabilities that fulfill the constrained weapon’s core military, deterrent, and coercive functions. States do not accept constraints on capabilities for which no substitute exists. I assemble an original dataset of over 100 arms control treaties spanning two centuries and trace substitutability across five least-likely cases, where it explains the pattern of agreement and noncompliance more fully than verification-centered or institutional accounts.
The three-player signaling model and original arms control dataset establish a framework for analyzing the structural conditions on credible communication, and speak to the utility of treating audience interdependence and strategic substitutability as prior to signal design under renewed great-power competition.International relationsPolitical scienceMilitary studiesalliancesarms controldeterrencegame theorynonproliferationtechnologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6jb614j7https://escholarship.org/content/qt6jb614j7/qt6jb614j7.pdfetdoai:escholarship.org:ark:/13030/qt8tp0b22q2026-06-02T06:35:26Zqt8tp0b22qCharacteristics and Control of Collective Electronic Transport in Low-Dimensional Charge-Density-Wave MaterialsTaheri, MaedehBalandin, Alexander A. A. A. B.2026-05-29This dissertation research deals with collective electronic transport and external control of charge-density-wave condensates in low-dimensional van der Waals materials. The charge-density-wave materials provide a unique platform for studying collective transport phenomena arising from strong electron–phonon coupling and offer potential pathways toward beyond-CMOS electronic functionalities. First, the dissertation describes the electrostatic modulation of the electron-lattice condensates in quasi-one-dimensional systems. In orthorhombic TaS3, a giant gate-induced modulation of the condensate charge density is demonstrated, exceeding predictions based on conventional geometrical capacitance by one to two orders of magnitude. The enhanced response originates from coupling between the applied electric field and the collective electron–lattice condensate, revealing an unusually large effective quantum capacitance. Complementary studies in top-gated h-BN/NbS3 heterostructures further demonstrate reversible electric-field control of the sliding charge-density-wave current. Secondly, the dissertation explains the electrical-gate control of the charge-density-wave phase transitions in quasi-two-dimensional 1T-TaS2 heterostructures. Electrostatic gating is shown to modulate the hysteretic transition between nearly commensurate and incommensurate charge-density-wave phases, indicating direct field-driven control of the strongly correlated mesoscale domains. The persistence of these phases at room temperature suggests opportunities for nonvolatile memory and reconfigurable electronic devices. Finally, the effects of radio-frequency excitation on the mesoscale charge-density-wave landscape in 1T-TaS2 thin films are described. The radio frequency driving reshapes domain configurations, modifies hysteretic transport, and induces metastable conductance states through dynamic reorganization of discommensuration networks. The measurements and theoretical modeling reveal the evolution of the periodic lattice distortion under radio frequency excitation. This dissertation research, covering a range of low-dimensional van der Waals materials, establishes pathways for controlling collective electronic phases in charge-density-wave materials and advances their prospects for future electronic and unconventional computing technologies.Electrical engineeringMaterials scienceApplied physicsQuantum physicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8tp0b22qhttps://escholarship.org/content/qt8tp0b22q/qt8tp0b22q.pdfetdoai:escholarship.org:ark:/13030/qt997383jg2026-06-02T06:35:21Zqt997383jgAlzheimer’s Disease Classification from Resting-State Brain Imaging: A Comparison of Support Vector Machine and Transformer ApproachesLennemann, LucyMichailidis, George2026-05-29Alzheimer's classification is essential to diagnosis and monitor a disease which severely impacts cognitive function. This thesis compares three representations of functional magnetic resonance imaging data, including correlation networks, causal relationships, and transformer derived patterns, and their efficacy for binary classification. Correlation networks are derived using sparse inverse covariance matrices; causal networks are derived using the Granger causality method. Standard preprocessing is run on a dataset of thirty images. The study evaluates the use of support vector machines with correlation and causal features versus two transformer architectures. The support vector machine with filtered causal features performed best with an area under the curve score of 0.8, with connections in the default mode network, hippocampus, and orbitofrontal lobes having the most predictive power. Transformers performed well given the limited data, but further study is needed on a larger dataset to assess the effectiveness of the proposed spatiotemporal architecture. These findings replicate clinical findings of vulnerable regions in early stages of Alzheimer's, and hyperconnectivity in Alzheimer's patients as brain regions try to compensate for neural degeneration.StatisticsCognitive psychologyArtificial intelligenceMedical imagingAlzheimer's diseasedeep learningML classificationrepresentational learningresting state fMRItransformerapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/997383jghttps://escholarship.org/content/qt997383jg/qt997383jg.pdfetdoai:escholarship.org:ark:/13030/qt3mv4h6cp2026-06-02T06:35:16Zqt3mv4h6cpPolicing the Margins: The Contradictions of Power and Violence in Rio’s Military PoliceMelo, VanessaBourgois, Philippe2026-05-29This dissertation examines policing in Rio de Janeiro through the paradox that police officers are simultaneously agents of state violence and subjects shaped by it. Focusing on the Polícia Militar do Estado do Rio de Janeiro (PMERJ) and the rise and fall of the Pacifying Police Units (UPPs), the study argues that police violence is not an institutional deviation but a constitutive feature of governance in contexts of deep inequality. Rather than treating violence as the outcome of individual misconduct or failed reform, the dissertation situates policing within broader political-economic and historical processes that produce and sustain coercive state practices.
Drawing on over a decade of engagement with Rio's police, including more than 100 in-depth interviews, ethnographic fieldwork in favelas and police institutions, and an extensive visual archive, the project develops an integrated analytical framework that brings phenomenology, micro-sociology, and political economy into dialogue. This approach shifts attention from why violence occurs to how it is lived, embodied, and reproduced in everyday policing. It shows how officers internalize contradictory institutional demands, navigating tensions between militarized practices and reformist discourses, particularly during the UPP period.
At the core of the analysis is the concept of contradictory habitus, through which officers emerge as subjects formed within competing moral and institutional logics. While some adapt to entrenched forms of violent policing, others experience reform as ethical rupture, generating forms of moral injury and disillusionment. The dissertation further extends the notion of embodiment beyond the individual to theorize territorial embodiment, demonstrating how spaces, infrastructures, and material traces -- such as the ruins of UPP projects in Complexo do Alemão -- carry and reproduce the rupturing of state interventions.
By reconceptualizing police officers as both perpetrators and subjects of violence, this study challenges dominant frameworks that individualize responsibility while obscuring institutional and political accountability. It argues that reforms such as body-worn cameras and community policing initiatives often become performative mechanisms of accountability, leaving intact the structural conditions that produce violence. Ultimately, the dissertation contends that meaningful transformation in policing requires not only institutional reform but a fundamental reconfiguration of the political and economic structures that govern inequality and sustain coercive state power.Cultural anthropologyLaw enforcementLatin American studiesMilitary studiesBrazilEmbodimentPolice ViolencePolitical HabitusRacialized InequalityTerritorial MarginalizationpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3mv4h6cpetdoai:escholarship.org:ark:/13030/qt6bh172nd2026-06-02T06:35:11Zqt6bh172ndMultiscale Modeling, Control and Soft Sensing in Semiconductor Manufacturing ProcessesOu, FeiyangChristofides, Panagiotis D2026-05-28The rapid advancement of modern technology is driven largely by improvements in semiconductor devices, which require manufacturing techniques capable of producing increasingly complex nanoscale three-dimensional structures with high precision, high throughput, and reasonable cost. Atomic layer deposition (ALD) and atomic layer etching (ALE) have emerged as critical techniques in advanced semiconductor manufacturing because their self-limiting surface reactions enable atomic-scale control of material deposition and removal, resulting in highly uniform thin films and precise surface modification. To better understand and optimize these processes, this dissertation develops multiscale modeling frameworks that integrate reactor-scale transport phenomena with surface reaction mechanisms to analyze atomic layer processes and identify optimal operating conditions, reactor geometries, and control strategies that improve precursor utilization and overall manufacturing efficiency. First, a multiscale simulation framework is developed for the Al2O3 ALE process to investigate process mechanisms and optimize operating conditions and reactor configurations. The framework is then extended to study temperature management and multiple control strategies that enhance process stability, efficiency, and robustness under disturbances by run-to-run control, model predictive control, and machine learning-based endpoint detections. Next, a multiscale model is developed for a novel area-selective ALD process for SiO2, where internally integrated etching steps enable selective growth on SiO2 surfaces while suppressing deposition on Al2O3 regions. This bottom-up selective deposition strategy provides a potential solution to lithography alignment challenges and offers guidance for optimizing etching durations to maintain high selectivity while maximizing process efficiency. In addition, this dissertation investigates machine learning-based soft sensing methods for semiconductor manufacturing processes using large-scale industrial data from Seagate Technology. A systematic framework for data cleaning, data aggregation, and ML modeling is developed. The proposed soft sensing models are applied to multiple semiconductor manufacturing processes, including plasma dry etching and slider milling processes, demonstrating their ability to predict wafer pass/fail outcomes and reduce reliance on costly offline metrology.Chemical engineeringChemistryCondensed matter physicsAtomic physicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6bh172ndhttps://escholarship.org/content/qt6bh172nd/qt6bh172nd.pdfetdoai:escholarship.org:ark:/13030/qt27n1f78n2026-06-02T06:35:05Zqt27n1f78nControlling Localization and Function of Polynucleotide Phosphorylase (PNPase) to modulate the accumulation of mitochondrial double-stranded ribonucleic acids (mtdsRNA)Short, Connor W.Koehler, Carla M2026-05-29Polynucleotide phosphorylase (PNPase) is a critical regulator of mitochondrial double-stranded RNA (mtdsRNA) homeostasis. Loss of PNPase leads to the export of mtdsRNA to the cytosol, triggering a Type-I interferon response via the MDA5-MAVS signaling axis. Because PNPase is essential for cellular viability, we developed a "plug-and-play" murine embryonic fibroblast (MEF) cell line utilizing a tamoxifen-inducible Cre-ERT2 system for endogenous PNPT1 knockout, paired with a FLP-in recombinase locus for the stable expression of PNPase variants. Upon knockout of endogenous PNPase, mtdsRNA rapidly accumulates and is exported to the cytosol, leading to MAVS recruitment to the mitochondrial outer membrane and eventual cell death via apoptosis. Notably, mitochondrial DNA (mtDNA) copy number remains stable and is not exported, identifying mtdsRNA as the primary inflammatory ligand. Functional localization studies revealed that targeting PNPase to the mitochondrial intermembrane space, but not the matrix, was sufficient to prevent mtdsRNA accumulation. Furthermore, expression of RNA-degradation-deficient PNPase mutants (S484A) rescued cell survival despite constitutive mtdsRNA export and induced a mitochondrial hyperfusion phenotype. Differential MDA5 activation patterns between catalytic mutants suggest that the polyadenylation state of escaping RNA—regulated by PNPase—influences the downstream immune response. This model provides a robust platform to dissect the compartment-specific roles of PNPase in RNA regulation, mitochondrial dynamics, and innate immune signaling.BiochemistryCellular biologyImmunologyAntiviral SignalingInterferonMitochondrial RNAModel SystemPolynucleotide Phosphorylaseapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/27n1f78nhttps://escholarship.org/content/qt27n1f78n/qt27n1f78n.pdfetdoai:escholarship.org:ark:/13030/qt63p677mt2026-06-02T06:35:00Zqt63p677mtIndustry Dynamics and Resource AllocationYun, YangkeunAsker, JohnVoigtländer, Nico2026-05-29This dissertation consists of three essays on industry dynamics and resource allocation. The first essay studies long-run common-pool resource management. Inefficiencies in open-access resources arise not only from resource use by existing participants but also from their capacity investment and the entry of new firms. This essay develops a model of firm dynamics in which firms interact through stock depletion and congestion, and estimates it using firm-level panel data from the American whaling industry (1804–1909). A tractable framework for optimal policy design is introduced by quantifying the shadow prices of externalities. Standard per-unit Pigouvian taxes substantially improve welfare but fall short of the first best: they correct stock externalities but leave congestion unpriced, leading to persistent overcapacity. Optimal regulation combines per-unit taxes with lump-sum fees to discipline entry, exit, and investment.The second essay, written jointly with Will Rafey, examines inefficiencies in water pricing and groundwater investment by regulated utilities in California. Regulatory constraints on retail pricing can preclude utilities from charging the shadow price of the resource, while shared aquifers give rise to common-pool externalities across neighboring utilities. This essay develops a dynamic model in which utilities set water prices and invest in pumping capacity, and estimates it using a panel of aquifer conditions, well capacity, and utility finances. Shadow prices of groundwater pumping are derived to quantify intertemporal mispricing and common-pool costs, and Pigouvian taxes and surface water reallocation policies are evaluated.The third essay studies how exporting and innovation affect efficiency gains from trade liberalization. Exporting can raise productivity through learning-by-exporting and stronger innovation incentives, but more productive firms are also more likely to export, and as their productivity and market share grow further, concentration can increase over time. This essay develops a model of strategic industry dynamics in which firms enter, exit, export, and innovate. Counterfactual simulations show that ignoring endogenous market structure overstates welfare gains from trade by an order of magnitude. R&D and entry subsidies interact differently across industry types—partial substitutes in concentrated industries but complements in innovative ones. EconomicsMarketingManagementFinanceapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/63p677mthttps://escholarship.org/content/qt63p677mt/qt63p677mt.pdfetdoai:escholarship.org:ark:/13030/qt0x31g4182026-06-02T06:34:55Zqt0x31g418Machine Learning-Enabled Multiplexed Immunoassays for Point-of-Care TestingPalanisamy, BarathDi Carlo, Dino2026-05-29Current clinical diagnostic workflows frequently rely on centralized laboratory infrastructure, leading to delayed decision-making and fragmented patient care. This dissertation presents the development of machine-learning-enabled multiplexed vertical flow assays (xVFA) as a versatile platform for decentralized point-of-care testing. By integrating advances in dual antigen/antibody assays, engineered synthetic fusion di-peptides, and logic-verified neural network-based classification models, this work addresses critical diagnostic gaps and pushes the frontiers of point-of-care diagnostics toward the analytical performance of centralized laboratories.First, the xVFA platform is developed for the rapid triage of newly recognized hyperglycemia to differentiate type 1 from type 2 diabetes. The assay simultaneously profiles endogenous insulin reserve, insulin sensitivity, and islet autoimmunity by measuring C-peptide, adiponectin, and IA-2 autoantibodies, successfully detecting both antigens and antibodies within the same assay. To improve solid-phase autoantibody detection, a juxtamembrane peptide was synthesized to mitigate nonspecific binding. A logistic regression model successfully classified clinical serum specimens with an area under the curve (AUC) of 0.929, establishing a framework for same-visit, physiology-informed triage.Second, the xVFA platform is applied to the complex serological landscape of European Lyme borreliosis, where the geographic diversity of Borrelia genospecies limits the efficacy of traditional assays. A redesigned sensing membrane incorporating novel synthetic fusion di-peptides was engineered to capture broad-spectrum antibody responses. Evaluated using a highly characterized European clinical cohort, a multi-layer perceptron neural network integrated with Monte Carlo dropout uncertainty quantification achieved 95.2% sensitivity and 97.4% specificity.Finally, the platform was used to tackle multiple tick-borne diseases (TBDs) by increasing the number of multiplexing spots. A high-density 37-spot xVFA was engineered to simultaneously screen for Lyme disease, babesiosis, and anaplasmosis from a single 20 µL serum sample. Hardware optimizations, including a hex-inlet vertical flow diffuser and a novel offset vent around periphery sensing membrane spots, ensured uniform reagent delivery. Parallel neural network classifiers combined with logic-gated interpretation reliably differentiated these common infections, with the pan-TBD classifier achieving an AUC of 0.981 in blinded testing.BioengineeringPathologyArtificial intelligenceImmunologyImmunoassayMachine LearningPoint-of-care diagnosticspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0x31g418etdoai:escholarship.org:ark:/13030/qt5w5539pc2026-06-02T06:34:50Zqt5w5539pcNonlinear Causal Discovery via Sequential Edge Orientation for Directed Acyclic Graphs and Mixed GraphsHuang, StellaZhou, Qing2026-05-29Recent advances in causal learning have established the identifiability of a directed acyclic graph (DAG) under additive noise models (ANMs), spurring the development of various causal discovery methods. However, most existing methods make restrictive model assumptions, rely heavily on general independence tests, or require substantial computation.
To address these limitations, we propose a sequential procedure to orient undirected edges in a completed partial DAG (CPDAG), representing an equivalence class of DAGs, by leveraging a pairwise additive noise model (PANM) to identify their causal directions. We prove that this procedure can recover the true causal DAG assuming a restricted ANM. Building on this result, we develop a novel constraint-based algorithm for learning causal DAGs under nonlinear ANMs. Given an estimated CPDAG, we develop a ranking procedure that sorts undirected edges by their adherence to the PANM, which defines an evaluation order of the edges. To determine the edge direction, we devise a statistical test that compares the log-likelihood values, evaluated with respect to the competing directions, of a sub-graph comprising just the candidate nodes and their identified parents in the partial DAG. We further establish the structural learning consistency of our algorithm in the large-sample limit. Extensive experiments on synthetic and real-world data sets demonstrate that our method is computationally efficient, robust to model misspecification, and consistently outperforms many existing nonlinear DAG learning methods.
We also study nonlinear causal structure learning in the presence of latent variables, where the latent projection of a DAG is represented by an acyclic directed mixed graph (ADMGs). Existing approaches are often limited by model assumptions and rely on iterative testing. To this end, we develop a sequential edge orientation framework for determining undetermined edges in a partial ancestral graph (PAG) that combines the strengths of constraint-based and score-based methods. We propose a graphical criterion for identifying edges whose orientations can be correctly inferred through conditional independence tests, enabling targeted edge evaluation rather than repeated global testing. For the remaining edges, we directly compare competing orientations using log-likelihood estimates obtained through a parameter estimation procedure tailored to nonlinear models that more accurately recovers the covariance structure. Through extensive simulations, we demonstrate that the proposed framework substantially improves the recovery of both causal and latent confounding relationships and achieves greater accuracy than existing competing methods.StatisticsArtificial intelligenceInformation technologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5w5539pchttps://escholarship.org/content/qt5w5539pc/qt5w5539pc.pdfetdoai:escholarship.org:ark:/13030/qt1tk2323p2026-06-02T06:34:45Zqt1tk2323pEmpirical depictions of the state policy environment regarding reproductive autonomy, 2010-2024: Design, construction, and validation of a novel legislative measure and examination of its relationship to population healthNayak, MonikaZewde, Naomi B2026-05-29Social scientists often consider the state policy context when investigating shifts in reproductive and population health responses to policy change or exogenous shock. For example, after the Dobbs v. Jackson Women’s Health Organization Supreme Court decision, U.S. states passed increasingly strict abortion service restrictions and total bans on provision of abortion, inciting a wide swath of natural experiment studies in public health, economics, public policy, and demography. In studying the associated or causal impacts, access to abortion and other reproductive health and social and economic constraints at the population level offer important insight into how heterogeneity across state contexts moderate the relationship between the national shock and outcomes. Previous studies adjust for the policy environment using 1) proxies, such as partisanship in state government, 2) single domain indices, such as those on abortion or contraception policy restrictiveness, or 3) specific indicators, such as Medicaid expansion or safety net program eligibility. This dissertation opts to operationalize and measure the state policy environment regarding reproductive autonomy as a multifaceted indicator. In paper 1, I define a structural construct and created a policy database of social, economic, and healthcare policies that reflect the state policy environment regarding reproductive autonomy. Using predetermined inclusion criteria for the data sources and each selected indicator, policy data were drawn from five key legislative resources and supplemental sources, covering a breadth of policy domains. The first study summarizes the contents of the policy dataset and the overall orientation of states across the fifteen year period, 2010-2024, and finds that all states have restrictive and expansive social, economic, and healthcare policies in effect, to varying degrees. The second study evaluates methodologies for consolidating a wide breadth of policy data into a usable, longitudinal measure of the state policy environment. Results include comparisons of the analytic and conceptual benefits and tradeoffs of composite scores, exploratory factor analysis, and LASSO penalized regression, three variable reduction approaches. The third study deploys one preferred measure in a series of analyses of population health outcomes with adjustments for state-level characteristics, and validation exercises evaluating the new measure against external but related measures. The third study poses evidence of construct validity and explanatory value of the new measure in population health analyses. The novel policy measure and database, which describe shifts in the state policy environment over time across multiple policy areas, contribute to descriptive and causal studies in two main pathways: 1) understanding heterogeneity across state-level structural conditions in population health research with targeted interrogation of codified policy areas of interest, 2) shifting the focus from individual-level determinants of health toward the structural.Public healthPolitical scienceEpidemiologydatabasereproductive autonomyreproductive healthreproductive justicesocial policystate policyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1tk2323phttps://escholarship.org/content/qt1tk2323p/qt1tk2323p.pdfetdoai:escholarship.org:ark:/13030/qt9kr3t9j62026-06-02T06:34:04Zqt9kr3t9j6Statistical methods for observational health data studiesLi, KellySuchard, Marc A2026-05-28Observational studies seek to utilize the large wealth of patient-level observational data captured during the routine course of clinical care to produce real-world evidence that then guides clinical practices.In this dissertation, I seek to improve the generation process for real-world evidence by developing novel methods and evaluating existing processes for two core tasks in observational health research: patient-level prediction and population-level effect estimation.
I first propose a transfer-learning Bayesian sparse logistic regression framework that leverages coefficient information from large-scale models fit to millions of patients and up to hundreds of thousands of features to inform model fitting in small-scale settings that struggle with small sample sizes and rare outcomes.
The transfer-learning approach consistently outperforms traditional L1-regularized logistic regression in discrimination, bias, and sparsity, under both real-world studies and simulations.
I then conduct two large-scale systematic evaluations across four national healthcare databases, 24 treatment comparisons, and 160 unique outcomes of propensity score (PS) methods, used for confounding control in treatment-effect estimation.The first study demonstrates that large-scale PS models that use tens of thousands of generic covariates consistently outperform traditional small-scale models, which typically only use \approx 50 hand-picked covariates in all metrics for balance, precision, and bias control.
The second study shows that 1:1 matching and inverse probability of treatment weighting perform the best among different adjustment strategies in large-scale models, but researchers should consider each strategy's operating characteristics when making the final decision.
Both studies also corroborate that empirical calibration substantially reduces performance differences across both model scales and adjustments, such that the overall model is less sensitive to the PS modeling choice.
Finally, I present two software tutorials supporting scalable and reproducible analytics on large-scale observational health data: Fine-Gray competing risk regression for comparative effectiveness analyses and a framework for standardized prevalence estimation.BiostatisticsHealth sciencesEpidemiologyInformation scienceBayesian sparse regressionObservational healthpredictive modelingPropensity scoretransfer learningapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9kr3t9j6https://escholarship.org/content/qt9kr3t9j6/qt9kr3t9j6.pdfetdoai:escholarship.org:ark:/13030/qt5735s3xr2026-06-02T06:33:59Zqt5735s3xrToward a Structure of Global Phenomenal ConsciousnessLee, Youngzie YoungzieMonti, Martin M2026-05-28Recent work in the structural approach to consciousness has shown promise as a research paradigm for the formal and empirical study of phenomenal qualities of experience, or qualia. Existing empirical work in this paradigm has primarily focused on characterizing qualia associated with narrower, local contents of consciousness through relational or similarity structures derived from behavioral judgments. A major next step for the structural approach is to extend these methods toward broader, phenomenally unified experiences, and to formalize the relationship between the phenomenal qualities of local and global aspects of consciousness. This dissertation presents both empirical and mathematical approaches to the structural characterization of broad qualia. First, linguistic feature-based representations generated by large language models are examined as approximations of human judgments of similarity between complex subjective experiences. The relationship between the relational structure of multimodal experiences and the relational structures of their constituent experiential components is then investigated using material perception as a case study and multimodal language models as comparison models. Finally, a category- and sheaf-theoretic framework is developed that integrates two currently disparate structural approaches into a unified formal description of the structure and quality of the phenomenally unified conscious experience. The dissertation concludes with a discussion of the remaining challenges and future directions toward a structural characterization of global phenomenal consciousness.PsychologyNeurosciencesCognitive psychologycategory theoryconsciousnesslanguagequaliastructuralismapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5735s3xrhttps://escholarship.org/content/qt5735s3xr/qt5735s3xr.pdfetdoai:escholarship.org:ark:/13030/qt9698k5832026-06-02T06:33:54Zqt9698k583From Mines to Native Jewelry Markets: Unravelling the Settler Political Economy of TurquoiseDorsey, Kristen BarbaraMithlo, Nancy M2026-05-28This thesis is an inquiry into turquoise, a mineral primarily used as a gemstone in jewelry, and one of many different significant materials used in Southwestern Native American cultures and traditions. Despite the diversity of Native jewelry form and materials, jewelry set with turquoise generally overshadows other types of Native-made jewelry as a coveted and collected object for non-Native consumers. This thesis examines settler desires for turquoise mined from Native homelands through time, starting with the territorial New Mexico colonial political economy of turquoise mining in the 19th century in which settler men attempted to construct a narrative that Native turquoise mines had been abandoned. The mineral is also collected for Victorian curio cabinets and displayed on the bodies of white settler women for whom the gem becomes their connection to dead Native sisters of the female settler imaginary. The mineral discussed in this thesis has saturated the Southwest Native American jewelry market for over a century.Today, Native jewelers navigate the enduring settler capitalist structure of the turquoise economy, where white mine owners control access to the uncut gem, and where commodified gems as jewelry function as they have since the 19th century as markers of a culture deemed to be of the past. The story of turquoise is the continuing story of Native dispossession: the theft of Native land and resources and the confinement of Natives to anachronistic space and time. It is also the story of refusal and Native contestation of the story of their disappearance and confinement to prehistory. Gender is central to this quintessential story of dispossession and settler capitalism. From the masculine settler subjects who claim the mines, to the white male ethnographers, to the white settler women who narrate and lend content to the imaginary story of the vanishing Indian and her fantasy of dead turquoise-wearing Native sisters, turquoise proves essential to the making of colonial subjectivities.When Native jewelers sell their turquoise creations in the Santa Fe jewelry market, they must navigate a colonial field of white mine owners and the trading post histories that mark Native labor, creativity, and jewelry as colonial commodities. Gender is central to how these colonial fields can be traversed. If the story of turquoise is the quintessential colonial story of theft and extraction in the Americas and elsewhere, in the Southwestern United States, the story reveals how a single mineral is extracted and commodified into an innocent gem that masks the story of conquest and dispossession. Transformed from a sacred object into an aesthetic object, turquoise tells the story of settler colonialism, revealing the specific gendered procedures and mythologies of settler property regimes in the Southwest.Native American studiesAestheticsMiningPolitical scienceNative studiesIndigenous CritiqueIndigenous FeminismNative American Art MarketNew MexicoSettler Capitalist CritiqueTurquoiseapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9698k583https://escholarship.org/content/qt9698k583/qt9698k583.pdfetdoai:escholarship.org:ark:/13030/qt0xb6t9vb2026-06-02T06:33:49Zqt0xb6t9vbAdvancing Shared Decision-Making for Management of Mitral Valve RegurgitationSallam, AminahGlenn, Beth A2026-05-27Mitral valve regurgitation (MR) is one of the most common valvular heart conditions in adults, and the choice between contemporary treatments—including surgical mitral repair, minimally invasive surgical approaches, and transcatheter edge-to-edge repair (TEER)—is increasingly preference-sensitive. As the therapeutic landscape has expanded, the decision facing patients and clinicians has become more complex, with meaningful tradeoffs in durability, recovery, time horizon to benefit, and patient-reported quality of life. Yet, shared decision-making (SDM) remains inconsistently operationalized in cardiac surgery practice, and the evidence inputs needed to support a well-informed encounter—mid-to-long-term patient-reported outcomes, age-appropriate comparative effectiveness data, and accessible patient-facing communication—are incomplete.This three-paper dissertation contributes empirical evidence to three domains of an adapted SDM framework for MR decision-making. Paper 1 develops and assesses the acceptability of an artificial intelligence-generated physician avatar for asynchronous perioperative communication, addressing the patient and surgeon communication arms of the framework through a mixed-stakeholder qualitative study at a high-volume cardiac surgery center. Paper 2 evaluates practice trends and three-year outcomes of transcatheter edge-to-edge repair (TEER) versus surgical mitral repair in patients younger than 70, contributing comparative-effectiveness evidence on time horizon to benefit and treatment risk using a multistate administrative cohort. Paper 3 characterizes mid-to-long-term patient-reported outcomes 3 to 5 years after surgical mitral repair for degenerative MR, and identifies predictors of decisional satisfaction, with an exploratory sub-analysis comparing patient-reported outcomes between surgical repair and TEER recipients at a single center.Together, the three papers triangulate evidence across qualitative, administrative, and patient-reported sources to inform contemporary MR decision-making. Findings indicate that AI-augmented asynchronous communication is acceptable to patients, caregivers, and clinicians; that surgical mitral repair is associated with superior freedom from major adverse valve related events at three years relative to TEER in patients under 70, particularly for degenerative etiology; and that 3- to 5-year patient-reported quality of life after surgical repair meets or exceeds general-population norms, with race emerging as the strongest predictor of decisional satisfaction in this cohort. The dissertation concludes by mapping these contributions back to the SDM framework, surfacing equity considerations that recur across the three studies, and outlining priorities for advancing operationalized SDM in MR care.Health sciencesPublic healthBiomedical engineeringSurgeryapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0xb6t9vbhttps://escholarship.org/content/qt0xb6t9vb/qt0xb6t9vb.pdfetdoai:escholarship.org:ark:/13030/qt5gc1w4kw2026-06-02T06:33:44Zqt5gc1w4kwQuantitative Interrogation of Biological Systems: Intracellular Mechanobiology and Interpretation of Multimodal OmicsChen, ChichiaLin, Neil2026-05-28Biological regulation relies on the complex integration of biochemical and physical cues. While single-cell analysis has emerged to interrogate the cellular heterogeneity for underlying regulation, mapping the network requires overcoming bottlenecks in both mechanobiology and computational biology. First, how physical mechanical signals are regulated within intracellular space remains a knowledge gap. Second, while high-dimensional multimodal omics demand sophisticated machine learning models, their "black box" opacity prevents researchers from translating mathematical outputs into biological mechanisms. Therefore, this dissertation advances single-cell quantitative analysis across two critical fronts. To resolve the mechanobiology bottleneck, this work establishes an image-based framework utilizing a uniaxial cell stretcher and high-resolution microscopy. This approach uncovers an anti-correlated relationship between nuclear and cytoplasmic deformations in live epithelial monolayers, demonstrating that this relationship is regulated by nucleo-cytoskeletal coupling via the LINC complex. To satisfy the demand for analytic approaches for multimodal omics, this work introduces PITCH-LM, a computational pipeline that couples a multi-layer perceptron with post-hoc interpretability methods and Large Language Models. This browser-based studio extracts context-dependent regulatory rules and translates mathematical outputs into actionable biological hypotheses. Ultimately, this dissertation provides the advanced quantitative strategies required to interrogate complex cellular behaviors and push toward a fully integrated systems biology.Systematic biologyBiomechanicsCellular biologyArtificial intelligenceMolecular biologyGeneticsCell mechanicsLarge Language modelMachine learningMechanobiologySingle-cell quantificationSystem biologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5gc1w4kwhttps://escholarship.org/content/qt5gc1w4kw/qt5gc1w4kw.pdfetdoai:escholarship.org:ark:/13030/qt1nh2q5z12026-06-02T06:33:39Zqt1nh2q5z1Fast approximations to the Bayesian dimension reduction of dissimilarity dataSheth, AmiHolbrook, Andrew J.2026-05-28Bayesian multidimensional scaling (BMDS) is a probabilistic dimension reduction tool that models and visualizes dissimilarities between pairs of objects in a low-dimensional Euclidean space. Compared to classic MDS, BMDS is more robust to model misspecification and supports posterior uncertainty quantification and joint estimation within hierarchical models. However, standard BMDS inference becomes computationally prohibitive as the number of data points N grows, requiring O(N2 ) operations per Markov chain Monte Carlo (MCMC) iteration to evaluate the likelihood or gradient. To combat this computational bottleneck, we invent fast approximations for the BMDS likelihood and gradient, which we integrate into novel MCMC algorithms for scalable Bayesian inference.Chapter 1 establishes a unified framework for BMDS used throughout this work. In Chapter 2, we propose and compare two sparse versions of BMDS that apply log-likelihood and gradient calculations to subsets of the observed dissimilarity data. These sparse variants let one specify a time complexity between O(N) and O(N2 ). Under simplified conditions, we prove posterior consistency for subsampled distance matrices. Chapter 3 introduces a tree-based approximation to the likelihood and a Gibbs sampler that leverages this structure, remaining compatible with hierarchical extensions and reducing computational complexity to O(N log N). We further establish consistency for the stationary measure of the proposed method, proving that it concentrates around the true latent configuration even as the total error of the surrogate likelihood diverges. Notably, this consistency holds in the infinite dimensional limit.These approximations greatly improve computational efficiency while preserving inferential accuracy as demonstrated through rigorous mathematical results and extensive experiments on simulated and real-world data with diverse structural properties. By enabling scalable posterior inference for high-dimensional dissimilarity data, this dissertation contributes to the broader development of robust Bayesian dimension reduction for large-scale analysis.BiostatisticsComputer scienceArtificial intelligenceInformation scienceBarnes-HutBayesian dimension reductionclusteringMCMCmultidimensional scalingphylogeographyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1nh2q5z1https://escholarship.org/content/qt1nh2q5z1/qt1nh2q5z1.pdfetdoai:escholarship.org:ark:/13030/qt8sg115hs2026-06-02T06:33:35Zqt8sg115hsTherapeutic Targeting of Neutrophils in Pre-Clinical Non-Small Cell Lung Cancer ModelsCrosson, William PaulDubinett, Steven M2026-05-28Lung cancer is the leading cause of cancer-related deaths in the United States and worldwide. While therapeutic options have improved with the introduction of immune checkpoint blockade (ICB) to the clinic, many patients do not respond to immunotherapy or develop resistance following initial response. A significant subset of non-small cell lung cancers (NSCLC) harbor somatic co-mutations in Kirsten rat sarcoma virus (KRAS) and Liver kinase 1 [LKB1, also known as serine/threonine kinase 11 (STK11)] genes, which are characterized by a predominance of neutrophils and an immunosuppressive tumor microenvironment (TME) often resulting in resistance to ICB. Utilizing a murine model of NSCLC, KPL-3M, with the KrasG12DP53-/-Lkb1-/- (KPL) genotype and an increased tumor mutational burden that recapitulates human disease, our studies revealed that all-trans retinoic acid (ATRA), a metabolite derived from vitamin A, sensitized KPL-3M tumors to PD-1 blockade. The ATRA and anti-PD-1 combination therapy improved local and systemic T cell activation and generated systemic tumor-specific immunity. To investigate the mechanism of ATRA-mediated anti-tumor effects, we conducted single cell RNA sequencing (scRNA-seq) of KPL-3M tumors treated with ATRA or vehicle control. ATRA reduced the abundance of neutrophils while enriched T and natural killer (NK) cells in the TME. scRNA-seq trajectory and RNA velocity analysis suggested an enrichment in neutrophil clusters associated with aging and effector function. ATRA increased expression of maturity and aging markers on neutrophils in KPL-3M tumors in vivo as well as differentiated in vitro from murine bone marrow and human peripheral blood progenitor cells. In vivo BrdU labeling experiments suggest a reduced neutrophil lifespan and increased clearance from the TME in ATRA-treated KPL-3M tumors. Neutrophils from ATRA-treated mice showed reduced immunosuppressive activities and increased secretion of immune activating cytokines, correlating with increased infiltration of T cells that are essential for ATRA-mediated anti-tumor efficacy. ATRA enhanced the anti-tumor efficacy of PD-1 blockade in multiple murine models of NSCLC in addition to the KPL-3M model. Taken together, these findings reveal that ATRA reshapes the TME by altering neutrophil composition and function to sensitize resistant tumors to ICB immunotherapy.Molecular biologyImmunologyBiologyOncologyCancerLungNeutrophilpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8sg115hsetdoai:escholarship.org:ark:/13030/qt1jg380vx2026-06-02T06:33:30Zqt1jg380vxAbolitionist Archival Imaginaries: The World Building Praxis of Memory Work on IncarcerationRobinson-Sweet, AnnaCaswell, Michelle2026-05-28This dissertation studies memory work on incarceration through the lens of critical archival studies and abolition feminism. In the U.S., around two million people are currently incarcerated, nearly half of the population has a family member who is or has been in prison, and the lifetime probability of incarceration for men is almost ten percent. Despite this widespread impact, the perspectives of incarcerated and formerly incarcerated people struggle to be heard. However, more archives are turning their attention to collecting oral histories, artwork, writings, and other materials from those with lived experiences of imprisonment. This dissertation studies the significance of this memory work through ethnographic research at three archives: After Violence Project, Rikers Public Memory Project, and Brown University’s John Hay Library. Through in-depth interviews with archives staff and formerly incarcerated contributors, observation of events at the sites, and study of their collections, the significance of memory work is identified at four levels: personal, practical, political, and epistemological. On the personal level, formerly incarcerated archives contributors describe the healing power of reclaiming one’s story in the wake of symbolic annihilation. The practical significance of memory work on incarceration is in fostering an archival praxis that prefigures a society centered on care and accountability instead of punishment. Describing the political applications of this memory work, Rikers Public Memory Project provides a case study for how archives can advance narrative change. And study of the Mumia Abu-Jamal papers demonstrates how memory work on incarceration challenges dominant epistemologies. Seen through the lens of abolition feminism, these levels of significance represent interconnected areas of struggle in ending carceral capitalism. This is the abolitionist archival imaginary: the use of archives to imagine and enact a post-carceral future. At a moment when archival praxis is threatened by state repression, the rise of AI technologies, and the expansion of digital surveillance, this study concludes by asserting the urgency of abolitionist archival imaginaries for reorienting memory work toward liberation.Information scienceAmerican studiesSociologyWomens studiesBlack studiesabolition feminismarchival studiescritical prison studiesprefigurative politicsprison abolitionsocial movement studiespubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1jg380vxetdoai:escholarship.org:ark:/13030/qt26h935052026-06-02T06:33:25Zqt26h93505The Subjugated Body as Visual System: Labor and Imperial Order in Court VI from Relief to PrintHarris, Wanda YSchneller, David2026-05-28This study examines how labor is made visible and structured within the visual and architectural program of the Southwest Palace at Nineveh, constructed under King Sennacherib in the late seventh century BCE, and traces how this system shifts as the imagery moves from relief to drawing to print. Rather than treating these images as fixed representations, it approaches them as a sequence of transformations across media that reorganize how labor is structured and made legible. Focusing on the reliefs of Court VI, where the quarrying, transport, and installation of monumental lamassu unfold across a sequence of scenes encountered through movement along the palace walls, this thesis argues that these images do not present discrete actions but organize bodies through repetition, posture, spacing, and hierarchy into a coordinated visual system. Laboring figures appear in substantial numbers, yet their presence is structured in ways that subsume individuality within a larger compositional order. What emerges is not a record of discrete actions, but a system in which bodies are positioned, aligned, and repeated, rendering labor visible while constraining subjectivity. As these reliefs are translated by British archaeologist Sir Austen Henry Layard into drawings and later into printed plates, this system is reconfigured through processes of recompositing, alignment, and compression. Fragmented stone surfaces are recomposed into more continuous images, damaged bodies are completed, and spatial arrangements clarified. In print, scenes are consolidated into single images that can be taken in at once, with bodies more tightly aligned, spacing compressed, and variation reduced, so that what changes is not the subject of labor itself, but the conditions of its legibility. This thesis argues that coerced labor is not simply depicted but organized into a visual system that produces a mode of representation in which the subjugated body functions as an instrument of imperial order.Art historyCultural anthropologyHistoryMuseum studiesapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/26h93505https://escholarship.org/content/qt26h93505/qt26h93505.pdfetdoai:escholarship.org:ark:/13030/qt37j9k1zp2026-06-02T06:33:20Zqt37j9k1zpEvaluation of Cluster Detection Methods in Applied Epidemiology: Addressing Ascertainment Bias, Validation, and Cost-Effectiveness of Spatiotemporal Surveillance for COVID-19Lu, PhoebeRimoin, Anne W.2026-05-28Public health departments play a critical role in identifying and investigating disease outbreaks. Traditional approaches to outbreak identification often rely on empirical observation, which can delay response and increase both disease burden and resources needed for control. Consequently, systematic methods that can be applied to routine surveillance data have gained interest as tools for earlier outbreak detection. Cluster detection methods such as the spatiotemporal scan statistic have demonstrated potential for improving outbreak detection, but the effects of case ascertainment, validation against related methods, and the cost-effectiveness of implementation remain underexplored. This dissertation is motivated by the California Department of Public Health’s (CDPH) use of the spatiotemporal scan statistic for COVID-19 cluster detection. Chapter 1 reviews the public health context for applying the spatiotemporal scan statistic and describes CDPH’s implementation. Chapter 2 evaluates socioeconomic predictors associated with COVID-19 testing rates in California to identify potential biases in electronic laboratory reporting (ELR) data, which often serve as the earliest available inputs for cluster detection. Because populations with limited testing are less likely to be represented in the ELR dataset, this underrepresentation may reduce sensitivity for detecting clusters in those communities. Across peak testing periods during the Omicron BA.1 wave and 2024-2025 respiratory virus season, rural census tracts associated with lower testing rates and a higher probability of reporting zero tests, suggesting the cluster detection model may under-detect clusters in rural areas. Chapter 3 compares the frequentist- and Bayesian-based spatiotemporal scan statistics against reported COVID-19 outbreaks. Although the Bayesian approach identified more clusters that matched to outbreaks overall, the additional complexity of model specification may not justify the modest improvement in sensitivity. Chapter 4 develops a framework for evaluating the cost-effectiveness of cluster detection methods for health departments, using COVID-19 as a case study. Cost-effectiveness depended on the transmission setting, detection threshold, outcome metric, and case ascertainment. Overall, this dissertation strengthens the methodological foundation for assessing the application of the spatiotemporal scan statistic in public health practice by addressing surveillance bias, method validation, and economic value.EpidemiologyBiostatisticsPublic healthbayesian inferencecluster detectioncost-effectivenesscovid-19infectious disease surveillancespatial epidemiologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/37j9k1zphttps://escholarship.org/content/qt37j9k1zp/qt37j9k1zp.pdfetdoai:escholarship.org:ark:/13030/qt0gb902qp2026-06-02T06:33:16Zqt0gb902qpBayesian Inference for Exponential-family Random Graph ModelsResch, JosephHandcock, Mark S2026-05-28In network data, such as those representing social and economic relationships, the connections between entities depend on one another. Exponential-family random graph models (ERGMs) are the standard tool for capturing this dependence. They are difficult to fit because their parameter space has a challenging geometry. Large regions of it produce networks that are either nearly empty or nearly complete, neither of which is wanted by practitioners. Common Bayesian methods either ignore this geometry or work around it inefficiently. This dissertation develops methodology that respects it. The first chapter shows that a recent variational method for ERGM estimation introduces a formulation that neglects tie dependence and works poorly in realistic settings. The second chapter proposes a new prior distribution for Bayesian ERGM inference. Unlike standard priors, it concentrates on parameter values that produce realistic networks. The third chapter shows that the new prior generalizes to modern sampling methods that use auxiliary samples. The methods are demonstrated through simulations and applied to a tailor shop network from a 1972 study.Social structureSocial researchEconomicsStatisticsBayesian inferenceExponential-family random graph modelsNetwork analysisNetwork degeneracyReference priorsSocial networksapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0gb902qphttps://escholarship.org/content/qt0gb902qp/qt0gb902qp.pdfetdoai:escholarship.org:ark:/13030/qt9n4144jx2026-06-02T05:03:24Zqt9n4144jxFrom tectonic stress field to surface deformation: A global analysisKuhasubpasin, BoontiganLithgow-Bertelloni, Carolina R.2025-01-01The state of lithospheric stress governs geological processes and surface deformation across multiple spatial and temporal scales. At long wavelengths, it is driven by mantle flow and variations in lithospheric structure. Understanding how these stress sources shape surface deformation is key to linking deep and surface processes. Yet, challenges remain: modeling stress from structural heterogeneity is complex, surface expressions of stress are difficult to isolate, and interactions between tectonic stress and surface processes are not fully understood. This thesis addresses these challenges by (1) analyzing how structural variations influence the tectonic stress field, (2) evaluating how different stress sources affect surface features—particularly faults and rivers, and (3) examining how faults enhance erosion through stress-induced deformation. I use gravitational potential energy (GPE) contrasts and finite element modeling in spherical geometry to simulate lithospheric stress fields and compare model predictions to observations from the World Stress Map. Results show that a GPE integration depth of 100~km yields the best agreement with observed stress orientations. Models without additional imposed isostatic adjustment outperform those using isostasy assumptions. These results demonstrate the sensitivity of stress fields to lithospheric structural parameters. I further show that fault orientations generally align with the azimuth of the most compressive horizontal principal stress, hereinafter referred to as the stress direction, consistent with Anderson’s theory of faulting. River flow directions, particularly for major rivers, are also influenced by nearby fault orientations. Different stress sources affect fault patterns in distinct ways: lithospheric heterogeneity more accurately predicts normal fault orientations, while mantle flow better explains reverse fault patterns. To quantify this relationship, I introduce a new metric for mantle flow influence, which serves as a proxy for lithospheric strength. Finally, I investigate how active tectonics influence erosional processes. I show that erosional efficiency is significantly elevated within approximately 100–150~km of fault traces, especially near reverse faults and long fault segments, and decays with distance following an inverse power-law relationship. Machine learning analyses reveal that fault proximity is a stronger predictor of erosional efficiency than either precipitation or lithology. These findings indicate that active tectonics, governed by lithospheric stress, influence landscape evolution not only through uplift but also by enhancing rock damage and erodibility.GeologyGeophysicsGeomorphologyActive faultsErosionLithospheric dynamicsMantle flowRiver networksTectonic stressenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9n4144jxhttps://escholarship.org/content/qt9n4144jx/qt9n4144jx.pdfetdoai:escholarship.org:ark:/13030/qt71d962ns2026-06-02T05:02:49Zqt71d962nsHow and Why We Comfort Others: Neural Mechanisms of Prosocial AllogroomingLim, Kayla YHong, Weizhe2025-01-01Comforting is a critical form of prosocial behavior, expressed as affiliative social touch directed toward distressed individuals. These behaviors play a key role in promoting emotional recovery and maintaining social cohesion. While extensively studied in humans, comforting has only recently been examined in rodents, where it offers a tractable model system for dissecting the underlying neural mechanisms.Chapters 1 and 2 establish the conceptual and methodological framework for this dissertation. Chapter 1 outlines the conceptual frameworks and roots of comforting behavior. Chapter 2 focuses on the behavioral expression of comforting in mice, characterizing its core features and motivational underpinnings, and synthesizes emerging evidence from rodent models regarding the neurobiology of how animals detect and respond to the distress of conspecifics and the mechanisms mediating social buffering effect of comforting behavior.
Subsequent chapters present a series of experimental findings that reveal a shared behavioral and neural architecture supporting comforting behavior in mice. Individual differences in parenting behavior predict variation in adult-directed prosocial comforting, measured as allogrooming toward stressed conspecifics. These two behaviors recruit partially overlapping neuronal populations in the MPOA. Through calcium imaging and a series of functional manipulation experiments, a shared MPOA-to-ventral tegmental area (VTA) projection is identified as a critical circuit element supporting both pup grooming and adult-directed prosocial behavior. Neuronal ensembles engaged during pup interaction are shown to be functionally necessary for allogrooming, suggesting that evolutionarily conserved caregiving circuits may be recruited to support broader forms of prosocial behavior. The final chapter extends this framework to examine how mice respond to social partners experiencing other negative internal states, including immune-induced sickness and nausea-like visceral malaise. Across all conditions, subject animals display increased allogrooming and sustained physical proximity (huddling) toward affected partners. Furthermore, the distribution of grooming across different body regions varies depending on the conspecific’s condition, suggesting that mice may be sensitive to somatic cues and capable of modulating their affiliative responses accordingly.
Together, these findings advance understanding of the neural basis of comforting behavior and highlight the MPOA as a central integrator of caregiving responses across multiple social contexts. This work provides a foundation for further studies into the biological mechanisms of prosociality and the capacity for social animals to respond flexibly to the needs of others. Neurosciencesenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/71d962nshttps://escholarship.org/content/qt71d962ns/qt71d962ns.pdfetdoai:escholarship.org:ark:/13030/qt6t2016vs2026-06-02T05:02:41Zqt6t2016vsGenres of Dictatorship: Dictator Novels, Counterfactuals, Dystopian Sci-Fi, and Refugee Texts of 20th Century and Contemporary U.S. LiteratureEncinas, AbrahamLopez, Marissa K.2025-01-01In Genres of Dictatorship, I argue that several texts of U.S. literature can be read as analyzing the problem of dictatorship. First, I address the question why these different forms of literature (counterfactuals, dystopian science-fiction, and memoirs) have not been thought of primarily as dictatorial genres in the same way that, for example, Latin America has a distinct dictator novel genre. By reading from an American hemispheric perspective, I theorize and critique the way that ideological perceptions of spacetime dictate literary-political ideas of where the dictator is located, the idea that the dictator is located there, in the global south, and then, in the past, and not here and now in the present of modernity. I call this ideological structure a metagenre of modernity. To analyze and critique this structure, in Chapter 1, I analyze two counterfactual novels, Philip K. Dick’s Man in the High Castle (1962) and Roberto Bolaño’s La literatura nazi en América (1996), to argue how disaffection is represented as an affective impulse toward alternate timelines that comment on the fascist dictatorship of the here and now. While in Chapter 1 I analyzed a U.S. novel and a Chilean novel to read across the geography of the American hemisphere, in Chapter 2 I read bookend moments of the 20th century to discuss the story of U.S. empire through the figure of the dictator. By comparing Sinclair Lewis’ It Can’t Happen Here (1935) with Octavia Butler’s Parable of the Talents (1998), I argue that U.S. culture has always been ripe for dictatorship. In the final chapter and Coda, I examine the figure of the refugee in contrast to the dictator. In Chapter 3, I study Loung Ung’s memoir, First They Killed My Father (2000), and Viet Thanh Nguyen’s novel, The Sympathizer (2015), to illustrate how refugee narratives are caught between genocidal dictatorships at home and dictatorial propaganda machines in the host country that attempt to revise and rewrite their stories. Finally, I end with a Coda in which I discuss Sabrina Vourvoulias’ Ink to show how technocratic governmentality constitutes digital dictatorial control against refugee migrant populations.American literatureLatin American literatureEthnic studiesDystopiasFascismLatin AmericaNovels of DictatorshipRefugee NarrativesScience Fictionenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6t2016vshttps://escholarship.org/content/qt6t2016vs/qt6t2016vs.pdfetdoai:escholarship.org:ark:/13030/qt3fk514vd2026-06-02T05:02:03Zqt3fk514vdElectro-dewetting-based Microdevices and a Cloud-based Design and Manufacturing Platform for Digital MicrofluidicsWang, QiningKim, Chang-Jin2025-01-01Electrowetting-on-dielectric (EWOD), discovered in the 1990s and explored by many in the 2000s, has opened the field of digital microfluidics (DMF), enabling diverse biomedical and optical applications. However, EWOD faces persistent issues, such as dielectric breakdown, electric charging, contact angle saturation, and biofouling. The first two issues are associated with the dielectric and hydrophobic layers required for EWOD devices. In 2019, a new liquid manipulation mechanism, electro-dewetting, was introduced and demonstrated to enable DMF. Without using dielectric or hydrophobic layer, electro-dewetting is free of the associated issues of EWOD. The first half of this dissertation investigates and leverages electro-dewetting for development of new microdevices. We establish a theoretical framework and assist a collaborating lab to develop a mathematical model that simulates droplet motion and surfactant transport in electro-dewetting. Next, we propose and confirm electro-dewetting works not only in air but also in oil environment, and perform a series of experiments to characterize electro-dewetting in oil. Then, we propose electro-dewetting-based manipulation of liquid shape for optical applications and demonstrate the feasibility by assembling a proof-of-concept device. A microfabricated pixel device is further developed to assess the potential for electro-dewetting-based reflective displays. The second half of this dissertation is to democratize EWOD DMF. In addition to the unsolved issues of EWOD, a significant challenge hindering the adoption of EWOD DMF is the lack of technological infrastructure, such as chip design tool, standard fabrication process, and user-friendly electronic control system. Our approach for democratization is to establish a cloud-based DMF design and manufacturing platform and build a versatile control system that would empower any users to design, obtain, and operate DMF chips. For chip design, we collaborate with another lab to establish a web-based DMF chip design platform. For chip manufacturing, we build a web-based chip manufacturing platform and demonstrate fabricating four types of EWOD chips, i.e., glass, paper, printed circuit board (PCB), and thin-film-transistor (TFT), to emulate the foundry service workflow in collaboration with other labs. For chip control, we improve a versatile control system, including expansion of its functionality to operate electro-dewetting-based DMF chip and establishment of web-based operating software.Mechanical engineeringElectrical engineeringBioengineeringdigital microfluidicselectro-dewettingelectrowettingEWODmicrofluidicsenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3fk514vdhttps://escholarship.org/content/qt3fk514vd/qt3fk514vd.pdfetdoai:escholarship.org:ark:/13030/qt3167p7z02026-06-02T05:01:59Zqt3167p7z0Autonomous Robotic Cataract Surgery– Efficient Lens Extraction and Safe Capsule CleaningLai, KevinTsao, Tsu-Chin TC2025-01-01Cataract is the most common cause of vision loss due to the natural formation of the cloudylens that blocks or diffracts visible light. The cloudy lens sits inside a transparent capsular
bag, and the typical cataract treatment is to remove the opaque lens from the capsular bag
and implant an intraocular lens that corrects vision. The surgeon uses surgical systems
to perform phacoemulsification by manipulating a handheld device and control the level of
irrigation, aspiration, and ultrasound with a foot pedal to extract the lens. While the overall
procedure is effective, human physiological limitations, including hand tremor and insufficient
visualization through existing sensors, hinder the development of efficient material transport
and operations on the thin capsule membrane.
This work presents a robotic system capable of performing autonomous lens extraction
and capsule cleaning through real-time feedback from a fluidics system and intraocular OCT
mounted on the instrument. The fluidics control system includes upstream sensors and
actuators that provide fast dynamics to account for uncertainties during procedures, and efficiently
balance the flow between irrigation and aspiration. In addition, the robotic system
integrates an intraocular optical coherence tomography probe that can visualize otherwise
invisible equatorial regions and use distance feedback to ensure surgical safety. Through
feedback and control, the developed system reduces the challenges of operation in the peripheral area and dynamic intraocular environment through a set of experimental trials in
ex-vivo pig eyes. Innovations are expected to improve or enable other surgical procedures
that are difficult or infeasible to perform with existing technologiesMechanical engineeringenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3167p7z0https://escholarship.org/content/qt3167p7z0/qt3167p7z0.pdfetdoai:escholarship.org:ark:/13030/qt85n604jw2026-06-01T06:36:09Zqt85n604jwBayesian Transfer Learning for High-Dimensional Regression Models via Adaptive ShrinkageJamshidian, Parsa MehdiTelesca, Donatello2026-05-28High-dimensional regression problems are common in biomedical research, where accurate inference and prediction may be challenging when target-study sample sizes are limited. Transfer learning offers a principled way to improve estimation and prediction by borrowing information from related source studies. However, naive borrowing from heterogeneous or biased sources can lead to negative transfer, thereby degrading model reliability and performance. This dissertation develops Bayesian transfer learning methodology for high-dimensional regression models designed to selectively borrow information from auxiliary sources and improve inference in a target study. The first part of the dissertation introduces BLAST, Bayesian Linear regression with Adaptive Shrinkage for Transfer, a Bayesian multi-source transfer learning framework for high-dimensional linear regression. BLAST combines global-local shrinkage priors with Bayesian source selection to identify informative auxiliary sources while discounting biased sources that may degrade target-study performance. Source selection and sparse regression are jointly accounted for in prediction and inference through Bayesian model averaging. The structure of the model admits efficient posterior simulation via a Metropolis-within-Gibbs sampling algorithm, enabling full posterior inference for the target regression coefficients.Building on this foundation, the dissertation extends the core transfer learning principles of BLAST to two additional outcome settings: binary outcomes via sparse logistic regression and right-censored survival outcomes under a high-dimensional Cox proportional hazards model. Each extension builds on the core principles of BLAST while introducing outcome-specific model structures and computational strategies for posterior inference. Performance is assessed through simulation studies and real data applications in cancer studies, demonstrating improvements over target-only analyses and state-of-the-art transfer learning methods.BiostatisticsMedicineMathematicsbayesian statisticshigh-dimensional datamodel averagingmonte carlo algorithmsshrinkage priorstransfer learningapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/85n604jwhttps://escholarship.org/content/qt85n604jw/qt85n604jw.pdfetdoai:escholarship.org:ark:/13030/qt3kp5q3hf2026-06-01T06:36:03Zqt3kp5q3hfKinetic-scale Solar Wind Current Sheets: Statistical Characteristics and Their Role in Energetic Particle TransportZhang, ZijinAngelopoulos, Vassilis2026-05-27The transport of solar energetic particles (SEPs) through the heliosphere is governed by their interaction with the highly variable interplanetary magnetic field. Yet standard quasi-linear models, which assume scattering by small-amplitude, randomly phased fluctuations, fail to account for current sheets, coherent, intermittent structures that pervade the solar wind. This dissertation develops a quantitative, observation-informed framework for understanding how kinetic-scale current sheets scatter energetic particles and shape their transport across the inner heliosphere. On the observational side, we analyze more than 175,000 current sheets identified by Parker Solar Probe (0.17 AU), Juno (1–5 AU), ARTEMIS, WIND, and STEREO using a robust, time-resolution-independent detection algorithm. We find that their normalized thicknesses (2–4 ion inertial lengths) and current densities (0.05–0.15 Alfvén current densities) remain broadly invariant across 0.17–5 AU and follow an inverse current density–scale relation spanning several orders of magnitude. Theoretically, we develop an exact Hamiltonian description of particle motion in force-free current sheets and conduct extensive test-particle simulations parameterized by the observed property distributions at 0.1 and 1 AU. We find that the scattering is dominated by geometrical chaotization, the destruction of the quasi-adiabatic invariant at separatrix crossings, which produces order-unity pitch-angle jumps within single interactions, a mechanism qualitatively distinct from resonant wave–particle diffusion. By evaluating the aggregate effect of multiple, sequential interactions through a Monte Carlo transport model, we show that both parallel and perpendicular diffusion coefficients exhibit asymptotically diffusive behavior, but with a perpendicular-to-parallel ratio that increases with particle energy and exceeds standard quasi-linear predictions above 1 MeV. These results identify current sheets as efficient drivers of energetic particle scattering throughout the inner heliosphere, offering a natural explanation for SEP transport phenomena that have long challenged purely wave-based models, including broad particle reservoirs and rapid longitudinal spreading.Plasma physicsPhysicsAstrophysicsAstronomyTheoretical physicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3kp5q3hfhttps://escholarship.org/content/qt3kp5q3hf/qt3kp5q3hf.pdfetdoai:escholarship.org:ark:/13030/qt0zq410dt2026-06-01T06:35:58Zqt0zq410dtEssays in Empirical Industrial Organization: Procurement, Production, and Firm IncentivesWu, YingjieAsker, John W2026-05-27This dissertation contains two essays in empirical industrial organization. Both essays study how firms respond to incentives, either from public procurement rules or from imperfect factor markets.Chapter 1 studies how delay days are classified after construction begins in California highway projects. A delay day can be recorded as a working day, a change-order day, or an excused weather non-working day. That classification determines whether the contractor pays liquidated damages, CalTrans pays for authorized additional time, or the contract is extended without a payment or penalty. I assemble a panel of CalTrans progress payments, match it to observed weather condition from the Global Historical Climatology stations, and study 1,916 paving projects auctioned between 2009 and 2017. The data show that the gap between CalTrans-recorded weather non-working days and externally measured adverse weather is large and grows over time, even though California weather does not follow the same upward trend. The paystub panel shows a strong relationship between weather-day claims and completion-time pressure. After controlling for observed adverse weather and pay-period length, a one-percentage-point increase in completion-time pressure is associated with a 0.53 percentage-point increase in the share of days classified as weather days. Since CalTrans can revise day classifications as a project moves towards finish, the recorded weather days do more than summarize field conditions. They also reflect the financial incentives faced by CalTrans and the contractor. Bidder characteristics explain little of the remaining weather gap, which suggests that the gap is shaped mainly during contract execution rather than through contractor selection at auction. A simple welfare calculation suggests that reducing delays by 10 percent would lower commuters’ costs by about $9 million per year over 2009– 2017, or roughly $83 million over the sample period. I argue that policy reforms should focus on the documentation and review of delay days, not only on the auction format.Chapter 2, co-authored with Michael Rubens and Mingzhi Xu, addresses the joint identification of wage markdowns and factor-augmenting productivity. We show that existing “production approaches” to markdown estimation do not separately identify factor price markdowns from factor-augmenting productivity levels. We propose a method to overcome this challenge and apply it to study the effects of ownership liberalization in the Chinese nonferrous metal industries from 1999 to 2006. We find that private firms have much higher labor-augmenting productivity than state-owned enterprises (SOEs). However, we also find that private firms exert greater monopsony power over their workers than SOEs, although this only holds for domestically-owned firms. This suggests that SOE privatization implies a trade-off between increased productivity and monopsony power, while FDI liberalization does not show the same trade-off.EconomicsManagementBusiness administrationOperations researchapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0zq410dthttps://escholarship.org/content/qt0zq410dt/qt0zq410dt.pdfetdoai:escholarship.org:ark:/13030/qt6k43938s2026-06-01T06:35:53Zqt6k43938sWrapped Fukaya Categories of Orbifolds and Hecke AlgebraKrutovskiy, RomanHonda, Ko2026-05-28The two leitmotifs of this dissertation are Fukaya categories of orbifolds and Hecke algebras. We develop methods and notions to study and compute wrapped Fukaya categories of global quotient Liouville domains. Of primary interest to us are cotangent bundles of smooth global quotient orbifolds, especially of symmetric products. Our methods allow us to show that A∞-algebras of cotangent fibers of such orbifolds are closely tied to their Hecke algebras. • Chapter 1 is based on the work in progress with Ko Honda, Yin Tian, and Tianyu Yuan. There, we study obstructions to smoothings of nodal orbicurves and wrapped Fukaya categories of T ∗ [X/G] for X a complex manifold equipped with an effective action of a finite group G.• Chapter 2 is based on the work [HKT25] with Ko Honda, Yin Tian, and Tianyu Yuan. There, we develop a cylindrical model for the wrapped Fukaya category of the symmetric product Symκ (T ∗M), and its Morse-theoretic counterpart. We use it to give a full computation of the A∞-algebra of a generic fiber in Symκ (T ∗S 2 ). • Chapter 3 is based on the work [KY23] with Tianyu Yuan. There, we develop a cylindrical model for symplectic cohomology of symmetric products of a Liouville domain. We also obtain a generalized version of the Viterbo isomorphism for this invariant.MathematicsTheoretical mathematicsApplied mathematicsTheoretical physicsFukaya categoryHecke algebraorbifoldsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6k43938shttps://escholarship.org/content/qt6k43938s/qt6k43938s.pdfetdoai:escholarship.org:ark:/13030/qt4qh2z4jz2026-06-01T06:35:48Zqt4qh2z4jzInvestigating the Developing Brain: Sleep and Neurodevelopmental Genetics Shape Early Functional ConnectivityChiem, EmilyDapretto, MirellaHernandez Sharma, Leanna M2026-05-27Infancy is a critical period for brain and behavioral development. Disruptions in neural architecture or the presence of genetic risk factors may alter developmental trajectories to increase vulnerability for neurodevelopmental disorders, such as Autism Spectrum Disorder (ASD). Sleep problems are a common feature in ASD and have been reported in infants prior to a formal diagnosis. While sleep and ASD are both in-part under genetic control, it remains unknown how genetic factors predisposing for sleep problems and ASD may influence the functional connectome in infancy. This dissertation aims to advance our understanding of how early sleep disruption is associated with functional brain connectivity in early life, as well as uncover how genetic predisposition for ASD and atypical sleep impact these patterns. In Study 1, functional magnetic resonance imaging (fMRI) data from infants at varying familial likelihood for ASD was used to examine how functional connectivity of the Salience Network related to parent-reported measures of sleep-onset problems and sensory over-responsivity. In Study 2, fMRI, genetic, and behavioral data from a non-clinically enriched sample of neonates were used to investigate the impact of ASD common genetic variation on thalamocortical functional connectivity and behavioral outcomes. Using data from the same population of neonates, Study 3 examined the impacts of genetic predisposition for abnormal sleep duration and genetically-predicted expression of a known sleep-regulatory gene – DEC2 – on thalamocortical functional connectivity and later behavior. The multimodal approach implemented throughout these three studies provide an integrative framework for understanding how the neurobiological and genetic underpinnings of sleep and ASD shape early neurodevelopment.NeurosciencesGeneticsMedical imagingDevelopmental biologyAutismGeneticInfantMRISleepapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4qh2z4jzhttps://escholarship.org/content/qt4qh2z4jz/qt4qh2z4jz.pdfetdoai:escholarship.org:ark:/13030/qt4sj0k6482026-06-01T06:35:44Zqt4sj0k648Leveraging Large-Scale Multi-Omics and Functional Data to Identify Cancer Drivers and Therapeutic TargetsFreeland, JackGRAEBER, THOMAS2026-05-27Cancer research has been transformed by the development of large-scale sequencing, functional genomics, and pharmacological screening technologies. These approaches have enabled systematic characterization of cancer genomes and direct interrogation of gene function across diverse biological contexts. However, some central challenges remain: distinguishing molecular alterations that functionally drive cancer from those that are incidental and identifying therapeutic vulnerabilities that can be exploited clinically.
This dissertation focuses on leveraging large-scale genomic, transcriptomic, and functional datasets to identify cancer drivers and therapeutic vulnerabilities. Chapter 1 investigates high-grade complex karyotype sarcomas, a heterogeneous group of aggressive tumors characterized by extensive genomic instability and poor clinical outcomes. Because these tumors harbor numerous genetic alterations, identifying clinically relevant drivers has been challenging. To address this, large-scale tumor profiling data were used to nominate candidate drivers of sarcoma development, which were then functionally evaluated using an in vivo transformation framework. This work identified oncogenic programs capable of driving sarcoma formation and recapitulating distinct sarcoma phenotypes, providing insight into the genetic mechanisms underlying sarcoma initiation, subtype specification, and therapeutic vulnerability.
Chapter 2 extends this work beyond a single disease context by developing a pan-cancer framework for integrating genetic dependency and pharmacological screening datasets. This chapter leverages large-scale CRISPR, RNAi, and drug sensitivity data across overlapping cancer cell line models to jointly model gene essentiality and drug response. By identifying shared structure between genetic perturbation and pharmacological phenotypes, this approach reveals systematic relationships between cancer dependencies and drug sensitivities and prioritizes underexplored genes with therapeutic potential.
Together, this work demonstrates how integrative analysis of multi-omics, genetic dependency, and pharmacological screening data can move cancer research beyond descriptive molecular catalogs toward the systematic identification of functional cancer drivers and therapeutically actionable vulnerabilities.BioinformaticsPharmacologyOncologyBiochemistryBiophysicspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4sj0k648etdoai:escholarship.org:ark:/13030/qt1v77q79b2026-06-01T06:35:39Zqt1v77q79bBayesian Difference-in-Differences Modeling for Environmental Exposure Data: Some Perspectives from the Aliso Canyon Health Research StudyArputhasamy, ValentinaBanerjee, Sudipto2026-05-28The 2015 to 2016 Aliso Canyon natural gas blowout, the largest recorded methane leak in US history, raised concern regarding potential adverse health effects among nearby communities exposed to hazardous air pollutants. Prior analyses using standard difference-in-differences (DiD) models reported elevated rates of low birthweight (LBW) and reduced birthweight among higher risk infants exposed during late pregnancy. However, traditional DiD approaches may fail to capture important features of the data, including spatial heterogeneity, nonstandard outcome distributions, and variation in exposure intensity.
To address these limitations, this dissertation develops three extensions of the DiD framework. First, we introduce spatial DiD models that allow treatment effects to vary across geographic regions. Second, we develop an ordinal logistic DiD framework to model birthweight severity among higher risk infants, providing robustness to skewness and heteroskedasticity in continuous outcomes. Third, we incorporate pollutant exposure estimates derived from a chemical transport model (CTM) into the DiD framework, enabling analysis of exposure intensity.
Using California birth record data, we find evidence of spatially heterogeneous effects, including an east to west gradient in risk, and a shift toward more severe birthweight and preterm birth classifications among infants with third trimester exposure. Incorporating CTM based exposure estimates reveals an impact of the blowout on birthweight even after adjusting for benzene exposure, suggesting that factors beyond pollutant exposure, such as psychosocial stress potentially caused by the blowout, may also have contributed to adverse birth outcomes. Together, these results highlight the importance of extending DiD methods to better capture the complexities of environmental exposure data.BiostatisticsEnvironmental healthPublic healthStatisticsAliso Canyon Natural DisasterBirth OutcomesCausal InferenceDifference-in-DifferencesSpatial Modelingapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1v77q79bhttps://escholarship.org/content/qt1v77q79b/qt1v77q79b.pdfetdoai:escholarship.org:ark:/13030/qt0jc4v0mv2026-06-01T06:35:33Zqt0jc4v0mvAn Integrated Mechanistic AI and 3D Spheroid Platform for Discovery and Validation of Combination Therapies in Drug-Resistant Prostate CancerLo, Chih-HuiLin, Neil2026-05-27Combination therapies are key to overcoming resistance to androgen receptor signaling inhibitors in prostate cancer. Current paradigms for developing synergistic therapies, however, remain chronically inefficient and resource-intensive due to the prohibitive scale of the combinatorial screening space and a lack of computational frameworks for navigating signaling crosstalk. This work introduces a hybrid in silico and in vitro lead discovery platform that integrates knowledge-augmented large language models (LLMs) with an oxygen-supplemented 3D spheroid system. By mechanistically evaluating multi-pathway interactions, the LLM framework nominates drug pairs with high predictive accuracy and interpretable rationales. This computational pipeline is complemented by an engineered 3D spheroid model that utilizes oxygen supplementation to mitigate artifactual necrosis, a common confounder that masks synergistic signals in standard screening. Through the accelerated and physiologically relevant screening of 3,592 natural products, this platform identifies a novel synergy between berberine and enzalutamide. Experimental validation corroborates the model’s rationale, supporting that this discovered combination achieves synergy via the simultaneous suppression of the PI3K/AKT/mTOR axis and the induction of AMPK-mediated stress. These findings demonstrate that integrating interpretable artificial intelligence with advanced 3D screening enables the scalable, rational discovery of synergistic therapies.BioengineeringTherapyArtificial intelligenceOncology3D spheroidsberberinedrug synergyenzalutamide resistancelarge language modelsprostate cancerpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0jc4v0mvetdoai:escholarship.org:ark:/13030/qt5ds844k32026-06-01T06:35:28Zqt5ds844k3Stochastic Optimization, Numerical Stability, and Privacy in Large-Scale Computational SystemsXue, Alexander JingNeedell, Deanna2026-05-28In this thesis, we discuss three topics related to stochastic optimization, numerical stability, and privacy. First, in Chapter 2, we study incomplete tensor linear systems under the tensor t-product. We develop a stochastic iterative method to solve these incomplete systems. We prove two theoretical convergence results under some general conditions on the correlation of the missingness pattern. To showcase the generality of these conditions, we provide three examples of missing data models that satisfy the necessary conditions. We then run experiments on synthetic data and video data to support the two theoretical results. In Chapter 3, we analyze the numerical stability of (accelerated) randomized Kaczmarz. We show that randomized Kaczmarz is not fully forward stable. Next, we prove a black-box result for iterative refinement stating that as long as the underlying solver satisfies some convergence condition, then the solver becomes forward stable when paired with iterative refinement. We apply this result to randomized Kaczmarz, conditionally stabilizing the method. Furthermore, we study a particular accelerated randomized Kaczmarz method and prove that similar to randomized Kaczmarz, it is not fully forward stable, but by combining with iterative refinement, it achieves conditional stability. We conclude with experimental data showing that accelerated randomized Kaczmarz with iterative refinement converges quickly and attains the best stability out of the analyzed methods. Lastly, in Chapter 4, we introduce a differentially private over-parametrized random feature model by perturbing the non-private estimator with a variable following a carefully chosen normal distribution. We then show that our model is fair by analyzing its excessive risk gap for each group. Lastly, we verify our results on synthetic data and available data sets, showing that our method achieves better generalization performance and is more fair than other differentially private random feature models.Applied mathematicsStatisticsComputer sciencedifferential privacykaczmarznumerical linear algebrastochastic processestensor recoveryapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5ds844k3https://escholarship.org/content/qt5ds844k3/qt5ds844k3.pdfetdoai:escholarship.org:ark:/13030/qt2dg353tw2026-06-01T06:35:23Zqt2dg353twJewish Identity in an Imperial Persian World: Elephantine, Diaspora, and the Negotiation of Jewishness under Achaemenid RuleJarvis, TylerBonesho, Catherine E.2026-05-28Jewish identity in the Second Temple period was formed within a succession of imperial contexts. From the Babylonian conquest through the Persian and Hellenistic periods, Jewish communities lived under foreign regimes that shaped the conditions under which Jewishness was maintained, adapted, and expressed. Yet scholarship on the Hebrew Bible and Judaism of the Second Temple period has not always accounted for the role of empire in the formation of Jewish identity, often reducing Jewish engagement with empire to models of either resistance to imperial rule or passive accommodation to it.This dissertation uses the Jewish military colony at Elephantine as its central case study for understanding how Jewish communities negotiated imperial rule. The Aramaic papyri and ostraca from Elephantine provide rare documentary evidence for a Jewish diaspora community living within the structures of the Achaemenid Empire. This study argues that Jewishness was not simply preserved under empire but was maintained and articulated through strategic participation in imperial structures. At Elephantine, Jewishness was maintained through communal labels, naming practices, devotion to YHW, ritual traditions, and ongoing relationships with leaders in Yehud and Samaria. At the same time, the community selectively adopted and redeployed imperial norms of loyalty, administration, and hierarchy, especially in its petitions for the rebuilding of the temple of YHW. From this evidence, the dissertation develops a three-part model of Jewish interaction with empire: maintained Jewish distinctiveness, selective mirroring of imperial ideology, and appeal to imperial hierarchy in moments of crisis. The reconstruction of imperial dynamics at Elephantine provides a historically grounded framework for reassessing literary portrayals of Jewish identity under empire, specifically within the Book of Esther, where the same tensions are reframed to articulate a particular vision of Jewish life within imperial systems. Ultimately, by centering the practical ways Jewish communities operated within imperial structures, this study demonstrates that participation in those structures was not necessarily opposed to Jewish distinctiveness but became one of the means through which Jewishness was maintained and articulated in the Second Temple period.Near Eastern studiesBiblical studiesJudaic studiesLiteratureAchaemenid EmpireBibleDiasporaElephantineEmpireJudaismpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2dg353twetdoai:escholarship.org:ark:/13030/qt7vt623b92026-06-01T06:35:18Zqt7vt623b9From Subjective to Principled Single-Cell Data Analysis: Evaluating Annotation Behavior, Optimizing Integration, and Benchmarking Visualization PipelinesZhai, ZhiqianLi, Jingyi2026-05-27Over recent years, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics have transformed transcriptomic research by enabling high-resolution gene expression profiling and making it possible to generate atlas-scale datasets efficiently and cost-effectively. Yet rigorous scRNA-seq analysis remains challenging because key tasks, including cell-type annotation, data integration, and visualization, are often affected by substantial methodological and practical limitations. Cell-type annotation relies on a flexible multi-step pipeline whose functions and parameter choices are often shaped by analysts’ judgment and expertise, introducing subjectivity that can reduce reproducibility. Integration across batches is essential for large-scale and multi-condition studies, but it requires balancing the removal of between-batch variation with the preservation of true cell identity. Existing integration workflows still lack principled annotation-free strategies for feature selection and hyperparameter tuning. Visualization of atlas-scale data is further complicated by computational demands and by the strong influence of preprocessing steps such as normalization and integration, which remain insufficiently evaluated in existing benchmark studies. This dissertation addresses these challenges through three complementary projects on subjectivity in annotation, principled integration optimization, and systematic benchmarking of large-scale visualization pipelines.
My first project examines how graduate students enrolled in an advanced bioinformatics course at UCLA performed cell-type annotation. We also collected information on their pipeline-tuning practices, annotation results, and educational and research backgrounds. Our study revealed that participants performed well in identifying major cell types but often struggled with closely related cell subtypes. Participants who achieved higher cell-type annotation accuracy tended to incorporate data-quality control into analysis pipelines or have prior publications with single-cell analysis. Subjective choices of parameter values influence clustering results, and tuning beyond default settings typically improves clustering accuracy. However, because cell-type label assignment is driven largely by prespecified marker genes and subjective judgments of their expression, parameter tuning has limited impact on improving annotation accuracy. We also identified a confirmation bias: prior expectations about cell types influence subjective annotation decisions. For comparison, we evaluated an AI agent, Biomni, for automated cell-type annotation and found its performance worse than 70–90% of participants. Our findings underscore the importance of transparent reporting of analysis pipelines, including parameter choice justifications and prior expectations about cell types.
My second project presents IntegrateRigor, a data-driven, method-agnostic framework that performs batch-stable gene selection and optimizes integration hyperparameters across state-of-the-art methods, without relying on cell identity annotations. IntegrateRigor selects genes whose expression patterns are stable across batches using a gene-wise likelihood-based batch stability score, excluding batch-sensitive genes that can bias batch alignment during integration. IntegrateRigor then identifies optimal integration results across methods and hyperparameters by defining a dataset-level integration score that balances between-batch variation removal against cell identity preservation. In a colorectal cancer single-cell and spatial transcriptomics dataset, IntegrateRigor revealed previously uncharacterized cancer-immune interface states in the tumor microenvironment that were masked by both underintegration under default settings and over-integration in previous literature. Across diverse single-cell and spatial transcriptomics datasets, IntegrateRigor consistently improves integration performance by balancing over-integration and under-integration. By transforming integration from a heuristic step into a statistically principled, dataset-adaptive procedure, IntegrateRigor improves the reproducibility and discovery power of large-scale single-cell analyses.
My third project proposes a comprehensive benchmark study evaluating 180 method pipelines that combine state-of-the-art normalization, integration, and visualization methods. We assessed performance on five semi-synthetic atlas-scale scRNA-seq datasets that mimic real data and include ground truths. Each pipeline was evaluated using ten quantitative metrics that assess visualization accuracy and scalability. This benchmark provided systematic, evidence-based guidance for selecting appropriate methods for visualizing atlas-scale single-cell data.StatisticsCellular biologyMolecular biologycell-type annotationintegrationsingle-cell RNA sequencingsubjectivityvisualizationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7vt623b9https://escholarship.org/content/qt7vt623b9/qt7vt623b9.pdfetdoai:escholarship.org:ark:/13030/qt5pm9c8ck2026-06-01T06:35:13Zqt5pm9c8ckHypoxia-inducible factor 1-alpha and regenerative failure initiate fibrosis through collagen maturation in dystrophic muscleHelzer, DanielCrosbie, Rachelle H2026-05-27Muscular dystrophies comprise over 30 genetically distinct disorders unified by progressive muscle wasting, wherein repeated cycles of degeneration and regeneration ultimately exhaust regenerative capacity. Central to this process is the extracellular matrix (ECM), which transiently expands during injury to scaffold myogenesis and is subsequently remodeled to homeostasis through tightly coordinated cell-ECM interactions. When these interactions are disrupted, as occurs across the muscular dystrophies, ECM remodeling fails, fibrosis follows, and muscle function is irreversibly lost. This thesis investigates the mechanisms governing cell-ECM interactions and ECM production across multiple disease contexts, from steady state to acute injury to chronic dystrophic pathology. Specifically, we focus on Duchenne muscular dystrophy, the most common and severe form of muscular dystrophy resulting from loss of dystrophin, and γ-sarcoglycanopathy, a severe form of limb-girdle muscular dystrophy.In Chapter 1, we provide a literature review of the reciprocal regulation between muscle stem cells (satellite cells) and the ECM. During homeostasis, satellite cells actively remodel the ECM to maintain quiescence; during regeneration, they tightly regulate ECM turnover to enable repair. Muscular dystrophy disrupts this reciprocity, impairing regeneration and dysregulating multiple muscle-resident cell populations. The dysregulation of these interactions in dystrophic muscle motivates the mechanistic studies that follow. Chapter 2 examines how ECM mechanical changes regulate fibro-adipogenic progenitors (FAPs) in the mdx mouse model of Duchenne muscular dystrophy (DMD). We demonstrate that increased matrix stiffness drives FAP activation and fibrosis through YAP/TAZ mechanosignaling. YAP inhibition in mdx mice prior to peak muscle necrosis and pro-fibrotic signaling reduces fibrosis, establishing mechanical stimulation as a key amplifier of the pro-fibrotic dystrophic phenotype.Chapters 3 and 4 investigate limb-girdle muscular dystrophy R5 using the Sgcg mouse model of γ-sarcoglycanopathy. Chapter 3 demonstrates that sarcospan stabilizes a compensatory sarcoglycan complex, restoring the dystrophin-dystroglycan-laminin connection and preventing muscle damage, identifying sarcospan as a structural pivot point for dystrophin-glycoprotein complex integrity. Chapter 4 reveals that fibrosis in Sgcg muscle is initiated not by excess collagen production but by HIF-1α–driven collagen modification that renders the matrix degradation resistant. Early-stage Sgcg collagen accumulates despite reduced fibroblast transcriptional activity, and HIF-1α inhibition in fibroblasts reduces collagen deposition and increases solubility. In vivo, the fibrogenic consequence of HIF-1α activity requires concurrent regenerative failure, supporting a two-hit model of fibrosis initiation. In Chapter 5, we used the D2.mdx model, a genetically distinct and more severe background for dystrophin deficiency than the standard mdx model, and found that increased RNA decay drives dystroglycan hypoglycosylation, establishing glycosylation enzyme transcript instability as a critical bottleneck for dystroglycan function and a candidate target for improving DMD therapies.Together, these studies reveal that fibrosis and ECM dysfunction in muscular dystrophy arise through convergent but mechanistically distinct disruptions of cell-ECM reciprocity, and identify multiple points of intervention—from matrix mechanics and structural adhesion to collagen maturation and RNA-level regulation—across genetically diverse muscular dystrophies.PhysiologyBlack studiesCellular biologyMolecular biologyBiochemistryextracellular matrixfibroblastfibrosismuscular dystrophyskeletal musclepubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5pm9c8cketdoai:escholarship.org:ark:/13030/qt8nm0g63r2026-06-01T06:35:08Zqt8nm0g63rEcos do naturalismo: As relações do ontem e do hoje na literatura brasileira do século XXICarvalho, IcaroLino, Patricia2026-05-27Esta tese de doutorado investiga a permanência, transformação e reconfiguração de métodos narrativos e estéticos associados ao naturalismo do século XIX na literatura brasileira do século XXI, tomando O Cortiço, de Aluísio Azevedo, como eixo comparativo central. A partir de um corpus composto por autores brasileiros oriundos das classes trabalhadoras racializadas, o texto propõe a existência de um neo-naturalismo, caracterizado principalmente pela retomada do determinismo social e da influência do meio, invertendo, porém, a lógica racista por conta da mudança da posição de autoria—agora ocupada por sujeitos historicamente silenciados. O texto discute esse tópico a partir de teorias literárias e sociais e estudos urbanos, com o intuito de demonstrar como a cidade contemporânea, a violência estrutural e a sobrevida da escravidão configuram novos modos de narrar.A hipótese central da pesquisa, a existência de uma afinidade estrutural entre o naturalismo clássico e parte da ficção brasileira atual, parte principalmente da predeterminação dos destinos das personagens e de certa submissão ao meio urbano, marcado pela herança escravocrata debatida por Saidiya Hartman. O corpus é delimitado e exposto de forma comparativa entre romances naturalistas do século XIX—e princípio do XX—e obras do século XXI que discorrem sobre experiências de personagens racializadas nas grandes cidades brasileiras: Rio de Janeiro, São Paulo, Belo Horizonte e Porto Alegre. O corpus analítico reúne romances, contos e poemas de autores brasileiros contemporâneos, entre eles José Falero, Geovani Martins, Conceição Evaristo, Jeferson Tenório, Sacolinha, Paulo Scott, Lívia Natália e Cristiane Sobral. Esses textos são lidos em diálogo direto com O Cortiço, entendido não apenas como marco do naturalismo brasileiro, mas como obra pioneira na representação literária da classe trabalhadora urbana. A tese demonstra que, embora o romance de Azevedo esteja atravessado por contradições ideológicas e por um imaginário racial problemático, ele inaugura uma gramática narrativa que continua a reverberar na literatura brasileira contemporânea.Mais do que investigar obras publicadas entre os últimos anos do século XX e as primeiras décadas do século XXI, tem-se como objetivo explorar como mecanismos estéticos e estruturais se dão de forma tão semelhante à do naturalismo clássico. Ainda que de uma posição histórica, política e autoral distinta, ambos os momentos literários trazem consigo a consequência da limitação da mobilidade social em desfechos trágicos. Ao pensar a literatura contemporânea como documento histórico, estabelece-se também diálogo com Lélia Gonzalez, Georg Lukács, Antonio Candido, Alfredo Bosi e Walter Benjamin. Há, no entanto, três autores-chave para esta análise: Flora Süssekind, por ser a primeira a teorizar sobre o neo-naturalismo no Brasil, Milton Santos, por analisar o espaço urbano brasileiro, e Saidiya Hartman pelo seu conceito de “afterlife of slavery.” Esses três teóricos constituem o esqueleto da pesquisa, permitindo-me compreender a persistência das hierarquias raciais e econômicas no Brasil pós-abolição como força estruturante das narrativas contemporâneas.A análise organiza-se em torno de quatro eixos fundamentais—o meio, as disputas de classe, a violência e a cidade—que funcionam como pontos de convergência entre o naturalismo clássico e as obras selecionadas para compor este neo-naturalismo moderno. A cidade emerge como cenário central dessas narrativas, atuando ativamente como agente de exclusão social, espelhando espacialmente as organizações herdadas de engenhos escravocratas. O antagonismo a essa sobrevida da escravidão aparenta residir justamente na mudança da posição de autoria. Enquanto o naturalismo clássico observava distantemente a classe trabalhadora a partir de um ponto de vista frequentemente marcado pela exotização e pela análise pseudocientífica, o neonaturalismo brasileiro caracteriza-se por escrever “de dentro,” sendo produzido por autores oriundos das próprias classes subalternizadas. Essa mudança transforma a relação entre narrador, personagem e leitor, substituindo o olhar do naturalista pela experiência compartilhada do espaço urbano. Dessa forma, o neo-naturalismo brasileiro do século XXI não deve ser entendido como uma simples repetição ou retorno cíclico ao naturalismo, mas como uma possível reconfiguração crítica do gênero.Finalmente, usa-se do conceito neo-naturalismo para definir uma corrente contemporânea que retoma procedimentos naturalistas de análise social, mas subverte ao deslocar o foco narrativo a partir de personagens, espaços e autores historicamente marginalizados. Diferenciando-se também pelo fato de o determinismo presente nessas obras não derivar mais de pressupostos cientificistas ou biologizantes, característicos do naturalismo oitocentista, mas de uma herança histórica marcada pela escravidão, pela segregação urbana e pela violência institucional.This doctoral thesis investigates the persistence, transformation, and reconfiguration of narrative and aesthetic methods associated with 19th-century Naturalism in 21st-century Brazilian literature, taking *O Cortiço* by Aluísio Azevedo as its central comparative axis. Drawing upon a corpus composed of Brazilian authors hailing from racialized working-class backgrounds, the text posits the existence of a "neo-naturalism," characterized primarily by a revival of social determinism and the influence of the environment—yet one that inverts the racist logic of its predecessor due to a shift in authorial position, now occupied by historically silenced subjects. The text examines this topic through the lenses of literary and social theory, as well as urban studies, aiming to demonstrate how the contemporary city, structural violence, and the afterlife of slavery shape new modes of narration.The research’s central hypothesis—that a structural affinity exists between classical Naturalism and a segment of contemporary Brazilian fiction—stems primarily from the predetermined fates of characters and a certain subjection to the urban environment, an environment marked by the legacy of slavery as theorized by Saidiya Hartman. The corpus is delineated and presented comparatively, juxtaposing Naturalist novels of the 19th—and early 20th—centuries with 21st-century works that explore the experiences of racialized characters in major Brazilian cities: Rio de Janeiro, São Paulo, Belo Horizonte, and Porto Alegre. This analytical corpus comprises novels, short stories, and poems by contemporary Brazilian authors, including José Falero, Geovani Martins, Conceição Evaristo, Jeferson Tenório, Sacolinha, Paulo Scott, Lívia Natália, and Cristiane Sobral. These texts are read in direct dialogue with *O Cortiço*, a work understood not merely as a landmark of Brazilian Naturalism, but as a pioneering text in the literary representation of the urban working class. The thesis demonstrates that, although Azevedo’s novel is fraught with ideological contradictions and a problematic racial imaginary, it inaugurates a narrative grammar that continues to reverberate throughout contemporary Brazilian literature. Rather than merely investigating works published between the final years of the 20th century and the first decades of the 21st, the objective here is to explore how aesthetic and structural mechanisms manifest in a manner so strikingly similar to that of classical naturalism. Although situated within distinct historical, political, and authorial contexts, both literary moments share a common consequence: the limitation of social mobility resulting in tragic outcomes. By conceptualizing contemporary literature as a historical document, this study also establishes a dialogue with Lélia Gonzalez, Georg Lukács, Antonio Candido, Alfredo Bosi, and Walter Benjamin. There are, however, three key authors central to this analysis: Flora Süssekind, for being the first to theorize on neo-naturalism in Brazil; Milton Santos, for his analysis of the Brazilian urban space; and Saidiya Hartman, for her concept of the "afterlife of slavery." These three theorists constitute the framework of this research, enabling me to understand the persistence of racial and economic hierarchies in post-abolition Brazil as a structuring force within contemporary narratives.The analysis is organized around four fundamental axes—the environment, class struggles, violence, and the city—which serve as points of convergence between classical naturalism and the works selected to represent this modern neo-naturalism. The city emerges as the central setting of these narratives, acting actively as an agent of social exclusion and spatially mirroring the organizational structures inherited from the slave-holding sugar plantations. The counterpoint to this afterlife of slavery appears to lie precisely in the shift in authorial position. While classical naturalism observed the working class from a distance—often through a lens marked by exoticization and pseudo-scientific analysis—Brazilian neo-naturalism is characterized by writing "from within," being produced by authors hailing from the very subalternized classes they depict. This shift transforms the relationship between narrator, character, and reader, replacing the naturalist’s detached gaze with a shared experience of the urban space. Thus, 21st-century Brazilian neo-naturalism should not be understood as a mere repetition of, or cyclical return to, naturalism, but rather as a potential critical reconfiguration of the genre.Ultimately, the concept of neo-naturalism is employed to define a contemporary current that revives naturalist methods of social analysis, yet subverts them by shifting the narrative focus toward historically marginalized characters, spaces, and authors. It further distinguishes itself by the fact that the determinism present in these works no longer stems from the scientistic or biologizing premises characteristic of 19th-century naturalism, but rather from a historical legacy marked by slavery, urban segregation, and institutional violence.LiteratureLatin American historyHistoryLatin American studiesBrazilHistoryLatin AmericaLiteratureNaturalismSocietypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8nm0g63retdoai:escholarship.org:ark:/13030/qt5wk445p42026-06-01T06:35:03Zqt5wk445p4Wildfires and Birth Outcomes in California: Risks and Mitigation StrategiesSong, Lanxinvon Ehrenstein, Ondine S2026-05-28The intensification of climate change has established wildfire smoke as a dominant and growing source of ambient air pollution in the western United States. Pregnant individuals and their developing fetuses are uniquely vulnerable to these exposures, yet important gaps remain regarding the effects of wildfire smoke on prematurity, fetal growth, critical windows of susceptibility, and the potential effectiveness of mitigation strategies. This dissertation provides a comprehensive evaluation of wildfire-related impacts on birth outcomes in California using large population-based birth cohorts and machine learning-based wildfire-specific fine particulate matter (PM2.5) estimates.Analyses of this large cohort identified distinct nonlinear relationships between wildfire-specific PM2.5 and eight birth outcomes, including preterm birth, low birth weight, and large for gestational age, establishing wildfire smoke as a robust threshold-free risk factor. A clear severity gradient was observed, including for very preterm birth, extreme preterm birth, and preterm birth with eclampsia. Findings further identified critical gestational windows of susceptibility, particularly during the second trimester, during which prenatal wildfire smoke exposure was consistently associated with adverse birth outcomes. Using a parametric G-computation framework to simulate hypothetical interventions, this research further quantified the prevention potential of hypothetical targeted smoke mitigation strategies. Results demonstrated that evidence-informed PM2.5 reduction strategies could avert a meaningful proportion of wildfire-attributable adverse birth outcomes, including preterm births and low birth weight. Equity-centered strategies prioritizing highly impacted and vulnerable populations may produce particularly large benefits, comparable to established clinical prevention measures. These strategies may also effectively reduce disparities in health burden across populations with differing vulnerability. Disaster-related stressors and emergency responses may also contribute to adverse perinatal outcomes beyond smoke exposure. Collectively, this dissertation demonstrates that wildfire-related impacts on birth outcomes are substantial yet potentially preventable through targeted and equity-centered interventions. These findings provide important evidence for integrating climate resilience into prenatal care, public health preparedness, and emergency management policy to protect maternal and neonatal health.EpidemiologyEnvironmental healthClimate changeEnvironmental engineeringAir pollutionBirth outcomesCounterfactual simulationEnvironmental epidemiologyPrenatal exposureWildfire smokepubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5wk445p4etdoai:escholarship.org:ark:/13030/qt2ms704sp2026-06-01T06:34:58Zqt2ms704spMaking and Sensing Sacredness: The Architecture and Materiality of CoricanchaMenéndez Pereda, AlbaNair, Stella Elise2026-05-28The Inca Empire, as Tahuantinsuyu is popularly known, constituted the largest Indigenous polity in the Americas prior to European invasion. At its height, its territory extended along the Pacific coast and Andean highlands, from present-day southern Colombia to northern Argentina. At the political and spiritual center of this vast empire was its capital, Cuzco, in present-day Peru, and, more precisely, the religious complex of Coricancha. This sacred center was the home of Inca ancestral rulers and the stage on which some of the most important ceremonies in the empire unfolded. Through an analysis of extant architecture, objects excavated at the site, and ethnohistoric documents, this dissertation foregrounds how the Inca made and sensorially experienced sacredness at the heart of their empire. Based on the location of Coricancha within Cuzco, its architectural design, and its materiality, I argue that the Inca did not create sacredness anew and instead developed it in relational terms. Thus, they built their most important religious center at a location that carried pre-imperial Inca historical significance and materiality, connecting it visually and conceptually with other sacred spaces in and around Cuzco, and used building materials imbued with symbolism. The objects excavated at the site provide insight into the richness of the ceremonies staged therein and the identity of the center’s inhabitants. This corpus, which reflects the material and artistic diversity of the empire, together with the sonic and visual experiences curated by the architects of the religious center, were the means through which ritual participants engaged with the sacred. I explore the materiality of Coricancha through Andean notions of personhood and relationality to foreground that the architecture at the site and objects excavated from it are not merely traces of ritual spaces and activities but the corporeality of and offerings to Coricancha. Not only was Coricancha a place, it was also an animate being that required care and veneration and that was embedded within a social-relational matrix. The conceptualization of Coricancha as a subject being studied rather than an object of study forces a reconsideration of the material evidence at the site.ArchaeologyCultural anthropologyHistoryArchitectureArchaeology of ReligionCoricanchaInca ArchitectureSacrednessTahuantinsuyupubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2ms704spetdoai:escholarship.org:ark:/13030/qt2tz280q92026-06-01T06:34:53Zqt2tz280q9Identifying lipid metabolic vulnerabilities in gliomas through integrative multi-omic analysisBayley, Nicholas AlexanderGraeber, Thomas Gnathanson, David A2026-05-28Gliomas are molecularly heterogeneous brain tumors with stagnantly poor clinical outcomes, underscoring the need for new therapeutic strategies. Although metabolic reprogramming is a hallmark of cancer, how recurrent genetic alterations and intratumoral cellular states diversity shape lipid metabolism and create targetable vulnerabilities in gliomas remains poorly understood. Here, we integrate lipidomic, transcriptomic, and genomic profiling of molecularly diverse gliomas and their derivative in vivo and in vitro model systems to define the landscape of metabolic heterogeneity and its functional consequences. We identify distinct lipid signatures and phenotypes associated with specific genetic alterations and transcriptional programs. Notably, we identify that deletion of CDKN2A remodels the lipidome by redistributing oxidizable polyunsaturated fatty acids into discrete lipid compartments, resulting in elevated lipid peroxidation and increased susceptibility to ferroptosis. These findings reveal a therapeutically exploitable link between a recurring genetic alteration and lipid metabolic vulnerability. Beyond genetic drivers, we demonstrate that lipid metabolism is tightly coupled to cellular state identity and plasticity. Glioma cellular states align on a metabolic continuum reflecting patterns in neurodevelopmental lineage hierarchies, stratifying tumor subpopulations by their reliance on de novo fatty acid synthesis versus lipid scavenging. Functional perturbations uncover state-specific dependencies: Vascular-like states require de novo fatty acid synthesis, OPC- and NPC-like states depend on microenvironmental lipid uptake, and GPC-like states exhibit metabolic flexibility, enabling adaptation between lipid acquisition strategies. Environmental perturbations further driver reversible remodeling of both lipid metabolic programs and cellular state composition, indicating that metabolic constraints actively shape tumor heterogeneity. Together, this research establishes lipid metabolism as a central regulator of both genetically encoded and cellular state-driven heterogeneity in glioma. By defining how metabolic rigidity and flexibility govern tumor cell survival, we identify actionable vulnerabilities that may inform the development of therapies for glioma.BioinformaticsNeurosciencesBiologyGeneticsgenomicsglioblastomagliomalipidomicstranscriptomicspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2tz280q9etdoai:escholarship.org:ark:/13030/qt9vb4v13h2026-06-01T06:34:39Zqt9vb4v13hAI-Driven Methods for Prostate Biparametric MRI Enhancement and Improved Clinical AssessmentSun, HaoranLi, Debiao2026-05-26Prostate cancer (PCa) is one of the most commonly diagnosed cancers worldwide. Prostate biparametric magnetic resonance imaging (bpMRI), consisting primarily of T_2-weighted (T2w) imaging, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps, plays a central role in PCa detection, lesion localization, risk stratification, and treatment planning. However, important limitations remain. T2w imaging is qualitative and relies on relative tissue contrast, limiting objective tissue characterization and reproducibility. Quantitative T2 mapping can provide direct tissue measurements but is not routinely acquired because it requires additional scan time and dedicated acquisition. DWI is critical for detecting clinically significant PCa, particularly in the peripheral zone, but is vulnerable to geometric distortion, artifacts, and limited spatial resolution.This dissertation develops artificial intelligence-driven methods to enhance prostate bpMRI and improve clinical assessment using routinely acquired MRI data. First, a deep learning method was developed to retrospectively estimate prostate T2 maps from conventional weighted MR images. The estimated maps preserved prostate tissue structure, showed agreement with reference T2 maps, and demonstrated lower T_2 values in tumor regions than in non-tumor regions. The clinical value of the estimated T2 maps was further evaluated for detecting clinically significant peripheral-zone PCa. Radiomic features extracted from estimated T2 maps provided complementary quantitative information to conventional T2w image features, and an integrative model combining both feature groups improved prediction of clinically significant cancer. Second, this dissertation addresses DWI reliability and image quality. An automated image-quality assessment framework, Automated Assessment of Image Quality (AutoIQ), was developed to quantify geometric distortion and classify prostate DWI scans as acceptable or severely distorted. By integrating segmentation-based and registration-based distortion metrics using an ensemble machine learning model, AutoIQ provided an objective quality-control decision that may support timely repeat acquisition. In addition, deep learning-based DWI super-resolution was explored to improve spatial detail and anatomical consistency while maintaining clinically practical acquisition. Together, these studies establish an AI-enhanced prostate bpMRI framework that retrospectively derives quantitative T2 information, enables DWI quality control, and improves DWI resolution from conventional prostate MRI, supporting a more objective, reliable, and decision-supportive prostate imaging workflow.BioengineeringBiomedical engineeringOncologyHealth care managementDeep learningDiffusion-weighted imagingMRIProstate cancerRadiomicsT2 mappingpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9vb4v13hetdoai:escholarship.org:ark:/13030/qt9vn4n7122026-06-01T05:03:29Zqt9vn4n712"A Legacy of Woes": Internalized Racism and Political Accountability in Contemporary KenyaHamilton, Jennifer AnnePosner, Daniel N2024-01-01Why do voters tolerate poorly performing politicians? In African contexts, existing theory highlights lack of information, ethnic voting, and clientelistic equilibriums as potential explanations. Another explanation has remained overlooked: accountability is impaired by internalized racism, a psychological phenomenon wherein members of disadvantaged racial groups implicitly or explicitly adopt racist attitudes, ideologies, and behaviors instilled and perpetuated by dominant racial power structures. Notwithstanding its centrality in much African scholarship, political scientists have not investigated internalized racism as an explanation for impaired accountability, in large part because social scientists lack appropriate methodological tools to systematically document internalized racism and its effects. My research addresses this gap. In this dissertation, I outline internalized racism’s character, origins, and relevance in African contexts. I then leverage data from an original survey among 600 Kenyan respondents toward two ends. First, I use survey data to develop and validate new explicit measures of internalized racism. Second, I use survey data to test three hypotheses regarding internalized racism’s impact on contemporary political attitudes, beliefs, and behaviors: (1) internalized racism alters political evaluations via motivated reasoning, (2) internalized racism increases perceptions and expectations of ethnic discrimination, and (3) internalized racism alters patterns of political participation. While the survey included an embedded experiment with a new intervention to reduce internalized racism, this initial attempt at causal identification did not prove successful. I therefore turn to observational analyses to test the three research hypotheses. I do not find compelling evidence that internalized racism affects political evaluations via racial motivated reasoning. However, I do find that internalized racism significantly predicts reported experiences of ethnic discrimination. Contrary to expectations, I do not find evidence that internalized racism depresses political efficacy or adversarial participation. However, I find evidence that internalized racism significantly predicts acceptance of clientelistic norms. The findings raising the possibility that internalized racism increases tendencies toward ethnic voting and drives persistent clientelism, thus impairing accountability and producing a self-fulfilling prophecy of poor-quality governance. This research adds depth to existing political science theory by shifting emphasis to racial identities in a context where scholars have deemphasized race in recent decades. Political scienceAfrican politicscolonial mentalityinternalized racismKenyapolitical accountabilityenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9vn4n712https://escholarship.org/content/qt9vn4n712/qt9vn4n712.pdfetdoai:escholarship.org:ark:/13030/qt2285492s2026-06-01T05:01:54Zqt2285492sSemantics in the Era of Large-Language ModelsHolur, Pavan SRoychowdhury, Vwani P.2024-01-01Recent autoregressive generation models such as SoRA, GPT-4, LLaVa-next have made significant headway in modeling the short-range co-occurrence statistics in language – at the level of tokens or “word pieces” – and in vision at the pixel/region resolution. Empirically, these models are trained and evaluated by querying the model using a partial token sequence $t_1, \dots, t_{k-1}$, sampling the conditional distributions $P(t_k | t_1, \dots, t_{k-1})$ over multiple turns, and finally judging the quality (called the “Semantics”) of the resulting generated sequence of tokens $t_{k+1}, t_{k+2}, \dots$ in context of the original prompt. Such setups have proven to approach (and, in some cases, exceed) human performance at pre-existing NLP tasks such as Sentiment/Topic Classification, Sentence-level Similarity and Question-Answering and Short Story Completion. As the length demand of these token-by-token generations grows, the corresponding efforts to train and deploy these models – specifically, the sheer volume of the training data, and the computational demands to jointly condition on many input prompt tokens at once -- grow exponentially. Today, many models exceed 100B parameters, cannot be hosted locally and draw significant power. Thus, many efforts today seek alternate means by which to extend the range of Semantic consistency of these generation models.This thesis presents a method to address this problem by asking the following question: What if instead of attempting to model the long-range semantics directly, we identify the Semantic information for shorter prompts, and then stow away the Semantics as a representation beyond simple tokens. From here, these intermediate representations can be manipulated, aggregated and extended within an augmented representation space of lower complexity: a “Workspace”. The estimation of such a Workspace can be formulated as a State Space model: the intermediate representations of the Semantics constitute samples from an underlying latent state process (designed to satisfy Markovian assumptions) and the generated text segments constitute the observable short contexts. Over a series of works (chapters) that include such diverse applications as discovering author biases and intentions across internet-scale social media by piecing together the clues in individual posts, to DNA sequence alignment and assembly of short reads with respect to a much larger reference genome, we develop the cognitive theory of the Workspace and a computational implementation -- and demonstrate empirical success at existing and constructed tasks -- of such a Workspace for the text modality.Electrical engineeringgenerative modelinglarge-language modelnatural language processingretrieval augmented generationsemanticsworkspaceenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2285492shttps://escholarship.org/content/qt2285492s/qt2285492s.pdfetdoai:escholarship.org:ark:/13030/qt0fb7q6cb2026-06-01T05:01:13Zqt0fb7q6cbSoft magnetoelastic bioelectronicsZHAO, XUNChen, Jun2024-01-01Bioelectronics are revolutionizing the future of human life by reshaping fields of medicine and healthcare into a more personalized form. Biomechanical-to-electrical energy conversion is a promising pathway to realize battery-free bioelectronics in the era of the Internet of Things. This conversion is available 24 hours a day and provides up to 100 W output for an average person. Current biomechanical energy conversion mechanisms, including piezoelectric and triboelectric effects, suffer limitations such as low current density and high internal impedance, which arise from their power generation mechanism. Additionally, their output performance is vulnerable to the humidity caused by body fluids, which limits their practical deployment in wearable and implantable bioelectronics. Therefore, there exists a demand to search for an unexploited biomechanical-to-electrical energy conversion mechanism featuring high current output, low internal impedance, waterproofness, and robustness under biomechanical stimulus. Thus, we discovered the giant magnetoelastic effect in a soft polymer system, which was further coupled with magnetic induction to invent a soft magnetoelastic generator (MEG) as a fundamentally new platform technology for building up human-body-powered soft bioelectronics. Soft magnetoelastic bioelectronics are intrinsically waterproof since the magnetic fields can penetrate water with negligible intensity loss. Thus, they demonstrated stable performance in wearable and implantable manners without any encapsulation. This breakthrough has opened alternative avenues for practical human-body-centered energy, sensing, and therapeutic applications. Second, the recent development of soft bioelectronic devices for disease prevention, diagnosis, and treatment presents pressing needs for the creation of an intimate interface between electronic systems and dynamically evolving biological tissues for precise physiological measurement. While ultrathin membrane-based soft bioelectronic devices can conform to biological tissue, mechanical mismatches between the solid materials and the biological tissue still exist. Thus, we developed an intimate liquid bioelectronic interface through the creation of a permanent fluidic magnet (PFM). PFM possesses the reconfigurable functionality to change its shape without losing its permanent magnetism. It will bridge the gap between liquid-state magnets and ferromagnetism, thereby unlocking a wide range of possibilities. Unlike traditional biomedical devices, liquid bioelectronics used for ambulatory monitoring enable the continuous assessment of biomechanical activity over extended periods. Uninterrupted monitoring provides a more comprehensive picture of a patient's health status, capturing any irregularities or abnormalities that may occur intermittently or during daily activities. Since magnetic fields can penetrate biological tissues due to their physical mechanisms, here we described the utilization of PFM for different biomedical applications, including injectable bioelectronics, and liquid acoustic sensors.
Bioengineeringliquid bioelectronicsmagnetoelastic effectsoft bioelectronicssoft fiberstextilewearable sensorenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0fb7q6cbhttps://escholarship.org/content/qt0fb7q6cb/qt0fb7q6cb.pdfetdoai:escholarship.org:ark:/13030/qt9tk6m3tn2026-05-31T05:03:43Zqt9tk6m3tnBecoming One Country, One System: Cantonese Opera in Post-Colonial Hong KongYeung, Ngai WanRees, Helen M2024-01-01This dissertation explores Cantonese opera in post-colonial Hong Kong, focusing on the interplay between local cultural practices and the pressures of Chinese nationalism. Using practice theory set out by Pierre Bourdieu, this study examines how changes in social hierarchy and cultural identity are manifested within the Cantonese opera world amidst institutional influences. Employing qualitative methods such as interviews, archival research, participant observation, and virtual ethnography, this project examines the professional, amateur, and educational Cantonese opera settings while locating interactions and conflicts within and between them. The influx of formally trained talents, often described as bearers of a “mainland Chinese” flavor or conservatory training and contrasted with locally trained performers who have not attended formal conservatories, suggests two distinctive sets of performance practice. However, an audiovisual analysis reveals that this binary, in fact, refers to a spectrum of practices. Looking through the lens of practice theory, the findings reveal that institutional support and societal attitudes towards public performances have significantly reshaped the ecosystem of Cantonese opera in Hong Kong. Rather than an artistic struggle, the issue of performance practices reveals how a performing art can transform into a battlefield where stakeholders negotiate artistic legitimacy and cultural identity. This dissertation aims to reveal the connectedness and motivations within the ecosystem and what such an analysis can contribute to the broader understanding of performing arts traditions.Performing artsCultural anthropologyAsian studiesCantonese operaHong KongIntangible cultural heritagePostcolonialismSustainabilityTraditional performing artsenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9tk6m3tnhttps://escholarship.org/content/qt9tk6m3tn/qt9tk6m3tn.pdfetdoai:escholarship.org:ark:/13030/qt64g6f0462026-05-31T05:02:37Zqt64g6f046Cultures of Foreign Policymaking: State Department Diplomats and Race in US-Africa StrategyMills, Naakoshie AwuramaHart-McGrath, Laurie Elizabeth2024-01-01This study examines how Black Americans’ interpellated experiences with racism impact diplomatic life and work in the African region, in the context of emerging diversity, equity, and inclusion initiatives that promote the recruitment of Americans from a variety of marginalized backgrounds into the Foreign Service, or diplomatic corps, in the US Department of State. Through an ethnography of the diplomatic lives of Black Americans at the Department of State, this dissertation uncovers the complexities of the foreign policymaking process: expertise communities, power assemblages in major diplomatic metropoles, like Washington, DC, bureaucratic ideologies, training and peer discipline, and the hubris of American exceptionalism in international politics. I consider the myriad of ways policy is constructed, incubated, and disseminated in everyday administrative actions, such as writing, elite educational circles, and ambiguous social, professional, and personal spaces in which the State Department trains and comports actors to be diplomats in a well-defined culture of foreign policymaking. Black Americans, who have endured ongoing anti-Black violence, oppression, exploitation, and discrimination from the nation’s inception, affirm the importance of a diversified diplomatic corps; one that truly represents the multicultural identity embedded within the idea of an American. I contend that while Black Americans learn, practice, and implement expected norms of diplomatic statecraft, their approach, nonetheless, is framed by their racial subjectivity, phenomenological understandings of Blackness as it moves across borders, and attention to the complex interplay between US empire and racial hierarchies in global governance. An embodied tension emerges for Black Americans hoping to reconcile centuries of US racism embedded in domestic and foreign policy–a legacy that structures their own marginalization as employees at the Department of State and throughout US society and has contributed to the subjugation of African partners in geopolitics and multilateral diplomacy. Situated ethnographically in Washington, DC, this dissertation reveals a constellation of Black policy actors who draw on and engage with notions of Blackness, Americanness, and diaspora to construct physical, digital, and interpersonal spaces that grapple with the historical and contemporary tensions of race in US foreign relations in Africa.African American studiesInternational relationsAfrican studiesAfrican AmericansDiplomacyRaceSub-Saharan AfricaUS Foreign Policyenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/64g6f046https://escholarship.org/content/qt64g6f046/qt64g6f046.pdfetdoai:escholarship.org:ark:/13030/qt4cs4z4842026-05-31T05:02:13Zqt4cs4z484The Influence of Macromolecular Design on Polyurethane Composites and Polyelectrolyte ComplexationIyer, DivyaSrivastava, Samanvaya2024-01-01This dissertation elucidates the significance of macromolecular design in two types of systems - (i) covalently-linked polyurethane networks and composites, and (ii) complexes of oppositely charged polymers (polyelectrolyte complexes) – unified by their macromolecular nature, yet distinguished by their design, governing principles, and applications. The first part of this dissertation highlights the profound effect of the chemistry of flexible polyurethane foams on their physico-mechanical and thermochemical properties; by harnessing these correlations, a simple strategy has been proposed for the facile classification of post-consumer-use flexible polyurethane foams for their chemical recycling. Following this, the upcycling of depolymerized polyurethane products (polyols) into lightweight high-strength organic-inorganic composites with superior thermal insulation and acoustic barrier performance has been discussed. The amenability of the compositing strategy with polyols from different sources, varying chemistries of the diisocyanate linker, and types of inorganic materials have been highlighted. This dual approach is a step toward enabling the circularity of post-consumer-use flexible polyurethane foam and informing the design of polyurethane-inorganic composites with unique properties. The second part of this dissertation delineates the phenomenon of polyelectrolyte complexation and examines the non-trivial role of salt ion valency and polyelectrolyte length asymmetry on complexation. Divalent ions have been shown to reverse the composition of PECs, favoring their sequestration in the complex phase, and hindering chain relaxation. Following this, the dramatic influence of polyelectrolyte length symmetry in controlling the polymer content of the complex phase has been discussed in the context of thermodynamics and the Voorn-Overbeek theory. Furthermore, length-asymmetry will be shown to alter the viscosity of the complex phase. These dual studies are aimed to serve as design guides for polyelectrolyte complexation occurring in environments (biological and medical systems, environmental and wastewater treatment applications) where little to no control can be exercised over the properties of the solution and polyelectrolytes.Chemical engineeringPolymer chemistryPolyelectrolyte ComplexationPolyurethane Compositesenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4cs4z484https://escholarship.org/content/qt4cs4z484/qt4cs4z484.pdfetdoai:escholarship.org:ark:/13030/qt9qd2h7m62026-05-30T05:03:42Zqt9qd2h7m6Nonlinear Lattice Dynamics and Spin-phonon Coupling in Magnetic Topological Heterostructures via Precision Optical SpectroscopyKang, Jin HoWong, Chee Wei2024-01-01Research on two-dimensional (2D) van der Waal (vdW) materials with structural benefits and topological materials with advantages due to unique electronic band structures has rapidly grown over the past 20 years in anticipation of them being a robust platform for uncovering emerging phenomena and implementing device structures. In addition, scholars have found that materials having both these properties simultaneously produce enhanced topological states due to their large surface to volume ratio. Furthermore, dedicated efforts over the past few years have led to extensions that encompass magnetism. The interplay between the topological band structures and their magnetic order is expected to demonstrate exceptional physical characteristics. The purpose of this thesis is to explore magnetic topological and 2D vdW materials via optical spectroscopic methods for analyzing their vibration dynamics with their symmetric group(s). First, MnBi2nTe3n+1 (n=1,2,3,4) magnetic topological heterostructures are examined by cryogenic polarized-Raman spectroscopy. MnBi2Te4 is the first-ever intrinsic antiferromagnetic (AFM) topological insulator arising by intercalating Mn-Te chains into Bi2Te3 crystals. Two resonances at 66 and 112 cm-1 show abnormal broadening in Raman linewidths below the Néel temperature because of spin-phonon coupling. In addition, the MnBi4Te7 heterostructure has an out-of-plane interlayer force constant three times weaker than Bi2Te3 by Davydov splitting of the A1g mode at cryogenic temperature. Second, we explore ferromagnetic (FM)-EuCd2As2, an excellent candidate for investigating Weyl physics due to its minimal number of Weyl points, via polarized magneto-Raman spectroscopy. Enhanced Raman intensities are observed below the Curie temperature due to the spin-phonon coupling. In addition, we demonstrate that A-mode peaks are manipulated by magneto helical-Raman spectroscopy because of the magneto-optic effect. Third, we also investigate electronic transition peaks of FM-EuCd2As2, which is a family of rare earth compounds of Eu. These lanthanide compounds have strong spin-orbit coupling due to the protected 4f-orbital block, and thus, the electronic transition state exists near phononic excitation level. We show the Zeeman effect coupled to the direction of cross-circular polarization configuration with the external magnetic fields. In addition, anomalies in intensity are observed in the low-frequency region between anti-Stokes and Stokes. Lastly, we seek to enhance the second harmonic generation (SHG) for 2D vdW MoS2 materials by intercalating a foreign species between the MoS2 layer, called bulk monolayer (BM)- MoS2. Due to its centrosymmety, the even-number of susceptibility term vanishes in bulk MoS2 materials. The foreign species causes reduced interlayer coupling due to an increased gap between the layers, which breaks the inversion symmetry of the materials and in turn, leads to an enhanced SHG signal. We find that BM-MoS2 has a signal 126 times higher than its monolayer and 21 times higher than a GaAs wafer. Electrical engineeringCondensed matter physicsMagnetismRaman spectroscopySecond harmonic generationSpectroscopyTopological materialsenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9qd2h7m6https://escholarship.org/content/qt9qd2h7m6/qt9qd2h7m6.pdfetdoai:escholarship.org:ark:/13030/qt85x253n32026-05-30T05:03:24Zqt85x253n3Vision Enhancement and Artificial Intelligence via Efficient and Interpretable Algorithms Inspired by Optical PhysicsZhou, YimingJalali, Bahram2024-01-01The remarkable success of physics in explaining nature and engineering machines is rooted in low-dimensional deterministic models that accurately describe a wide range of natural phenomena. In contrast, Artificial Intelligence (AI) led by neural networks has introduced an alternate data-driven computational framework, achieving astonishing performance in various fields. However, this impressive feat comes at the cost of challenges, including the need for massive datasets, high computational cost, and limited interpretability. To address these issues, there is a growing interest in hybrid approaches that merge the laws of physics and physical systems with AI. This dissertation focuses on developing efficient and interpretable algorithms for vision enhancement and AI applications by drawing inspiration from the principles of optical physics. Unlike traditional algorithms that rely on hand-crafted empirical rules or neural networks that are data-driven and computationally heavy, physics-inspired algorithms leverage laws of nature as blueprints, resulting in low dimensionality, high efficiency, and full interpretability. The dissertation begins with the principles of Photonic Time Stretch, a hardware technique grounded in optical physics for ultrafast and single-shot data acquisition, which seeded the ideas herein. A unified physical and mathematical framework is derived based on the governing Nonlinear Schrödinger Equation (NLSE). Inspired by this physical principle, a novel class of algorithms called PhyCV (Physics-inspired Computer Vision) is developed for image and video processing tasks, including edge detection, low-light enhancement, and motion detection. These algorithms demonstrate superior performance in vision enhancement and are well-suited for applications on mobile and edge devices with constrained resources. Furthermore, a novel physics-based AI model called the "Nonlinear Schrödinger Network" is proposed, which treats the NLSE as a trainable model in the numerical domain to learn complex patterns and nonlinear mappings from data. This physics-inspired approach provides a more interpretable and parameter-efficient alternative compared to black-box neural networks, achieving comparable or better accuracy on nonlinear classification tasks while significantly reducing the required number of parameters.Electrical engineeringenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/85x253n3https://escholarship.org/content/qt85x253n3/qt85x253n3.pdfetdoai:escholarship.org:ark:/13030/qt3fb0h5142026-05-30T05:02:36Zqt3fb0h514Amides and Cyclic 1,2,3-Trienes as Synthetic Building BlocksBulger, Ana SydelleGarg, Neil K.2024-01-01This dissertation describes the development of reaction methodologies that utilizeunconventional building blocks in chemical synthesis. One major effort involves the net hydrolysis
of amides to give carboxylic acids via the nickel-catalyzed cleavage of traditionally inert amide
C–N bonds. Additionally, the development of the first enantioselective reaction of amide
electrophiles proceeding with the usage of a chiral catalyst through C–N bond activation is
disclosed. At the other end of the reactivity spectrum, studies of minimally explored strained
intermediates including cyclic 1,2,3-trienes and azacyclic 1,2,3-trienes are reported. These efforts
expand on fundamental structure and reactivity of the cyclic trienes, give rise to new mechanistic
understanding, and allow access to polycyclic products.
Chapter one outlines the current state of the art in nickel and iron-catalyzed cross-couplings
of traditionally inert electrophiles. Specifically, recent advances in base-metal-catalyzed reactions
of phenol, aniline, ester, and amide derivatives that proceed via aryl or acyl C–O/C–N bond
activation are described. This brief review chapter should provide context for some of the
subsequent studies presented in this dissertation. Furthermore, summarizing recent efforts in this
field is expected to highlight the utility of base-metal-catalyzed cross-couplings of traditionallyinert
electrophiles in organic synthesis.
Chapter two describes the development of a nickel-catalyzed net hydrolysis of amides. The
methodology strategically employs a nickel-catalyzed esterification using 2-(trimethylsilyl)-
ethanol, followed by a fluoride-mediated deprotection in a single-pot operation. The selectivity
and mildness of this transformation are demonstrated through competition experiments and the
net-hydrolysis of a complex valine-derived substrate. This strategy addresses a limitation in the
field with regard to functional groups accessible from amides using transition metal-catalyzed C–
N bond activation.
Chapter three describes an enantioselective Mizoroki–Heck cyclization of amide
electrophiles. In recent years, numerous cross-coupling reactions of amide electrophiles have been
disclosed, however, this study constitutes the first known enantioselective variant proceeding
through amide C–N bond activation using a chiral metal catalyst. Ligand design and reaction
optimization are discussed, as well as a brief scope of the reaction. Additionally, the synthetic
utility of the method is demonstrated through synthetic elaborations to enantioenriched,
medicinally-relevant heterocycles and products bearing multiple stereocenters. This study
establishes a proof-of-concept for using amide electrophiles in asymmetric catalysis.
Chapter four describes the development of strained 1,2,3-cyclohexatrienes, which have
remained underexplored historically, as synthetic building blocks. Studies of the reactivity of the
unsubstituted 1,2,3-cyclohexatriene, as well as its mono- and disubstituted derivatives are reported,
thus expanding the scope of reactions known for such intermediates. Combined computational and
experimental studies elucidate the factors controlling regioselectivity in reactions of an
unsymmetrical strained triene. Furthermore, the potential utility of strained trienes in rapidly
generating complex scaffolds is demonstrated through the integration of triene trapping reactions
into multistep synthetic sequences to access polycyclic products. These studies highlight the
potential of these traditionally avoided species in synthetic chemistry.
Chapter five describes the mechanistic study of a C–C bond fragmentation that was
observed in Chapter four studies. Experimental deuterium-labeling studies are combined with
computational transition-state analysis to suggest that a carbonyl-retro-ene mechanism is
operative. These studies demonstrate the synergy between experimental and theoretical chemistry
and should promote the usage of both to better understand complex reaction mechanisms.
Chapter six illustrates the development of the reactivity of strained azacyclic 1,2,3-trienes.
Experiments show the precursor to the pyridone triene can be generated on gram-scale and that it
can be trapped efficiently in (4+2) cycloadditions. Current and future efforts are focused on
discovering the modes of reactivity available to these trienes, examining the regioselectivity of
unsubstituted and substituted variants, and performing intramolecular trappings. These studies
should provide predictive understanding for the reactivity and selectivity of a previously
unexplored class of strained cyclic intermediate and should give access to diversely substituted
pyridone products.Organic chemistryAmidesCatalysisCycloadditionSynthesisenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3fb0h514https://escholarship.org/content/qt3fb0h514/qt3fb0h514.pdfetdoai:escholarship.org:ark:/13030/qt2wk1s48s2026-05-30T05:02:11Zqt2wk1s48sFrom Germline to Somatic: ASXL1’s Multifaceted Role in Development and DiseaseLin, IsabellaArboleda, Valerie A2024-01-01Genetics underpins all of biology, and is the most rapidly evolving field in biomedical research. The ASXL gene family are critical players in rare developmental disorders and various cancers, bridging seemingly disparate areas of study. This dissertation provides a comprehensive examination of the role of ASXL1 mutations in the intersection of rare genetic disorders and cancer, highlighting the multifaceted impact across developmental biology, genetics, and metabolism.Chapter 1 explores the broad regulatory roles of the ASXL gene family in developmental and oncogenic processes, detailing the molecular architecture, clinical and epigenetic consequences of ASXL mutations, and identifies potential avenues for research and therapeutic development. Chapter 2 investigates how ASXL1 mutations disrupt epigenetic regulation in Bohring-Opitz Syndrome (BOS) patient-derived tissues, impacting gene expression through dysregulation of Wnt signaling pathways. This cross-tissue effect offers potential therapeutic targets. Chapter 3 also utilizes a multi-omics approach to explore the effect of ASXL1 mutations across development in induced pluripotent stem cells , neural progenitor cells , and neural crest cells, identifying defects in neurodevelopment and cell migration. Chapter 4 delves into metabolic dysregulations in BOS, highlighting mitochondrial dysfunction and metabolic inflexibility that could underlie clinical manifestations of the syndrome. Chapter 5 presents a unique familial case with ASXL1 missense mutation, expanding the phenotypic spectrum associated with ASXL1 mutations and suggesting distinct metabolic phenotypes.In Chapter 6, a direct comparison of ASXL1 mutations in acute myeloid leukemia and BOS reveals shared molecular pathways, advocating a unified gene-centric approach to studying these disorders. Chapter 7 discusses the implications of BLM gene mutations in clonal hematopoiesis of indeterminate potential genes, linking germline disorders and cancer predisposition.
By integrating multi-omics analyses, patient-derived cellular models, and comprehensive metabolic characterizations, this work establishes crucial connections between rare genetic disorders, metabolic defects, and cancer, underscoring the potential for cross-disease insights to inform targeted therapies. This dissertation contributes to our understanding of specific disorders like BOS and reveals shared molecular dysregulations between rare germline disorders and cancer. These findings pave the way for developing targeted therapeutic interventions that could benefit a broad spectrum of conditions.
GeneticsAcute myeloid leukemiaASXL1Bloom SyndromeBohring-Opitz syndromeMetabolismMulti-omicsenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2wk1s48shttps://escholarship.org/content/qt2wk1s48s/qt2wk1s48s.pdfetdoai:escholarship.org:ark:/13030/qt2n0055dr2026-05-30T05:01:53Zqt2n0055drLow Complexity Coding and Learning Techniques for Resilient Millimeter-Wave NetworksDogan, Mine GokceFragouli, Christina P2025-01-01Millimeter-wave (mmWave) communication is a promising technology for meeting the high data rate, low latency, and high reliability demands of data-intensive applications such as virtual reality, cloud gaming, and autonomous systems. Despite its potential, mmWave communication faces significant challenges including sensitivity to link blockages, interference to passive users, limited theoretical understanding of fundamental performance limits, and the computational complexity of evaluating these limits in large networks. This dissertation addresses these challenges through low-complexity techniques and analyses in four main parts: Multilevel Coding, Hybrid Scheduling, Coexistence with Passive Users, and Gomory-Hu Trees. In the first part, we develop Multilevel Diversity Coding (MDC) schemes, which are proactive mechanisms that offer resilience without prior knowledge of blockages. Multilevel codes accommodate distinct Quality of Service (QoS) requirements of various information streams with different priorities. Despite their advantages, these codes may have high design and operational complexity. To address this challenge, this dissertation proposes low-complexity MDC schemes. First, we develop symmetric MDC schemes that encode information streams across space and time. We introduce an optimization framework to efficiently select the design parameters such that distinct QoS requirements are accommodated, while suitably balancing the trade-off between average rate and graceful performance degradation. Additionally, we develop a lower-complexity MDC scheme that approximates well the aforementioned trade-off. Our evaluations, carried out also within ns-3, show that these codes lead to favorable trade-offs between rate, delay, and outage probability. Extending this, we develop a low-complexity asymmetric MDC scheme that leverages unequal blockage probabilities to further improve reliability. We characterize its achievable rate region and derive capacity regions of certain scenarios. We show that our scheme is information-theoretically optimal when there are two priority levels across information streams. In the second part of the dissertation, we develop a hybrid scheduling mechanism that determines which network paths to use and how to schedule them to achieve a desired end-to-end packet rate in the presence of link blockages and channel variations. Towards this goal, we first develop proactive transmission mechanisms to build resilience in advance, while achieving a high packet rate. Building on this, we design an efficient path selection algorithm and integrate it with our deep reinforcement learning approach. Our evaluations show that the hybrid mechanism enables agile, decentralized adaptation to network dynamics in realistic environments. The third part of the dissertation focuses on mitigating interference to passive users. The mmWave spectrum is shared by both active users and passive users, where the latter do not transmit and are therefore difficult to detect, yet can be significantly affected by interference. We develop a method that limits the interference to passive users with a small penalty to the throughput of active users. We formulate a linear program, derive lower bounds on the achievable rates of active users, and characterize the number of required paths to achieve a target rate while limiting the interference. Finally, we establish a connection to the problem of (information theoretically) secure communication over mmWave networks. In the final part of the dissertation, we focus on the efficient evaluation of performance limits and show that the well-known Gomory-Hu algorithm can be used to efficiently compute information-theoretic rate characterizations, such as capacity for every pair of nodes in the network.Electrical engineeringComputer engineeringComputer scienceDeep Reinforcement LearningMillimeter-Wave CommunicationsMultilevel Diversity CodingResilient Transmission MechanismsSchedulingUltra-Reliable Low-Latency Communicationsenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2n0055drhttps://escholarship.org/content/qt2n0055dr/qt2n0055dr.pdfetdoai:escholarship.org:ark:/13030/qt1dc8p8c12026-05-30T05:01:38Zqt1dc8p8c1Worth the Welcome? The Political Economy of Immigration and Gender Across East Asia and Asian DiasporasPark, JieunPeters, Margaret E.2025-01-01The three papers in this dissertation examine the political economy of immigration through a gendered lens, analyzing how economic calculations and gender dynamics shape immigration attitudes across East Asian host countries and toward Asian immigrants abroad. Drawing on original survey experiments in Japan, South Korea, Australia, and the U.S., alongside observational data including the Ipsos World Refugee Day Global Survey conducted across 28 countries, I investigate how economic considerations interact with gender to influence public reception of immigrants. In the first paper, I demonstrate that women's opposition to immigration in Japan stems from labor market vulnerabilities, with Japanese women showing increased hostility toward female immigrants when presented with information emphasizing immigrants' economic necessity. In the second paper, I establish that individuals with experiences of anomalous weather events may react defensively to climate refugees—especially women in South Korea, where gender-based economic inequality and restrictive immigration policies are pronounced. In the third paper, I reveal that greater favorability toward Asian immigrants over other non-white immigrant groups in the U.S. and Australia is linked to perceptions of Asians imposing lower fiscal burdens, with this economic calculation more evident among highly educated citizens. This research contributes to political economy theories of immigration by addressing significant gaps in the literature on understudied East Asian contexts, while emphasizing how economic considerations and gender dynamics create varying patterns of immigrant inclusion and exclusion across different host societies.Political scienceAsian DiasporaAustraliaClimate ChangeEast AsiaGenderImmigrationenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1dc8p8c1https://escholarship.org/content/qt1dc8p8c1/qt1dc8p8c1.pdfetdoai:escholarship.org:ark:/13030/qt0jt310d12026-05-30T05:01:15Zqt0jt310d1The Undercurrents of Institutionalization: How Asian American and Native American Pacific Islander-Serving Institutions (AANAPISIs) Navigate a Racialized Process to Promote Pacific Islander Student SuccessGogue, Demeturie Toso-LafaeleChang, Mitchell J.2024-01-01Asian American and Native American Pacific Islander-Serving Institutions (AANAPISIs) have played an integral role in transforming the ways higher education institutions in the United States and Pacific Islands serve Asian American and Pacific Islander (AAPI) students. Although scholarship on AANAPISIs has demonstrated how these institutions and programs have promoted AAPI student success, few have discussed how these programs and services are sustained beyond the federal grant’s tenure. Moreover, although the federal grant program is intended to serve both Asian Americans and Pacific Islander communities, much of the empirical research has either used the AAPI panethnic label without considering the different educational experiences of Pacific Islanders from Asian Americans within these sites or overlooked how AANAPISIs have served Pacific Islander students specifically. To address these oversights in higher education research and discourse, this study documented how two AANAPISIs—one in California and the other in the Pacific Islands—with a targeted interest in Pacific Islander student success institutionalized aspects of their grant-funded programs and services.Using qualitative research methods within an embedded, multiple case study design, this study highlighted the various strategies employed at both AANAPISIs to help facilitate the process of institutionalization. More specifically, AANAPISI administrators were able to demonstrate the success of their programs, adjust institutional processes and practices to support their efforts, and build relationships with key stakeholders both within the institution and beyond. Although findings demonstrated a pathway to institutionalization, participants in the study also shared challenges they encountered in the process of garnering institutional investment to sustain AANAPISI efforts, demonstrating how the process of institutionalization is ultimately racialized. Moreover, these challenges varied significantly by site, illuminating the importance of considering contextual factors when navigating the process of institutionalization.Findings from this study offered effective strategies that can be leveraged to help move AANAPISIs toward institutionalization; however, this study also exemplified the ways in which higher education institutions operate as racialized organizations and the impact that this has on institutional processes that are often framed as race neutral. By bringing awareness to this reality, AANAPISI scholars and practitioners must reimagine efforts toward institutionalization that can challenge the centrality of whiteness in higher education and promote the success of students, like Pacific Islanders, who are often overlooked or made invisible.Higher educationSociologyAANAPISIsInstitutionalizationPacific IslandersRacialized Organizationsenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0jt310d1https://escholarship.org/content/qt0jt310d1/qt0jt310d1.pdfetdoai:escholarship.org:ark:/13030/qt4gr0k9sb2026-05-29T06:34:16Zqt4gr0k9sbLabel-Efficient Machine Learning: Structure-Driven Semi-Supervised Learning on Graphs and Self-Supervised Disturbance MappingHardiman-Mostow, Harris HowardBertozzi, Andrea L2026-05-26This dissertation develops novel semi-supervised and self-supervised learning algorithms, motivated by settings where labeled training data is scarce or unavailable, respectively. We study two core problems in graph-based semi-supervised learning: how to construct a meaningful graph, and how to exploit its structure. To address the former, we derive backpropagation equations to precisely integrate graph construction and graph-based learning into a neural network, so end-to-end training directly learns data embeddings conducive to high-quality graphs. Our novel Graph Learning Layer (GLL) demonstrates improved generalization and adversarial robustness compared to the standard softmax classification head across vision datasets, architectures, and label rates. To tackle the latter, we leverage graph curvature to design coreset selection and active learning routines that dynamically target different regions of the graph according to its topology. We also present an algorithm that modifies the graph's multiscale connectivity based on active learning acquisitions, enhancing local structure. On a variety of benchmark datasets, our methods consistently outperform existing work in coreset quality, sequential and batch active learning, and computational efficiency, reaching higher accuracy faster and with fewer labels. Extending the theme of label-efficiency to disaster response with remote sensing imagery, we design a self-supervised vision transformer trained on near-global, time-series synthetic aperture radar (SAR) data to detect and delineate land disturbances. On three distinct real-world natural disasters, our approach yields accurate delineations and surpasses prior SAR disturbance methods, representing an important step towards operational and label-free disturbance monitoring using modern artificial intelligence tools.Applied mathematicsactive learningdeep learninggraph learningremote sensingself-supervised learningsemi-supervised learningapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4gr0k9sbhttps://escholarship.org/content/qt4gr0k9sb/qt4gr0k9sb.pdfetdoai:escholarship.org:ark:/13030/qt0fk9691k2026-05-29T06:34:11Zqt0fk9691kThe American Dilemma in Election Administration: How Street Level Bureaucrats Racialize VotingHERNDON, MICHAELBarreto, Matthew Alejandro2026-05-26Problem: The COVID-19 pandemic has ushered in a new reliance on non-traditional voting methods, as over one third of voters utilized vote-by-mail (VBM) in the 2024 general election. While this sweeping transition has increased convenience for many voters, it has opened a door for another form of voter suppression: ballot rejection via signature discrepancy. In the 2024 general election, over 580,000 VBM ballots were rejected nationwide - with the most popular reason being a signature discrepancy. Importantly, these ballot rejections are not randomly distributed and are instead felt unequally by racial minorities. VBM ballot rejections are an important component to election outcomes, yet the processes leading to these types of rejections has yet to be empirically scrutinized.Methodology: My dissertation investigates disproportionate ballot rejection through two approaches. First, I conduct an observational analysis using detailed voter data from California and Washington to measure gaps in rejection rates across counties, demographic groups, and elections. Unlike prior studies that examine single contests, I track ballot rejection over time. Second, I use survey experiments to test psychological mechanisms that can explain why certain groups face higher rejection risk and offer insight into what interventions election administrators might employ to reduce these disparities.Results: The results of my observational analysis show that non-White voters see their ballots rejected up to four times as often as White voters, but that this relationship is largely driven by age and the fact that non-White voters tend to be younger on average. My survey experiments find that the average lay-person is more likely to accept White signatures and reject non-White signatures while holding all else equal (e.g. authenticity, penmanship, etc.) and that racial attitudes influence the process of signature verification even when controlling for partisanship, age, and other demographic factors.Conclusions: My dissertation underscores 1) the theoretical importance of understanding election administration as a bureaucracy that is vulnerable to biased decision-making, 2) the existence of important demographic and regional disparities in vote-by-mail ballot rejection rates, and 3) the practical need for reforms that ensure election systems are not undermined by bias or discrimination, especially in VBM signature review which entails the most ambiguity and subjectivity.Political scienceSociologyPublic administrationSocial psychologyBiasElectionsMailRaceSignaturesVotingapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0fk9691khttps://escholarship.org/content/qt0fk9691k/qt0fk9691k.pdfetdoai:escholarship.org:ark:/13030/qt30r0f6c22026-05-29T06:34:06Zqt30r0f6c2Modeling Spatial Heterogeneity in Health Outcomes with Marked Log-Gaussian Cox Processes: Methods for Retrospective Environmental StudiesZhou, LinyuBelin, Thomas RLiu, Honghu2026-05-22Many environmental and epidemiological studies produce spatially referenced data with two linked layers: the locations at which observations occur, such as participant residences or cases, and the outcomes measured among the observed individuals, such as continuous health measurements recorded at those locations. Spatial clustering in the observed sample can reflect population density, recruitment, access to care, or other spatially patterned factors, while spatial variation in outcomes may reflect exposure gradients and other spatially structured determinants. This dissertation develops Bayesian marked log-Gaussian Cox process (LGCP) models that separate the observation-location process from the conditional outcome process while allowing layer-specific covariates and latent spatial structure.Chapter 1 introduces the environmental-exposure setting near the Inglewood Oil Field in Los Angeles and frames the central modeling goals: interpretable inference and calibrated uncertainty under uneven observation patterns and spatially varying exposure. Chapter 2 presents a baseline single-component marked LGCP and a parsimonious two-component mixture extension with covariate-gated component weights. This mixture formulation represents residual heterogeneity through latent components that may differ in both event intensity and mark behavior. Using simulations and geocoded health measurements, the chapter examines when the mixture model can separate point-pattern heterogeneity from outcome variation that is not fully explained by measured covariates.Chapter 3 adds spatial structure to the mark layer and compares Markov chain Monte Carlo (MCMC), Integrated Nested Laplace Approximation (INLA), and hybrid workflows within fixed computational representations. The results emphasize practical trade-offs between speed and uncertainty calibration, particularly for variance components and parameters involved in the mixture structure. Chapter 4 extends the framework to a temporal change-point model for marks that may shift abruptly over time while retaining the spatial LGCP occurrence model. Simulation studies evaluate recovery of the change time and change magnitude, along with evidence summaries for detecting time-localized shifts. Together, these chapters develop a Bayesian marked point-process workflow for separating the process governing where observations occur from the process governing how outcomes vary among observed individuals in retrospective environmental studies.BiostatisticsEnvironmental studiesEnvironmental healthEpidemiologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/30r0f6c2https://escholarship.org/content/qt30r0f6c2/qt30r0f6c2.pdfetdoai:escholarship.org:ark:/13030/qt5wz0w7sq2026-05-29T06:33:52Zqt5wz0w7sqDepression Detection of Reddit CommentsChan, ClaytonHandcock, Mark2026-05-22As mental health awareness increases, there has been growing interest in developing machine learning models to assist with early detection of depression. Building on prior studies that have explored this task, this study aims to replicate and extend previous findings using a different dataset and models. Approximately 10,000 comments were collected from each of the subreddits r/depression and r/AskReddit, resulting in a dataset of roughly 20,000 Reddit comments. The task is a binary classification problem to detect depressive language and to compare the performance of classical machine learning models with modern deep learning models. The classical models—Logistic Regression, k-Nearest Neighbors, and Multinomial Naive Bayes, were evaluated alongside a feedforward neural network and a fine-tuned BERT model. Results show that BERT achieved the highest performance at approximately 85.2% accuracy and 86.2% recall for the depressed class. However, Multinomial Naive Bayes had the second best performance with roughly 79.8% accuracy and 88.7% recall for the depressed class despite its simplicity. These findings suggest that in some applications, it may be advantageous to accept a modest reduction in predictive performance in exchange for substantially lower computational costs and model complexity.StatisticsArtificial intelligenceMental healthClinical psychologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5wz0w7sqhttps://escholarship.org/content/qt5wz0w7sq/qt5wz0w7sq.pdfetdoai:escholarship.org:ark:/13030/qt3sr7v7km2026-05-29T06:33:48Zqt3sr7v7kmForecasting Apple Inc.'s Stock Price with Classical Time Series and Sentiment AnalysisMo, KathyWu, Yingnian2026-05-11Stock price forecasting has long been an area of active research in both statistics and financial mathematics. A large portion of that work has relied on classical time series models, such as ARIMA, GARCH, and exponential smoothing, which are well understood and continue to serve as strong baselines. More recently, advances in natural language processing have introduced a new direction: using transformer-based models to extract sentiment from financial news and incorporate those signals into forecasting pipelines. This thesis sits at the intersection of both traditions.We build directly on the work of Berninger, who applied classical time series models to forecast Apple Inc.'s monthly opening stock price and found that a consensus average of all model forecasts produced the lowest root mean square error (RMSE) over a 12-month test window. We extend that framework by using Python instead of R, use different stocks, which are Tesla (TSLA), NVIDIA (NVDA), and Microsoft (MSFT), alongside Apple (AAPL) and the S&P 500, and update the sample period to span June 2010 through December 2024. Beyond this, the primary contribution of this thesis is the implementation of a transformer-based sentiment model, FinBERT, into the forecasting pipeline. Monthly price data is sourced from Yahoo Finance via the yfinance Python library.We compare seven models in total: ARIMA, ARIMA+GARCH, Holt-Winters exponential smoothing, Vector Autoregression (VAR), linear regression on ARIMA residuals, a consensus average, and a FinBERT sentiment-augmented ARIMA. Results show that ARIMA and Holt-Winters remain the strongest short-term forecasters, while FinBERT sentiment adjustments provide additional interpretable signal when tuned appropriately. The consensus average continues to perform well over the full 12-month horizon, consistent with Berninger's original finding. These results suggest that classical methods remain competitive and that sentiment-based adjustments from modern NLP models can complement but not yet replace them on monthly data.StatisticsComputer scienceFinanceARIMAFinBERTSentiment AnalysisTime Seriesapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3sr7v7kmhttps://escholarship.org/content/qt3sr7v7km/qt3sr7v7km.pdfetdoai:escholarship.org:ark:/13030/qt7r86n7sj2026-05-29T05:38:29Zqt7r86n7sjMémoire et Patrimoine: The Present-Day Impact of the History of Slavery in FranceMarcelino, Taryn JMarchant, Elizabeth2024-01-01This doctoral dissertation traces how local organizing groups and municipal entities faire oeuvre de mémoire [make work of memory] to advance the recognition of the history of the transatlantic slave trade in Nantes, France. Unlike many metropolitan cities in Europe, the city attempts to reconcile its colonial past. However, I argue that the city still falls into discursive traps and utilizes distancing mechanisms through exhibits and memorials to avoid acknowledging the ongoing legacies of racism and anti-blackness in the afterlife of slavery. This dissertation demonstrates that the fight for memory in Nantes is a crucial intervention since the concept of race and the memory of colonial hierarchies do not correspond with the core tenets of French republicanism and secularism. France’s distancing mechanisms allow a dismissal of racial rhetoric in the country, reflecting a global amnesia that continually harms Black and brown communities. This analysis is centered on Nantes, due to its extensive shipbuilding activities, which was the primary hub of the transatlantic slave trade in the country. Although often overlooked, Nantes’ unprecedented steps towards recognizing its colonial history make the city a crucial case study for understanding French debates and silences about enslavement, racial violence, and contemporary structures of inequality. I engage with cultural sites in Nantes to analyze the discursive structures and practices that shape how the history of slavery is officially presented in Nantes. I delve extensively into Nantes’ local archives to uncover how local organizations in Nantes attempt to shift the representation of slavery within French public space. “Mémoire et Patrimoine: The Present-Day Impact of the History of Slavery in France” contributes to ongoing conversations on how to address the ongoing legacies of racism in the afterlife of slavery. Gender studiesenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7r86n7sjhttps://escholarship.org/content/qt7r86n7sj/qt7r86n7sj.pdfetdoai:escholarship.org:ark:/13030/qt9tp081mv2026-05-29T05:03:21Zqt9tp081mvUnraveling Knowledge Sourcing: Firm Capabilities and Technological Recombination in Chinese CitiesZhang, YiouRigby, David2024-01-01The global integration of markets for many goods and services has fueled the rise of the knowledge economy and pushed innovation to the center of models of competitive advantage. At the same time, the knowledge landscape has become more uneven with new technology increasingly generated in relatively few, large cities characterized by deep pools of skilled workers and dense agglomerations of technology-intensive firms.The standard model of innovation has also been reworked. Less and less do we think of new knowledge as produced by individual economic agents engaged in varied forms of learning and in-house research and development. Rather, new technology combinations are seen as emerging from local and non-local collaborations among groups of firms and other economic actors and through more general forms of interaction with buyers and suppliers. The more fractured geography of ideas has granted multi-locational firms clear advantages over their single-plant competitors. First, they are able to locate establishments in different knowledge environments to access multiple, localized forms of buzz and spillovers. Second, embedding plants in different knowledge clusters assists multi-plant firms overcome constraints on effective knowledge flows that are imposed by different forms of proximity. Third, local embedding also gives multi-plant firms the ability to shape the character of knowledge development over space, as well as some control over who is able to exploit specific knowledge assets.
Economic geographers have been rather slow in exploring the connections between firm organization and the production of ideas within the knowledge economy. These connections have been more extensively developed within the fields of international business and management. However, scholars in these cognate fields are focused almost exclusively on multi-national enterprises that distribute activities across national borders. Relatively little attention is paid to multi-locational firms that largely operate within a single country. It is these firms upon which most attention is placed in this dissertation.
Three broad questions are explored in the main chapters of this dissertation that focus on knowledge production in the domestic operations of Chinese multi-locational firms. The first question is who are the “main agents of change” that drive innovation within Chinese city-regions. Within contemporary economic geography, considerable research examines how local structures condition regional development possibilities. I challenge that logic, exploring whether capabilities are more likely to emerge within the firm and to flow across spatial boundaries than they are to be built within the region flowing across firm boundaries. Analysis targets technological diversification within the establishments of multi-locational firms that are distributed across Chinese cities. Results suggest that the knowledge structure of firms is more important than the knowledge structure of cities in shaping the character of diversification within the establishments of multi-unit firms.
The second question explored in the dissertation asks whether multi-plant firms, that distribute their R&D efforts over several cities, produce different kinds of technological knowledge within these R&D units, whether that knowledge is linked to the broader knowledge stocks of the cities where they are located, and whether firms benefit from the geographical distribution of knowledge production. To explore these issues, a linked firm-patent dataset is constructed covering the period 2001-15. Those data reveal significant differences in the character of knowledge production at different R&D sites of multi-unit firms. Furthermore, those differences can be linked to the underlying knowledge bases of the cities where the R&D units are placed. This confirms arguments regarding the knowledge sourcing behavior of multi-locational firms. Finally, I show that the average complexity value of the patents produced by multi-unit firms increases as they distribute their knowledge production across a larger number of cities.
The third research question in the dissertation asks how knowledge recombination within Chinese cities scales with city-size and the number of technology components found at the city level. That question is then extended to examine how new technology combinations diffuse across the Chinese urban system. Once more, patent data form the basis for the empirical analysis of the questions raised. Individual technologies are defined by the sub-classes identified in the International Patent Classification. New technology combinations are captured by novel pairings of these technology classes that are found on the same patents. The location of these pairings is traced at the city-level and over time. Analysis of technology recombinations shows that they scale super-linearly with city-size and the number of technologies available at the city-level. This means that new knowledge combinations are increasingly likely to be found in larger cities that contain more diverse sets of knowledge. Subsequent analysis shows that new technology recombinations diffuse across the Chinese urban system conditioned by the geographical proximity of cities, by the technological proximity of different urban areas and as the social proximity between cities increases.
GeographyChinaMulti-locational FirmsRegional InnovationTechnology Developmentenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9tp081mvhttps://escholarship.org/content/qt9tp081mv/qt9tp081mv.pdfetdoai:escholarship.org:ark:/13030/qt5ks225vw2026-05-29T05:02:26Zqt5ks225vwBidirectional Relationships between Cardiovascular Disease and Lung Cancer, and Racial Disparities of Cardiovascular Disease and Depression in Lung Cancer PatientsPan, YancenZhang, Zuo-FengHashibe, Mia2025-01-01Background: Lung cancer is the second most common cancer among men and women in the US, with an estimated 226,650 new cases (11% of all cancer types) expected to be diagnosed in 2025. It also accounts for the highest proportion of cancer-related deaths, with an estimated 124,730 deaths (20% of all cancer types) in 2025. Lung cancer patients have relatively poor survival outcomes compared to those diagnosed with most of other cancer types, though the number of people living with lung cancer is expected to increase over the coming decades.Cardiovascular disease (CVD) has been the leading cause of death in the US, responsible for 941,652 deaths in 2022. From 2017 to 2020, approximately 9.9% of US adults (28.6 million) were affected by CVD, including coronary heart disease (CHD), heart failure (HF) and stroke. The total burden of CVD, including direct and indirect costs, was $417.9 billion from 2020 to 2021.
Cardio-oncology, a multidisciplinary field focused on cardiotoxicity related to cancer treatment, has emerged over the past several decades. Lung cancer and CVD share multiple risk factors, including tobacco smoking, age, sex, diet, alcohol consumption, overweight/obesity, diabetes, and environmental and occupational exposures.
Depression is also a prevalent condition. According to 2023 reports, an estimated 17 million adults in the US, were affected by depression, with approximately 7% of Americans experiencing a 12-months persistent prevalence of major depressive disorder. Lung cancer patients may face severe mental and physical stress due to a poor prognosis, relatively low survival rate, and side effects of treatments.
Previous studies have reported that Asian, Native Hawaiian, and Pacific Islander (ANHPI) lung cancer patients have poor experiences related to diagnosis and treatments. Lung cancer risk and healthcare outcomes after diagnosis may vary across ANHPI subgroups, due to diversity and heterogeneity in acculturation, disease-related stigma, and socioeconomic status.
Objective and Specific Aims: The aim of this study is to investigate bidirectional associations between CVD and lung cancer, as well as racial and ethnic disparities in cardiovascular disease and depression among lung cancer patients. The specific aims are: (1) to investigate the associations between the prevalence of CVD conditions (heart failure, ischemic heart disease and stroke) and lung cancer incidence in an older population; (2) To investigate the risk of CVD (heart failure, ischemic heart disease and stroke) among older lung cancer patients who are ANHPI or non-Hispanic White (NHW); (3) To investigate the risk of depression among older lung cancer patients among ANHPI and NHW populations.
Methods: We utilized the SEER-Medicare dataset to identify first primary lung cancer cases. The CVD conditions of interest were ischemic heart disease, heart failure and stroke/transient ischemic attack.
For Aim 1, we conducted a nested case-control study among lung cancer cases aged 70 years or older at diagnosis between 2004 and 2019, and non-cancer controls selected from Medicare beneficiaries. Each case was matched to a control based on age, sex, state, and index year. We used conditional logistic regression to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for differences in CVD prevalence at least 5 years prior to the index year between cases and controls. We also performed logistic regression for CVD at least 10 years prior to the index year.
For Aim 2 and 3, we conducted matched cohort studies, including ANHPI and NHW lung cancer patients who were 66 years and older and diagnosed between 2000 and 2017. Each ANHPI patient was matched to three NHW patients on age of diagnosis (+/-1 year), calendar year of diagnosis (+/-1 year) and sex. We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% CIs for CVD and depression risk comparing ANHPI to NHW patients, and among ANHPI subgroups.
Results: In Aim 1, lung cancer risk was higher among individuals with ischemic heart disease more than 5 years prior to the index date (OR=1.22, 95% CI=1.13-1.33), compared to individuals without ischemic heart disease. Analyses were adjusted for demographic covariates, socioeconomic status (SES), baseline Charlson Comorbidity index (CCI), tobacco use, alcohol consumption and obesity. Stratified analyses showed elevated lung cancer risk associated with ischemic heart disease more than 5 years prior to diagnosis among females (OR=1.37, 95% CI=1.23-1.53), NHW individuals (OR=1.17, 95% CI=1.09-1.26) and individuals of other races (OR=1.49, 95% CI=1.14-1.96).
In Aim 2, compared to NHW lung cancer patients, ANHPI lung cancer patients had a lower incidence of heart failure (HR=0.64, 95% CI=0.53, 0.76) and ischemic heart disease (HR=0.76, 95% CI=0.60, 0.95). Specifically, Chinese, Japanese and other Asian lung cancer patients had lower incidences of both conditions compared to NHW patients. However, when compared to Chinese lung cancer patients, several ANHPI subgroups, including Filipino, Indian/Pakistani, other Southeast Asian and Pacific Islander lung cancer patients had higher risks of heart failure. Indian or Pakistani lung cancer patients also had a higher risk of ischemic heart disease compared to Chinese patients (HR=1.76, 95% CI=1.05, 2,96).
In Aim 3, when compared to NHW lung cancer patients, overall ANHPI lung cancer patients had a lower incidence of depression (HR=0.60, 95% CI=0.50-0.73). Specifically, Chinese, Japanese, Filipino, Asian Indian or Pakistani lung cancer patients experienced lower incidence of depression than NHW patients. However, Korean lung cancer patients had a higher incidence of depression compared to Chinese patients (HR=1.62, 95% CI=1.05-2.51).
Conclusions: We observed an increased risk of lung cancer among individuals with pre- existing ischemic heart disease. Although overall ANHPI lung cancer patients experienced lower risks of cardiovascular disease and depression compared to NHW patients, certain subgroups, such as Indian or Pakistani patients (higher CVD risk) and Korean patients (higher depression risk) faced elevated health burdens relative to Chinese patients. These findings suggest the possible benefit of targeted lung cancer screening among patients with ischemic heart disease and underscore the needs for tailored healthcare strategies for specific ANHPI subgroups. Further research is warranted to understand the underlying mechanism behind these associations, and studies with larger, disaggregated samples of ANHPI populations are needed to inform equitable cancer and CVD prevention and clinical care.
Epidemiologyenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5ks225vwhttps://escholarship.org/content/qt5ks225vw/qt5ks225vw.pdfetdoai:escholarship.org:ark:/13030/qt2578k5cd2026-05-29T05:01:40Zqt2578k5cdThe Imperial Double: Imposture, Succession, and the Case of the False Neros in Imperial LiteratureAkiyama-Kim, JasmineMartelli, Francesca K. A.Spielberg, Lydia M.2024-01-01This dissertation examines the idea of imitation in Roman imperial succession using the case study of the emperor Nero. Analyzing an array of texts and authors, it argues that Nero is consistently remembered as confounding the categories of original and copy, reality and representation. We are told, for example, that Nero assimilated the Great Fire of Rome to the Fall of Troy (Tac. Ann. 15.39.3); that he performed onstage wearing a mask of his own face (Suet. Ner. 21.3); that he tried to make his lover Sporus into a copy of his deceased wife Poppaea Sabina (Dio Chrys. Or. 21.6-10); and that he authorized the False Neros who imitated him after his death (Tac. Hist. 2.8-9, Dio Chrys. Or. 21.10, Suet. Ner. 57.2). This dissertation posits that these and other instances of Nero’s doubling are symptoms of his double reception: legitimized and delegitimized by the same imperial system, he demonstrates how the strategies of repetition and doubling that sustained the principate also constructed its dysfunction. Each chapter engages with a different theorization of mimesis in which Nero is enmeshed: intertextuality, imperial cult, exemplarity, and imperial Greek mimesis, which engages with both Platonic concepts of mimesis and exemplarity. The first two chapters focus on Tacitus’s Histories and Annals: the first interrogates the uneasy difference between the emperor Nero and his pretenders, and the second investigates a similar unsteady relation between the emperor and his images. The third chapter traces Suetonius’s construction of a mimetic genealogy of Nerones in De Vita Caesarum, and the fourth chapter explores how Nero is conceived by a selection of imperial Greek authors (Dio Chrysostom, Plutarch, Pseudo-Lucian, and Philostratus), who, like Nero, are invested in imitating the Classical Greek past. Guiding my analysis is the distinction articulated by Deleuze between Plato’s copy versus his simulacrum, and, correspondingly, the imperial successor versus the impostor. While the copy/successor maintains internal fidelity to the Platonic form, the simulacrum/impostor looks like the form but contains oppositional content. The simulacrum/impostor does not merely negate the original; rather, it destabilizes the difference between original and copy. All of the authors under examination frame Nero as an impostor rather than a successor, yet my analysis shows that the distinction between the two positions is more ambiguous and less hierarchical than the authors theorizing imperial succession sought to construct. Classical studiesenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2578k5cdhttps://escholarship.org/content/qt2578k5cd/qt2578k5cd.pdfetdoai:escholarship.org:ark:/13030/qt1170d1rd2026-05-29T05:01:23Zqt1170d1rdSelf-Assembly of Functional Polyelectrolyte-Nanoparticle MaterialsHolkar, Advait SuhasSrivastava, Samanvaya2024-01-01This dissertation delineates my research on the electrostatically driven self-assembly on a wide range of materials. These materials include zeolites where metal cations act as structure directing agents, polyelectrolyte nanoparticle complexes with tunable interparticle separation, stabilized polyelectrolyte complex coacervate microdroplet dispersions and complex coacervate protocells with encapsulated enzymes to model biomolecular condensates. In all these systems, coulombic interactions play a central role in governing the functionality of the resultant material in distinct ways. For zeolites, the valency of the metal cation influences their micropore topology and the co-precipitation of different structures can be accomplished by tuning the cation content. In the case of polyelectrolyte-nanoparticle systems, the charge ratio of these two components directs the average interparticle distance in the complex which can be effectively manipulated. The coacervate microdroplet dispersions were studied in high throughput, and the addition of comb-polyelectrolytes suppress their coalescence and provide long-term stability against high ionic screening. Enzymes spontaneously partition into these coacervate domains due to their heterogenous surface charge, and such stabilized coacervate microdroplets can serve as in vitro protocells models and can elucidate the reaction-diffusion kinetics of enzymatic reactions.Chemical engineeringCondensed matter physicsenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1170d1rdhttps://escholarship.org/content/qt1170d1rd/qt1170d1rd.pdfetdoai:escholarship.org:ark:/13030/qt14w0c8c72026-05-28T06:33:49Zqt14w0c8c72-Categorical Affine Symmetries and Quantum Boson AlgebrasQunell, Samuel DavidRouquier, Raphaël A2026-05-26We study the representation theory of quantum groups via categorical tools. We pursue two reciprocal research directions in this thesis; we find new categorifications of known results for quantum groups and then use this extra categorical structure to deduce new results within the traditional theory. We focus on affine quantum groups since they are poorly understood categorically despite having the richest representation theory and despite being important in many applications.Our first main result is the construction of a 2-representation of the positive part of affine quantum enveloping algebras on their finite-dimensional counterparts in type An. These 2- representations naturally extend the right-multiplication 2-representation of U + q (sln+1) on itself and are closely related to evaluation morphisms of quantum groups. We also show that the induced 1-representation exists in types D4 and C2. As an application, we show that a certain quotient of this 1-representation in type An is isomorphic to a prefundamental representation and produce a new proof of the prefundamental character formula. Our construction of this prefundamental is essentially dual to one of Jang, Kwon, and Park, and we explain the connection to their work in detail.MathematicsTheoretical mathematicsApplied physicsAffine Lie algebraCategorificationCategory theoryQuantum boson algebraQuantum groupRepresentation theoryapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/14w0c8c7https://escholarship.org/content/qt14w0c8c7/qt14w0c8c7.pdfetdoai:escholarship.org:ark:/13030/qt0k22c0r02026-05-28T06:33:44Zqt0k22c0r0Data-Driven Characterization of Vertical Structural Response in Instrumented Buildings Using Dense Sensor NetworkBolourani, AnahitaBozorgnia, YousefBurton, Henry V.2026-05-26Earthquake engineering practice has traditionally focused on the horizontal components of ground motion, while the vertical component and its effects on building response have received less attention. This dissertation addresses this gap by developing a data-driven framework to characterize vertical structural response in instrumented buildings and to improve understanding of vertical dynamic properties, floor-level acceleration demands, and code-based vertical seismic load effects. Using sensor recordings from past earthquakes, complemented by numerical simulation and code-oriented evaluation, the research provides recommendations to inform future provisions of the United States building codes. The study uses dense triaxial acceleration recordings from the Community Seismic Network at the NASA Jet Propulsion Laboratory campus. Six mid-rise JPL buildings, ranging from four to nine stories, are analyzed using earthquake records that were consistently recorded across the selected buildings. A Python-based data preparation and processing pipeline is developed to retrieve, verify, convert, filter, and align the recorded building response. The processed records are then used to identify vertical and horizontal structural response frequencies through Fourier amplitude spectra, power spectral density, transfer functions, and coherence functions. The results show that vertical response frequencies are generally higher than horizontal frequencies. Across the six buildings, dominant vertical frequency is typically concentrated in the approximately 5–10 Hz range, while dominant horizontal frequency occurs at the fundamental horizontal mode, approximately 0.7–1.8 Hz. To interpret the measured vertical frequency ranges captured using sensor recordings, a refined three-dimensional numerical model of JPL 183 is analyzed. The simulation results show that vertical modal mass is distributed across many modes, and that substantially more modes are required to capture vertical response than are required for horizontal modal response. Stiffness-perturbation studies are then used to distinguish global vertical response from slab-dominated vibration. A key finding is that the dominant vertical response is often not the lowest identified vertical frequency but instead occurs at higher frequency ranges associated with global vertical response, slab vibration, or coupled global-local behavior. This dissertation also evaluates vertical and horizontal floor response spectra using 5%-damped pseudo-spectral acceleration computed from measured floor accelerations. The vertical floor response spectra show strong period dependence, with the largest vertical amplification generally concentrated in the short-period range of approximately 0.08–0.25 seconds. Vertical acceleration demand generally increases with height. Horizontal floor response spectra show a different pattern: short-period horizontal amplification is generally more modest, while stronger amplification occurs at intermediate-to-longer periods associated with horizontal modal response. Comparisons with ASCE/SEI 7-22 Chapter 13 show that current horizontal height-amplification assumptions capture the general concept of increasing acceleration demand with height but do not fully represent the measured period dependence. For vertical response, the results indicate that the current code framework does not provide an explicit vertical floor-response-spectrum or height-amplification model. Therefore, a separate vertical floor-amplification factor is strongly recommended for future consideration in nonstructural component design. Finally, the dissertation evaluates two approaches in the current building codes (ASCE, 2022) for calculating vertical seismic load effect: the period-independent expression based on 0.2S_DS and the period-dependent expression based on 0.3S_av. The two approaches are compared using NGA-West2 ground-motion models for more than 3,000 seismic scenarios and ASCE 7 Hazard Tool data for 19,316 California sites. The results show that the relationship between the two expressions depends strongly on the vertical period and seismic design category. The 0.2S_DS expression can underestimate vertical seismic demand for moderate vertical periods, especially around 0.05–0.20 seconds in higher seismic design categories, while it can be overly conservative for very short and long vertical periods compared to results derived from 0.3S_av. Based on these findings, the dissertation provides period- and seismic-design-category-dependent recommendations for calculating vertical seismic load effects and proposes refinements to improve the long-period behavior of the vertical design spectrum. Overall, this dissertation demonstrates that vertical structural response is an important structural dynamic phenomenon that should be characterized using measured data, physical interpretation, and period-dependent design considerations. The findings provide empirical evidence and building code-oriented recommendations for improving the treatment of vertical response in future building code provisions.Civil engineeringElectrical engineeringEnvironmental engineeringData-Driven MethodsFloor Response SpectraSeismic Load EffectsStructural Sensor NetworkVertical Structural Responseapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0k22c0r0https://escholarship.org/content/qt0k22c0r0/qt0k22c0r0.pdfetdoai:escholarship.org:ark:/13030/qt4gs940q92026-05-28T06:33:39Zqt4gs940q9Green Schools and Hot Playgrounds: Assessing the outcomes of greening thresholds on elementary school campuses in Los Angeles, CaliforniaWall, KathrineTurner, V. KellyKim, Minjee2026-05-26The urban heat island effect is a well-studied and well-documented phenomenon describing the tendency of cities to be hotter than their surrounding suburban and rural environments. Within cities, differences in land use regimes, the built environment, and proximity to vegetation and water create systems of microclimates. Intraurban temperature variation and increased likelihood of climate-change induced heatwaves have highlighted the need for heat-conscious micro-scale urban design. This is especially true on school campuses, where common building materials and site design can create unsafe conditions for children, who already experience heightened vulnerability to heat. Greening thresholds represent a potential policy instrument to address heat dangers on campuses, but their effectiveness and limitations are understudied. This research addresses this gap using the Los Angeles Unified School District’s Green Schools for All Goal- which calls for 30% greening across all campuses-as a case study. Four elementary school campuses are compared: two that have met the threshold and two that have not. Using the SOLWEIG environmental model, this study simulates both existing conditions and a refined policy scenario in which greening is narrowly defined as tree canopy cover. The study examines usable space on the playground in each scenario in order to assess whether greening thresholds are meaningful indicators of thermal safety and whether a more refined design strategy improves cooling potential. The results demonstrate that baseline climatic conditions are widely determinant in greening’s success in increasing usable space, even in cases where ‘greening’ is narrowly defined as tree canopy. This research supports the growing consensus that heat mitigative design should be site specific to best ensure that the interventions are effective and equitable.Urban planningEnvironmental healthRegional studiesGreening ThresholdsHeat PolicyHeat SafetyMicroclimatesSchoolsUrban Heatapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4gs940q9https://escholarship.org/content/qt4gs940q9/qt4gs940q9.pdfetdoai:escholarship.org:ark:/13030/qt9dv0x8jt2026-05-28T06:33:34Zqt9dv0x8jtLifestyle, Metabolic, and Vascular Risk Factors for Atherosclerotic Cardiovascular Disease: Evidence from Observational Studies Using G-ComputationWu, ChuyueNianogo, Roch A.Wong, Nathan D.2026-05-26Atherosclerotic cardiovascular disease (ASCVD) develops over decades through the accumulation of metabolic, behavioral, and vascular risk factors. Understanding how early-life cardiometabolic dysfunction contributes to later ASCVD, estimating the long-term effects of sustained interventions, and identifying for whom subclinical disease markers are most informative are central to precision prevention. This dissertation applies causal inference methods to longitudinal observational data from the Coronary Artery Risk Development in Young Adults (CARDIA) study and the Multi-Ethnic Study of Atherosclerosis (MESA) to address these questions across the life course.The first aim examined whether the association between young-adulthood insulin resistance and later-life ASCVD is mediated through midlife metabolic syndrome (MetS), and whether this pathway differs by race. Using parametric g-computation for causal mediation analysis in CARDIA, this aim found that insulin resistance in young adulthood was associated with increased later-life ASCVD risk, with a substantial proportion of this effect mediated through midlife MetS. Mediation through MetS was stronger among Black participants than White participants, whereas midlife lifestyle factors contributed little independent mediation.The second aim emulated a target trial to estimate the effects of sustained hypothetical interventions on modifiable health behaviors and metabolic factors from young adulthood on ASCVD risk in CARDIA. Using the parametric g-formula, this aim evaluated interventions on smoking, alcohol use, physical activity, diet quality, adiposity, lipids, glucose, and blood pressure, including both single-component and multi-component strategies. Intervention effects were summarized both as 34-year cumulative ASCVD risk and as interval-specific cumulative risks across follow-up. Single-component interventions generally produced modest reductions in long-term ASCVD risk, whereas multi-component combined strategies yielded greater benefit in predicted ASCVD risk.The third aim evaluated heterogeneity in the association between coronary artery calcium (CAC) and incident ASCVD in MESA using a generalized heterogeneous treatment effect framework. CAC was strongly associated with higher ASCVD risk overall, but its prognostic value varied across subgroups. Greater CAC-attributable excess risk was observed among individuals with higher carotid intima-media thickness, older age, elevated systolic blood pressure, higher interleukin-6, low ankle-brachial index, and a positive family history of cardiovascular disease.Overall, this dissertation shows how causal inference methods can strengthen life-course ASCVD research and inform earlier, more tailored prevention strategies.EpidemiologyPublic healthBiostatisticsCardiometabolic diseaseCausal inferencepubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9dv0x8jtetdoai:escholarship.org:ark:/13030/qt1m2760q32026-05-28T06:33:29Zqt1m2760q3Development and Assessment of the Clinical 5DCT Workflow for use in Respiratory Motion Management for Radiation TherapyMiller, Claudia RobertaLow, Daniel A2026-05-26Lung cancer is responsible for the largest number of cancer related deaths worldwide. In its diagnosis and treatment, respiratory motion poses a considerable challenge to imaging. Radiation therapy (RT) is routinely used in the treatment of this disease; however, it heavily relies on accurate and reliable imaging. Four-dimensional computed tomography (4DCT) is widely considered the standard for lung cancer RT treatment planning (TP) in which CT projections are sorted into 8-10 breathing phase bins that are used to reconstruct each respective breathing phase and create a loop of images that can be used for target tracking during respiration. However, 4DCT suffers from poor image quality with motion artifacts like diaphragm doubling or streaking, which compromises images and leaves them unusable for target identification and subsequent TP. Five-dimensional CT (5DCT), a model-based CT reconstruction method, has been developed as an alternative capable of producing artifact-free images. This thesis aimed to improve the clinical 5DCT workflow, evaluate performance differences between 4DCT and 5DCT modalities in target identification, and assess the impact of 5DCT-informed target volumes on dosimetric calculations in TP. The original 5DCT workflow used the abdominal surface to calibrate a breathing surrogate for use in motioning modeling and was updated to use the internal diaphragm dome position. Additionally, a tertiary back up to 5DCT utilizing this breathing signal calibration, termed “4DCT HiFi”, was developed. These updates allowed not only for improved calibrations but also a form of backup to the 5D model that were qualitatively rated. The 4DCT, 4DCT HiFi, and 5DCT scans for 14 patients were qualitatively compared via scoring by 6 experts of the reconstruction performance with rates of 1 (very good), 2 (flawed, but useful), and 3 (not useful). Calibration correlation (R2) improvements where abdomen and diaphragm calibrations ranged from 0.31-0.99 and 0.73-0.997, respectively. 4DCT HiFi scoring resulted in scores of 2.07 ± 0.74, 2.24 ± 0.64, and 2.07 ± 0.27 for 4DCT, 4DCT HiFi, and 5DCT, respectively. Next, the 4DCT and 5DCT clinical imaging protocols were compared for use in tumor volume identification across 20 patients. Five physicians contoured internal target volumes (ITV) for both techniques in two blinded sessions separated by a 30-day washout period to prevent recall bias. Both motion-compensated images were subsequently rated on image quality (1: Good, 2: Flawed, 3: Unusable). ITVs were quantitatively compared using volume percent difference (VPD), Hausdorff Distance (HD), mean distance to agreement (MDA), Dice Similarity Coefficient (DSC), and Jaccard Index (J). Statistical significance in scoring difference was evaluated using Wilcoxon signed-rank testing. A linear mixed-effect model (LMM) was used to assess inter-observer variability across contours as well as account for random effects from patient and physician observer bias. Accuracy of the 5DCT image reconstructions were reported as residual model error(mm). Qualitative scorings averaged 1.57 ± 0.73 and 1.24 ± 0.45 for 4DCT and 5DCT, respectively. All metrics showed statistically significant differences in comparative metrics (p<0.05), with the LMM adjusted mean for VPD, HD, MDA, DSC, and J being -16.34%, 8.22mm, 1.61mm, 0.70, and 0.55, respectively. 5D model residual errors were 0.71-2.93mm. We also evaluated dosimetric implications of target differences, treatment plans were initially optimized using 4DCT-defined ITVs and subsequently recalculated on 5DCT targets to quantify the dosimetric impact of target variance between modalities. Significant reductions in target coverage were observed when 4D plans were evaluated on the 5D geometry. The median percentage dose to 100% and 90% of the target volume decreased from 95% to 78% (p < 0.001) and 100% to 89% (p < 0.001), respectively. Median percentage dose to the 5DCT-informed ITV exhibited a statistically significant decrease (p < 0.001), dropping from 113.8% to 102.8%. Overall, the 5DCT workflow was improved, significant differences in 4DCT and 5DCT-informed targets were found, and the subsequent impact on dose distribution was highlighted. These results demonstrate significant differences between modalities. 5DCT offers an improved alternative via artifact-free quantitative image reconstruction.TherapyOncologyBiostatistics4DCT5DCTMotion-ManagementRadiation therapyTreatment Planningapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1m2760q3https://escholarship.org/content/qt1m2760q3/qt1m2760q3.pdfetdoai:escholarship.org:ark:/13030/qt3453x04r2026-05-28T06:33:24Zqt3453x04rOld Age in Contemporary Chilean Film and Literature (2010-2025)Mora, CristianCortínez, Verónica2026-05-26Discussions about old age have been increasingly prevalent in the media. Issues such as low pensions, rising healthcare costs, caregiving burdens, and loneliness have brought aging into public debate elderly after long remaining in the background. This shift reflects broader demographic trends: life expectancy continues to increase while global fertility declines. As a result, many societies must adapt to a reality in which older people constitute a significant proportion of the population, requiring both political reforms and cultural change, given that the “old” label has long carried “pejorative connotations,” as Simone de Beauvoir noted in her seminal essay Old Age (1972).In this context, the study of the representation of old age in contemporary media is particularly relevant, especially due to its influence on social discourse. Scholarship by Pamela Gravagne, Raquel Medina, and Barbara Zecchi has laid the groundwork for cultural aging studies. Building on their work, this dissertation examines the representation of old age in contemporary Chilean cinema and literature (2010-2025), an area that remains largely understudy.Since 2010, Chilean cinema has had a significant increase in films centered on older protagonists, from Sebastián Silva’s Gatos viejos (Old Cats 2010) to Maite Alberdi’s Oscar-nominated La memoria infinita (The Eternal Memory 2023), more than thirty films have focused on old age. Although to a lesser extent, Chilean writers such as Andrea Jeftanovic and Maivo Suárez, among many others, have also shown interest in the subject, following the footsteps of renowned Chilean and Latin American boom writer José Donoso. This project examines the representation of old age in a collection of Chilean fiction films, documentaries, novels, and short stories. These works coincide with the rapid growth of Chile’s people aged sixty and older, who are projected to be 30 percent of the total population in 2050 (INE). I argue that these works employ three narrative frameworks to depict their subject: decline, resistance, and renewal. As Pamela Gravagne notes in The Becoming of Age (2013), the current dominant narrative when illustrating old age is “inevitable decline” (19), which has been the prevalent type of depiction of the elderly for a long time. This dissertation demonstrates that Chilean directors and authors are representing old age as a heterogeneous experience in which decline coexists with resilience and renewal. Examples include Alfredo Pourailly’s La fabulosa máquina de cosechar oro (The Fabulous Gold Harvesting Machine 2024), which explores aging in relation to labor, and Nicol Ruiz’s La nave del olvido (Forgotten Roads 2020), which addresses sexuality and the possibility of new beginnings. Through close reading and comparative analysis, this study reveals a sustained effort to move beyond both prejudice and idealization, offering a nuanced portrayal of aging that both reflects present realities and anticipates future challenges. Los debates sobre la vejez han ganado protagonismo en los medios de comunicación. Problemas como las pensiones bajas, el aumento de los costos de la salud, el trabajo que supone el cuidado de personas mayores y la soledad han situado el envejecimiento en el centro del debate público, tras haber permanecido durante mucho tiempo en un segundo plano. Este cambio refleja tendencias demográficas más amplias: la esperanza de vida sigue aumentando, mientras que la fertilidad mundial disminuye. Como resultado, muchas sociedades deben adaptarse a una realidad en la que las personas mayores constituyen una proporción significativa de la población, lo que requiere tanto reformas políticas como un cambio cultural, dado que la etiqueta de “viejo” ha tenido durante mucho tiempo “connotaciones peyorativas”, como señaló Simone de Beauvoir en su influyente ensayo La vejez (1972). En este contexto, el estudio de la representación de la vejez en los medios de comunicación es particularmente relevante, sobre todo debido a su influencia en el discurso social. Los trabajos académicos de Pamela Gravagne, Raquel Medina y Barbara Zecchi han sentado las bases para los estudios culturales sobre el envejecimiento. Partiendo de su trabajo, esta tesis examina la representación de la vejez en el cine y la literatura chilena contemporánea (2010-2025), un ámbito que sigue estando en gran medida sin estudiar. Desde 2010, el cine chileno ha experimentado un aumento significativo de películas centradas en protagonistas de edad avanzada; desde Gatos viejos (2010), de Sebastián Silva, hasta La memoria infinita (2023), de Maite Alberdi, más de treinta películas se han enfocado en la vejez. Aunque en menor medida, escritores chilenos como Andrea Jeftanovic y Maivo Suárez, entre muchos otros, también han mostrado interés en el tema, siguiendo los pasos del renombrado escritor chileno y del boom latinoamericano José Donoso. Este proyecto examina la representación de la vejez en una selección de largometrajes de ficción, documentales, novelas y cuentos chilenos. Estas obras coinciden con el rápido crecimiento de la población chilena de sesenta años o más, que se prevé que represente más del 30% de la población total en 2050 (INE). Sostengo que estas obras emplean tres marcos narrativos para retratar su tema: el declive, la resistencia y la renovación. Como señala Pamela Gravagne en The Becoming of Age (2013), la narrativa dominante a la hora de ilustrar la vejez es el “declive inevitable” (19). Esta tesis demuestra que los directores y autores chilenos están representando la vejez como una experiencia heterogénea, en la que el declive coexiste con la resiliencia y la reinvención. Dos ejemplos son La fabulosa máquina de cosechar oro (2024), de Alfredo Pourailly, que explora el envejecimiento en relación con el trabajo, y La nave del olvido (2020), de Nicol Ruiz, que aborda la sexualidad y la posibilidad de nuevos comienzos. A través de una lectura detallada y un análisis comparativo, este estudio revela un esfuerzo sostenido por ir más allá tanto de los prejuicios como de la idealización, ofreciendo una representación matizada del envejecimiento que refleja las realidades actuales y anticipa los retos del futuro.AgingCinematographyLatin American literatureLanguageHispanic American studiesAging narrativesAging studiesChilean cinemaChilean literatureGender and sexualityOld Ageapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3453x04rhttps://escholarship.org/content/qt3453x04r/qt3453x04r.pdfetdoai:escholarship.org:ark:/13030/qt3pf5t4wv2026-05-28T06:33:18Zqt3pf5t4wvFrom Ithaca to Beijing: The Reception of Greco-Roman Classics in China, 1989-2022.Liu, TianranNguyen, KellyPurves, Alex C2026-05-26My dissertation explores the reception and appropriation of Greco-Roman classics in contemporary China. Examining several different media, including poetry, visual art, and scholarship, I explore how Chinese authors and artists have used Greco-Roman classics to both challenge and redefine claims of western cultural hegemony. Working under two sometimes stifling hegemonies—the cultural imbalance between China and the West, on the one hand, and the suppressive authoritarian regime of People’s Republic of China (PRC), on the other-Chinese authors and artists have created an in-between space of negotiation in which neither western or Chinese authority is stable, and cultural identity instead emerges through ongoing process of reinterpretation and contestation. My first two chapters examine how, in the wake of Tiananmen Crackdown, Chinese poets used Greco-Roman classical motifs, and in particular the story of Odysseus, to express feelings of displacement and disillusionment. I argue that the Odyssey provided poets a means to resist the uniform discourses imposed by the Chinese state. My third chapter studies Chinese visual art, demonstrating how contemporary visual artists have reinterpreted Greco-Roman classics to challenge the authority and cultural prestige of antiquity and propose a dynamic vision of hybrid cultural identity. My final chapter explores how Chinese leading classicist Liu Xiaofeng attempts to use Greco-Roman classics to create a stable moral foundation for society that can ultimately strengthen the Chinese authoritarian regime. Throughout, my dissertation draws on postcolonial and diaspora theory to shed new light on the cultural interactions between China and the West, a point of considerable urgency in the post-COVID political climate in which the United States and China have grown increasingly alienated from each other.Classical studiesChinese literatureComparative literatureEast Asian studiespubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3pf5t4wvetdoai:escholarship.org:ark:/13030/qt0bs607d12026-05-28T06:33:14Zqt0bs607d1Regularity of Evolutionary Free Boundary ProblemsCollins, CarsonKim, Christina2026-05-22This dissertation studies free boundary problems, a type of nonlinear partial differential equation in which one simultaneously searches for a solution ? and domain Ω(?), such that ? satisfies an overdetermined boundary value problem in Ω(?). More specifically, we consider evolutionary free boundary problems, in which the free boundary is an interface which moves over time, with various motion laws. The first problem we discuss is a model for incompressible tumor growth in the presence of a diffusive nutrient, in which the motion of the tumor interface is given by Darcy’s law. The second problem and third problems we discuss are rate-independent models of droplet motion, in which the motion of the droplet contact line is mediated by a balance between energy minimization and frictional forces opposing motion. The fourth and final problem is the two-phase Muskat problem with surface tension and gravity, which models the motion of two incompressible, nonmixing fluids in a porous medium; for example, oil and water underground.In Chapter 2, we discuss the aforementioned tumor model, which can be interpreted as a Hele-Shaw flow with coupled nutrient source term. Since the nutrient diffuses over time and is consumed by the tumor, there is no comparison principle for the coupled system. We overcome this with a novel Hopf-Lax type estimate for the pressure, which we combine with barrier arguments to establish Hölder continuity of the hitting time of the tumor. With obstacle problem techniques, we are able to use this Hölder continuity to establish generic regularity of the tumor boundary. This work has been published in [CJK25].In Chapter 3, we consider a rate-independent model of droplet motion based on the Bernoulli one-phase problem. This system models the phenomenon of contact angle hysteresis, in which a droplet resting on a solid surface has nonconstant contact angle, due to microscopic roughness of the solid. The evolutions we study are produced as limits of a certain discrete time scheme, in which the Dirichlet data of the one-phase problem is varied over time. Using comparison techniques, we are able to show that the nearly sharp ? 1,1/2− regularity of the contact line is preserved over time by these evolutions. This work has been released as a preprint in [CF24].In Chapter 4, we consider an energetic rate-independent model based on an experimental setup known as the Wilhelmy plate. In this experiment, a plate is inserted vertically into a container of liquid and moved up and down. This allows measurement of the liquid-plate contact angles when the plate is advancing and receding, which are generally different due to hysteresis. In our model, the motion of the contact line is dictated by a balance between minimization of capillary energy and gravitational energy subject to volume constraint, and friction along the liquid-plate contact line. For this model, we study a limit in which the ratio between the container radius and plate radius tends to infinity. Along subsequences, we show convergence to an energetic rate-independent system in an unbounded exterior domain, where the volume constraint disappears. The limiting problem can be treated by the same comparison principle techniques as in Chapter 3, so its discrete scheme solutions are expected to have the same ? 1,1/2− contact line regularity. This work has been released as a preprint in [CF25]. In Chapter 5, we study the two-phase Muskat problem with surface tension and gravity. Our analysis is based on the formal structure of the problem as a 2-Wasserstein gradient flow. We present a brief proof that wherever fluid interface is graphical, it has an ? 2 bound for the graph curvature estimated in terms of the energy-dissipation rate. Consequently, at smaller scales we can show that the interface is has uniform Lipschitz and ? 1,1/2 estimates in terms of the dissipation rate. Our proof is based on a variational method to approximate the optimal dissipation rate. This chapter is partially based on a work in preparation with Inwon Kim and Andrej Zlatoš.MathematicsApplied mathematicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0bs607d1https://escholarship.org/content/qt0bs607d1/qt0bs607d1.pdfetdoai:escholarship.org:ark:/13030/qt5xj0h9xd2026-05-28T06:33:08Zqt5xj0h9xdAn Improved Post-Training Framework for Small Language Models Based on Group Relative Policy Optimization (GRPO)Tattersall, CaseyZhu, Yuhua2026-05-26Pre-trained large language models require post-training to become useful problem solvers, and DeepSeek’s Group Relative Policy Optimization (GRPO) has emerged as the dominant reinforcement learning algorithm for this purpose. However, current GRPO research is primarily focused on larger models, while many practical applications may be better served by smaller specialized models that can run locally on consumer-grade hardware. This thesis investigates how a GRPO-based training pipeline can be modified to work effectively at the small-model scale, using multi-digit multiplication as a controlled environment with cheap, verifiable, binary rewards.
Starting from Qwen2.5-1.5B-Instruct, we train and compare five LoRA-adapted models on a single NVIDIA A100 GPU through Google Colab. Our baseline GRPO run improves substantially over the base model, but suffers from mid-training instability and stagnant performance on harder problems. We then propose and evaluate three modifications: zeroing the KL coefficient on uninformative prompts whose rollout group produces no reward variation, adopting the DAPO modifications proposed by ByteDance, and adding a multi-armed bandit-inspired data sampler that shifts training budget toward problem types at the frontier of the current model’s ability. The first and third modifications produce substantial gains. Our best model reaches 100%, 94%, 92%, 69%, and 80% accuracy on 2×2, 3×3, 4×4, 5×5, and out-of-distribution 6×3 multiplication problems respectively, up from 89%, 30%, 0%, 0%, and 1% for the base model. We then investigate each model’s responses to better understand the strategies they have learned and confirm that the final models are robust to variation in prompt format and therefore are not overfitting.StatisticsComputer scienceArtificial intelligenceArtificial IntelligenceFine-TuningGRPOLLMPost-trainingReinforcement Learningapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5xj0h9xdhttps://escholarship.org/content/qt5xj0h9xd/qt5xj0h9xd.pdfetdoai:escholarship.org:ark:/13030/qt43w403j12026-05-28T06:33:03Zqt43w403j1Gendered Racial Microaggressions and Disordered Eating in Black Women: Psychosocial Mediators and Physiological Health ImplicationsParker, JordanTomiyama, A. Janet2026-05-26Gendered racial microaggressions are defined as the nuanced expressions of oppression that Black women encounter at the intersection of their racial and gender identities. Though a robust body of literature documents associations between discrimination and adverse health outcomes for Black women, few studies have investigated this through a lens that acknowledges Black women’s unique experiences with oppression and disordered eating is rarely examined in this context. Given growing rates of disordered eating overall, among Black women, and their association with adverse mental and physical health outcomes, this dissertation aimed to characterize the association between gendered racial microaggressions and disordered eating among Black women, using cross-sectional, longitudinal, and experimental frameworks. In addition, I examined self-silencing as a psychosocial mechanism explaining this association. Building on independent bodies of literature linking each of these outcomes to subclinical indices of cardiovascular disease, I also examined whether gendered racial microaggressions, self-silencing, and disordered eating together were implicated in the increased burden of cardiovascular disease observed among Black women. Results of Study 1 established positive associations between gendered racial microaggressions and disordered eating in Black women. For specific domains of gendered racial microaggressions, self-silencing mediated this association. Relationships between gendered racial microaggressions and disordered eating were replicated longitudinally in Study 2. The positive association between these constructs also led to an indirect effect of gendered racial microaggressions on cardiovascular outcomes over a four-year timespan. In Study 3, I aimed to experimentally manipulate the experience of gendered racial microaggressions but failed to find evidence of greater disordered eating risk when compared to a control condition. Results of this dissertation add to the growing body of literature documenting associations between Black women’s experiences with discrimination and their health outcomes and behaviors. These findings are the first to establish self-silencing as a mechanism linking gendered racial microaggressions and disordered eating. They are also the first to establish and provide longitudinal evidence suggesting that Black women’s experiences with gendered racial microaggressions are associated with greater engagement in emotional eating, which later predicts increased cardiovascular disease risk. Taken together, these results suggest that Black women’s unique experiences of discrimination, and specifically the subtle, nuanced, expressions of oppression that they contend with, are relevant to understanding pathological eating behaviors and health outcomes observed in this demographic.PsychologyWomens studiesPhysiological psychologyBlack studiesBlack womenCardiovascular diseaseDiscriminationDisordered eatingHealth psychologyIntersectionalityapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/43w403j1https://escholarship.org/content/qt43w403j1/qt43w403j1.pdfetdoai:escholarship.org:ark:/13030/qt67s170sg2026-05-28T06:32:57Zqt67s170sgEssays on the Economics of Higher Education: Transfer Students, Peer Effects, and the Pandemic ShockLee, James YuiLleras-Muney, Adriana2026-05-26This dissertation consists of three essays on the economics of higher education. In Chapter 1, I investigate the peer effects of community college transfer students on direct admits. In the short run, I find a small but statistically significant positive effect of transfer exposure on the grades of direct admits that is likely driven by a “curve effect”. In the long run, I find that direct admits who are more exposed to transfer students in their college career graduate faster with no statistically significant effects on labor market outcomes. In Chapter 2, I provide an update on the performance of transfer students and analyze the peer effects of community college transfer students on each other. Transfer student academic outcomes have improved over time, but student interviews show signs of discrimination, mainly from competitive student clubs. In the short run, I find a small positive peer effect for transfer students. In the long run, I find positive effects for academic outcomes but possibly negative effects for post-graduation labor market outcomes. In Chapter 3, I explore how cohorts graduating during and after the COVID-19 pandemic performed. While some courses may have been ineffectively taught at the onset of the pandemic, student academic performance is not hurt by how much time they spent learning remotely. However, I find evidence of human capital loss as spending an additional quarter learning remotely leads to lower paying jobs - by about $4,000 in annual income.Higher educationEducationEconomicsEconomic theoryCommunity college educationcommunity collegepandemicpeer effectstransfer studentsUCLAapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/67s170sghttps://escholarship.org/content/qt67s170sg/qt67s170sg.pdfetdoai:escholarship.org:ark:/13030/qt7088h46p2026-05-28T06:32:52Zqt7088h46pEssays on Economic HistoryMolligo, PatrickCosta, Dora L2026-05-26This dissertation studies the historical development of federal education policy in the United States, with particular emphasis on vocational education, the G.I. Bill, and the relationship between human capital policy and labor markets during the twentieth century. Across three essays, I examine how federal education programs expanded workforce training opportunities while also shaping patterns of inequality and educational access.The first chapter provides a historical analysis of the Smith–Hughes Federal Vocational Education Act of 1917, the first major federal commitment to vocational education below the collegiate level. Using archival reports from state boards of vocational education and federal agencies, I document the administrative structure, political origins, and long-run expansion of federally supported vocational training programs in agriculture, trades and industry, and home economics. The chapter also introduces a newly digitized dataset constructed from annual vocational education reports spanning 1918 to 1962.The second chapter examines the effects of the G.I. Bill on vocational training decisions among veterans of World War II and the Korean War. While existing research primarily focuses on college attendance, I show that the G.I. Bill also substantially increased participation in vocational education. Using cohort-level variation in military service rates and Census microdata, I estimate the effects of veterans’ educational benefits on vocational school completion and labor market outcomes. The results suggest that vocational education represented an important and understudied channel through which the G.I. Bill affected human capital accumulation.The third chapter studies the relationship between the G.I. Bill and Historically Black Colleges and Universities (HBCUs). I examine how Black veterans accessed postsecondary education within segregated educational systems and analyze the role HBCUs played in absorbing increased educational demand after World War II. Together, the essays contribute new empirical evidence on the development of federal human capital policy and the historical relationship between education and labor markets in the United States.Labor economicsEconomic historyEconomic theoryEconomicsEconomic HistoryG.I. BillHBCUSmith-Hughes ActVocational Educationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7088h46phttps://escholarship.org/content/qt7088h46p/qt7088h46p.pdfetdoai:escholarship.org:ark:/13030/qt9dx644j62026-05-28T06:32:47Zqt9dx644j6“Exactly Wrong”: Failure and Relationality in Andy Warhol’s Polaroids, 1969–1978Duncan, ThomasBaker, George T.2026-05-26This dissertation presents the first extended analysis of Andy Warhol’s Polaroids. In 1969, Warhol began taking Polaroids that challenged photographic norms, placing many of them in pocket-sized albums to create new formal and conceptual relations. As Warhol never exhibited his Polaroids, they are typically dismissed as either thoughtless snapshots or merely as source material for other artworks; yet they demonstrate methodical interventions into popular and artistic photographic conventions. These private interventions—embracing poor quality, using nontraditional display formats, subverting portraiture, and abstracting the body—stand in contrast with Warhol’s rising public prominence during this period. Using as a starting point the artist’s aphorism “when you do something exactly wrong, you always turn up something,” this study theorizes Warhol’s use of Polaroid as an evolving dynamic of failure and relationality, contending that his refusal of mastery and exploration of “wrong” subject matter, including aimless performance, gender nonconformity, and queer sexuality, served as catalysts for the establishment of new photographic relations.
Warhol’s shifting approach to Polaroid coincided with a number of artistic and social transformations in the 1960s and 1970s. While the first chapter assesses his relationship with fellow photographer Brigid Berlin, it also examines how their work engaged with conceptualism and performance art. The second chapter interrogates Warhol’s photo albums featuring portraits of gender-nonconforming models of color at a time when anxieties about the intersection of race and gender variability were on the rise. This was the same moment that the culture surrounding gay male sexuality became increasingly liberated. Accordingly, the third chapter analyzes Warhol’s abstraction of the male nude as an ambivalent critique of this newly visible culture. To chart the course of these developments, this dissertation considers Warhol’s photography alongside that of modernist, postmodernist, and other queer photographers, linking historical modes of production that have long been viewed as unrelated.Art historySexualityAfrican American studiesEthnic studiesFailurePolaroidQueernessRaceRelationalityWarholpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9dx644j6etdoai:escholarship.org:ark:/13030/qt4xx6r5mp2026-05-28T06:32:41Zqt4xx6r5mpQuantifying the Primary Drivers of Recent Dryness in the AmazonChang, AlexFu, Rong2026-05-26The Amazon rainforest, a vital component of Earth’s carbon and hydrological cycles, has undergone significant changes to its climate in recent decades. These include increases in vapor pressure deficit (VPD) that reflect greater atmospheric moisture demand, delays in the wet season onset (WSO) that result in longer dry seasons, and increases in the frequency and intensity of compound hot-dry-windy (HDW) extremes. Consequently, the Amazon has experienced increased ecosystem stresses and elevated wildfire risks from these changes. Through a combination of observation-based attributions and climate model simulations provided by the Coupled Model Intercomparison Project Phase 6 (CMIP6), we determine the mechanisms behind the VPD increases, WSO delays, and increases in compound HDW extremes; whether they are primarily attributable to climate change or natural variability; and how they are all interconnected. Specifically, we find that the VPD increases, WSO delays, and increases in compound HDW extremes are primarily driven by anthropogenically forced increases in temperature, and decreases in humidity also play a secondary role in these changes. They are all interconnected via the intensification of the Hadley Cell over tropical and subtropical South America, highlighting large-scale dynamic and thermodynamic changes to the climate of the Amazon as a whole.Atmospheric sciencesClimate changeEnvironmental scienceMeteorologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4xx6r5mphttps://escholarship.org/content/qt4xx6r5mp/qt4xx6r5mp.pdfetdoai:escholarship.org:ark:/13030/qt1nf103zw2026-05-28T06:32:35Zqt1nf103zwSite- and Stereoselective Ni-Catalyzed Cross-Coupling Reactions: Reaction Development and Predictive ModelingBucci, Erin MarieDoyle, Abigail G.2026-05-26Chapter 1 describes the development of a conceptually novel mechanistic framework for C(sp3 )–Me bond formation using two distinct Ni catalysts capable of cross-coupling sterically and electronically diverse alkyl halides (chlorides and bromides) with methyl radical generated photocatalytically from benzaldehyde dimethyl acetal. Additionally, by modifying the alkyl substituents on the acetal coupling partner, we demonstrate cross-couplings beyond methylation to access an array of 1°–1° and 1°–2° alkyl–alkyl bonds.Chapter 2 describes experimental and computational mechanistic studies aimed at elucidating the reaction mechanism for the cross-coupling reaction discussed in Chapter 1. We examine the speciation of Ni in the complex catalytic system and evaluate the influence of substrate identity on the predominant C–C bond forming cycle. Specifically, we provide support for cooperativity between an in situ-generated (bpy) NiI (X) catalyst that facilitates XAT and inner sphere C–C bond formation and a (Tp*)NiII(acac) cocatalyst that captures methyl radical and engages in concurrent outer-sphere SH2 coupling.Chapter 3 describes a data-science-guided approach to streamline development of asymmetric Ni-catalyzed cross-coupling reactions. We developed a descriptor generation strategy that accounts for changes in the enantiodetermining step with catalyst or substrate identity, allowing us to model reactions involving distinct ligand and substrate types. As validating case studies, we collected data on enantioselective nickel-catalyzed C(sp3 ) couplings and trained statistical models with features extracted from the transition states and intermediates proposed to be involved in asymmetric induction. These models allow the optimization of poorly performing examples reported in a substrate scope and are applicable to unseen ligands and reaction partners. This approach offers the opportunity to streamline catalyst and reaction development; quantitatively transferring knowledge learned on sparse data to chemical spaces.Organic chemistryChemistryComputational chemistrycatalysiscross-couplingmachine learningnickelphotocatalysisapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1nf103zwhttps://escholarship.org/content/qt1nf103zw/qt1nf103zw.pdfetdoai:escholarship.org:ark:/13030/qt52c3206f2026-05-28T06:32:29Zqt52c3206fEstimating long-term health effects of housing instability in the US using a lifecourse frameworkMobley, TaylorMayeda, Elizabeth Rose2026-05-26The United States (US) is facing a housing affordability crisis. Over 30% of adults experience a high housing cost burden by formal economic definitions, and 69% report being “very concerned” about the cost of housing. As a result, many people in the US experience unstable housing, which includes frequent or involuntary residential moves and homelessness. Current evidence suggests that housing instability is harmful to health: it is associated with adverse health outcomes across multiple lifecourse periods, and select, short-term interventions targeting housing instability appear to improve health outcomes. To date, limited work has examined its long-term health effects using a lifecourse framework. In this dissertation, I estimated long-term health effects of housing instability in the US using a lifecourse framework and used causal inference methods to evaluate common sources of bias in lifecourse epidemiology, including selection bias and time-varying confounding. Further, I examined potential sources of heterogeneity and specified policy-relevant estimands, strengthening the real-world relevance of the findings. In Aim 1, I estimated the effect of childhood housing instability, measured by experiencing a residential move due to financial difficulties before age 16 years, on mortality among Health and Retirement Study participants aged (i) 50-60 years and (ii) >60 years at baseline using inverse probability of treatment-weighted Kaplan Meier curves. Findings indicated small, long-term effects of childhood housing instability on mortality among participants aged 50-60 years at baseline, but not among participants aged >60 years at baseline. In Aim 2, I examined the relationship between childhood housing instability and functional impairment trajectories among Health and Retirement Study participants aged 50, 60, and 70 years at baseline under a hypothetical scenario in which everyone in the sample remained in the study through the end of follow-up. Effect estimates were obtained using generalized estimating equations with weights to account for early-life confounding, censoring due to loss to follow-up, and censoring due to death. Findings from this aim suggest small effects of childhood housing instability on functional impairment in mid-life that diminished in late-life after accounting for early-life confounding and selection due to loss to follow-up and death. In Aim 3, I estimated the effect of a hypothetical intervention eliminating involuntary residential moves compared with the natural course of involuntary residential moves on mortality over 50 years (ages 0-5 years through mid-life) in a national US cohort study using the parametric g-formula. Results from this aim suggested that the hypothetical intervention eliminating involuntary residential moves had a small, long-term protective effect on mortality, resulting in a 2% reduction in deaths in mid-life (50 years of follow-up) had everyone remained in the study throughout follow-up. Across all 3 aims, I found evidence of small, long-term effects of housing instability on morbidity and mortality in mid- and late-life in national US cohort studies.EpidemiologyPublic healthAmerican studiespubliceScholarship, University of Californiahttps://escholarship.org/uc/item/52c3206fetdoai:escholarship.org:ark:/13030/qt3ct584wt2026-05-28T06:32:23Zqt3ct584wtNAD+ Transporter SLC25A51 Is a Novel Regulator of Glucose Stimulated Insulin SecretionArias, Nataly JaquelineDivakaruni, Ajit2026-05-26Pancreatic beta cells secrete insulin in response to elevated blood glucose through tightly regulated metabolic coupling. While several unique metabolic features of beta cells are known to support glucose-stimulated insulin secretion (GSIS), additional specialized pathways likely remain unidentified. This thesis demonstrates that rapid NAD+ efflux across the mitochondrial inner membrane via the transporter SLC25A51 is a previously unrecognized hallmark of beta cell metabolism. We show that isolated mitochondria from beta cells rapidly lose NAD+, impairing complex I-linked respiration in a manner fully rescued by exogenous NAD+. Knockdown of Slc25a51 reduces ATP-linked respiration and blunts GSIS, establishing mitochondrial NAD+ compartmentation as a key regulator of insulin secretion. This work reveals a novel metabolic feature essential for beta cell function with implications for type 2 diabetes.Cellular biologyMolecular biologyBiochemistrybeta cellbioenergeticsinsulin secretionmetabolismmitochondriaNAD+application/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3ct584wthttps://escholarship.org/content/qt3ct584wt/qt3ct584wt.pdfetdoai:escholarship.org:ark:/13030/qt8kn3x19d2026-05-28T06:32:18Zqt8kn3x19dAdaptive insulin resistance preserves brown adipocyte thermogenic capacityNagari, RohithTontonoz, Peter J2026-05-26The involution of thermogenic brown adipose tissue (BAT) to a lipid-rich, energy-storing phenotype is a major barrier to the clinical application of UCP1 thermogenesis. We have identified an orphan transmembrane protein expressed selectively in BAT that is required to prevent this transition. This factor is upregulated in response to anabolic stimulation and limits BAT lipid storage by suppressing insulin-activated glucose uptake. Accordingly, loss of this factor accelerates involution by inappropriately elevating glucose uptake and triglyceride synthesis in BAT, ultimately rendering mice cold-intolerant. Conversely, overexpression of this factor impairs insulin-responsive glucose uptake across all adipose depots and impairs diet-induced hypertrophy of white adipose tissue. These findings demonstrate a pathway through which brown adipocytes restrain anabolic signaling to remain poised for thermogenic challenge.PhysiologyCellular biologyMolecular biologyNutritionAdiposeObesityThermogenesisUCP1publiceScholarship, University of Californiahttps://escholarship.org/uc/item/8kn3x19detdoai:escholarship.org:ark:/13030/qt5k8521bg2026-05-28T06:32:12Zqt5k8521bgAlgebraic structure of knot Floer and Khovanov-Rozansky homologyPopovic, DavidSarkar, Sucharit2026-05-26We prove the structure theorem for knot Floer homology by explicitly characterizing finitely generated free Z ⊕ Z-graded chain complexes over F[U,V ]/(UV ) . Each knot Floer complex splits canonically as a direct sum of a standard complex and possibly several local systems. We make further progress towards resolution of the geography problem for knot Floer homology by classifying the standard complexes whose local equivalence classes admit lifts to chain complexes over F[U, V ]. Restricting to knots of knot Floer width at most two, we additionally classify such local systems and show that they can be realized by exhibiting them in certain iterated cables of the figure-eight knot.In the second part of this dissertation, we prove an analogous structure theorem for Khovanov-Rozansky homology. Counterparts to knot Floer concordance invariants τ , ϵ, and φj are constructed. MathematicsTheoretical mathematicsHOMFLY homologyKhovanov-Rozansky homologyknot Floer homologyknot homology theorieslink Floer homologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5k8521bghttps://escholarship.org/content/qt5k8521bg/qt5k8521bg.pdfetdoai:escholarship.org:ark:/13030/qt07z3n2r32026-05-28T06:32:07Zqt07z3n2r3Cell Surface Proteomic Atlas Decodes the Molecular Logic of Retinal Identity and Synaptic FunctionCavallini, MartinaPeng, Yirong2026-05-26The inner plexiform layer (IPL) of the retina is organized into discrete synaptic laminae, each containing circuits that extract distinct features of the visual scene. The retinal neurons that participate in these circuits have been classified into hundreds of distinct types, however, the molecular mechanisms by which they achieve lamina-specific connectivity during development remain incompletely understood. Cell surface proteins (CSPs) mediate synaptic partner recognition and wiring specificity, yet the full complement of CSPs expressed at the IPL surface during circuit assembly has not been systematically characterized.To address this, we utilized iPEEL (in situ cell-surface proteome extraction by extracellular labeling), a Cre-dependent proximity biotinylation approach in which horseradish peroxidase (HRP) is expressed on the extracellular face of targeted neurons. Applying iPEEL across five Cre driver lines, each targeting distinct neuronal populations with defined stratification patterns, during peak synaptogenesis in the mouse IPL yielded 1,172 IPL-associated CSPs (IPL-CSPs). Integration of this proteome with single-cell RNA-sequencing datasets from cone bipolar cells (CBCs), retinal ganglion cells (RGCs), and amacrine cells (ACs) provided cell-type-specific resolution of IPL-CSP expression. IPL-CSP gene signatures contain sufficient information to cluster cells by cell type, with minimal overlap with the top 2,000 highly variable genes typically used for classification. Using hdWGCNA, we identified co-regulated IPL-CSP modules that are correlated with distinct stratification patterns. Pruning analyses established that 100–150 IPL-CSPs are sufficient for high-fidelity cell-type classification, defining a minimal molecular identity code for each cell class.Focusing on a defined circuit, we identified a role for IgLON5 in maintaining direction selectivity (DS). Functional characterization of Iglon5 knockout mice revealed reduced direction selectivity, enlarged receptive field centers (~50% expansion), and reduced surround suppression, without detectable anatomical defects or loss of overall RGC response diversity. IgLON5 preferentially localizes to inhibitory synapses on starburst amacrine cells, consistent with a role in maintaining the inhibitory synapse organization that underlies DS computation.Together, these findings provide a comprehensive atlas of the IPL cell-surface proteome, demonstrate that neuronal identity can be encoded by a compact and co-regulated set of IPL-CSPs, and reveal a role of IgLON5 in direction-selective circuit function.NeurosciencesDevelopmental biologyOphthalmologyOptometrycircuitigloninner plexiform layeripeelproteomicsretinaapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/07z3n2r3https://escholarship.org/content/qt07z3n2r3/qt07z3n2r3.pdfetdoai:escholarship.org:ark:/13030/qt9qf1r7x52026-05-28T05:04:42Zqt9qf1r7x5Marginalization in the Movement: The Effect of Intersectionality on Activist LeadershipRobertson, Crystal DeniseFrasure, LorriePérez, Efrén O.2024-01-01Under what conditions does intersectionality influence the strategies of social movement leaders? Social identities are crucial for social movement organizations (SMO), serving as one of the strongest mobilizers for movement participants. Activists utilize group identities to recruit and retain participants (Polletta and Jasper 2001), similarly, the social identities of the movement’s leadership are important for mobilization processes. Psychology research suggests people expect leaders to hold more privileged identities such as Whiteness or masculinity (Rosette, Leonardelli, and Phillips 2008; Scott and Brown 2006). While Black women can achieve leadership positions, they tend to do so in organizations on the decline (Rosette and Livingston 2012a). Between the downward trajectory of the organizations and discrimination from within (H. M. Hurwitz 2019) leaders with multiple marginalized, or intersectional, identities face tremendous challenges in leading activist organizations.I argue intersectional activists take resistance against their intersectional identity into account in their strategizing and therefore prefer more moderate tactics rather than techniques considered radical. I speculate this includes a hesitation to include intersectional political agendas into their organizing for fear of backlash. This framework identifies the concern of discriminatory resistance as a key factor in intersectional leaders' tactical selection and assesses the potency of this factor’s influence on public opinion. Social psychology research finds that as intersectional activists' leadership status is more fragile, they are more likely to act on behalf of the group and react more harshly when someone defies group norms (Ellemers and Jetten 2013).
Given the symbiotic relationship between leaders and followers, I assess each group’s perception of public support for intersectional leadership and their freedom in emphasizing issues.
This multi-method project seeks to understand intersectional leadership within social justice organizations and how these dynamics influence American political behavior. Utilizing survey experiments and in-depth interviews, I focus this analysis on Black women’s incorporation into activist organizations' public reception to movements focusing on them, public response to Black women’s leadership, and how Black women in social movements manage these attitudes while organizing.
Political scienceSocial psychologyblack politicsintersectionalitypolitical psychologysocial movementsenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9qf1r7x5https://escholarship.org/content/qt9qf1r7x5/qt9qf1r7x5.pdfetdoai:escholarship.org:ark:/13030/qt9px323xt2026-05-28T05:04:39Zqt9px323xtThe Falcon Has Flown: Funerary Rituals as Performance, Power Display, and Transformation During the Royal Funeral in New Kingdom Egypt (circa 1550-1070BC).Brown, Nicholas RobertCooney, Kathlyn MWendrich, Willemina Z2024-01-01This research focuses on the performance and enactment of the royal funeral in antiquity, which is studied through the lens of New Kingdom royal burials in the Valley of the Kings (ca. 1550-1070BC). This allows for a redefinition of how we think about ancient Egyptian royal burials from the perspective of human action, by closely examining what objects were used to create a divine space, how they functioned within the final ceremonies to transfigure a deceased king into a god, and to understand better the transition of power between one ruler and the next. The research is approached holistically, engaging with archaeological, art historical, and textual evidence. The first chapter (Chapter 1) introduces the reader to the research questions, methodologies, and theoretical frameworks that are used while analyzing and investigating the royal funeral in ancient Egypt. The following chapter (Chapter 2) provides the reader with an overview of the landscape of the Valley of the Kings, its topographical features, and spends significant time hypothesizing how the king’s body was moved from their place of death to the final interment in the royal necropolis as well as possible participants and audience members for the king’s burial. The theme of creating a sacred space is woven throughout this chapter, as man-made features of the landscape, like enclosure walls and boundary foot paths, are discussed; examples are derived from both the Valley of the Kings in Luxor as well as the Royal Wadi at Amarna.
The following three chapters (Chapters 3-5) are the main case studies for this research, which cover the reigns of Tutankhamun (18th Dynasty), Merenptah (19th Dynasty), and Ramses IV (20th Dynasty). All three provide archaeological, iconographic, textual, and/or material culture remains with which to study the royal funeral. Evidence as preserved is discussed in-depth, including relevant contextual and religious symbolism considerations of specific objects, icons, and/or social histories in order to ground the reader in the material better. The themes of creating the sacred (for both objects and the tomb), the king’s transformation into a divine being in the hereafter, as well as the transition between one ruling party and the next are all dispersed throughout each individual chapter. Finally, the concluding chapter (Chapter 6) summarizes the results of the research and suggests multiple avenues of further research. Here the author postulates about the role(s) of the king and his high officials in the royal funeral, audience participation on the day of the burial, how the king’s burial ensured the successful transition of the deceased into the hereafter, and what the royal funeral meant for manufacturing power and legitimizing rule in ancient Egypt.
Near Eastern studiesArchaeologyenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9px323xthttps://escholarship.org/content/qt9px323xt/qt9px323xt.pdfetdoai:escholarship.org:ark:/13030/qt8kj6r0qt2026-05-28T05:03:40Zqt8kj6r0qtThe Portrayal of Memory of the Spanish Civil War and Francoist Dictatorship in 21st Century Catalan LiteratureSoley Mateu, Ainade Zubiaurre, Maria Teresa2025-01-01This dissertation examines how Catalan literature written after 2000 engages with the legacy of the Spanish Civil War and Francoism, arguing that memory in these works functions not as a static historical account, but as a dynamic process shaped by emotion, identity, and contemporary cultural demands. While studies of Spanish historical memory often foreground Castilian language texts, this project places Catalan literature at the center of the inquiry, emphasizing how its cultural and regional specificity generates distinct approaches to remembering, contesting and reframing the past. Structured thematically across four chapters, the study traces a progression from individual trauma to collective justice. Chapter 1 establishes the theoretical and historiographical framework, drawing on memory studies to position literature as a site for negotiating silenced or overlooked histories. Chapter 2 analyzes Pa negre by Emili Teixidor and País íntim by Maria Barbal, both of which depict the lingering effects of repression through rural childhood narratives. Chapter 3 focuses on the concept of postmemory in works by Marta Marín-Dòmine, Aida Llorenç and Esther Lorenzo, and Carme Riera, examining how the following generation reconstructs familial histories through archival means. Chapter 4 considers Jo soc aquell que va matar Franco by Joan-Lluís Lluís and Justícia by Guillem Clua, exploring how literary representations of justice and national identity engage with recent political tensions in Catalonia.
This dissertation argues that Catalan literature not only engages with the past, but also reshapes the present by confronting unresolved historical trauma, reclaiming suppressed voices and challenging institutional silences. Through a progression that moves from personal recollection to familial transmission and collective justice, the selected works trace how memory evolves across generations and historical moments. They highlight how literature becomes a space for negotiating cultural identity, asserting symbolic justice, and resisting the erasure of Francoist violence. Moreover, the texts underscore the emotional and political stakes of remembering in a region where historical memory remains entangled with identity and contemporary debates around national sovereignty. By foregrounding Catalan-language texts, this study broadens the scope of memory studies and affirms literature’s role in shaping how societies understand—and act upon—their histories.
LiteratureRegional studiesEuropean studiesCatalan LiteratureCataloniaCivil WarFrancoismMemory Studiesenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8kj6r0qthttps://escholarship.org/content/qt8kj6r0qt/qt8kj6r0qt.pdfetdoai:escholarship.org:ark:/13030/qt7t76m2362026-05-28T05:03:32Zqt7t76m236Identity, Economy, and Secessionism in Comparative PerspectiveGatter, Kevin MichaelPosner, Daniel N2024-01-01What motivates the emergence of secessionist movements, and why do individuals support secession once it becomes a salient issue? Scholars have argued that identitarian and economic factors motivate the emergence of secessionism and shape individuals’ preferences on secession, but they have examined only social groups among which secessionist movements have emerged and have not identified the specific ways in which identitarian and economic motivations shape secessionism in different types of regions. My project pioneers a novel approach to secessionism by identifying the universe of potentially secessionist social groups and introducing a theory of why the causal processes motivating secessionism might be different for different regions or groups. Using large-N cross-national analyses, case studies, interviews, and surveys, I examine how cultural identity and economic dynamics shape secessionism across a variety of contexts.I test traditional theories of secessionism using an original dataset containing the universe of potentially secessionist social groups in Europe and the former Soviet Union. To reduce bias from selecting cases on the outcome of interest, my dataset includes a set of groups with potential but unrealized secessionism alongside groups that mobilized for secession. I show that theories that emphasize the role of natural resources and relative deprivation in motivating secessionism lose their explanatory power when tested over this improved sample. I supplement this quantitative analysis by investigating the systematic reasons why economic grievances motivate secessionists in some places while identity-based grievances motivate secessionism in others. Through interviews, ethnographic research, and process tracing, I examine secessionism in Catalonia, Montenegro, Quebec, and Scotland. I show that in Scotland, economic factors have primacy in motivating secessionism, whereas identity-based factors drive secessionism in Quebec. In Catalonia, identitarian and materialist concerns interact to motivate secessionism, while anti-regime pressures encouraged secessionism in Montenegro.I then examine survey data from Catalonia, Northern Ireland, Scotland, and Wales to explore how cultural identity, socioeconomic status, and partisanship predict support for secession. I show that while regional identity is generally a strong predictor of support for secession, socioeconomic status and partisanship are weaker indicators, which problematizes traditional theories of public opinion and suggests that secession is fundamentally distinct from other political issues.This project advances our identification of the causal mechanisms behind secessionism at both the cross-national and individual levels. My cross-national analysis shows that identity and economic factors are indeed important drivers of secessionism, while disaggregating secessionist regions shows how these factors shape secessionism in different contexts.Political scienceContentious politicsSecessionismSubstate nationalismTerritorial politicsenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7t76m236https://escholarship.org/content/qt7t76m236/qt7t76m236.pdfetdoai:escholarship.org:ark:/13030/qt7rw318tx2026-05-28T05:03:27Zqt7rw318tx“We, as a community, are not complete without each other”: A Mixed Methods Study of Social Networks and Mental Health among Advocates of LGBTQ+ Vietnamese American Issues in Orange CountyHuynh, JamesThomas Tobin, Courtney S2024-01-01PurposeThe overall purpose of this dissertation is to understand how social networks, through kinship-making and coalition building, shape the distress, anxiety, and well-being of advocates engaged in LGBTQ+ issues in Vietnamese American communities in Orange County, CA through queer and trans of color critique lenses. The specific aims are to (1) characterize advocates’ sociodemographic profiles; (2) elucidate diverse conceptualizations and practices of community organizing; and (3) evaluate the impacts of kinship network characteristics on the psychological and social well-being of these advocates.
Methods
The dissertation used a two-stage mixed paradigm (critical, constructivist, and post-positivist) and mixed methods (ethnography and social network analysis) case study design. Viet Rainbow of Orange County (VROC), an LGBTQ+ advocacy organization, was the primary community partner. The first stage used an exploratory sequential design where four months of ethnographic field work and semi-structured interviews established the sampling frame for the second stage. The second stage used a convergent design where qualitative data (eight months of ethnographic field work and semi-structured interviews) and quantitative data (egocentric social network collection and self-administered health questionnaires) were collected simultaneously. Thematic and narrative analysis of ethnographic field work and semi-structured interviews fully addressed Aims 1 and 2. Statistical network analysis, including network visualization and descriptive statistical analyses, fully addressed Aim 3. I integrated qualitative and quantitative findings in the discussion.
Results
Aim 1: 51 advocates participated in semi-structured interviews and participant observation (with observations lasting between four to twelve months). Advocates were majority Vietnamese (82%) and majority cisgender (62%), with 38% identifying as trans or non-binary and 39% identifying as queer. Five themes emerged: (1) the separation between queerness and Vietnameseness; (2) the impact of family of origin on gender identity formation especially for those socialized as Vietnamese daughters but who now identify as trans, non-binary, or gender non-conforming; (3) the political orientations of queerness and non-binaryness; (4) the significance of allyship, and (5) the intersection of religion with beliefs surrounding gender and sexuality.
Aim 2: Drawn from the same 51 semi-structured interviews as Aim 1 and 12 months (January 2023 - 2024) of intensive participant observation, four themes surfaced: (1) organizing as a constellation, (2) non-profitization of radical politics, (3) politics of non-disposability, and (4) gestures to queer and trans Vietnamese political approaches.
Aim 3: 38 participants completed cross-sectional egocentric network surveys (N = 628 alters, 666 total nodes). Larger networks and higher number of closer relationships tended to correlate with better mental health, while certain types of homophily, such as religion and gender, may provide protective health effects. Although specific associations between network structural characteristics and mental health were not statistically significant, they generally followed expected patterns, with social support density associated with better mental health and influence/conflict density linked to poorer mental health outcomes.
Discussion
The findings illuminate the complexities inherent in navigating intersecting identities and systemic oppressions within LGBTQ+ Vietnamese American communities. Intergenerational trauma and community support emerge as pivotal themes, highlighting the resilience fostered through supportive relationships despite the pervasive presence of trauma. Religion emerges as both a source of solace and struggle, underscoring the nuanced relationship between religious communities and mental well-being. Moreover, the exploration of gender identity and advocacy reveals discordant experiences, emphasizing the need for intersectional approaches in understanding and addressing the diverse needs within LGBTQ+ communities. This dissertation underscores the imperative of supporting grassroots social movements in promoting the well-being of LGBTQ+ Vietnamese Americans and in resisting and undoing community-level levers of oppression.Public healthAsian American studiesLGBTQ studiesLGBTQMental HealthMixed MethodsOrange CountySocial NetworksVietnameseenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7rw318txhttps://escholarship.org/content/qt7rw318tx/qt7rw318tx.pdfetdoai:escholarship.org:ark:/13030/qt6dw8z3bz2026-05-28T05:03:07Zqt6dw8z3bzPsychosocial Work Characteristics, Cognitive Function, and Subjective Memory among Midlife and Older Adult Workers in the United StatesGuardiano, MeganLi, Jian2025-01-01BackgroundPsychosocial work conditions and cognitive health are growing concerns among the aging United States (U.S.) workforce. Under a preliminary enactive-Biopsychosocial Model framework and utilizing the validated Job Demand-Control (JDC) and Effort-Reward Imbalance (ERI) models of psychosocial work, this dissertation explored associations of U.S. midlife and older adult workers’ perceived JDC and ERI with both objective and subjective cognitive health outcomes, while accounting for physical, psychological, and social characteristics.
Aims
This dissertation aimed to examine longitudinal associations of JDC and ERI psychosocial work characteristics with changes in cognitive function and subjective memory among U.S. workers in midlife and older age.
Methods
Data were obtained from two U.S. population-based datasets, the Midlife in the United States (MIDUS) study and the Health and Retirement Study (HRS), to explore study aims among midlife and older workers, respectively. For each worker sample, self-reported JDC and ERI psychosocial work categorical characteristics were the baseline exposure (MIDUS II, 2004-2006; HRS 2006-2008). Continuous cognitive outcomes of objective cognitive function test performance (MIDUS: Brief Test of Adult Cognition by Telephone; HRS: modified Telephone Interview for Cognitive Status) and subjective memory perceptions were measured at baseline and in subsequent follow-up survey waves (MIDUS III, 2013-2014; HRS 2008-2018). Covariates included demographic, socioeconomic, lifestyle, health, and work characteristics. Generalized estimating equations with multivariable models adjusting for covariates were conducted to analyze the longitudinal associations of baseline JDC and ERI with changes in cognitive outcomes.
Results
Results are presented in four manuscripts. Among workers in midlife, high reward, independently and in combinations with effort, was significantly associated with increased cognitive function over an approximate nine-year period. Among older workers, high job control, independently and in combinations with job demand, was associated with increased cognitive function across up to 12 years of follow-up. Both high job control and high reward characteristics were significantly associated with improved subjective memory perceptions among both worker cohorts.
Conclusions
Findings suggest long-term associations of psychosocial work with cognitive health among these U.S. worker samples. Together with future research investigations, nursing practice may incorporate assessments and education of psychosocial work and cognitive health concerns in workplaces and communities.NursingOccupational psychologyAgingcognitive functioneffort-reward imbalancejob demand-controlpsychosocial worksubjective memoryworkersenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6dw8z3bzhttps://escholarship.org/content/qt6dw8z3bz/qt6dw8z3bz.pdfetdoai:escholarship.org:ark:/13030/qt3xh9q4qf2026-05-28T05:02:27Zqt3xh9q4qfChaperone Proteins: Bridging Protein Accumulation with Epigenetic Mechanisms and Gene Regulation in Arabidopsis thalianaBoone, Brandon AustinJacobsen, Steven E2024-01-01DNA methylation has been studied for decades and is necessary to maintain cellular homeostasis from humans to plants. However, there remains a dearth of information regarding the function of proteins that bind DNA methylation. It remains unclear if plant DNA methylation binding proteins bind DNA methylation, which methylation sequence context (CG, CHG, or CHH) they prefer, or how these proteins interpret DNA methylation to regulate gene or transposable element (TE) expression. Therefore, we studied the methyl-CpG-binding domain (MBD) protein family in Arabidopsis thaliana to expand on the mechanisms of DNA methylation-mediated gene regulation. We discovered that Arabidopsis MBD proteins MBD5 and MBD6 redundantly regulate the expression of promoter-methylated genes and TEs by recruiting chaperone proteins ACD15, ACD21, and SLN. We further discovered that the MBD5/6 complex co-opts the oligomerization capacity of small heat shock proteins (sHSPs), ACD15 and ACD21, to accumulate the MBD5/6 complex at sites of high-density methylation creating nuclear foci which are further regulated by SLN. Although chaperone proteins often interact with bound complexes, the surprising specificity with which ACD15, ACD21, and SLN directly regulate the MBD5/6 complex is novel and unprecedented for DNA methylation binding proteins. Furthermore, proper accumulation is necessary for gene regulation by the MBD5/6 complex suggesting protein accumulation at DNA methylation sites is an integral step in DNA methylation-mediated gene regulation in plants.
Our results suggest this chaperone-mediated accumulation is not specific to Arabidopsis proteins as protein accumulation was caused by sHSPs from other organisms including humans, archaea, bacteria, protists, and other plant species. Using a chimeric protein approach, we show that MBD6 foci can be replicated using these sHSPs, in an ACD15/ACD21 independent manner, leading to subtle gene silencing. We further demonstrate chaperone-mediated accumulation using these sHSPs by targeting specific genomic loci using the SunTag, dCa9 targeting system. These results support the role of protein accumulation in DNA methylation pathways while providing novel mechanisms for protein localization in chromatin environments.
Evidence is growing that efficient protein accumulation and localization are important for the function of chromatin complexes. The discovery of phase separation events and membrane-less compartments has led to a reevaluation of how protein dynamics can impact chromatin function through novel and potentially overlooked mechanisms. Our work has added to this evidence by demonstrating that plants utilize chaperone proteins to efficiently and specifically accumulate DNA methylation-binding proteins, forming an organized and functional accumulation.
Molecular biologyBiochemistryGeneticsChaperonesDNA MethylationEpiGeneticsGene RegulationGene SilencingPhase Separationenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3xh9q4qfhttps://escholarship.org/content/qt3xh9q4qf/qt3xh9q4qf.pdfetdoai:escholarship.org:ark:/13030/qt0k9300zn2026-05-28T05:01:20Zqt0k9300znTranslating Pain, Communicating Care: Representing Expertise, Kinship, and Disability Through the Distress and Discomfort Assessment ToolParks, RachelThroop, Christopher J2024-01-01This dissertation describes an ethnographic project exploring pain, communication, and disability. Assessing and relating to pain is a fundamental endeavor, not just of Western biomedical practitioners, but of any type of caregiver, and it is a task that is rendered even more difficult when pain is experienced by people who do not have access to words. The DisDAT, or Distress and Discomfort Assessment Tool, is a form developed in the UK designed to allow clinicians to avoid, or at least mitigate, distress among nonverbal patients by documenting their personalized distress and contentment cues. I conducted my primary ethnographic research for this dissertation embedded within and alongside a clinical team at my field site as they seek to facilitate the adoption of the DisDAT at their hospitals. In addition, I used creative autoethnographic photography to explore the resonances between my personal experiences of pain and disability and those of my interlocutors. Ethnographic methods used in this study included qualitative interviews, content analysis, participant-observation, and autoethnography.This ethnographic project takes the DisDAT tool itself as a starting point to explore several distinct but nested aims. I ask what the goals and difficulties of the DisDAT implementation project can reveal about bureaucracy and the pursuit of quality in the U.S. medical system as a while. I also explore how family members and professionals understand signs of pain or distress and translate them into the “vocabulary” represented by the DisDAT, along with how kinship and the work of care are constituted through the form. Finally, I use autoethnography to examine what claims can be made about the universality versus individuality of both pain and disability and the communication of pain experience through artistic work.These are complex theoretical questions, and also urgent pragmatic ones that caregivers must seek to answer every day. By engaging ethnographically with medical caregivers and family members of patients who are involved in the DisDAT project, I hope to assist them in answering these questions for themselves, as well as contribute to the evolving anthropologicalliterature on this topic.Disability studiesMedicinecarechildrencommunicationdisabilitydistresspainenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0k9300znhttps://escholarship.org/content/qt0k9300zn/qt0k9300zn.pdfetdoai:escholarship.org:ark:/13030/qt0704r3qw2026-05-28T05:01:11Zqt0704r3qwContested Bodies: Fictions of Brownness and LatinidadSolis, SamanthaLópez, Marissa K2024-01-01This dissertation traces a genealogy of brown performance and aesthetics from the 1980s to the present. As the title suggests, contestation is a central part of the framework I use to think through the ways that contemporary artists of color negotiate the racial scripts and legacies of white supremacy that structure their lives and professional opportunities. I am interested in brownness as a disidentification with identity politics and their commodification in the contemporary United States. Focusing on brown(ed) bodies as sites of contestation—textual imaginaries of bodies, bodies of scholarship, bodies on stage, on screen, and online— I ultimately theorize brownness as a queer ecology: as a set of unruly connections, perpetuating slippages between racialized identities that at once offer the potential for antiracist and anti-imperial solidarities and also the opportunity for neoliberal cooptation through multicultural discourse and the commodification of brown aesthetics through popular media and celebrity cultures. Chapter 1, which centers on Gloria Anzaldúa and Richard Rodriguez, considers contentious critical histories of Chicano literary studies, leading me to a theorization of queer brown ecologies as the nourishment, attunement to, and production of texts committed to thinking in community. Chapter 2 continues this theorization through the works of friends and artists Gil Cuadros and Laura Aguilar, and their queer worldbuilding: I argue that attuning to the queer and brown media ecologies created and made possible by these artists and their friends highlights the affecting/affective power of friendship as a mode of queer life. Chapter 3 focuses on the career of poet and novelist Melissa Lozada-Oliva to consider the brown commons made manifest in the queer media ecologies—digital and physical—by which we encounter contemporary poetry. Finally, Chapter 4 focuses on the contestation and protest, complicity and corporatization that crystallize in the celebrity discourse of actor and influencer Vanessa Hudgens. I suggest that affective, unruly brown texts create possibilities for anticolonial, anticapitalist solidarities based in the brown convergences US imperialism and racism has produced.Hispanic American studiesEthnic studiesenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0704r3qwhttps://escholarship.org/content/qt0704r3qw/qt0704r3qw.pdfetdoai:escholarship.org:ark:/13030/qt5m95d20d2026-05-27T05:02:41Zqt5m95d20dThe impact of germline variability on the molecular landscape of pancreatic adenocarcinomaChen, Caroline YenjungBoutros, Paul C2025-01-01Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with rising incidence that is projected to become the second leading cause of cancer-related mortality within five years. The influence of germline genetic features on tumour heterogeneity is poorly understood and represents a novel avenue for defining clinically meaningful classifications to improve diagnostic and therapeutic approaches. Here we present the impact of two primary contributors to germline variability: sex and ancestry, along with common genetic variants (as represented by a polygenic risk score or PRS) on the somatic mutational landscape of PDAC. We discovered ancestry-specific mutation patterns, particularly in global point mutation density and in the driver gene GNAS. While no overall ancestry associations were found in KRAS (the predominant driver in PDAC), evaluation of KRAS variants indicated a trend towards ancestry-related variation, in which poor risk G12D variants were more frequent in those of European ancestry, and favorable risk G12R variants were more frequent in those of African ancestry. Notably, higher inherited genetic risk was significantly associated with increased somatic mutation burden including higher percent genome alteration, greater global point mutation density and increased driver SNVs in MUC4 and SNHG14. Our study demonstrates that germline ancestry influences PDAC tumour heterogeneity, and further investigation into these associations could refine precision oncology strategies. Furthermore, PRS may represent a non-invasive tool to identify individuals at higher risk of developing genomically unstable, and thus potentially more aggressive, PDAC. In summary, our study demonstrates how germline factors including ancestry, sex and common variants influence the somatic mutational landscape of PDAC. These findings underscore the potential for germline-informed approaches to improve risk assessment and guide personalized treatment strategies.GeneticsOncologyenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5m95d20dhttps://escholarship.org/content/qt5m95d20d/qt5m95d20d.pdfetdoai:escholarship.org:ark:/13030/qt4x4033h42026-05-27T05:02:37Zqt4x4033h4Modeling gene regulatory logic in the innate immune responseSchiffman, AllisonHoffmann, Alexander2025-01-01This thesis highlights the regulation of the interferon β (IFNβ) enhancer by NFκB and IRF as well as epigenomic changes driven by NFκB and IRF. Precise regulation of the immune cytokine IFNβ is essential for human health. Classic studies established that the transcription factors NFκB and IRF function synergistically in activating IFNβ expression. The regulation of IFNβ promoter activity has been a touchpoint of mammalian gene control research since the discovery of functional synergy between two stimulus-responsive transcription factors (TFs) nuclear factor kappa B (NFκB) and interferon regulatory factors (IRF). However, subsequent gene knockout studies revealed that this synergy is condition-dependent such that either NFκB or IRF activation can be dispensable, leaving the precise regulatory logic of IFNβ transcription an open question.In Chapter 2, we develop a series of quantitative enhancer states models of IFNβ expression control and evaluate them with stimulus-response data from TF knockouts. Our analysis confirmed that TF synergy is a hallmark of the regulatory logic but that it need not involve NFκB, as synergy between two adjacent IRF dimers is sufficient.
In Chapter 3, we evaluate the stimulus specificity of the IFNβ enhancer. We found that a sigmoidal binding curve at the distal site renders the dual IRF synergy mode ultrasensitive, allowing it only in conditions of high IRF activity upon viral infection. In contrast, the proximal site has high affinity and enables expression in response to bacterial exposure through synergy with NFκB. However, its accessibility is controlled by the competitive repressor p50:p50, which prevents basal IRF levels from synergizing with NFκB, such that NFκB-only stimuli do not activate IFNβ expression. The enhancer states model identifies two commonly used modes of synergy that are accessed differentially in response to different immune threats, enabling a highly stimulus-specific but also versatile regulatory logic for stimulus-specific IFNβ expression.
In Chapter 4, we investigate mechanisms of enhancer formation driven by NFκB and IRF. In response to specific pathogens, the NFκB pathway and the IRF pathway modify the chromatin state and epigenome, increasing chromatin accessibility and depositing new transcriptional enhancer marks. The mechanisms by which this epigenomic remodeling occurs remains unclear. It has been thought that only a distinct subset of transcription factors, called “pioneer factors,” can bind to and open condensed chromatin. However, recent evidence has shown that NFκB can bind to nucleosomal DNA and displace histone-DNA contacts. We test the hypothesis that NFκB increases chromatin accessibility solely through its DNA binding activity, without requiring additional proteins to be recruited by its transcriptional activation domain. We additionally profile the dynamic enhancer formation process and find evidence for different mechanisms involving various histone methyltransferases, histone acetyltransferases, RNA polymerase, and other cofactors.ImmunologyApplied mathematicsgene regulatory circuitsinnate immunityinterferon betathermodynamic modelsenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4x4033h4https://escholarship.org/content/qt4x4033h4/qt4x4033h4.pdfetdoai:escholarship.org:ark:/13030/qt4348v9b92026-05-27T05:02:08Zqt4348v9b9The Impact of Work-Family Conflict on Changes in Lipid Risk Factors, Allostatic Load, and Perceived Stress Among Workers: Four-Year Longitudinal Evidence from the Midlife in Japan StudySaiki, MayumiLi, Jian2025-01-01Background: A growing number of studies have reported that occupational psychosocial factors increase the risk of CVD. CVD risk factors, such as hypertension and dyslipidemia, are prevalent among younger individuals who have dual roles at both work and home. This suggests that conflict between these two domains may cause stress that contributes to CVD. Work-family conflict has two directions: work-to-family conflict (WFC), where work roles interfere with family roles, and family-to-work conflict (FWC), where family roles interfere with work roles. Although many workers experience work-family conflict, research examining its effects is still lacking.
Allostatic load (AL) represents a phase of failing adaptation to stress, which is a main biological mechanism that explains the pathway from stress to disease. Lipid risk factors are well-established cardiovascular biomarkers, and the associations between allostatic load index (ALI) and CVD have also been documented. Both ALI and lipid risk factors can capture the wear and tear of the biological system prior to the disease manifestation. Research also indicates that increased perceived stress leads to CVD risk factors, such as unhealthy dietary choices, health risk behaviors, elevated waist-to-hip ratio, lower cardiovascular reactivity, and increased glycosylated hemoglobin levels. Therefore, this dissertation research used the Midlife in Japan longitudinal data set to investigate the impact of WFC and FWC on risk factors for cardiovascular disease that included increased blood lipid risk factors, ALI, and perceived stress, with three aims listed below:
Aim 1: To investigate the impact of baseline WFC and FWC on changes in lipid levels measured at baseline and at a four-year follow-up point.
Aim 2: To examine whether WFC and FWC at baseline would be associated with changes in ALI measured at baseline and at a four-year follow-up point.
Aim 3: To investigate whether exposure to work-family conflict would be associated with changes in perceived stress measured at baseline and at a four-year follow-up point.
Methods: This study used longitudinal data from the Midlife in Japan study with a four-year interval. Inclusion criteria were participants employed at baseline without missing variables of interest. Each study had a different number of participants. Aims 1 and 2 included 152 participants, and Aim 3 included 419 participants. Work-family conflict was measured using a validated scale with eight items (each WFC and FWC has four items), and higher scores indicated higher levels of WFC or FWC. WFC and FWC were used as continuous variables in this study.
Aim 1 assessed the associations of baseline WFC and FWC with changes in metabolic risk factors between baseline and follow-up using Generalized Estimating Equations (GEE) linear regression. Results were reported as beta coefficients (beta) and 95% confidence intervals (CIs).
Aim 2 examined the four-year longitudinal changes in the ALI using two methods: comparing ALI scores at baseline and follow-up, named “Two ALI approach,” and creating one ALI score considering changes at baseline and follow-up, named “One ALI approach.” GEE negative binomial regression was used for analyses. The results were reported by count ratios (CRs) and 95% CIs.
Aim 3 investigated the associations of baseline WFC and FWC with changes in perceived stress measured by the Perceived Stress Scale 10-item version (PSS-10). For this analysis, in addition to continuous WFC and FWC, they were dichotomized at the median to create binary (low and high) WFC and FWC. The higher perceived stress scores indicated higher levels of perceived stress. GEE linear regression was applied in this study. The results were reported as beta and 95% CIs.
Results:
Aim 1: After adjusting for baseline sociodemographic, work and family-related, and lifestyle factors, the fully adjusted model showed that WFC was significantly associated with increased low-density lipoprotein cholesterol (LDL-C), with beta = 1.73, 95% CIs (0.13, 3.33), as well as the increased total cholesterol (TC)/high-density lipoprotein cholesterol (HDL-C) ratio, with beta = 0.04 (0.00, 0.09). However, FWC was not significantly associated with changes in any lipid risk factors.
Aim 2: In the final model, our results demonstrated that WFC at baseline was significantly associated with increased ALI using “Two ALI approach” CR = 1.15, 95% CIs (1.03, 1.28) and “One ALI approach” CR = 1.15, 95% CIs (1.01, 1.32), whereas FWC did not show statistical significance in both methods.
Aim 3: In the fully adjusted model, dichotomized and continuous WFC were significantly associated with increased perceived stress, with beta = 2.33, 95% CIs (1.28, 3.37) and beta = 1.53, 95% CIs (0.93, 2.13), respectively. Both dichotomized and continuous FWC were also significantly associated with increased perceived stress across models. The final model showed dichotomized and continuous FWC with beta = 2.29, 95% CIs (1.35, 3.22) and beta = 1.30, 95% CIs (0.80, 1.80), respectively.
Conclusion: Our findings indicated a significant impact of WFC on LDL-C, TC/HDL-C ratio, and ALI, but no significant impact of FWC on lipid risk factors and ALI. In contrast, both WFC and FWC increased perceived stress compared at baseline and at a four-year follow-up time point. Since lipid risk factors, ALI, and perceived stress are associated with CVD, it is crucial to mitigate the effects of work-to-family conflict. Stress management that addresses work-family conflict can be beneficial when occupational health nurses develop interventions to enhance workers’ cardiovascular health. Intervention studies to mitigate work-family conflict will be warranted. NursingOccupational psychologyAllostatic Load IndexJapanLipid Risk FactorsLongitudinal StudyPerceived StressWork-Family Conflictenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4348v9b9https://escholarship.org/content/qt4348v9b9/qt4348v9b9.pdfetdoai:escholarship.org:ark:/13030/qt26z8c7q12026-05-27T05:01:41Zqt26z8c7q1After the Final Bell: A Critical Ethnography with a Youth-Serving Community-Based Education Non-ProfitCohen, AbbieHoward, TyroneRogers, John S2025-01-01Out-of-school time (OST) educational non-profits can offer positive and affirming programming to students who attend chronically underfunded public schools. Despite this, OST non-profits, similarly to public schools, have been impacted by the privatized restructuring and reform of the past several decades. One striking difference, though, is the impact of OST non-profits’ reliance on the philanthropy of individuals, foundations, and corporations to run their programming. This critical and participatory ethnography investigates an OST non-profit from the top-down and the bottom-up to illuminate the complex racialized systems and structures that sustain and limit these organizations, and highlights the power of participatory methods to transform scholarship, communities, and individuals. The questions guiding my study are: 1) How do youth-serving community-based non-profits manage the tension between private philanthropy and the needs of their students?
2) How do organizational stakeholders define success? Using critical theory I find three overlapping tensions facing the non-profit of focus. First, I capture a tension of measuring success—how wealthy actors exert their definitions of success that are defined by a desire for easy-to-digest quantifiable data points rather than built on the collective or collaborative ideas of the on-the-ground stakeholders. Second, I introduce how the pressure of expansion influences the organization to grow beyond its community-based mission in order to be more visible to white elite donors. Finally, I demonstrate a tension of performance—how the organization performs for its donors, and how its donors perform for the organization. Drawing on rich ethnographic data, I demonstrate how power is reproduced to those at the top of a structurally and socially designed hierarchy—where elite donors and board members, defined by whiteness and capital, influence and transform the non-profit.This dissertation highlights an overlooked educational learning and developmental site while capturing how the privatized restructuring of public education has infiltrated the OST non-profit landscape. This research deepens scholarship, practice, and policy on democratic goods, privatization, the non-profit sector, and philanthropy. Finally, my participatory methods disrupt conventional ethnography by collectively building towards more justice and solidarity as defined by community partners themselves.Educational sociologyEducation policyEducationcritical ethnographynon-profitsparticipatory researchphilanthropypublic educationraceenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/26z8c7q1https://escholarship.org/content/qt26z8c7q1/qt26z8c7q1.pdfetdoai:escholarship.org:ark:/13030/qt0594f2qp2026-05-27T05:01:32Zqt0594f2qpPalladium-Catalyzed Reactions of Cyclic Allenes and Strain-Promoted Reactions of Cyclic 1,2,3-TrienesWitkowski, Dominick ChristopherGarg, Neil K2025-01-01 This dissertation describes the development of methodologies that engage strained cyclic intermediates in complexity-generating reactions. One major effort involves the transition metal-catalyzed interception of strained cyclic allenes, which has been accomplished using palladium catalysis. Additionally, the study of alternative minimally explored strained intermediates including cyclic 1,2,3-trienes and heterocyclic 1,2,3-trienes are reported. These studies contribute to fundamental understanding of structure and reactivity of transient strained compounds and give rise to polycyclic products. Computational studies relating to the mechanism of strained intermediate generation from Kobayashi precursors are also reported. Chapters one and two are related to the development of palladium-catalyzed reactions of strained cyclic allenes. Chapter one describes the development of a modular annulation reaction of strained allenes and arylpalladium species. This methodology employs aryl halides and cyclic allene precursors to generate fused heterocyclic products via the formation of two new bonds and a new center. Chapter two described the development of a catalyst-controlled annulation reaction of strained allenes and -allylpalladium species. This methodology employs vinyl benzoxazinones and cyclic allenes precursors to generate two isomeric products with high selectivity based on the ligand employed. The development of these palladium-catalyzed reactions demonstrates that despite their high reactivity and short lifetimes, strained cyclic allenes efficiently engaged in catalytic processes, to access complex products, including examples with absolute stereocontrol.
Chapter three describes the development of strained 1,2,3-cyclohexatrienes, which have remained underexplored historically, as synthetic building blocks. Studies of the reactivity of the unsubstituted 1,2,3-cyclohexatriene, as well as its mono- and disubstituted derivatives are reported, drastically expanding the scope of reactions known for such intermediates. Combined computational and experimental studies elucidate the factors controlling regioselectivity in reactions of an unsymmetrical strained triene. Furthermore, the potential utility of strained trienes in rapidly generating complex scaffolds is demonstrated through the integration of triene trapping reactions into multistep synthetic sequences to access polycyclic products. These studies highlight the potential of these traditionally avoided species for broader use in synthetic chemistry.
Chapter four details the study of six-membered heterocyclic 1,2,3-trienes, particularly 4,5-didehydropyridones. Computational studies of the structure of such species, as well as the development of a synthetic route to access precursors to the same, are reported. Scope studies demonstrate the utility of six-membered azacyclic 1,2,3-trienes for accessing complex nitrogen-containing heterocycles, and trends in the regioselectivity observed therein are explored through computational studies. Collectively, this study demonstrates the value of six-membered azacyclic 1,2,3-trienes, a previously unexplored class of strained cyclic intermediates, in heterocycle synthesis, while pushing the limits of strained intermediate chemistry.
Chapter five describes the reaction mechanisms of the fluoride-mediated generation of selected strained intermediates from Kobayashi precursors. We interrogate several mechanistic aspects using Density Functional Theory (DFT) calculations and find that the eliminations to form alkynes and alkenes can take place through primarily two different mechanisms. This study is one of the few theoretical studies on Kobayashi eliminations for the generation of strained intermediates. It is anticipated that this report will enable the rational design of new strained intermediate precursors for future uses in synthesis.ChemistryOrganic Chemistryenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0594f2qphttps://escholarship.org/content/qt0594f2qp/qt0594f2qp.pdfetdoai:escholarship.org:ark:/13030/qt4mk962hm2026-05-26T06:32:50Zqt4mk962hmMultiracial Family Socialization and IdentitiesGoodwin, DejaCollett, Jessica2026-05-22Family racial socialization is a complex yet essential process for multiracial individuals—people with mixed-race ancestry and parents from two or more distinct racial groups. Learning about racial identities and location in the racial order is complicated by social positions between traditional racial categories and within interracial families. With the potential for multiple racial identification options and a variety of racial socialization experiences, multiracial people’s perceptions of their socialization processes and self-identification can provide key insights for understanding how race is learned, negotiated, and reproduced. My dissertation seeks to understand how multiracials experience racial socialization across different family structures (i.e., with or without siblings; with or without multiracial parents) and develop their racial identities. Building on a foundation of literature that identified approaches to racial socialization and established the essential influence of family in identity development, my work illuminates how multiracial people’s racial socialization experiences may vary by familial resources and differently influence their racial identities.To conduct this research, I interviewed 140 multiracial individuals about their racial socialization experiences, family dynamics, and racialized senses of self. My findings, presented in three empirical chapters, focus on patterns of family racial socialization experiences, racial socialization in institutions outside of the family, and the experiences of later-generation multiracials (i.e., multiracial participants who have a parent with multiracial ancestry). The first empirical chapter illustrates the distinction between explicit and implicit family racial socialization, finding that participants who experienced explicit family racial socialization describe strong senses of their racial identities while those who experienced implicit racial socialization describe weaker senses of their racialized selves. The next chapter examines how participants who described implicit family racial socialization acquire resources for racial learning, finding that social institutions outside of the family (i.e., schools, neighborhoods, media) can serve as socializing institutions with the capacity to provide both explicit and implicit racial socialization. The final chapter specifically examines later-generation multiracial participants (i.e., those with a multiracial parent), finding unique patterns of early racial consciousness and mother-led socialization not seen amongst first-generation multiracial participants. Each empirical chapter includes research implications for future studies and practical implications for multiracial families.SociologyPsychologyDevelopmental psychologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4mk962hmhttps://escholarship.org/content/qt4mk962hm/qt4mk962hm.pdfetdoai:escholarship.org:ark:/13030/qt8rk6z2r52026-05-26T06:32:45Zqt8rk6z2r5Towards Universal Event ExtractionParekh, Tanmay DevendraPeng, NanyunChang, Kai-Wei2026-05-22Extracting structured knowledge from unstructured text "in the wild" remains a fundamental challenge for AI, often bottlenecked by data scarcity and the brittle reasoning of standard models in specialized domains. This dissertation introduces a unified framework for Universal Event Extraction that successfully bridges the gap between open-domain generalizability and high-stakes real-world applications. To drive this generalizability, we propose targeted synthetic data generation pipelines that dynamically adapt to novel schemas and domains, overcoming the manual annotation bottleneck. Simultaneously, to resolve the discriminative reasoning failures of off-the-shelf language models, we architect specialized multi-agent frameworks that enhance planning and reasoning by decomposing complex extraction into iterative, verifiable steps. Crucially, this combined approach can now extract arbitrary event types across new languages and domains in completely zero-shot and low-resource settings. Grounding the application of this universal paradigm, we demonstrate its profound real-world efficacy by successfully deploying it for automated global epidemic forecasting. Ultimately, this thesis establishes a scalable foundation for transforming raw text into structured event graphs, powering deeper semantic understanding and strategic decision-making for next-generation AI models.Computer scienceArtificial intelligenceInformation technologyagentic frameworksepidemic predictionevent extractioninformation extractionlarge language modelssynthetic data generationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8rk6z2r5https://escholarship.org/content/qt8rk6z2r5/qt8rk6z2r5.pdfetdoai:escholarship.org:ark:/13030/qt9zn4822m2026-05-26T06:32:40Zqt9zn4822mBreaking the Rules, Synthetic Efforts Toward Keramaphidin B, and Uplifting OthersGonzalez, JordanGarg, Neil K2026-05-22This dissertation describes both synthetic endeavors and outreach initiatives that challenge conventions and drive innovation. The thesis begins with discussion on the importance of rules in synthetic chemistry and the benefits of trying to break them, specifically through the lens of generating “forbidden” compounds and activating inert bonds. The bulk of this dissertation details studies toward the total synthesis of keramaphidin B, a complex macrocyclic alkaloid. Key to the overall synthetic approach is the use of an unconventional building block, a strained cyclic allene, to construct the core of the natural product. Cyclic allenes are overlooked in the context of total synthesis, and herein the power of these species in constructing molecular complexity is examined. The final foray recounts important outreach initiatives that reflect shifting paradigms beyond the laboratory. The chapters are more specifically outlined as follows.Chapter one offers a focused perspective on rule-breaking in chemical synthesis. Despite commonly accepted norms, the general solution to anti-Bredt olefins and the activation of amides via nickel catalysis represent advances in synthesis that overturn decades of conventional thinking. This chapter highlights the importance of viewing rules as foundational guidelines rather than absolute truths. Challenging rules can prompt innovation, leading to fundamental discoveries and practical advances.Chapters two and three detail comprehensive efforts toward the total synthesis of keramaphidin B leveraging Diels–Alder cycloadditions of geometrically-distorted cyclic allenes. Chapter two focuses on model system studies involving azacyclic allenes and pyrones, which result in the construction of the azadecalin core of the natural product and simultaneous installation of a challenging quaternary center with excellent overall selectivity. Several approaches to construct the [2.2.2]-bridged bicycle via iminium Diels–Alder cycloadditions are also described. Chapter three provides a complete account of strategies pursued toward achieving the total synthesis of keramaphidin B, with a central focus on an undisclosed Diels–Alder cycloaddition between and azacyclic allene and a pyridone. This successful approach results in the direct construction of the core of the natural product in only four steps. Subsequent challenging C–C bond forming reactions were developed, ultimately enabling exploration of the synthetic endgame. Collectively, these studies illustrate the ability of unconventional cyclic allenes to enable complex molecule synthesis.Chapter four describes the success of the Organization for Cultural Diversity in Science (OCDS), a graduate student-led group focused on empowering the next generation of scientific leaders. Impact is delineated across four areas: (A) Academic Lecture Series, in which speakers from underrepresented groups are invited to share their scientific work with the UCLA community; (B) Professional Development, wherein networking events and grant-writing workshops are programmed to ensure equitable access to resources for all students; (C) Scientific Outreach, in which aspiring scientists from the greater Los Angeles area are introduced to research opportunities; and (D) Community Building, where connections are fostered within the UCLA student community. Through these efforts, OCDS has made a significant positive impact at UCLA.Organic chemistryChemistryAnalytical chemistryComputational chemistryOutreachStrained IntermediatesTotal SynthesispubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9zn4822metdoai:escholarship.org:ark:/13030/qt9s19z60j2026-05-26T06:32:35Zqt9s19z60jBehavioral, computational, and neural insights into human learning and memoryPeck, FlemingRissman, Jesse2026-05-22The brain rapidly integrates incoming information with internal models of the world to guide behavior over short timescales, drawing on both goal-directed and implicit processes. A central challenge is that the relevance of information often depends on context. Context shapes how information is interpreted, which knowledge is used to guide behavior, but can also bias behavior in unproductive ways when it is non-diagnostic. Understanding how context is integrated during learning and used to guide behavior provides insight into the mechanisms underlying adaptive cognition. In Chapter 2, I review the literature on statistical learning and context-dependent learning, examining how paradigm design factors may contribute to differences in prior findings and highlighting opportunity to extend existing models to account for hierarchical learning. Chapter 3 presents evidence of context-dependent statistical learning with visual stimuli and uses recurrent neural network models to show how hidden layer representations support context-appropriate predictions in the absence of explicit context cues. Chapter 4 shows that context reinstatement can impair memory for the temporal order of objects from the same context, using eye-tracking measures of gaze behavior and generalized linear mixed-effects modeling to index retrieval. Chapter 5 applies machine learning to EEG data from working memory tasks, including a context-dependent control task (e.g., DPX), extracting a broad set of spectral and non-spectral time series features to successfully predict cognitive performance while showing limited generalization to psychopathology, highlighting both strength and weakness of brain-behavior modeling. Overall, this dissertation comprises empirical and theoretical advances in the understanding of human learning and memory drawing on the study of behavior, neural activity, and computational modeling.NeurosciencesPsychologyBehavioral psychologyCognitive psychologycontexteye trackingneural networksstatistical learningtemporal memoryworking memoryapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9s19z60jhttps://escholarship.org/content/qt9s19z60j/qt9s19z60j.pdfetdoai:escholarship.org:ark:/13030/qt0h95w5n72026-05-26T06:32:29Zqt0h95w5n7A Case Study Examining Social Emotional Learning Through LGBT-Inclusive Education: Lessons in Practice from Scotland's Experience with Representative Curriculum and InstructionCiszek, DariuszOmwami, Edith M2026-05-22This case study examines Scotland's national approach to LGBT-inclusive education through the lens of social and emotional learning (SEL). LGBT youth face significant adversity in school settings, including elevated rates of bullying, mental health challenges, and academic disengagement, yet SEL programs often fail to adequately address their social and affective needs. In 2021, Scotland became the first country to mandate LGBT-inclusive education across all public schools, implementing a comprehensive instructional model through its government-designated partner, Time for Inclusive Education (TIE). TIE develops and supports schools in implementing an LGBT-inclusive curriculum to address gender-based stereotypes and homophobia in schools.With a focus on illustrative practice, this case study examines how TIE's instructional model integrates anti-bias education into an inclusive curriculum to support students' social-emotional competencies during middle childhood. Using the CASEL Five as a conceptual framework, the analysis draws on classroom field notes reconstructed as instructional vignettes, semi-structured interviews, diary studies, survey data, and curricular documents. Findings reveal that TIE's instructional model provides consistent opportunities for students to engage with SEL capacities across CASEL's five domains, with particular emphasis on social awareness. Through dialogic instruction, scenario-based learning activities, visual media, and calibrated content exposure, students engaged in perspective-taking, examined (cis)heteronormative social norms, and, through SEL practice, challenged gender-based stereotypes and homophobic language. Moreover, the study found that active perspective-taking functioned as a foundational social cognitive competency among youth. The research extends the current scholarship by illustrating how LGBT-inclusive education can simultaneously advance school-based equity programming and support essential social-emotional competencies that benefit all students. The study offers educators, policymakers, and SEL practitioners an empirically grounded model for integrating LGBT diversity curricula within transformative social-emotional learning.Educational psychologyCurriculum developmentLGBTQ studiesEducation policyGender studiesCurriculum and InstructionLGBT YouthSocial and Emotional Learningapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0h95w5n7https://escholarship.org/content/qt0h95w5n7/qt0h95w5n7.pdfetdoai:escholarship.org:ark:/13030/qt6wb9f66f2026-05-26T06:32:24Zqt6wb9f66fMultiscale Modeling and Soft Sensing of Semiconductor Manufacturing ProcessLin, AndreaChristofides, Panagiotis PDC2026-05-22The continued scaling of semiconductor devices toward sub-5 nm technology nodes requires manufacturing techniques capable of achieving atomic-level precision on complex three-dimensional architectures. Area-selective atomic layer deposition (AS-ALD) and atomic layer etching (ALE) have emerged as pivotal strategies for bottom-up fabrication, utilizing self-limiting surface reactions to enable self-aligned patterning and precise material removal. To optimize these processes and address challenges such as selectivity degradation and equipment variability, this thesis develops integrated multiscale modeling and data-driven frameworks that bridge fundamental surface kinetics with industrial-scale process control.First, a multiscale simulation framework is developed for the AS-ALD of SiO2 on Al2O3 and SiO2 surfaces to investigate the mechanisms of selective growth. This framework couples a microscopic Monte Carlo-based collision model with reactor-scale computational fluid dynamics (CFD) to analyze how precursor transport delays and inhibitor coverage influence surface-level selectivity. The study further introduces integrated etching steps within the ALD cycle, providing a strategy to recover selectivity by periodically removing unwanted nucleation and identifying optimal etching durations for maximum process efficiency. Next, an adaptive multivariable run-torun (R2R) control framework is developed for the integrated AS-ALD/ALE process. By utilizing exponentially weighted moving average (EWMA) controllers and a sequential relay protocol, the framework demonstrates the ability to maintain process saturation and selectivity even under significant kinetic disturbances and operational drifts.Furthermore, this thesis investigates machine learning-based soft sensing and virtual metrology using industrial data from dry etching and polishing processes. A physics-structured framework utilizing Graph Neural Networks (GNNs) is proposed to capture the physical coupling and connectivity between heterogeneous equipment subsystems. The results demonstrate that these tool-informed graph architectures significantly improve predictive accuracy for process outcomes compared to traditional data-driven models. This integrated approach enables real-time monitoring and fault detection, reducing the reliance on costly offline metrology and providing a robust path toward intelligent, data-driven semiconductor manufacturing.Chemical engineeringMaterials scienceArtificial intelligenceArea Selective Atomic Layer DepositionCFD SimulationMachine LearningProcess ControlSemiconductor ManufacturingSoft Sensingapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6wb9f66fhttps://escholarship.org/content/qt6wb9f66f/qt6wb9f66f.pdfetdoai:escholarship.org:ark:/13030/qt2rm5p5gc2026-05-26T06:32:19Zqt2rm5p5gcRotationally Symmetric Plabic Graphs and the Totally Nonnegative Lagrangian GrassmannianShevchenko, OlhaGalashin, Pavel2026-05-22This dissertation introduces and initiates the systematic study of a new totally nonnegative Lagrangian Grassmannian LGR ≥0 (n, 2n), the subset of the totally nonnegative Grassmannian consisting of subspaces isotropic with respect to a skew-symmetric bilinear form R. We characterize the cell structure of LGR ≥0 (n, 2n), prove that it is homeomorphic to a closed ball, and show that each cell admits a representation by a rotationally symmetric plabic graph. A new phenomenon distinguishes this setting from previously studied cases: outside of a restricted range, a plabic graph cannot be both reduced and symmetric. To handle this, we develop new techniques for working with non-reduced symmetric plabic graphs.MathematicsTheoretical mathematicsApplied mathematicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2rm5p5gchttps://escholarship.org/content/qt2rm5p5gc/qt2rm5p5gc.pdfetdoai:escholarship.org:ark:/13030/qt8fs4d7bp2026-05-26T06:32:13Zqt8fs4d7bpStability Theory of Two-Dimensional Deterministic and Stochastic Shear FlowsArbon, RyanBedrossian, Jacob2026-05-22We consider the quantitative stability properties of Couette flow and near-Couette shear flows in various two-dimensional partial differential equations from fluid dynamics. These include the Navier-Stokes equations without periodicity assumptions, the Boussinesq system on R 2 , and Navier-Stokes on T × [−1, 1] with Navier-slip boundary conditions and viscosity dependent stochastic forcing. In each of these settings, we seek to establish stability results for initial data which is quantitatively small in the viscosity parameter(s). Throughout, we use hypo coercive energy frameworks in low-order anisotropic Sobolev spaces.In Chapter 2, we focus on Navier-Stokes in two-dimensions, where the horizontal variable x lies in R. With ν > 0 the viscosity, we prove that for perturbations of size O(ν 1/2 ) up to logarithmic corrections in some cases, the Couette flow v = (y, 0) is nonlinearly stable. Furthermore, we demonstrate enhanced dissipation and inviscid damping of the perturbation at the nonlinear level. This is the first quantitative stability result on these unbounded domains to prove enhanced dissipation and inviscid damping. Additionally, we demonstrate Taylor dispersion-type decay rates for the low horizontal frequencies |k| ≤ ν. Our result is proven for vertical variables y ∈ R, [0, ∞), and [−1, 1], with Navier-slip boundary conditions as appropriate. Additionally, we describe as a corollary how our results translate to the β-plane model from atmospheric dynamics. This work has led to the publication [AB25].In Chapter 3, we analyze the fully dissipative Boussinesq system with fluid vorticity ν and temperature dissipation κ. Building on techniques from Chapter 2, together with a symmetrization method, we show that the Couette flow v = (y, 0) and temperature ρ = 1−by are stable for vorticity and temperature perturbations of size O(µ 1/2+), where µ ≈ min(ν, κ). Our result includes proving enhanced dissipation, Taylor dispersion, and inviscid damping results. Additionally, the norms used in this chapter allow for control of higher-order vector fields. This work has led to the publication [Arb25].In Chapter 4, we consider nearly-Couette flow on T × [−1, 1] with Navier-slip boundary conditions and additive stochastic forcing of ν 5/6ΦdVt + ν 2/3+αΨdWt , where α > 1/12 is a parameter, ΦdVt is white-in-time noise with spatial correlation in H3 0 ([−1, 1]), and ΨdWt is white-in-time noise with spatial correlation in a lower-order Sobolev space H and acts only on x-dependent Fourier modes. We allow for near-Couette initial data with x-mean-component O(1) in H3 0 , plus an O(ν 1/2+) x-mean component in H plus a O(ν 1/2+α ) x-dependent component in H. We split the perturbation into its x-mean ω0 and the remainder ω6=. After rescaling in amplitude, we prove that the enhanced dissipation of ω6= leads to an averaging principle as ν → 0, where ω6= rapidly approaches statistical equilibrium, while ω0 approaches an averaged process ω¯0, which satisfies a closed nonlinear evolution equation. We show that the convergence holds for long times T/ν1−γ , γ ∈ [0, 1/3), and we interpret the result as a stability theorem for the shear component of the velocity. This constitutes the first averaging principles of its type for Navier-Stokes systems with ν-dependent forcing. This work has led to the publication [AB26]. MathematicsFluid mechanicsTheoretical mathematicsApplied physicsEnhanced DissipationHypocoercivityQuantitative StabilityShear Flowsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8fs4d7bphttps://escholarship.org/content/qt8fs4d7bp/qt8fs4d7bp.pdfetdoai:escholarship.org:ark:/13030/qt0b2522242026-05-26T06:32:07Zqt0b252224Essays on Labor Unions and InequalityKowarski, JonathanLleras-Muney, Adriana2026-05-21This dissertation examines how labor market institutions and large-scale shocks shape inequality in economic and health outcomes. The first two chapters study the determinants and consequences of labor unionization in the United States, while the third examines changes in inequality in life expectancy during the COVID-19 pandemic.
Chapter 1 studies the decline in U.S. private-sector unionization from 1948 to 2019. I argue that an underappreciated driver of union decline is the interaction between a decentralized, firm-based bargaining system and firm turnover. Because unions are organized within firms, membership declines unless new firms are unionized quickly enough to offset the entry of non-union firms. Using a novel union formation dataset that corrects for “raid” elections and an accounting framework incorporating firm entry, I show that the postwar collapse in union formation rendered mid-century unionization levels unsustainable. Nearly half of long-run union decline is explained by the gradual erosion of unionization generated during the New Deal and WWII era through firm turnover, a force quantitatively comparable to postwar declines in union formation and larger than all other combined factors.
Chapter 2 estimates the causal effects of newly formed labor unions on worker outcomes using linked records from the Census Bureau’s Longitudinal Employer–Household Dynamics program and the National Labor Relations Board. I construct a panel of more than two million incumbent workers employed at over 4,000 establishments that held union elections between 2002 and 2010 and use a difference-in-differences design comparing workers in establishments where unions narrowly won or lost elections. I find no evidence that incumbent workers in newly unionized establishments experience long-run gains in earnings or employment. Earnings effects are heterogeneous but rarely positive, and unions do not increase worker retention at election establishments. I further show that union organizers disproportionately target establishments already on high-growth trajectories and that post-union changes in hiring and productivity may attenuate earnings gains. Together, these findings suggest that the economic benefits of newly formed unions in the contemporary U.S. private sector are limited.
Chapter 3 examines changes in the relationship between income and life expectancy in California during the COVID-19 pandemic. Using census tract-level mortality and income data from 2015 to 2021, I document large declines in life expectancy in both 2020 and 2021 and show that the relationship between income and life expectancy steepened substantially during the pandemic. The gap in life expectancy between high- and low-income communities widened sharply, with disproportionately large declines among Hispanic, Black, and Asian populations.
Together, these essays demonstrate how institutions and shocks shape inequality over time, highlighting how labor market organization, firm dynamics, and differential exposure to public health shocks contribute to persistent inequality.EconomicsLabor relationsLabor economicsPolitical scienceEconomic theoryeconomicsinequalitylabor economicslabor unionsmortalityunionizationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0b252224https://escholarship.org/content/qt0b252224/qt0b252224.pdfetdoai:escholarship.org:ark:/13030/qt9jb2x11x2026-05-26T06:32:02Zqt9jb2x11xVariable Stiffness Polymer Integrated with Stretchable Joule Heating Electrodes for Refreshable Braille DisplaysHong, HyeonJiPei, Qibing2026-05-22Refreshable Braille displays are essential assistive technologies that provide tactile access to written information for individuals who are visually impaired. However, most existing devices remain limited to single-line formats, largely because dense multi-line systems are difficult to achieve using conventional actuator architectures based on discrete transducers. Several actuator technologies have been explored for refreshable Braille, yet major challenges remain in scaling, power consumption, mechanical complexity, durability, portability and cost.
This dissertation presents a scalable actuation platform for multi-line refreshable Braille displays based on a phase-changing variable stiffness polymer (VSP) integrated with stretchable Joule heating electrodes (JHE). In this platform, localized Joule heating temporarily softens selected regions of the polymer membrane, pneumatic pressure drives out-of-plane deformation, and cooling-induced re-stiffening mechanically latches the raised state without continuous hold power. By separating addressing from mechanical work, this strategy enables a compact membrane-based architecture for dense tactile arrays.
Chapter 1 outlines the technological need for accessible and scalable Braille hardware, reviews the functional requirements and scaling barriers of multi-line refreshable Braille displays, and introduces variable stiffness polymer actuation as the central strategy of this work.
Chapter 2 establishes the common VSP/JHE platform framework and examines a first conductor implementation based on a low-resistance hybrid electrode composed of carbon nanotube (CNT) and silver nanowire (AgNW). This hybrid system demonstrates localized Joule heating and Braille-relevant taxel actuation, but also reveals conductor-level bottlenecks associated with resistance variation and a narrow safe operating window.
Chapter 3 addresses these limitations through a carbon nanotube electrode redesign within the same fabrication framework. The CNT-only heaters provide improved electrothermal reproducibility, stable strain-accommodating actuation, and long-cycle durability.
Chapter 4 integrates the optimized CNT-based platform into a portable 4 × 10 cell prototype with 240 independently addressable taxels and demonstrates system-level operation, including multi-line refresh and human-subject validation.
These results demonstrate that variable stiffness polymer membrane integrated with stretchable Joule heating electrodes provides a viable route toward compact, energy-efficient, and scalable multi-line refreshable Braille displays. The dissertation establishes a coherent actuator architecture that combines material design, electrode engineering, device integration, and human-centered validation, and provides a foundation for future high-density tactile interfaces for assistive technology.Materials scienceNanotechnologyElectrical engineeringpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9jb2x11xetdoai:escholarship.org:ark:/13030/qt4vj6z3442026-05-26T06:31:56Zqt4vj6z344Aging with Autonomy: Global, Regional, and Local Planning and Design for Aging in PlaceZhang, ChendiLoukaitou-Sideris, Anastasia2026-05-22As global life expectancy rises and birth rate declines, aging is becoming a pressing policy and planning challenge worldwide. To address the aging crisis, Aging in Place (AIP) - the ability for older adults to live independently and safely in one’s home and community - has become central in aging policy discourse. Although AIP is widely endorsed for its cost-effectiveness and support for older adults’ heterogeneous preferences, research remains limited on quantitative AIP capacity evaluation across regions, particularly in underrepresented developing countries with a large aging population like China. This dissertation addresses this gap through three interrelated studies. Building on existing AIP evaluation frameworks, Essay One creates an AIP assessment protocol and uses harmonized global aging survey datasets from 37 countries to examine cross-national disparities of AIP capacity. I find that there is a disparity of AIP capacity among older adults in different countries. AIP domains like financial confidence and social and civic participation are to be improved. Older adults in countries with higher economic capacity, health expenditure, and social development have higher AIP capacity. Essay Two focuses on AIP capacity disparity and the impacts from life history and socio-environmental factors at a regional scale in China. Drawing from the China Health and Retirement Longitudinal Study dataset, Essay Two finds that there are spatial disparities of AIP capacity, and life history, socio-demographic, economic, and environmental factors impact AIP capacity in China. Essay Three zooms into the neighborhood scale for older adults with only one child in Shanghai. Building on findings of the first two essays, I conduct a survey of 950 older adults who AIP in Shanghai on transportation, housing, and technology access across inner-city and suburban districts. Results show moderately favorable AIP overall, with inner-city residents reporting better conditions than suburban residents. Spatial, socioeconomic, and behavioral inequalities remain in two urban settings. Collectively, these studies offer cross-national, multi-scalar, and life-course-informed empirical analysis of AIP capacity evaluation. They provide new insights into AIP theory, spatial disparities, and life-course-informed, and socioeconomic barriers to aging with autonomy and well-being. They also offer evidence-based, equitable, and context-sensitive recommendations for age-friendly policies.Urban planningPublic healthAgingAge-Friendly PlanningAging in PlaceChinaCross-National Comparative AnalysisOlder AdultsSpatial Analysisapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4vj6z344https://escholarship.org/content/qt4vj6z344/qt4vj6z344.pdfetdoai:escholarship.org:ark:/13030/qt7b68558j2026-05-26T06:31:51Zqt7b68558j“Why are you letting him die?”: Infrastructural Abandonment and the Unevenness of HIV Care in PeruJauregui, Juan CarlosWilson, Bianca D.M.2026-05-22Despite free access to antiretroviral therapy in Peru, sustained engagement in HIV care remains inconsistent, with viral suppression rates falling short of global targets and higher mortality in regions such as Loreto in the Peruvian Amazon. While prior research emphasizes individual-level barriers, socioeconomic disadvantage and stigma, less attention has been given to how broader social and institutional conditions shape the organization and delivery of healthcare itself. This study employs a constructivist grounded theory approach to examine how structural constraints are translated into the organization and delivery of HIV care in practice. The primary data source for the study were in-depth semi-structured interviews with 20 HIV healthcare providers in Lima and Loreto.. Participants included physicians, nurses, psychologists, obstetricians, and community health workers across hospital and primary care settings. Interviews explored experiences delivering HIV care and perceived structural constraints. A secondary data source included public documents and government websites covering health and HIV policies and programs. Data were analyzed using constant comparative methods and memoing, with community advisory board member-checking.Findings introduce the concept of infrastructural abandonment to explain how political-economic forces, institutional arrangements, and clinical practices produce system-wide instability. At the macro level, centralized development, erosion of the HIV program, and global marginality shape differential access to treatment and innovation. Institutionally, fragmented administrative pathways and limited capacity generate delays and interruptions in care. Clinically, providers sustain care through improvisation and discretionary decision-making under scarcity. These findings reframe HIV care engagement as contingent on the stability of healthcare infrastructures. The concept of infrastructural abandonment extends structural frameworks by specifying how inequality is enacted within care delivery, revealing how health systems do not merely respond to inequity but actively organize it through differential reliability, access, and protection. This work calls for a reorientation of HIV policy and health systems strengthening efforts toward addressing structural inequities in resource distribution and institutional capacity, challenging conditions that produce stratified care and uneven valuations of whose lives are protected. A pesar del acceso gratuito a la terapia antirretroviral en el Perú, la continuidad en la atención del VIH sigue siendo inconsistente, con tasas de supresión viral que permanecen por debajo de las metas globales y una mayor mortalidad en regiones como Loreto, en la Amazonía peruana. Si bien investigaciones previas han enfatizado las barreras a nivel individual, la desventaja socioeconómica y el estigma, se ha prestado menos atención a la manera en que condiciones sociales e institucionales más amplias configuran la organización y prestación misma de la atención en salud. Este estudio emplea un enfoque de teoría fundamentada constructivista para examinar cómo las restricciones estructurales se traducen, en la práctica, en la organización y prestación de la atención del VIH. La principal fuente de datos del estudio consistió en entrevistas semiestructuradas en profundidad realizadas a 20 proveedores de atención del VIH en Lima y Loreto. Los participantes incluyeron médicos, enfermeras, psicólogos, obstetras y trabajadores comunitarios de salud en entornos hospitalarios y de atención primaria. Las entrevistas exploraron experiencias en la prestación de atención del VIH y percepciones sobre las restricciones estructurales. Una fuente secundaria de datos incluyó documentos públicos y sitios web gubernamentales relacionados con políticas y programas de salud y VIH. Los datos fueron analizados mediante métodos comparativos constantes y elaboración de memorandos analíticos, con validación por parte de miembros de un grupo asesor comunitario.Los hallazgos introducen el concepto de abandono infraestructural para explicar cómo las fuerzas político-económicas, los arreglos institucionales y las prácticas clínicas producen inestabilidad sistémica. A nivel macro, el desarrollo centralizado, el debilitamiento del programa de VIH y la marginalidad global configuran un acceso diferencial al tratamiento y a la innovación. A nivel institucional, trayectorias administrativas fragmentadas y capacidades limitadas generan retrasos e interrupciones en la atención. A nivel clínico, los proveedores sostienen la atención mediante la improvisación y la toma discrecional de decisiones en contextos de escasez. Estos hallazgos replantean la continuidad en la atención del VIH como contingente a la estabilidad de las infraestructuras sanitarias. El concepto de abandono infraestructural amplía los marcos estructurales al especificar cómo la desigualdad se materializa dentro de la prestación de atención, revelando cómo los sistemas de salud no solo responden a la inequidad, sino que también la organizan activamente a través de acceso desigual y protección diferenciada. Este trabajo llama a una reorientación de las políticas de VIH y de los esfuerzos de fortalecimiento de los sistemas de salud hacia el abordaje de las inequidades estructurales en la distribución de recursos y la capacidad institucional, cuestionando las condiciones que producen una atención estratificada y valoraciones desiguales sobre qué vidas son protegidas.Public healthEpidemiologyHealth care managementContinuity of CareGlobal HealthHealth SystemsHIV/AIDS CarePeruStructural InequalitypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7b68558jetdoai:escholarship.org:ark:/13030/qt18m6k38s2026-05-26T06:31:46Zqt18m6k38sNews on the Nerves: Psychophysiological Processes in Media Selection and AvoidanceCarbone, MiaSoroka, Stuart2026-05-21The evolution of technology has afforded people more choice than ever over what media they do or do not consume. At the same time, this has paved the way for news avoidance to flourish. Studies on news avoidance find that many report avoiding the news because of its negative emotional consequences. This dissertation seeks to develop a deeper understanding of news avoidance using a unique methodological approach. Chapter 1 acknowledges the juxtaposition between the findings of the news avoidance literature with the findings of literature that uses psychophysiology to capture activation and attention. It then proposes an experiment that uses psychophysiology to capture news avoidance. Chapter 2 consequently conducts the proposed study. I find that, on balance, most people leave content when they are less activated. Still, about 30% of the time, people leave content when their activation is increasing. Chapter 3 seeks to understand if threat to social identity, particularly gender identity, can help to explain why people may leave when their activation is increasing. Using an experiment similar to Chapter 2, I find that amongst women, identity strength moderates the relationship between activation and avoidance. The findings of this dissertation are crucial for the understanding of what drives news avoidance, and for how we think about experiments in a world with endless options.CommunicationPhysiologyPolitical scienceSocial psychologyaffectnews avoidancephysiologyselective exposuresocial identityapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/18m6k38shttps://escholarship.org/content/qt18m6k38s/qt18m6k38s.pdfetdoai:escholarship.org:ark:/13030/qt817193342026-05-26T06:31:41Zqt81719334Writing on the Walls of Paris: Graffiti and the Body in French Literature, Film, and PhotographyHanzalik, Danielle MaeBrozgal, Lia N2026-05-22This dissertation constructs a comparative analysis of photographic, filmic, and literary representations of political graffiti in Paris during the second half of the 20th-century (1954-2000). I propose that political graffiti is textual, spatial, and corporeal. In other words, graffiti is conceived of as a text that is anchored in an urban space and that implicates the individual body of the passerby in order to incorporate this passerby into a greater political body. Through graffiti’s inscription in public space, I argue that the passerby’s physical body becomes an allegory for a greater metaphorical political body. Each of the four chapters is organized around a key political moment: the Algerian War, May 1968, the Women’s Liberation Movement of the 1970s, and protests in the Parisian banlieues in the 1980s and 1990s. In each chapter, I analyze both historical graffiti, captured by photographers, and fictional graffiti, represented in literature and film, producing a trans-medial analysis that echoes the trans-medial nature of graffiti itself.Via this cross-disciplinary focus, this dissertation contributes a new perspective on graffiti to the emerging fields of urban humanities and graffiti studies, as well as to traditional French and Francophone Studies. While the field of French and Francophone Studies has historically concentrated on literary analysis, scholars, in the last three decades, have embraced the study of visual media, particularly that of film. This research, which unites graffiti, film, and literary studies, pushes this trend even further to include objects of ephemera, an object of ephemera that is both visual and textual, and its mediatic representations. Within the field of graffiti studies, my project also breaks new ground. Since the 1980s, graffiti scholarship has predominantly interrogated graffiti from either a sociological or historical angle, whereas my project seeks to link the historical and sociological to graffiti’s cultural and literary impact. Thus, my analysis of graffiti bridges the gap between the humanities and urban studies.French literatureEuropean studiesFilm studiescontemporary literaturefilmgraffitiphotographypoliticsvisual studiesapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/81719334https://escholarship.org/content/qt81719334/qt81719334.pdfetdoai:escholarship.org:ark:/13030/qt0n15p6782026-05-26T06:31:36Zqt0n15p678Foundation Models for Partial Differential Equations: Learning Spatiotemporal Dynamics at ScaleLiu, YuxuanSchaeffer, Hayden2026-05-22Modeling spatiotemporal dynamics governed by partial differential equations (PDEs) remains a fundamental challenge in computational physics, requiring machine learning models that can generalize across diverse physical systems, parameter ranges, geometries, and temporal scales. This thesis focuses on the development of scalable PDE foundation models through three novel transformer-based architectures. First, we introduce PROSE-FD, a multimodal foundation model that fuses symbolic system information with trajectory data to enable zero-shot, non-autoregressive operator learning. Second, we present Vision In-Context Operator Networks (VICON), which leverage contextual information to be a few-shot learner for multi-physics systems, demonstrating strong robustness to imperfect or missing measurements. Finally, we propose BCAT, a block causal transformer for autoregressive prediction that effectively captures complex spatial dependencies by utilizing previous frames as contextual priors. To ground this approach, we provide a rigorous theoretical analysis establishing BCAT as a universal approximator for dynamical system solution operators. Together, these frameworks provide a diverse set of methodologies for learning and predicting complex spatiotemporal systems, spanning multimodal operator learning, in-context learning, and causal autoregression. These models advance the state-of-the-art in PDE modeling and lay the foundation for future research in scalable, generalizable models for scientific computing.Applied mathematicsComputer scienceArtificial intelligenceMachine LearningPDE Foundation ModelsSpatiotemporal Dynamicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0n15p678https://escholarship.org/content/qt0n15p678/qt0n15p678.pdfetdoai:escholarship.org:ark:/13030/qt9311d4mn2026-05-26T06:31:30Zqt9311d4mnMetals, Air Pollution and Parkinson’s Disease: Perspectives from Gene-Environment Interactions, Immune Responses and MetabolomeShao, KanghongRitz, Beate R.Paul, Kimberly C.2026-05-22Parkinson’s disease (PD) is world’s second most common neurodegenerative disorder, with age-standardized rates of prevalence, disability and deaths increasing more rapidly than any other neurological disorder. Toxic metals and air pollution are two widespread yet preventable health threats through policy and technology. Although they have been identified as major environmental risk factors for PD, the biological mechanisms underlying their toxicity that lead to PD pathology are not well understood. In this dissertation, we investigated gene-environment interactions and metabolomic signals associated with lead exposures as well as immune responses associated with air pollution in Parkinson’s disease to better understand potential biological mechanisms underlying their toxicity that contributes to PD pathology.
First, in a population-based case-control study conducted in three agricultural counties (Kern, Fresno, and Tulare) in central California, the Parkinson’s Environment and Gene (PEG) Study, we investigated the potential modifying effect of iron-load polygenic risk scores (PRS) on the associations between DNA methylation estimated tibia, patella and blood lead levels and PD risk. We estimated tibia, patella, and blood lead levels using DNA methylation biomarkers and created iron-load PRS (transferrin saturation, ferritin level, serum iron level, and total iron binding capacity) using genetic profiles for 757 participants (521 cases and 236 controls). We found genetic susceptibility to iron deficiency represented by PRS for low iron load may enhance the effect of cumulative lead exposure on the development of PD and saw evidence for larger effect modification from this genetic predisposition to iron deficiency among women, suggesting iron may play a protective role against neurotoxicity of lead, especially among women.
Second, in the same PEG population (n = 806), we investigated the effects of mixtures of traffic-related air pollution (TRAP) on PD outcomes (PD onset and progression symptoms) and further evaluated whether and how these air pollution exposures affect immune cell compositions in PD patients and controls. We estimated 1-, 5- and 10-year average concentrations of criteria pollutants (nitrogen dioxide, particulate matter less than 2.5 microns, ozone, and carbon monoxide) and air toxics (benzene, chromium, lead, and nickel) prior to index years using land use regression models and the California Line Source Dispersion Model, version 4 (CALINE4), and applied quantile g-computation method to estimate joint effects on PD risk and immune cell compositions, and weighted Cox proportional hazards models for progressive symptoms. We found that mixture of TRAP was associated with increased risk of PD and a more motor dysfunction during progression and these air pollution exposures were associated with alterations in peripheral immune cell compositions which indicate chronic immune activation. PD patients showed more pronounced T cell-related alterations suggesting more severe immune dysfunction (exhausted), while controls showed increase in CD8+ T cells and decreases in plasmablast and granulocytes, indicating differential immune response.
Lastly, we investigated perturbations in the serum metabolome for lead exposures among PEG patients and controls who had both DNA methylation and metabolome profiles (n = 675). We conducted high-resolution metabolomic profiling of serum samples and estimated tibia, patella and blood lead levels using DNA methylation profiles. We identified metabolites and metabolic pathways associated with lead exposures, with PD patients and controls showing shared perturbations in pro-inflammatory lipid and amino acid metabolism pathways. Furthermore, PD patients exhibited weaker signals of these pathways with cumulative lead exposure but greater susceptibility to short-term exposure, whereas controls showed more pronounced enrichment of lipid and amino acid metabolism pathways with cumulative lead exposure.
In summary, our studies indicate that lead and air pollution exposures may contribute to Parkinson’s disease through multiple biological mechanisms, including iron homeostasis, immune dysfunction, and metabolic disruption. We demonstrate that genetic susceptibility to iron deficiency enhances vulnerability to lead neurotoxicity. Our findings provide evidence that mixture of traffic-related air pollution increases PD risk and accelerates motor dysfunction, and air pollution induces alterations in peripheral immune system, with PD patients showing more severe immune exhaustion related to T cells than controls. Moreover, our study provides insight into shared and differential perturbations in pro-inflammatory lipid and amino acid pathways associated with cumulative and short-term lead exposures between PD patients and controls.EpidemiologyNeurosciencesPathologyImmunologypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9311d4mnetdoai:escholarship.org:ark:/13030/qt2h52c3c42026-05-26T06:31:25Zqt2h52c3c4Acting Outward while Looking Inward: Southern Baptist Missionaries’ Responses to the Conservative ResurgenceChalfoun, AndrewTimmermans, Stefan2026-05-22This dissertation examines political and social fractures between American evangelicals that shape how international missionaries perform and understand their work. Specifically, I study the long-term impact of the Southern Baptist “conservative resurgence,” a late twentieth century effort by Christian Right activists to take over America’s largest Protestant denomination. I focus on the International Mission Board (IMB), which is the Southern Baptist Convention (SBC)’s main agency for evangelization outside of the United States and Canada. The project traces how IMB leaders resisted pressure from conservative activists in the 1980s and 1990s and outlines how leaders’ response to activist pressure altered the organization’s structure. Although Southern Baptists are often used to exemplify the rightward political trajectory of American evangelicals, this study reveals the surprising fragility of activists’ influence within the denomination’s largest agency. Drawing on insights from political and organization sociology, my findings deepen our understanding of American evangelicals’ political engagement and provide unique insights into the recent transformation of international missionary activity.SociologyReligious educationPolitical scienceOrganization theoryChristian RightEvangelicalsMissionariesOrganizationsSocial MovementsSouthern Baptist ConventionpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2h52c3c4etdoai:escholarship.org:ark:/13030/qt6cf0x2462026-05-26T06:31:20Zqt6cf0x246Essays in Macroeconomics and International Economics: The Role of Labor MarketsGurkova, EkaterinaOhanian, Lee EItskhoki, Oleg2026-05-22This dissertation studies the role of labor markets in the transitions following aggregate shocks. Labor markets play an important role in transmitting aggregate shocks to individuals, as changes in macroeconomic conditions directly or indirectly affect labor demand and, therefore, wages, unemployment, and the human capital investment of individual workers. This dissertation examines the economy’s transition dynamics following sectoral and trade shocks, focusing on transmission mechanisms such as worker reallocation and skill acquisition.
The first chapter examines the impact of temporary sectoral shocks on human capital accumulation and introduces a structural model to quantify their long-term general equilibrium effects. I use Spain’s economic boom from 1995 to 2007 as an example of a positive labor demand shock in construction and low-skill services, and show that it led to a permanent decline in educational attainment and sectoral reallocation among young workers. To study the long-term aggregate effects of this shock and conduct counterfactual analysis, I develop a lifecycle model in which workers endogenously choose education and sectoral employment under imperfect human capital transferability. The model is then used to analyze the aggregate and distributional effects of the boom and to quantify the contribution of human capital accumulation to Spain’s productivity dynamics over the transition.
The model replicates the employment and enrollment dynamics observed during the boom, albeit with smaller magnitudes of change. It highlights the importance of human capital accumulation and the rising opportunity cost of education in driving workers’ reallocation across sectors and in explaining the long-term decline in aggregate labor productivity after the bust. Furthermore, the counterfactual analysis shows that productivity spillovers from human capital play a critical role in shaping aggregate productivity dynamics and contribute to the total losses over the transition.
The second chapter is joint work with Elhanan Helpman and Oleg Itskhoki and is published in the Journal of International Economics as ``Trade Liberalization, Wage Rigidity, and Labor Market Dynamics with Heterogeneous Firms.'' It argues that adjustment to trade liberalization is associated with substantial labor reallocation across firms within sectors. Transition dynamics exhibit rich patterns that vary across firms with different productivity levels and across workers attached to these firms. High-productivity exporters expand employment on impact. However, among lower-productivity firms, some close on impact, others lay off workers on impact and close at a later date, while still others gradually reduce their labor force and remain in the industry. In these circumstances, jobs that pay similar wages ex ante are not equally desirable ex post, because after the trade shock, high-productivity incumbents pay higher wages and provide more job security than low-productivity incumbents. The gains from trade resulting from a decline in the consumer price index outweigh the losses from wage cuts, job destruction, and capital losses among incumbent firms, and these losses increase with the extent of labor market frictions.EconomicsLabor economicsEconomic theoryapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6cf0x246https://escholarship.org/content/qt6cf0x246/qt6cf0x246.pdfetdoai:escholarship.org:ark:/13030/qt3rp2j7s02026-05-26T06:31:15Zqt3rp2j7s0Cardiac Rhythms Shape Brain Oscillations: Implications for Cognition and SchizophreniaSargent, Kaia StavigYee-Bradbury, Cindy M.2026-05-22Oscillatory activity in the brain plays a fundamental role in neural computation and communication. A rapidly growing body of evidence suggests that oscillatory brain dynamics are shaped by slower autonomic rhythms such as respiration and heart rate. Heart rate variability (HRV) is an index of autonomic function that has consistently been linked to cognitive functioning and mental health, but the possibility that such associations are due to a causal influence of cardiac rhythms on cortical activity has not been examined. Given that certain psychiatric disorders – most notably, schizophrenia (SZ) – are associated with both cortical oscillatory abnormalities and lower HRV, evidence of a mechanistic relationship between central and peripheral oscillatory dynamics could have important implications for how psychiatric illness is conceptualized and treated. The present dissertation examined how heart rhythms modulate cortical oscillations among healthy participants and individuals diagnosed with SZ to understand how heart-brain interactions shape cognition and mental health. In healthy participants, robust phase-amplitude coupling (PAC) was observed between HRV phase and the amplitude of EEG rhythms, with stronger heart-to-brain than brain-to-heart effects. Patients with SZ exhibited relatively lower HRV-EEG PAC in the alpha and theta bands. Functionally, lower coupling was associated with deficits in sustained attention, and HRV-EEG coupling was a better predictor of diagnosis than either HRV or EEG alone. Furthermore, HRV cycles were found to modulate coherence in the default mode network (DMN) and central executive network (CEN) in healthy participants, whereas individuals with SZ did not show HRV modulation of connectivity in either network, raising the possibility that deficient autonomic regulation of cortical coherence contributes to patterns of dysconnectivity that characterize SZ. Finally, a slow-paced breathing (SPB) manipulation was used to experimentally increase cyclic HRV. SPB led to improved performance on a working memory task, higher HRV-EEG coupling in all frequency bands, and higher CEN connectivity in the beta and gamma bands. These findings support the view that cognitive operations rely not merely on oscillatory activity within the brain but on a hierarchically organized system of oscillations across the brain and body, which may point to novel approaches for improving cognitive functioning and mental health.PsychologyNeurosciencesMental healthCognitive psychologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3rp2j7s0https://escholarship.org/content/qt3rp2j7s0/qt3rp2j7s0.pdfetdoai:escholarship.org:ark:/13030/qt0xp6c2bq2026-05-26T06:31:10Zqt0xp6c2bqMembership Inference Attacks Against Tabular Synthetic Data Under Conservative Threat ModelsWard, JoshuaCheng, Guang2026-05-21Tabular synthetic data has emerged as a promising mechanism for privacy-preserving data sharing, enabling organizations in sensitive domains such as healthcare and finance to publish data products that preserve statistical utility while limiting risk to individuals in their training sets. The empirical privacy of synthetic data releases is most rigorously assessed through Membership Inference Attacks (MIAs). However, existing MIAs for tabular synthetic data share two critical limitations: they frequently assume unrealistic adversarial knowledge of the generative model's implementation that would be withheld under responsible release practices, and they operate exclusively over the Cartesian feature space of a single observation, failing to capture vulnerabilities introduced by Large Language Models and relational data generators whose inductive biases differ fundamentally from earlier generative models.This dissertation addresses both limitations through three contributions. The first introduces the Generative Likelihood Ratio Attack (Gen-LRA), a No-Box MIA grounded in an influence function framework, accompanied by theoretical results characterizing what the attack measures and when it succeeds. Gen-LRA achieves state-of-the-art performance across a benchmark spanning 35~datasets, 9~generative architectures, and over 1,500 synthetic dataset configurations.The second contribution studies privacy risks specific to Large Language Model (LLM)-based tabular generators, which encode tabular rows as strings and generate records via autoregressive text production---a paradigm invisible to feature-space attacks. We introduce LevAtt, a No-Box MIA that targets memorized sequences of numeric digits in synthetic string representations, achieving near-perfect membership classification on some state-of-the-art generators.The third contribution formalizes membership inference for the multi-table synthetic data setting, where a user's information is distributed across multiple interconnected tables in a relational database. We show that single-table MIAs systematically underestimate user-level privacy leakage in this setting, and introduce MT-MIA, a No-Box attack that represents each user as a heterogeneous subgraph and leverages Graph Neural Networks to perform user-level membership inference.Together, these three works advance a principled framework for realistic, architecture-aware privacy auditing of tabular synthetic data.StatisticsArtificial intelligenceComputer scienceFinanceGenerative ModelsMembership Inference AttacksPrivacy AuditingTabular Synthetic DataUnsupervised Learningapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0xp6c2bqhttps://escholarship.org/content/qt0xp6c2bq/qt0xp6c2bq.pdfetdoai:escholarship.org:ark:/13030/qt7m05p6n82026-05-26T06:31:05Zqt7m05p6n8Development of General Methods for Nucleophilic FunctionalizationRuos, Madeline EstherDoyle, Abigail G2026-05-21Chapter 1 describes development of a visible-light photoredox-catalyzed method that enables nucleophilic amination of primary and secondary benzylic C(sp3 )–H bonds. A novel amidyl radical precursor and organic photocatalyst operate in tandem to transform primary and secondary benzylic C(sp3 )–H bonds into carbocations via sequential hydrogen atom transfer (HAT) and oxidative radical-polar crossover (ORPC). The resulting carbocation can be intercepted by a variety of N-centered nucleophiles, including nitriles (Ritter reaction), amides, carbamates, sulfonamides, and azoles for the construction of pharmaceutically-relevant C(sp3 )–N bonds under unified reaction conditions. Mechanistic studies indicate that HAT is amidyl radical-mediated and that the photocatalyst operates via a reductive quenching pathway. These findings establish a mild, metal-free, and modular protocol for the rapid diversification of C(sp3 )–H bonds to a library of aminated products.Chapter 2 describes the discovery and characterization of multiple N-(hetero)aryl, Nbenzyl and N-alkyl derivatives of 9-mesityl-3,6-di-tert-butyl-10-phenyl acridinium photocatalyst. The catalytic performances of these catalysts as photo-oxidant or photo-reductant (via in situ generated acridine radical) were compared in three model reactions. We also identified improved catalytic conditions for a previous cyanoarene-catalyzed nucleophilic amination reaction using a synthesized N-cycloheptyl acridinium catalyst (up to 98% yield).Chapter 3 describes the discovery and development of several new (hetero)aryl sulfonyl fluoride reagents that have enhanced deoxyfluorination reactivity, improved physical properties, and excellent safety profiles compared to those of PyFluor and other fluorination reagents such as PBSF and DAST. To select structurally diverse reagents, we computed a virtual library of (hetero)aryl sulfonyl fluorides and leveraged training set design principles to broadly survey structure–activity relationships in a model deoxyfluorination reaction. We developed predictive models to optimize sulfonyl fluoride reagents for the deoxyfluorination of a key intermediate used in the synthesis of RIPK1 inhibitor GDC-8264. The top-performing reagents demonstrated broad applicability across diverse alcohol substrate classes, including complex natural products and active pharmaceutical ingredients, highlighting the power of data science-enabled approaches in reagent development.Chapter 4 discusses and reflects on the design of reaction datasets in ways that are conducive to data-driven modeling, emphasizing the idea that training set diversity and model generalizability rely on the choice of molecular or reaction representation. As demonstrated throughout developments in chapters 1–3, models can codify our understanding of chemical reactivity and serve a useful purpose in the development of new synthetic processes via, for example, evaluating hypothetical reaction conditions or in silico substrate tolerance. Perhaps the most determining factor is the composition of the training data and whether it is sufficient to train a model that can make accurate predictions over the full domain of interest. We additionally discuss the experimental constraints associated with generating common types of chemistry datasets and how these considerations should influence dataset design and model building.Organic chemistryChemistryBiochemistryCatalysisChemical ReactionsFluorinationMachine LearningOrganic ReactionsPhotoredox Chemistryapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7m05p6n8https://escholarship.org/content/qt7m05p6n8/qt7m05p6n8.pdfetdoai:escholarship.org:ark:/13030/qt5s50s19q2026-05-25T06:34:42Zqt5s50s19qEarth, Fire, and Ice: An Exploration of Libertine symbols and philosophies regarding the place of the human in the Natural world and Christianity in Early Modern Songes PhilosophiquesClarke, KendellThomas, Dominic2026-05-21This dissertation examines why Robert Lenoble’s broad-stroked assertion about the objectified natural world does not apply to the early modern songes philosophiques genre. Throughout the three chapters, I argue that the genre’s allowance for philosophical conversations interweaves with the authors’ libertinism, as the works in question decentralize human existence in favor of a universalist analysis. My analysis spans 130 years, beginning with Cyrano de Bergerac’s novel L’Autre Monde: Les États et les Empires de la Lune et Les États et les Empires du Soleil, published in 1657, and ending with Giacomo Casanova’s Icosaméron, published in 1788, readers can perceive a timeline that depicts different aspects of French early modern philosophical fiction, as we move from a decade after the Thirty Years' War to a year before the decade-long French Revolution.The libertinism of Cyrano de Bergerac, Simon Tyssot de Patot, Charles Tiphaigne de La Roche, Voltaire, and Giacomo Casanova signaled their deconstruction of the Christian God, as understood by the French Catholic majority. In addition to this deconstruction, I posit that their movement away from the Catholic majority is integrated within their novel’s differentiation between the found society and the traveler’s society of origin. I divide the dissertation into three parts and analyze one element and its manifestations at a time.The first chapter, “Human Assimilation to Vegetality, Animals, and Minerality: The Questioning of the Great Chain of Being and Faith in Early Modern Songes Philosophiques”, argues that songes philosophiques further a discourse pertaining to the validity of Christian and Western-based hierarchies of Beings through the process of assimilating the European to soil and vegetality. The second chapter, “Water, Ice, and the Cold: Exploring the Mind and the Entanglement of Tyranny, Christianity, and Libertinism in Early Modern Philosophical Fictions”, posits that, in early modern French philosophical fiction, water, ice, and cold each signify distinct stages of libertinism. Water is a metaphor for the initial stages of moving away from French Catholic culture and dogma, as it often mirrors one’s inner world. The way one navigates and interacts with aquatic space demonstrates their personal analyses, hesitations, and overall thought processes regarding their relationship with the nuanced early modern French religious and sociopolitical landscapes. The final chapter, “Fire, the Sun, and the full elemental spectrum: Promethean advancement vs. Zeusian anthropophagous destruction in 17th and 18th century songes philosophiques”, argues that between 1657 and 1788, the representations of fire, heat, and the Sun in Les Songes Philosophiques and the imaginary voyage narratives changed in response to the expansion of libertinism, heliocentrism, and a desire for governmental and societal change.It is through this that I posit that Robert Lenoble’s assertion that the natural world was objectified in the early modern era is far too generalized and completely effaces the nuanced marked effect of the intersection of libertinism, the Little Ice Age, the Scientific Revolution, and the philosophical fiction's use of metaphor to highlight the era’s rising skepticism of the Christian faith.French literatureEuropean historyModern literatureReligionapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5s50s19qhttps://escholarship.org/content/qt5s50s19q/qt5s50s19q.pdfetdoai:escholarship.org:ark:/13030/qt92c5x3hr2026-05-25T06:34:37Zqt92c5x3hrComputational approaches for enhancing epigenomic analyses across species, individual, and disease contextsMaciejewski, Emily AnneErnst, Jason J2026-05-21Epigenetic marks such as DNA methylation (DNAm) and histone modifications play an essential role in cellular function and data from these marks can provide important biological information. However, this data raises analytical challenges. This dissertation takes place in three parts, all themed around developing and applying computational approaches in response to these challenges.One set of challenges is that there are gaps in profiling DNAm samples both across different species and across the whole genome, which we address in Chapters 2 and 3. Our first set of goals is thus to develop imputation methods to computationally expand available DNAm data. For this, we first develop a deep learning-based method to impute DNAm samples representing species and tissue combinations not available in current cross-species compendia. We show this method yields strong imputation performance and use it to impute 19,786 new samples. We next develop a method to expand DNAm array samples to the whole genome by leveraging a reference of sequencing-based whole-genome DNAm data.Another set of challenges is that while there is an established approach to annotate the genome into chromatin states by learning patterns across epigenetic maps such as of histone modifications through the method ChromHMM, applying annotations to new applications raises application-specific challenges, which we address in Chapter 4. Specifically, we focus on addressing these challenges in the context of epigenetic variation across individuals and the relationship between chromatin and Huntington’s disease (HD), demonstrating how chromatin states can be useful in these contexts.Computer scienceGeneticsCellular biologyPathologypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/92c5x3hretdoai:escholarship.org:ark:/13030/qt235313qk2026-05-25T06:34:31Zqt235313qkWarriors of the Wake: The Black Male Rapper as Mythological Subject and ArchetypeWilliams, AlexanderBradley, Adam2026-05-21Warriors of the Wake: The Black Male Rapper as Mythological Subject and Archetype examines a blended set of discographies from eight contemporary Black male rappers to explore how Black masculinity can be lived and performed as a meta-fiction reflecting hauntological and postcolonial anxieties of the 21st century, from racial capitalism, intergenerational trauma, and political marginalization to militaristic state violence and an anti-Black carceral complex. In my project, I argue that the Black male rapper can be characterized as a sociopolitical actor participating in a multiscalar mythology built from the hip-hop canon’s intertwining narratives. Through key songs from the discographies of Childish Gambino (The Outcast); J. Cole (The Sage); Drake (The Popstar); Pusha T (The “Old-School” Remnant); Montana of 300 (The Monster); Kanye West (The Uncle Tom); Tyler, the Creator (The Paradox); and Big Freedia (The Gender Bender), I hope to reveal how and why other Black men use these rappers’ discographies and personas as formative components in identity formation, culture creation, and racial politics. This project offers a reading of the aforementioned rap personas by analyzing Black, postcolonial, Romantic, and cultural literatures that reorient Black masculinity’s value in the cultural mythologies and value systems intervening between colonial systems and the world we experience. Ultimately, to read these rapper personas as mythological archetypes requires a praxis of literary thinking and framing that explores what it means to read for, with, and against these personas as texts. Using a hauntological approach, this project examines how haunting is embedded within the very language and experiential modality of these rappers’ poetics to provide a critical framework for understanding the ongoing legacies of slavery and Jim Crow in the contemporary moment. After constructing these archetypes, the project closes by analyzing the future of hip-hop, Black masculinity, and the para-critical questions of Black representation looming as emergent and developing technologies around AI, commodification, and surveillance continue to dominate and shape our understanding and lived experience of the human.Black studiesMusicLiteratureWomens studiesBlack feminismBlack masculinityHauntingHip-HopRomanticismSlaveryapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/235313qkhttps://escholarship.org/content/qt235313qk/qt235313qk.pdfetdoai:escholarship.org:ark:/13030/qt23h5w80v2026-05-25T06:34:26Zqt23h5w80vPerforming in the Margins: Global Entertainment Technologies, Artistic Labor, and the Survivalist Modernity of Folk Performance in China, 1876-1966Shi, YuGoldman, Andrea S2026-05-21This dissertation offers a technological, labor, and cultural history of Chinese folk performance from the late imperial period to the early PRC. Focusing on non-elite practitioners, it examines folk performance as a dynamic, profit-driven cultural practice shaped by global entertainment capitalism, emerging theatrical and media technologies, and grassroots strategies of survival amid the uneven modernization of a globalizing China. Drawing on underutilized media artifacts alongside textual, visual, and oral archives, this study reconstructs a nonlinear and non-teleological history of grassroots artistic modernization.The dissertation contributes to three major historiographical conversations. First, by centering non-elite performers’ engagements with global entertainment technologies, it intervenes in the history of technology by historicizing the art-technology nexus in vernacular cultural practice. Departing from existing narratives that often privilege elite experts, formal institutions, or industrial innovation, this project foregrounds the improvised and ambiguous ways grassroots cultural laborers navigated the tensions between artistic tradition and new sociotechnical ecologies.Second, this dissertation reframes the history of popular culture by shifting attention from middle-class discourse to the survival strategies of lower-status cultural laborers. Rather than treating art solely as an aesthetic pursuit, it understands artistic practice as a pragmatic means of livelihood. It argues that folk performance survived through the opportunistic strategies of its practitioners, a process conceptualized here as the “survivalist modernity” of Chinese folk performance. This term describes the ways historical actors navigated modernity’s structural transformations through pragmatic and often contingent responses. In this sense, survivalist modernity offers a history from the perspective of those who experienced modernization less as a coherent project of progress than as a series of confusions, pressures, constraints, and exclusions.Third, by treating performance as both physical and cultural labor, this project illuminates the chaotic professionalization of folk performers and the shifting hierarchy between professionalism and amateurism in folk performance from the late Qing to early Maoist China. In doing so, it integrates a socio-economic dimension into the cultural history of performance, understanding professionalization not as a linear process of institutional maturation, but as an open-ended and temporary restructuring of cultural labor practices, knowledge, working conditions, and forms of organization.Asian historyHistoryArt historyPerforming artspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/23h5w80vetdoai:escholarship.org:ark:/13030/qt2jm355sg2026-05-25T06:34:21Zqt2jm355sgExtreme Calabi–Yau Varieties and the Topology of the Hilbert Scheme of PointsSingh, JasTotaro, Burt2026-05-21In Chapter 1, I will survey the foundational theory of K3 surfaces and showcase a proof of the unboundedness of elliptic K3 surfaces through the unboundedness of a discrete invariant known as the minimal multisection degree. This invariant was used crucially in previous boundedness results for elliptic Calabi–Yau varieties.
In Chapter 2, I reproduce my article (first published in Mathematische Zetischrift) wherein I constructed a family of smooth, projective Calabi–Yau varieties in each dimension with conjecturally extremal (and nevertheless extreme) discrete invariants. Namely, their index and the sum of their Betti numbers grows doubly-exponentially in dimension.
In Chapter 3, I present my approach to a work-in-progress study of the topology of Hilbert schemes of points in affine space, and compute the integral homology of the Hilbert scheme of 5 points in affine space.MathematicsApplied mathematicsalgebraic geometrycalabi yauhilbert schemetopologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2jm355sghttps://escholarship.org/content/qt2jm355sg/qt2jm355sg.pdfetdoai:escholarship.org:ark:/13030/qt8sg530pb2026-05-25T06:34:16Zqt8sg530pbFactors Influencing Chronic Disease Control: Exploring Individual-, Provider-, and System- related Factors in Three Healthcare SettingsAryal, AnuPonce, Ninez A.Moucheraud, Corrina2026-05-21The increasing prevalence of chronic diseases poses a significant challenge to health systems, where achieving optimal disease control among those under treatment often seems out of reach. Scholars suggest a fundamental shift from episodic care toward the Chronic Care Model (CCM), yet system-level redesign efforts often assume structural changes alone will lead to better outcomes, paying insufficient attention to motivation, support systems, and environmental factors enabling productive client-provider interactions. This dissertation augments the CCM with two additional frameworks—PRISM and Andersen's behavioral model—to examine patient and provider activation in chronic disease management, exploring a) patients' motivation (Study 1), b) providers' support systems (Study 2), and c) system supports for both (Study 3), across three settings in distinct national and economic contexts.In Study 1, I investigated how medication adherence impacts blood pressure control among adults with hypertension in urban Thailand, an upper-middle-income country with universal health coverage. I found a high prevalence of antihypertensive non-adherence (67%) and uncontrolled blood pressure (53%; BP ≥140/90 mmHg), and a significant association between longer medication gaps and poor BP control. Unintentional forgetfulness and fear of long-term side effects emerged as barriers, while family reminders served as a key facilitator.In Study 2, I examined provider and system perspectives in the Philippines, assessing chronic disease care in primary care, specifically, Rural Health Units. I found persistent structural gaps in laboratory capacity, medicine availability, staffing, and referral systems. Key barriers included rising NCD burden, resource shortages, and COVID-19 disruptions. Facilitators included supportive local leadership, nurse deployment, community health worker home visits, and established hypertension and diabetes clubs.In Study 3, I studied a whole-person assessment tool in a U.S. county health system serving a predominantly Medicaid population. Among assessments from people with hypertension, only 32% had BP <130/80mmHg, and among people with type 2 diabetes mellitus, 39% had HbA1C <7%. Red-zone scores (i.e., poor well-being in that domain as flagged by the assessment) in at least one whole-person domain were associated with lower odds of disease control (AOR = 0.81, p < 0.001), with risk compounding as affected domains accumulated. Pilot intervention evidence showed small improvements across all whole-person domains.Collectively, these findings highlight that improving chronic disease outcomes requires intentional investment in patient support mechanisms, provider enablement, and system infrastructure across diverse settings.Public healthHealth sciencesHealth care managementAdherenceChronic care modelChronic diseaseHealth systemPrimary careWhole personpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8sg530pbetdoai:escholarship.org:ark:/13030/qt0g38t4c82026-05-25T06:34:11Zqt0g38t4c8Esophageal Cancer Risk, Prognosis, and Survivorship: The Role of Indoor Air Pollution, Prognostic Modeling, and Cardiovascular Diseases in Post-Surgical PatientsLiu, KuangyuZhang, Zuo-Feng ZFZHashibe, Mia MH2026-05-21Background:Esophageal cancer (EC) is one of the most lethal malignancies globally, with substantial geographic variation in both incidence and survival. While tobacco and alcohol are established risk factors, other environmental exposures and host-level determinants remain poorly understood. Survival outcomes among EC patients also vary widely due to differences in demographic, lifestyle, clinical, and treatment-related factors. As treatment advances improve survival for some EC patients, long-term health outcomes, particularly cardiovascular disease (CVD), have become increasingly important. However, evidence regarding environmental risk factors, prognostic factors, and long-term survivorship outcomes in EC remains limited.Objective and Specific Aims:The overall objective of this dissertation is to elucidate the role of environmental exposures, prognostic determinants, and long-term CVD outcomes in EC. The specific aims were to: (1) estimate the association between indoor air pollution (IAP) exposures and EC incidence; (2) identify host-level demographic, socioeconomic, lifestyle, and genetic prognostic factors for EC survival and evaluate whether machine learning survival models provide improved predictive performance beyond traditional Cox regression; and (3) examine the association between surgical approach (open versus minimally invasive esophagectomy) and long-term risk of CVD among EC survivors.Study Design and Population:For Aim 1, a population-based case–control study was conducted using data from the Jiangsu Four Cancer (JFC) study. For Aim 2, a prospective cohort design was used to analyze EC patients identified in the UK Biobank. For Aim 3, the SEER–Medicare database was used to assess long-term cardiovascular outcomes among older EC patients who underwent esophagectomy.Statistical Methods:For Aim 1, unconditional logistic regression models with a semi-Bayesian shrinkage approach were used to estimate associations between EC incidence and multiple indoor air pollution exposures, evaluated as individual exposures and as a combined exposure using weighted risk score. For Aim 2, multivariable Cox proportional hazards models were used to examine associations between host-level and treatment-related factors and all-cause mortality among EC patients. Machine learning survival models, including random survival forests and gradient boosting methods, were implemented to evaluate predictive performance and compared with Cox regression models using the concordance index (C-index). For Aim 3, Cox proportional hazards models were employed to estimate the association between EC surgical approach and long-term CVD outcomes, including heart failure, ischemic heart disease, stroke, and composite CVD.Results:In Aim 1, multiple indoor air pollution (IAP) sources were positively associated with esophageal cancer risk. Solid fuel use for heating showed the strongest association (semi-Bayesian adjusted odds ratio [SB-aOR] = 1.76, 95% CI: 1.56–1.98), followed by coal used for cooking (SB-aOR = 1.52, 95% CI: 1.36–1.69) and passive smoking (SB-aOR = 1.44, 95% CI: 1.31–1.58). Summary exposure measures demonstrated a dose-response relationship. Participants in the highest quartile of weighted risk score had 2.58 times the odds of esophageal cancer (95% CI: 2.25–2.97) compared with those in the lowest quartile. In Aim 2, cumulative smoking burden (per 10 pack-years: HR = 1.07, 95% CI: 1.01–1.13) was associated with worse overall survival whereas overweight status (HR = 0.69, 95% CI: 0.54–0.88), high physical activity (HR = 0.72, 95% CI: 0.55–0.93), supplement use (HR = 0.80, 95% CI: 0.65–0.99), prior Barrett’s esophagus (HR = 0.52, 95% CI: 0.35–0.78), and esophagectomy (HR = 0.23, 95% CI: 0.18–0.28) were associated with improved survival. The polygenic risk score was independently associated with poorer survival (per standard deviation increase: HR = 1.17, 95% CI: 1.06–1.29), but it added little incremental predictive value. Machine learning survival models did not meaningfully outperform the Cox model. Specifically, the C-index was 0.68 for Cox, 0.70 for random survival forest, and 0.68 for XGBoost; XGBoost showed poorer calibration.In Aim 3, minimally invasive esophagectomy was not associated with overall composite cardiovascular disease risk compared with open esophagectomy (HR = 0.98, 95% CI: 0.66–1.46). Likewise, no overall associations were observed for heart failure, ischemic heart disease, or stroke. However, minimally invasive esophagectomy was associated with improved overall survival during the first 5 years after surgery (HR = 0.80, 95% CI: 0.65–0.97), while an exploratory higher risk of heart failure was observed during 5–10 years of follow-up among minimally invasive esophagectomy recipients (HR = 3.35, 95% CI: 1.31–8.52).Conclusions and Public Health Implications:Together, these findings show that EC is shaped by factors operating across the continuum of disease, from environmental exposures before diagnosis to treatment and survivorship after diagnosis. The results support IAP as a potentially important and modifiable contributor to EC risk. For prognosis, post-diagnosis treatment variables explained substantially more survival variation than increasingly complex modeling approaches, suggesting that careful predictor selection may be more important than model complexity in modest-sized EC cohorts. For survivorship, the largely null association between surgical approach and overall CVD risk suggests that long-term cardiovascular burden in EC survivors may be driven more by aging, comorbidity, and cancer treatment exposures than by incision type alone. Collectively, these findings support a broader public health approach to EC that emphasizes prevention of environmental exposures, more holistic prognostic assessment, and survivorship care attentive to both cancer and non-cancer outcomes.EpidemiologyPublic healthMedicineOncologyCancer survivorshipEsophageal cancerIndoor air pollutionapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0g38t4c8https://escholarship.org/content/qt0g38t4c8/qt0g38t4c8.pdfetdoai:escholarship.org:ark:/13030/qt9x8528262026-05-25T06:34:06Zqt9x852826Digital phenotyping identifies objective measures of physiology, behavior, and emotional expression associated with depressionSeok, DarsolFreimer, Nelson B2026-05-21Depression and anxiety, the two most common mental health conditions, consist of heterogeneous combinations of altered physiology, behaviors, and emotional states. The lack of scalable, objective methods to measure these dimensions is a major obstacle to elucidating underlying mechanisms and improving care for those who experience these conditions. Here, I show that such measurements can be obtained from sensor data passively and continuously collected from smartphones and smartwatches. First, I demonstrate the feasibility of collecting such data from a large sample of diverse individuals who completed an extensive array of self-report measures while providing objective digital phenotyping data. Second, I discuss various strategies for processing, cleaning, and analyzing these data, all of which I implemented in a robust processing pipeline. Finally, I identify associations between these digital phenotyping features and self-reported measures of depression and anxiety. From 12-months of sensor data provided by 3,751 consenting participants (diverse in age, sex at birth, race, ethnicity, and severity of depression symptoms), I extracted 427 digital features, yielding assessments of physiology, behavior, and emotional expression; evaluations in the latter category included novel measurements of facial expression, typing sentiment, and speech. Most digital features were significantly associated with overall levels of depression and anxiety. I identified stronger associations, however, by analyzing the features in relation to 19 specific symptom dimensions (constructs), which were aggregated from 69 individual symptoms through consensus of clinical experts. While most features were associated with multiple symptom constructs, each construct displayed distinctive patterns of association to different features. Notably, suicidality, perhaps the most consequential dimension of depression, was especially strongly associated with features that scalably and longitudinally quantify variability in facial expression, typing sentiments, heart rate, and sleep. Additionally, I identified subsets of feature-construct associations that differ substantially across groups defined by age, sex, or symptom severity. My results demonstrate that consumer devices can provide an objective and scalable framework for phenotyping depression and anxiety and dissecting the complexity of these conditions, key steps toward precision mental health.NeurosciencesPsychologyMental healthbehaviordepressiondigital phenotypingdimensional psychopathologysmartphoneswearablespubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9x852826etdoai:escholarship.org:ark:/13030/qt26r213hb2026-05-25T06:34:01Zqt26r213hbHigh-angular-resolution Astronomy with Photonic LanternsKim, Yoo JungFitzgerald, Michael P2026-05-21Diffraction fundamentally limits resolvable angular scales, constraining our ability to detect close-in exoplanets and resolve the inner regions of circumstellar disks and galaxies. Traditionally, building larger telescopes or longer-baseline interferometers has been the primary method to reduce the angular scale of the diffraction limit, which has led to new discoveries at the finest angular scales.This dissertation explores a novel path toward efficient measurement below the diffraction limit in astronomy using photonic techniques. The central concept is spatial-mode-based measurement, or spatial mode demultiplexing (SPADE), in which photons are detected in a modal basis rather than in pixels, enabling more efficient extraction of spatial information at subdiffraction scales. While SPADE has been well studied theoretically and demonstrated in stable laboratory environments, its practical application to astronomical observations has remained largely unexplored due to the challenge of implementing such measurements in dynamic, ground-based conditions.Part I details the first practical application of spatial-mode-based measurements in astronomy using photonic lanterns (PLs), which are fiber-based mode-converters with one multimode input and multiple single-mode outputs. Feeding these single-mode outputs to a diffraction-limited spectrometer enables a new form of spectroastrometry. I present the conceptual study, laboratory validation, and on-sky demonstrations of this PL spectroastrometry. Using a 19-port PL-fed visible spectrometer (FIRST-PL) installed at the Subaru Telescope, and through a dedicated postprocessing technique using simultaneously recorded focal-plane images, we achieved an unprecedented centroid precision of 50 µas in Hα in a 10-minute observation, leading to the first detection of nearside-farside asymmetry in the disk around the classical Be star β CMi. Another demonstration on the Wolf-Rayet and O-type star binary WR 140 on He I 1.083µm line using a 3-port PL-fed infrared spectrometer further established the method’s robustness and potential with fewer mode-count PLs. Finally, I discuss the lessons learned, ongoing developments, and considerations for future PL-fed spectrometers.Part II takes a more fundamental step beyond Part I. Whereas Part I treated the photonic lantern as a fixed mode-sorting device, Part II explores how the mode-sorting device itself can be tailored to specific science goals by introducing controlled interference between outputs using a photonic integrated circuit (PIC). I explore two applications: augmenting image reconstruction of complex scenes, and realizing an active coronagraph to achieve high-contrast imaging at the diffraction limit. In the first case, a pairwise beam combination of PL outputs is used to diversify spatial frequency coverage. In the second case, the PIC is designed to combine coronagraphy with the sensing and correction of starlight leakage, which is a key practical limitation for high-contrast imaging at small separations. I present the conceptual designs and paths toward experimental implementation.Together, this work transitions spatial-mode-based measurement from a theoretical concept to a practical technique for astronomical observations, while also outlining pathways for its broader and optimized application. These results establish photonic technologies as a practical and powerful path toward high-angular-resolution astronomy. AstronomyOpticsPhysicsAnalytical chemistryastronomical instrumentationhigh-angular-resolutionphotonic lanternphotonicsspectroastrometryspectroscopyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/26r213hbhttps://escholarship.org/content/qt26r213hb/qt26r213hb.pdfetdoai:escholarship.org:ark:/13030/qt66c1s2vq2026-05-25T06:33:56Zqt66c1s2vqThe Messenger and the Message: How Identity and Voice Shape Social Perception in Contentious CommunicationJiang, ElizabethShih, MargaretDeVoe, Sanford2026-05-21Workplaces have become increasingly visible platforms for voicing stances on contentious social and political issues, with companies and employees speaking up about topics ranging from socioeconomic inequality to race and gender representation. Yet the risks of speaking up are consequential: individuals and companies face potential reputational harm if their messages are delivered or received poorly.In this dissertation, I examine the influence that messenger identity and message framing exert on the way workplace voice is perceived. Drawing on voice, social identity, and social perception research, I investigate how the demographic identity of messengers and the framing of their message jointly shape audience evaluations. I explore how these two factors affect perceptions of messengers and of the message itself. In Chapter 1, I focus on understanding messenger-side perceptions of motivation. Across one pilot and three preregistered experiments (N = 2,240), I demonstrate a negative messenger-message discordance effect: when presenting group-based workplace disparities, messengers with socially dominant identities are perceived as less other-interested and more self-interested than messengers with non-dominant identities. However, I find that changing the message framing to incorporate explicit personal advocacy against inequality mitigates this disadvantage for the socially dominant messenger and boosts perceived other-interest for all messengers. In Chapter 2, I turn my attention to message-side perceptions using workplace advertisements. Across four preregistered experiments (N = 5,125), I show that advertisements featuring stereotype-congruent versus stereotype-incongruent portrayals of models elicit divergent evaluations from liberal and conservative audiences. These evaluations vary in direction and magnitude depending on the racial identity of the models depicted, revealing the importance of how message content and framing affect the way audiences respond.Together, these findings advance research on the influence that messenger identity and message framing have in shaping the perception of workplace voice on contentious topics. This work emphasizes the importance for employees and organizations to carefully consider who speaks up, how they do so, and to what audiences when navigating an increasingly polarized social and workplace landscape.CommunicationSocial psychologyFilm studiesPolitical sciencecommunicationrisky and contentious topicssocial identitysocial perceptionsvoiceapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/66c1s2vqhttps://escholarship.org/content/qt66c1s2vq/qt66c1s2vq.pdfetdoai:escholarship.org:ark:/13030/qt7ww491jh2026-05-25T06:33:51Zqt7ww491jhLEAN ON ME: Spousal and Friendship Social Support Influencing Dementia Among Older AdultsChurchill, NicolaNianogo, RochArah, Onyebuchi2026-05-21This dissertation aims to understand the mechanism through which social support affects dementia, as well as the potential synergistic effect of multiple social support types on dementia, and evaluate potential interventions, particularly for individuals in racial and ethnic and sex/gender subpopulations. Specifically, the aims are as follows: Aim 1: To examine the joint effects of positive spousal social support and closeness to spouse on dementia and cognitive impairment risk overall and by subgroups defined by race/ethnicity and sex/gender; in Aim 2: To identify potential factors that can modify the effect of friendship social support on the risk of dementia and estimate subgroup heterogenous effects across these factors in the overall and racial/ethnic subgroups; and Aim 3: To examine the extent to which depression mediates the effects of positive spousal social support on dementia and cognitive impairment risk and evaluate if these effects vary by sex/gender subpopulations. These research aims were carried out by using the 2006-2020 Health and Retirement Study (HRS) cohort of U.S. older adults, age 50 and older and their spouses. The results of these aims demonstrated the complex relationships between social support and dementia/cognitive impairment risk. There is a need for more representation of older adults from racial and ethnic subpopulation. In particular, expanding participation among Black and Hispanic individuals is essential to overcoming the small sample size limitations. Future research should explore the nuanced social mechanisms that govern how support from a partner effects long term cognitive health and whether quality in addition to frequent interactions with friends can affect dementia risk.EpidemiologyMental healthAgingCausal InferenceDementiaDepressionEpidemiologySocial Supportapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7ww491jhhttps://escholarship.org/content/qt7ww491jh/qt7ww491jh.pdfetdoai:escholarship.org:ark:/13030/qt03q8w3pq2026-05-25T06:33:46Zqt03q8w3pqSex Chromosomal Versus Gonadal Contributions to Cell-Type Specific and Spatial Sex Differences in Adipose and Liver TissuesZhao, YutianYang, Xia2026-05-20Cardiometabolic diseases (CMDs), including obesity, diabetes, dyslipidemia, metabolic dysfunction-associated steatotic liver disease (MASLD), and cardiovascular diseases, are the leading causes of global mortality and exhibit marked sex differences driven by three primary sex-biasing factors: activational effects of sex hormones, organizational effects of gonadal sex, and sex chromosomal effects. How these factors shape gene regulation across diverse cell populations at single-cell and spatial resolution remains poorly understood. This dissertation employs single-cell and spatial transcriptomic approaches across various mouse models to dissect contributions of distinct sex-biasing factors to sex differences in peripheral metabolic tissues under CMD conditions. In gonadal adipose tissue of diet-induced obese mice, estradiol (E2) elicited cell-type- and sex-specific responses, including female-biased adipose stem and progenitor cell (ASPC) transcriptomic pattern shifts and stemness program remodeling, sex-shared suppression of inflammation in macrophages, and female-biased enhancement in ASPC-macrophage intercellular communications. In the liver of the same obese mice, E2 induced concordant effects between sexes across hepatocyte subtypes, along with more pronounced male-biased downregulation of genes and associated pathways. Intercellular signaling into or from hepatocyte subtypes and Kupffer cells was additionally more prominent in male mice. Together, the sex concordance or discordance of E2 effects is highly dependent on both tissue and cell type in the setting of obesity. Using the Four Core Genotype (FCG) mouse model in metabolic dysfunction-associated steatohepatitis (MASH), gonadal sex and sex chromosome complement were found to be differentially associated with liver cellular composition and the transcriptional landscape across both parenchymal and non-parenchymal cells. Cell-type-conserved metabolic processes, including energy homeostasis and autophagy, were co-regulated by both sex factors; in contrast, cell-type-specific pathways related to individual cell-type functions were regulated in a sex-factor-dependent manner. Sex factor-biased genes in disease-associated Trem2+ macrophages diverged functionally, with XX-biased genes enriched for phagocytic and lipid oxidation pathways, whereas testis-biased genes were implicated in oxidative stress and lipid storage. Gonadal sex was associated with differences in hepatic stellate cells (HSCs), manifested as a greater abundance of activated HSCs and higher enrichment of collagen fibril organization pathways in testis-bearing mice compared to ovary-bearing mice. Spatial neighborhood analysis further resolved five functional microenvironments recapitulating lobular zonation and fibrosis, with the myeloid cell-enriched niche showing a higher proportion in XY-bearing mice. Collectively, our findings partitioned the contributions of individual sex-biasing factors to the regulation of gene programs across metabolic tissues in multiple disease models, providing cell-type-resolved molecular insights that can inform sex-specific precision therapeutic strategies for CMDs.Molecular biologyCellular biologyGeneticsImmunologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/03q8w3pqhttps://escholarship.org/content/qt03q8w3pq/qt03q8w3pq.pdfetdoai:escholarship.org:ark:/13030/qt2bx4b3x32026-05-25T06:33:40Zqt2bx4b3x3Coherent Choices of Nullhomotopies in Pointed Infinity-CategoriesBrown, Thomas RushXu, Zhouli2026-05-21If C is an ∞-category, then the functor (−)⋆∆0 freely adjoins a terminal object to C. Having the join construction (−) ⋆ ∆0 as an explicit operation on simplicial sets, rather than only knowing the ∞-category C ⋆ ∆0 up to equivalence in Cat∞, is what allows the treatment of limits and colimits in ∞-categories in terms of simplicial sets. In this thesis, we give an analogous functor, (−)⋄ : sSet → sSet, which freely adjoins a zero object to an ∞-category. We then prove that the induced functor of ∞-categories Cat∞ → Catpt ∞ gives a left adjoint to the inclusion Catpt ∞, → Cat∞, via the classification of coCartesian fibrations. This adjunction, in turn, shows that an object x of an ∞-category C is a zero object if and only if the canonical inclusion C ,→ C⋄ admits a left inverse z : C⋄ → C whose restriction to {x}⋄ is the constant map to x. Just as the join construction allows us to treat (co)limits in ∞-categories more like (co)limits in 1-categories (as opposed to thinking of them as homotopy (co)limits), so also z : C⋄ → C allows us to treat fiber sequences in stable ∞-categories more like kernels in abelian categories.MathematicsTheoretical mathematicsApplied mathematicsHigher categoriesInfinity categoriesK-theoryStable infinity categoriesapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2bx4b3x3https://escholarship.org/content/qt2bx4b3x3/qt2bx4b3x3.pdfetdoai:escholarship.org:ark:/13030/qt6px189ds2026-05-25T06:33:35Zqt6px189dsIntegrating Machine Learning and Longitudinal Modeling to Optimize the Liver Transplant Continuum: From Waitlist Dropout to Post-Transplant SurvivalMelehy, AndrewBui, Alex2026-05-19Real-world clinical data is longitudinal in nature, owing to the multiple observations made over time when an individual interacts with the healthcare system. Despite the ubiquitous nature of longitudinal electronic heath record (EHR) data, many studies are limited to time-to-event analyses, clustering based on basic metrics, or the simple static modeling of dynamic processes. With the abundance of EHR data now available, an exploration and update of longitudinal data analysis frameworks is warranted to most accurately model patient experiences.This dissertation focuses on the exploration of longitudinal (e.g., time series) and clustered data analysis approaches for real-world clinical data in the specific domain of liver transplantation (LT). I begin by examining traditional approaches to longitudinal analysis in the LT domain, establishing common areas for improvement using existing 2 methodologies, and exploring novel methods with machine and reinforcement learning (ML/RL).This work leveraged large publicly available registry databases from the Scientific Registry of Transplant Recipients and United Network for Organ Sharing, as well as a granular multi-center dataset containing data on LT recipients. The explored traditional approaches included using linear mixed effects to model transplant center variation in waitlist mortality and logistic regression for the evaluation of novel longitudinal risk factors in transplant recipients with acute-on-chronic liver failure. The ML/RL approaches included the use of offline reinforcement learning to improve LT donor- recipient matching and the creation of a longitudinal deep survival network to better assess risk in hepatocellular carcinoma patients listed for LT.This collection of studies demonstrates that with advanced techniques the full power of longitudinal health information can be leveraged for state-of-the-art predictive performance. Specifically, the developed waitlist dropout model for HCC patients has predictive accuracy surpassing any previous published model. This work in HCC highlights how the strong predictive performance of an ML model can be integrated into an existing clinical framework that is used and accepted by the medical community, with demonstration of the impact of implementing our model in the system using simulation. Finally, this works offers a fundamentally new framework for data-driven donor-recipient matching optimization through the use of ML/RL, providing proof-of-concept that could serve as the basis for future intelligent decision support in LT.MedicineBiomedical engineeringBioinformaticsHealth care managementapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6px189dshttps://escholarship.org/content/qt6px189ds/qt6px189ds.pdfetdoai:escholarship.org:ark:/13030/qt62r5015g2026-05-25T06:33:30Zqt62r5015gEssays in International MacroeconomicsHe, BangyuWeill, Pierre-OlivierItskhoki, Oleg2026-05-15This dissertation studies how financial frictions, market structure, and policy interventions shape exchange rate dynamics and capital flows across countries at different stages of financial development. The three chapters jointly provide a unified perspective on international macroeconomics by emphasizing the role of constrained arbitrage, financial market depth, and official capital flows in determining macroeconomic outcomes. Chapter 1 revisits the uncovered interest parity (UIP) puzzle using daily data on exchange rates and interest differentials for both developed and emerging market economies over the past two to three decades. The large number of observations allows for a detailed analysis of the dynamics of the Fama coefficient and its response to major global events, including the global financial crisis, the European sovereign debt crisis, and the COVID-19 pandemic. Using a decomposition approach, the chapter evaluates several leading explanations of UIP deviations. The results suggest that different theoretical channels explain different dimensions of the data, while models based on financial market frictions are particularly well suited to account for the high-frequency dynamics of the Fama coefficient. Chapter 2 develops a theoretical framework to study the role of arbitrageurs in foreign exchange markets. The chapter highlights two opposing forces generated by arbitrage activity: a substitution effect that stabilizes exchange rates by absorbing shocks, and a wealth effect that can amplify exchange rate fluctuations when arbitrageurs incur losses. The model shows that the relative importance of these effects depends on the level of arbitrageur wealth and market depth. In advanced economies with deep and liquid foreign exchange markets, the substitution effect dominates and exchange rates remain relatively stable. In emerging market economies with thinner markets and limited arbitrageur capital, the wealth effect becomes more important, contributing to larger deviations from uncovered interest parity and more frequent foreign exchange interventions by central banks. Chapter 3 studies the catalytic impact of IMF lending on official development assistance (ODA) in low-income countries. Using a country-year panel dataset and an instrumental variables strategy to address selection bias in IMF program participation and lending size, the chapter estimates the causal effect of IMF disbursements on external financing. The findings show that IMF lending significantly increases ODA inflows and that larger IMF disbursements catalyze more substantial external financing. At the same time, the catalytic effect exhibits diminishing returns with respect to program size. The results highlight the important role of IMF programs in mobilizing official capital flows in economies where private financial markets remain underdeveloped. Taken together, the chapters demonstrate that financial frictions and constraints on capital allocation are central to understanding exchange rates, capital flows, and policy interventions in the international economy. While advanced economies, emerging market economies, and low-income countries differ substantially in financial market depth and the composition of capital flows, the dissertation shows that a unified framework centered on market frictions and risk-bearing capacity can help explain macroeconomic outcomes across these diverse settings.EconomicsEconomic theoryFinanceapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/62r5015ghttps://escholarship.org/content/qt62r5015g/qt62r5015g.pdfetdoai:escholarship.org:ark:/13030/qt61k599jv2026-05-25T06:33:24Zqt61k599jvHuman medial temporal lobe theta oscillations underlying visual exploration and approach-avoidance conflict in real-world spatial navigationZubair, Humza NisarBari, AusafSuthana, Nanthia2026-05-18Theta oscillations in the medial temporal lobe (MTL) are linked to spatial navigation, memory, and active decision making, processes disrupted in disorders such as Alzheimer’s. However, the role of MTL theta activity during naturalistic human behavior remains poorly understood. Much prior work in humans has relied on stationary laboratory tasks or surface EEG, paradigms that fail to capture the complexity of theta and real-world navigation. Describing MTL theta in freely-moving human behavior is therefore essential for relating intracranial electroencephalographic activity with the navigation and behavioral impairments observed in neuropsychiatric disorders.Critical differences exist across species which obfuscate our comprehension of theta physiology. In rodents, hippocampal theta is expressed continuously during movement, while human theta occurs in transient bouts and is influenced by context and navigational challenges. This dissertation explores how MTL theta activity is modulated by behavioral context during naturalistic navigation, with an emphasis on visual exploration, locomotive planning, and threat-reward evaluation. Using intracranial recordings from human epilepsy patients with chronically-implanted neurostimulation (RNS®) implants, we demonstrate that theta activity in the hippocampus and amygdala is dynamically elevated during specific context-dependent periods of navigation. In Chapter 1, we demonstrate that MTL theta power is significantly elevated during periods when saccadic eye movements occur, particularly during periods with overt memory demands. The largest increases are observed during saccades with more exploratory gaze patterns, during saccades of trials with better memory performance, and during saccades in the early planning period of each route. In Chapter 2, we introduce a novel paradigm in which participants navigate under different levels of approach-avoidance conflict, while tracking goal locations and mobile threats. We demonstrate a double dissociation in which theta in the hippocampus increases during high-conflict contexts, and theta in the amygdala is elevated during high-risk contexts. These findings collectively suggest that human MTL theta is a flexible and context-sensitive signal which supports prospective planning and decision-making during real-world navigation. We provide a framework for understanding oscillatory dynamics in freely moving humans, which will inform the development of effective context-dependent neuromodulation interventions for patients with diseases of the hippocampus and/or amygdala.NeurosciencesBiomedical engineeringCognitive psychologyhuman navigationiEEGmedial temporal lobetheta oscillationsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/61k599jvhttps://escholarship.org/content/qt61k599jv/qt61k599jv.pdfetdoai:escholarship.org:ark:/13030/qt8bb6x8sj2026-05-25T06:33:19Zqt8bb6x8sjSpintronic Magnetic Textures for Next-Generation AI Computing PlatformsWang, TianyiWang, Kang L.2026-05-18As Moore’s Law slows down, the scaling of CMOS-based devices is reaching fabrication limits, which raises concerns about its scalability. Besides, computing systems based on the von Neumann architecture also face bottlenecks from the separation of memory and computing units, leading to latency, energy inefficiencies, and limited throughput. Additionally, as the technology node develops, standby power consumption also increases tremendously, which leads to even more energy consumption. Spintronics, which exploits the electron's spin alongside its charge, offers a compelling alternative. With features of non-volatility, high density, and energy efficiency, spintronic devices show promise not only as advanced memory solutions but also for unconventional in-memory computing. These technologies have the potential to succeed CMOS as future AI computing platforms. Driven by these motivations, my doctoral study focuses on magnetic domain walls and skyrmions on their potential of applications in next generation AI applications due to their nanoscale dimensions and ultrafast dynamics. Our research demonstrates that domain wall dynamics can be harnessed for an experimental in-memory convolution device, enabling efficient implementations of convolution-based tasks. By integrating with magnetic tunnel junction and leveraging voltage controlled magnetic anisotropy, a probabilistic computing framework is further developed for Bayesian neural network, where intrinsic stochasticity enables tunable Gaussian-distributed signal generation for uncertainty-aware inference. In parallel, skyrmions and antiskyrmions are explored for stochastic computing, including the observation of a stripe-to-skyrmion transition and a skyrmion–antiskyrmion–skyrmion intertwined state. These dynamics are utilized to demonstrate stochastic bitstream generation, highlighting the potential of transient spin textures for probabilistic computation. Overall, my doctoral study establishes a unified spintronic framework that bridges magnetic materials, device physics, and AI hardware, providing a pathway toward energy-efficient, high-performance, and trustworthy computing systems beyond CMOS.Electrical engineeringEngineeringComputer scienceArtificial intelligenceapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8bb6x8sjhttps://escholarship.org/content/qt8bb6x8sj/qt8bb6x8sj.pdfetdoai:escholarship.org:ark:/13030/qt27b2g59j2026-05-25T06:33:14Zqt27b2g59jEssays on Firm Dynamics, Networks, and Economic GrowthOkumura, KokiBurstein, Ariel2026-05-15This dissertation studies how firm-level networks shape innovation incentives, aggregate growth, and welfare. Across three essays, it shows that both the private and social returns to innovation depend on how knowledge flows and production relationships connect firms. These channels create gaps between private and socially efficient innovation decisions, with implications for R&D policy and labor-market policy.
Chapter 1 studies inventor mobility as a channel of knowledge diffusion. Using German patent records linked to employer-employee data, it documents inventor mobility, wage changes, and productivity growth at destination firms that are consistent with inventor mobility transmitting knowledge across firms. It develops a quantitative endogenous-growth model in which inventors create knowledge within firms and transmit it when they move. The model shows that mobility shapes long-run growth through the allocative efficiency of inventors and the diffusion of knowledge across firms. Banning non-compete clauses raises mobility and long-run growth but can lower welfare through excessive vacancy posting.
Chapter 2 studies how production networks shape R&D allocation between incumbent innovation and entry. Using matched Japanese firm-to-firm transaction and patent data, it documents the life-cycle accumulation of trading relationships, relationship persistence, and links between trading partners' innovation. It develops a dynamic model in which accumulated trading relationships shape the returns to innovation and entry. Quantitatively, the decentralized economy over-allocates R&D to variety creation by entrants and young firms because firms do not internalize dynamic network-formation externalities. A uniform entry tax captures most of the planner gains but misses the age-dependent allocation of incumbent R&D.
Chapter 3 studies innovation policy in a dynamic oligopoly with product-market rivalry and technology-spillover networks. It develops and estimates a tractable endogenous-growth model with empirically measured firm-to-firm networks for a large set of U.S. firms. The quantitative analysis shows that aggregate R&D investment falls short of the socially efficient level, static product-market distortions and dynamic R&D misallocation reinforce each other, and uniform R&D subsidies deliver limited welfare gains because private-social return gaps vary across firms' network positions.
Together, the chapters show that the design of optimal R&D and labor-market policies depends critically on the network structures linking firms.EconomicsStatisticsLabor economicsEconomic theoryEconomic GrowthFirm DynamicsNetworkspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/27b2g59jetdoai:escholarship.org:ark:/13030/qt048858cc2026-05-25T06:33:09Zqt048858ccMolecular and Epigenetic Control of Mesenchymal Stem Cells by Histone Variant MacroH2A1 in Skeletal DevelopmentAhmad, Amr BadreldinChang, Jia2026-05-15Epigenetics plays a crucial role in bone development by influencing gene expression without altering the underlying DNA sequence. While it is well known that mesenchymal stem cell lineage-specific differentiation is mediated by the interplay between transcriptional regulators and their epigenetic modifiers, it remains unclear how histone variant proteins contribute to this process. Here, we show that histone variant macroH2A1 is a key regulator of the intrinsic bone morphogenetic protein signaling pathway, which is involved in MSC lineage differentiation and osteoid release. macroH2A1 loss halts the osteogenic differentiation capacity of mesenchymal stem cells during skeletal development, independent of osteoclastogenesis. Moreover, mice lacking macroH2A1 under the Prrx1 promoter display a low bone mass phenotype and defective bone growth at an early age. Notably, macroH2A1 controls Bone Morphogenic Protein 4 signaling via the phosphorylation of receptor-regulated Smads (R-Smads 1, 5 and 9). Mechanistically, macroH2A1 controls osteogenesis by protecting the promoter regions of pro-osteogenic markers from the trimethylation of histone H3 at lysine 27 gene repression. Thus, the epigenetic function of mH2A1 is pivotal for skeletogenesis and could potentially be utilized to mitigate prevalent degenerative bone conditions.Molecular biologyBiologyGeneticsBioinformaticsBone biologyEpigeneticsSkeletal developmentStem cell biologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/048858cchttps://escholarship.org/content/qt048858cc/qt048858cc.pdfetdoai:escholarship.org:ark:/13030/qt6b3990bn2026-05-25T06:33:05Zqt6b3990bnRational function progressions and the density versions of the Diophantine equationsLim, Zi LiTao, Terence Chi-Shen2026-05-18In this thesis, we discuss Szemerédi type theorems for three term rational function progressions and two dimensional rational function corners. Moreover, we also consider the density versions of some Diophantine equations over finite fields, cyclic rings and primes. The methods and techniques in number theory and additive combinatorics for these two topics are presented in this dissertation.MathematicsTheoretical mathematicsApplied mathematicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6b3990bnhttps://escholarship.org/content/qt6b3990bn/qt6b3990bn.pdfetdoai:escholarship.org:ark:/13030/qt6nt6j0b92026-05-25T06:33:00Zqt6nt6j0b9Cone Beam Computed Tomography Based Study on the Association Between Temporomandibular Joint Osteoarthritic Changes and Vertical Skeletal Growth Patterns in Asymptomatic PatientsGlavin, Monica KayMessadi, Diana V2026-05-15This study evaluated the association between vertical skeletal growth pattern and radiographic temporomandibular joint (TMJ) osteoarthritic changes in an asymptomatic adult population using cone-beam computed tomography (CBCT). TMJ osteoarthritis (OA) is characterized by degenerative osseous changes, yet similar radiographic findings may also represent adaptive remodeling, particularly in patients without symptoms. Understanding how craniofacial morphology influences these changes is clinically relevant in orthodontic diagnosis and treatment planning.A retrospective cross-sectional study was conducted using CBCT scans from 170 adult patients (340 TMJs) obtained from the UCLA Oral and Maxillofacial Radiology archive. Vertical skeletal pattern was classified using the sella-nasion to mandibular plane (SN-MP) angle into brachyfacial, mesofacial, and dolichofacial groups. Each TMJ was evaluated for radiographic features according to standardized Diagnostic Criteria for Temporomandibular Disorders (DC/TMD)-based imaging definitions and categorized as normal, indeterminate, or osteoarthritic. Generalized estimating equation (GEE) models were used to assess associations while accounting for bilateral joint correlation, with adjustment for age and sex.Of the 340 joints, 195 were normal, 87 indeterminate, and 58 osteoarthritic. Dolichofacial joints demonstrated significantly greater odds of OA compared with mesofacial joints (OR = 2.34, p = 0.0449). Female sex (OR = 2.43, p = 0.0209) and increasing age (OR = 1.04 per year, p = 0.0182) were also significant predictors. No significant associations were observed in the indeterminate versus normal analysis. Sensitivity analysis combining OA and indeterminate joints showed that only age remained significant. Feature-level analysis revealed that osteophytes and erosions were significantly associated with the dolichofacial pattern, whereas flattening and subcortical sclerosis were widely distributed across groups.These findings suggest that vertical skeletal pattern, particularly a dolichofacial pattern, is associated with increased likelihood of radiographic TMJ osteoarthritis in asymptomatic adults. Importantly, this association appears to be driven by features specific to degenerative disease rather than nonspecific remodeling. Clinically, careful differentiation between adaptive and degenerative radiographic findings is essential, and consideration of skeletal pattern, age, and sex may enhance interpretation of TMJ imaging in orthodontic patients.DentistryBiomedical engineeringBiologyMedical imagingCBCTorthodonticsosteoarthritistemporomandibular jointvertical skeletal patternapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6nt6j0b9https://escholarship.org/content/qt6nt6j0b9/qt6nt6j0b9.pdfetdoai:escholarship.org:ark:/13030/qt3c36001z2026-05-25T06:32:50Zqt3c36001zThalamic Neurexin-1-Mediated Interaction with Cortical Progenitors Drives Excitatory NeurogenesisNguyen, Claudia VuBhaduri, Aparna2026-05-13While the thalamus has largely been thought of as a relay center for sensory information destined for the cortex, mounting evidence demonstrates that it also plays a vital developmental role. The thalamus matures in parallel with the cortex and together these areas form early reciprocal projections. According to the protocortex hypothesis, extrinsic thalamic signaling is necessary for refining cortical areas and cell types, but the mechanism by which these inputs shape human cortical fate specification remains largely unexplored. Stem cell-derived brain organoids provide a tractable in vitro system to study aspects of human neurodevelopment that are difficult to probe in traditional animal models. Here, we fuse cortical and thalamic organoids to study this process. Leveraging single-nuclei RNA-sequencing and cellular imaging techniques, we discover that thalamic signals during a progenitor-rich critical period drive developmental gene expression changes and shift cell fate toward increased excitatory neurogenesis. We also discover that neurexin-1 mediates thalamic axon physical contacts with primate-enriched outer radial glia; genetic perturbation of NRXN1 reduces these contacts and significantly attenuates upper layer neurogenesis. These findings suggest a novel neurexin-1-mediated mechanism for thalamic regulation of outer radial glia cell fate and provides evidence that this process may contribute to human cortical expansion. Thus, this study helps to establish the foundation for how developmental extrinsic signaling may confer vulnerability to neurodevelopmental and neuropsychiatric disorders, particularly those characterized by thalamocortical dysregulation.NeurosciencesBiologyDevelopmental biologyGeneticsbraincortexdevelopmentneuronstem cellsthalamuspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3c36001zetdoai:escholarship.org:ark:/13030/qt99v8s5p62026-05-25T06:32:45Zqt99v8s5p6Signaling Bottlenecks in Lineage Control of Engineered T Cell Fate for Cancer ImmunotherapyZhou, KuangyiYang, Lili2026-05-13Chimeric antigen receptor (CAR)-T cell therapy has transformed the treatment of hematologic malignancies and is now extending into autoimmune disease, yet its broader promise in cancer therapy remains constrained by three interrelated bottlenecks: the unit economics of autologous manufacturing, the failure of conventional CAR-T products to control solid tumors, and the limited functional persistence of infused cells. A growing consensus holds that the fate of an engineered T cell — its lineage identity, functional state, and epigenetic configuration at the moment of infusion — determines whether it will persist and function in the patient. This dissertation tests the central thesis that signaling bottlenecks acting on lineage control are dominant determinants of engineered T cell fate, and that resolving them across distinct cellular platforms offers a generalizable strategy for next-generation cell therapy.I develop this thesis through three complementary experimental systems spanning donor peripheral blood (PB)-derived, hematopoietic stem and progenitor cell (HSPC)–derived, and induced pluripotent stem cell (iPSC)–derived CAR-T cell platforms. First, we establish a "Super-Killer" culture method that resolves a co-stimulatory signaling ceiling in donor-derived MAIT cells, generating IL-15–armored mesothelin-targeting CAR-MAIT cell products with intrinsic tissue tropism and durable anti-tumor activity across orthotopic lung and ovarian cancer xenograft models. Second, I contribute mechanistic work to an HSPC-derived CAR-NKT cell platform, demonstrating that promoter methylation of IFN-γ response genes and loss of STAT1 phosphorylation together install a hypoimmunogenic state that protects allogeneic products from host rejection. Third, and most mechanistically developed, I identify the IKKβ-dependent canonical NF-κB pathway as a critical signaling node through which tonic CAR engagement diverts iPSC-derived T cell development toward innate-like fates. I show that pharmacological calibration with the selective IKKβ inhibitor TPCA-1 — corroborated by IKK2 gain- and loss-of-function genetics and single-nuclei multiomics — restores the BCL11B-governed αβ T cell program and installs a stem cell–like memory phenotype with enhanced mitochondrial fitness and sustained in vitro anti-tumor function.Collectively, this work positions signaling and developmental epigenetic state as engineerable variables for cell therapy, and establishes mechanistically defined strategies for generating allogeneic, off-the-shelf CAR-T cell products across donor and stem cell sources.Molecular biologyCellular biologyImmunologypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/99v8s5p6etdoai:escholarship.org:ark:/13030/qt03w223r72026-05-23T05:01:06Zqt03w223r7Automated microscale technologies for improving antibody-secreting cell line developmentMellody, Michael PatrickDi Carlo, Dino2025-01-01Antibody development is a cornerstone of modern biotherapeutics, yet conventional methods for screening and expanding productive clones remain labor-intensive and inefficient. This thesis presents a suite of microscale technologies that streamline and enhance key stages of this process—from single-cell secretion analysis to autonomous expansion of selected clones.First, we developed a platform using hydrogel nanovials functionalized via orthogonal click chemistry to capture secreted antibodies selectively from individual hybridoma cells. This approach improved the specificity and sensitivity of single-cell secretion assays, enabling robust identification of high-producing clones in high-throughput formats.
Next, we introduced a novel "capped nanovial” system that physically seals single cells or microcolonies within compartmentalized hydrogel cavities using gelatin-polyethylene glycol (PEG) spherical caps. These caps serve dual roles: acting as a semi-permeable diffusion barrier to reduce signal crosstalk in secretion assays, and as a customizable biochemical surface for modulating cell-cell interactions. This multi-particle architecture supports longitudinal tracking of single-cell growth, functional screening, and co-culture assays within isolated compartments.
To propagate top-performing clones identified via these technologies, we designed and implemented an automated, artificial intelligence (AI)-assisted cell culture platform. This robotic system integrates a liquid handling workstation, robotic arm, incubator, and real-time imaging with a Python-based scheduler and structured query language (SQL) database for experiment tracking. A recurrent neural network processes live images to assess confluency and autonomously triggers expansion protocols, enabling asynchronous and scalable single-cell outgrowth with minimal human intervention. Together, these microscale and automated tools form an integrated pipeline that accelerates the identification, screening, and propagation of desirable cell lines.
BioengineeringAutomationHigh throughput screeningMachine learningSingle cell screeningenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/03w223r7https://escholarship.org/content/qt03w223r7/qt03w223r7.pdfetdoai:escholarship.org:ark:/13030/qt4kp2h31j2026-05-22T06:33:12Zqt4kp2h31jBehavioral state, movements, and decision-making across timescalesMelin, Maxwell DChurchland, Anne K2026-05-19The transformation of external sensory cues into appropriate motor actions is one of the most fundamental functions of the brain. This dissertation examines how internal
behavioral states shape neural activity and decision-making across multiple timescales. First,
we describe a novel platform for chronic, simultaneous electrophysiological recordings across
multiple brain regions, enabling stable, longitudinal tracking of neural populations. Next, we
demonstrate that latent changes in task engagement are characterized not by loss of task
encoding, but by increased influence of spontaneous, task-independent movements, which
serve as a proxy for latent state. Finally, we track neural activity across learning using the
novel methods described here, revealing how cortical and striatal representations reorganize
as animals transition from novice to expert performance. Together, this work demonstrates
that internal state dynamically shapes distributed neural activity, providing a framework for
understanding decision-making across both rapid state fluctuations and long-term learning.NeurosciencesBioengineeringBehavioral psychologyBehavioral neuroscienceElectrophysiologyNeuroscienceSystems neuroscienceapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4kp2h31jhttps://escholarship.org/content/qt4kp2h31j/qt4kp2h31j.pdfetdoai:escholarship.org:ark:/13030/qt0s2957jn2026-05-22T06:33:07Zqt0s2957jnStep-Discounted Regret for Regret-Guided Search Control in AlphaZero GomokuLin, FanchaoWu, Ying Nian2026-05-19To address AlphaZero’s low sample efficiency caused by self-play always starting from the initial position, regret-guided search control (RGSC) was proposed: restarting training from states where the model is expected to make larger mistakes. It outperformed the original AlphaZero in Go, Othello, and Hexx. In this thesis, we study whether the same method is also applicable to 9×9 Gomoku. This is because the average game length of Gomoku is much shorter, and at the same time local threats and forcing lines usually determine the outcome very quickly. We first adapt RGSC into an AlphaZero-style Gomoku agent, and examine three questions: whether it truly identifies states with higher regret, whether those high-regret states are later corrected through replay, and whether such corrections lead to faster learning. Under a limited compute budget, although standard RGSC successfully selected states with actually higher regret values, and replay also effectively reduced these mistakes, these phenomena did not quickly translate into playing strength, and it did not show the kind of advantage reported in prior work. To improve RGSC so that it better fits the characteristics of Gomoku, we further propose a step-discounted variant of regret. It places more emphasis on near-future errors, while the rest of the RGSC framework remains unchanged. Under the same training budget, this variant clearly outperforms standard RGSC, and can reach stronger results more quickly.StatisticsComputer scienceArtificial intelligenceComputer engineeringAlphaZeroGomokuMonte Carlo Tree SearchRegret-Guided Search ControlStep-Discounted Regretapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0s2957jnhttps://escholarship.org/content/qt0s2957jn/qt0s2957jn.pdfetdoai:escholarship.org:ark:/13030/qt0bc9j9gd2026-05-22T06:33:02Zqt0bc9j9gdBut How Do I Get There?: Transportation Equity and Public Library Access in an Automotive CultureSalisbury, SarahPosner, Miriam2026-05-19Physical access to public libraries is essential to patrons wishing to take advantage of the various social benefits they offer, but in a car-centric society, these benefits are significantly less accessible for patrons without cars. After several decades of car-oriented development in both library siting and transit planning, American public transportation systems have come to pose challenges in access to public services for those who rely upon them for mobility. Thus, a study of equitable library access must include a study of the spatial inequities to which inadequate American transit systems give rise. This is especially imperative because the non-driving population intersects with a number of other marginalized identities whose limited transportation options tend to reinforce cycles of inequity. And while it is not possible to entirely resolve this equity imbalance within the framework of an urban landscape designed for drivers, smaller-scale interventions brought about by collaboration between libraries and transit agencies can help to bridge the access gap somewhat for non-driving library patrons.Library scienceUrban planningTransportationPublic LibrariesPublic TransitSpatial EquityUrban Planningapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0bc9j9gdhttps://escholarship.org/content/qt0bc9j9gd/qt0bc9j9gd.pdfetdoai:escholarship.org:ark:/13030/qt6fq113zz2026-05-22T06:32:57Zqt6fq113zzStaging Strategies: Theatricality, Remaking, and the Museum Life of 1960s and 1970s Feminist Performance ArtFerrara, Lidia RuthKwon, Miwon2026-05-19This dissertation analyzes how, in the 1990s and early 2000s, American artists Joan Jonas (b. 1936), Senga Nengudi (b. 1943), and Carolee Schneemann (1939-2019) reconceived their performance-oriented artworks as arrangements of objects and media that could circulate long into the future. Between the early 1960s and the late 1970s, Jonas, Nengudi, and Schneemann made hybrid artworks combining live performance with disparate materials and objects, along with time-based mediums like film and video. In a moment when curators sought to historicize the radical experiments of the 1960s and 1970s through survey exhibitions, when the museum was becoming increasingly experience-oriented, and when art historians were reassessing and expanding established narratives of postwar artmaking, all three artists were encouraged by curators and peers to return to their innovations of decades prior and find ways to re-present them for museum display. Constituted by durational events and disposable or multi-purpose materials, their works did not simply remain as such when not on display. At the turn of the twenty-first century, the artists were faced with the challenge of revisiting artworks that could not be simply packed, shipped, and re-installed; they had to be remade.In the 1990s, Schneemann began reconstructing the spaces and settings that had formerly served as backdrops for her own performing body. In 2002, Nengudi began remaking her performative and sculptural experiments with pantyhose—known as her R.S.V.P. series—from the late 1970s. Beginning in 1994, Jonas re-presented her performance works of the 1970s as static installations of props, drawings, video monitors, and film projection. Few scholars have closely examined how, exactly, works by Jonas, Nengudi, and Schneemann have been able to resurface since the decades of their initial making. Chapters devoted to each artist investigate their works’ in-between moments of transition and transformation, arguing that the artists used remaking to participate directly in their own art world reception at the turn of the new millennium. Established terms for the remaking of artworks, such as replication, refabrication, or reenactment, are shown to be insufficient for analyzing these dynamics. This dissertation casts beyond the disciplinary bounds of art history and turns to the “again-ness” of theatrical performance to analyze the museum life of works by Jonas, Nengudi, and Schneemann. Close examination of key works and exhibitions by each artist demonstrates how theatrical techniques and materials—the delineated stage set and props, the logics of rehearsal, the mechanics of theatrical reconstruction—propelled the remaking of their 1960s-70s works. I argue that the space of theater, its material effects, its mechanics of repetition, its raced and gendered associations, functioned for these artists as a zone of agential possibility and a tool to enable their continued institutional visibility into the twenty-first century.Art historyPerforming artsMuseum studiesTheaterWomens studiesfeminismmuseumperformance artremakingtheaterpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6fq113zzetdoai:escholarship.org:ark:/13030/qt1w18t1t42026-05-22T06:32:52Zqt1w18t1t4Running on Race: How Minority Candidates Navigate Race in Campaign Messaging in Contemporary American CampaignsNguy, Joyce HuangMasuoka, Natalie2026-05-19How do candidates of color navigate race in contemporary American campaigns? Existing scholarship has largely emphasized a “deracialization” strategy, in which candidates of color avoid explicit discussions of race to minimize racial backlash and improve electoral viability. However, as the number of candidates of color continues to grow and racial demographics and norms shift, I argue that the contemporary political environment has altered both the costs and incentives of talking about race. Further, dominant theories of race in campaigns do not adequately explain the behavior of non-Black minority candidates, particularly Asian and Latino candidates. I advance a framework in which racial identity messaging can function as a strategic political asset, though the ways candidates deploy race are shaped and constrained by their racial position. I examine this through two dimensions of campaign messaging: candidate self-presentation and issue emphasis/framing. Using original content analyses of the official campaign biographies and issue platforms of all candidates running in the 2024 U.S. House primary elections, I show that contemporary campaign behavior does not reflect a universal deracialized strategy. Instead, most Asian and Latino candidates, particularly Asian candidates, foreground their racial and ethnic identities in campaign self-presentation, and unlike Black candidates, do so even in majority-White constituencies. Republican candidates of color also use racial and ethnic narratives to signal conservatism and distinguish themselves ideologically. I further show that candidates of color exhibit patterns consistent with racial issue ownership by emphasizing issues associated with their racial and ethnic groups and explicitly invoking identity in policy discussions. To examine how minority candidates are perceived by voters, I fielded a nationally representative survey experiment among White Americans. I find that a hypothetical Asian candidate is perceived as less patriotic and less representative of America than an equivalent White candidate, while White Democratic respondents perceive a Latino candidate as more competent on civil rights and immigration. By moving beyond the Black-White binary that has long dominated scholarship on race and campaigns, this project contributes to broader debates about representation and the evolving role of race in American politics.Political sciencePublic administrationAmerican studiesEthnic studiesCampaignsElectionsMinority CandidatesRacial Appealsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1w18t1t4https://escholarship.org/content/qt1w18t1t4/qt1w18t1t4.pdfetdoai:escholarship.org:ark:/13030/qt1gj0w3gj2026-05-22T06:32:47Zqt1gj0w3gjMachine-Learning-Based G-Computation for Life-Course SociologyJeon, NanumWu, Yingnian2026-05-19Life-course research asks how employment and family trajectories shape long-run outcomes, yet most empirical studies rely on a cluster-then-regress strategy that cannot recover causal effects when treatment and covariates evolve jointly over time. I propose a two-stage framework that integrates sequence analysis with g-computation. In the first stage, multi-channel sequence analysis and clustering identify empirically prevalent trajectory types whose medoids serve as counterfactual interventions for which positivity is empirically defensible. In the second stage, the causal effect of each medoid trajectory is estimated via g-computation with forward simulation of time-varying covariates via SuperLearner. A semi-synthetic simulation calibrated to the NLSY79 reveals a clear estimator hierarchy: cluster-adjusted regression recovers only a small fraction of the true causal effect, while flexible g-computation recovers the true effects most accurately. An empirical application to NLSY79 data reveals substantial wealth effects of sustained full-time employment and stable family formation, with notable divergence between parametric and flexible estimators. The framework offers life-course researchers a practical approach for defining trajectory interventions and estimating their causal effects.StatisticsApplied mathematicsArtificial intelligencepubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1gj0w3gjetdoai:escholarship.org:ark:/13030/qt3vt5t9bk2026-05-22T05:02:07Zqt3vt5t9bkDesign of Digital-to-Time-Converter for TX-TX Self-Interference CancellationLowrance, AsishPamarti, Sudhakar2024-01-01Many portables and IoT devices employ multi-radio systems. The number of differenttransceivers/antennas is increasing, especially with multi-antenna and phased array systems. Operating
multiple transmitters, power amplifiers, and antennae in close physical proximity, simultaneously, in
nearby frequency banks leads to unwanted TX-to-TX signal coupling which, in the presence of power
amplifier non-linearity, causes intermodulation distortion products (IMD3) that are problematic e.g.,
causing transmit spectral mask violations. There is a strong need to eliminate or suppress such TX-to-TX
signal coupling and the IMD3 products to satisfy FCC spectral mask requirements and enable
simultaneous multi-band transmission. Prior work has explored TX-TX cancellation for simultaneous transmission of NB and WLAN signals using analog techniques. However, the analog implementation is bulky and power hungry. A digital implementation for TX-TX self-interference cancellation can alleviate these issues. This thesis explores the design and implementation of a Digital-to-Time-Converter(DTC) as a part of digital TX-TX cancellation for the simultaneous transmission of NB and WLAN signals in 22FDX Technology. The designed DTC demonstrates consistent performance across process and temperature corners without calibration and has the necessary step-size and range to meet the TX-TX cancellation requirement.EngineeringDigital-to-Time-ConverterSelf-InterferenceTX-TX Cancellationenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3vt5t9bkhttps://escholarship.org/content/qt3vt5t9bk/qt3vt5t9bk.pdfetdoai:escholarship.org:ark:/13030/qt3tk1v18k2026-05-22T05:02:03Zqt3tk1v18kDancing with the Hips, Butt, and Pelvis: Pleasure, Respectability, and Intracommunal Literacy in Majorette/Dance Team PerformanceCorbett, SaroyaO'Shea, Janet M2024-01-01I highlight the racialized and gender politics of dancing with the hips, butt, and pelvis (h/b/p) and I examine the engagement and complex relationship with the politics of respectability and pleasure in Black culture in the United States. Additionally, I explore Black social dance as a means to attend to the lived experiences of people in Black communities. I focus on the autonomy of creative communities to form their own standards and aesthetics for generating their cultural materials; these standards may function inside and/or outside of mainstream narratives. The creators of these cultural products are influenced by the dominant culture and other external narratives, yet their materials operate within the community’s own value system, which may not be legible to outsiders. Additionally, I discuss how Black women negotiate these systems to create individual and communal practices that utilize the h/b/p to find joy and pleasure in their existence. Overall, the goal of this research is to provide a deeper understanding of how Black folks navigate creative practices of the everyday in interracial environments. When Black folks are out of the direct view of the White gaze, they are able to create spaces that are fashioned to their unique perspectives, values, and inclinations.To contextualize these movement cultures, I explore the use of the h/b/p in Black majorette/dance team performance, a movement practice that originates from the marching band culture at Historically Black Colleges and Universities (HBCUs). The Black majorette/dance team genre is popular throughout parts of the U.S. South and comprises three main groups: HBCU teams, youth teams, and queer teams. I focus this research partially on my fieldwork in the Southern Louisiana region and the two main dance teams at Southern University and A&M College (Southern University): Fabulous Dancing Dolls (FDD), the team that performs with the band during football season, and the Gold ‘N Bluez (GnB), the team that performs at basketball games and other school events. In addition to the dance teams on Southern University’s campus, I refer to examples in the wider Black majorette/dance team genre and Black popular culture to contextualize matters pertaining to U.S. Black culture and community.DanceAfrican American studiesWomen's studiesBlack StudiesDanceHBCUMajorettePolitics of PleasureTwerkenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3tk1v18khttps://escholarship.org/content/qt3tk1v18k/qt3tk1v18k.pdfetdoai:escholarship.org:ark:/13030/qt201682cn2026-05-21T06:32:00Zqt201682cnProstate Cancer Risk Stratification With Germline Genomic BiomarkersZeltser, NicolePajukanta, Paivi E2026-05-15The genome has historic potential to inform clinical care, and prostate cancer represents an excellent candidate for the application of precision genomic medicine. Prostate cancer is a heterogeneous disease, requiring accurate risk stratification to inform appropriate management strategies while avoiding unnecessary burden to quality-of-life. Clinical decision making can occur at several points across the prostate cancer diagnostic pathway: after indications for screening initiation and frequency, prior to biopsy, and when choosing from various management and treatment paradigms. Prostate cancer is also a heritable disease, with both rare and common DNA variants implicated in disease risk. This dissertation investigates the clinical validity and utility of genomic biomarkers in applications across a range of prostate cancer clinical contexts.The initiation of prostate cancer screening is informed by age, family history, and race-related risk factors, and occurs through the measurement of serum levels of prostate specific antigen (PSA). Tailoring screening frequency by prostate cancer risk profile can reduce rates of over-diagnosis. In the second chapter of this dissertation, I assess the risk-stratification ability of a polygenic hazard score (PHS) for the diagnosis of prostate cancer, specifically among the higher-risk patient population indicated by a baseline serum PSA level greater than 1.0 ng/mL. We utilized a matched study design to control for known pre-biopsy risk factors and detected 7-fold greater odds of a clinically significant cancer diagnosis among men in the fifth PHS quintile compared to the first PHS quintile. Our results support further investigation of a tiered biochemical and genomic screening strategy to guide screening frequency. At diagnosis, prostate tumors are stratified by risk of metastasis and death, typically via clinical risk groups that consolidate PSA, stage, and grade information. Diagnostic grade is assessed by a pathologist from biopsied tumor samples. Grading inaccuracies can be caused by unrepresentative sampling of heterogeneous tumors, and they can be revealed through the examination of the entire prostate after a radical prostatectomy treatment. In the third chapter, I investigate the ability of two prostate cancer polygenic risk models to predict upgrading at prostatectomy. In a unique biomarker validation cohort hosted by the Early Detection Research Network (EDRN), I found that neither polygenic score was predictive of upgrading of initially low-grade tumors, in contrast to several informative clinical features. Our results imply that polygenic risk scores lack the sensitivity to resolve grading accuracy and may be more useful when applied to longer-term outcomes. Translational genomic research relies on the participation of domain experts from a variety of fields, particularly clinicians. While polygenic risk score (PGS) models are now easily accessible to non-geneticists, the use of available software tools for the application of existing PGS models to new data requires substantial technical expertise. In chapter four, I develop an R package for PGS model application that is specifically designed for easy use by the computational layman. ApplyPolygenicScore provides utilities for processing genomic data, applying PGS models, and automated visualizations of standard PGS analyses. I report benchmarking of accuracy and performance against existing tools and demonstrate a typical use-case via a case study of genomic BMI predisposition in cancer patients. My tool helps accelerate further translational PGS research by expanding analytical opportunities to a wider range of researchers. In summary, this dissertation contributes to the body of translational genomic research that is steadily compiling evidence for the validity and utility of genomic biomarkers in prostate cancer management.GeneticsMedicineBioinformaticsOncologyBiomarkersCancer geneticsPolygenic riskProstate cancerapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/201682cnhttps://escholarship.org/content/qt201682cn/qt201682cn.pdfetdoai:escholarship.org:ark:/13030/qt6kh6582z2026-05-21T06:31:56Zqt6kh6582zEmpress Epistemologies: Intersectional Resistance through Black Women’s Reggae MusicBernard, Racquel SimoneFink, Robert2026-05-18"Empress Epistemologies: Intersectional Resistance through Black Women's Reggae Music" investigates how reggae, and particularly its Rastafarian ideological foundations, enables and constrains Black women's struggles against intersecting oppressions of racism, sexism, classism, and heteronormativity. Framing reggae as both spiritual practice and political discourse, the dissertation reads vocal timbre, musical arrangement, and lyrical semiotics as key sites through which artists theorize gendered and racialized liberation. Across case studies of Judy Mowatt, Marcia Griffiths, Queen Ifrica, Etana, Koffee, Jah9, Lila Iké, Kelissa, Shuga, and women of color in the contemporary Los Angeles reggae scene, the project advances "empress epistemologies" as a term for Black women's spiritual-intellectual production within and against masculinist reggae cultures.
Musicologically, the dissertation intervenes in scholarship that privileges low frequencies in reggae by centering women's voices—across low, mid, and high vocal registers—as politically consequential sonic practices in a male-dominated industry. It argues that women's so-called "natural" higher timbres, in dialogic tension with bass-heavy sound systems, carry distinct political and affective force, enabling artists to contest Rastafarian and Black nationalist theologies that profess liberation while subordinating women. Through close readings of Mowatt's and Griffiths's repertoires, the study illuminates womanist theologies, Black collective identity, and Caribbean critiques of feminism as a North American import, while foregrounding the complex interplay of race, class, and religion in Jamaican gender politics.
Subsequent chapters examine Queen Ifrica's corpus to interrogate the limits of heteronormative "empress" discourse and the persistence of homophobia within ostensibly liberatory reggae projects, and then trace a contemporary genealogy of Black Women's Reggae (BWR) through Etana and other reggae revival artists who rework womanist legacies amid shifting debates about sexuality. The final chapter offers an ethnographic account of women of color in Los Angeles reggae, analyzing interviews with singers and a deejay to show how reggae functions as spiritual technology, political education, and a vehicle for self-actualization in diasporic, racialized contexts. Taken together, the dissertation demonstrates that Black Women's Reggae is both analytic and archival: a critical site where sound, spirituality, and embodiment generate alternative epistemologies of resistance, belonging, and futurity.Music historyCaribbean studiesBlack studiesMusicWomens studiesBlack Women's ReggaeIntersectionalityPostcolonial Music StudiesRastafariVocal Timbre and RegisterWomanist Epistemologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6kh6582zhttps://escholarship.org/content/qt6kh6582z/qt6kh6582z.pdfetdoai:escholarship.org:ark:/13030/qt40k1g9n52026-05-21T06:31:51Zqt40k1g9n5Exploring Employment Patterns, Family Disruption, and Gender InequalityJeon, NanumBrand, Jennie E2026-05-18Stratification research has long sought to explain divergent economic outcomes by identifying the discrete events and durations that shape them: years of employment, transitions in and out of marriage, time spent on welfare. This work has produced important insights into how specific exposures and transitions become consequential for later inequality. Yet two lives composed of the same events and durations can still unfold very differently. This dissertation argues that what separates such lives is not only the events themselves but the structure through which those events unfold over time—the patterned configurations across employment, family, and state institutions that are difficult to capture when trajectories are reduced to isolated transitions or cumulative durations. This limitation is especially consequential for understanding women’s lives, where work, family, and welfare engagements are tightly interwoven through marriage, childbearing, caregiving, and labor-market disruption.The dissertation develops a framework of trajectory stratification and a set of computational tools for measuring trajectory structure as a stratifying mechanism. Drawing on the National Longitudinal Survey of Youth 1979 and 1997 cohorts, three empirical chapters develop distinct strategies for capturing trajectory structure. Chapter 2 identifies post-divorce trajectory types as causal units of stratification, combining sequence analysis with machine-learning-based g-computation, and shows that they form a clear protective hierarchy with racially stratified access. Chapter 3 introduces trajectory atypicality—distance from the cohort-specific centroid in a continuous embedding space generated from large language models—as a relational measure of employment instability, and finds that wage penalties for atypicality are sharply gender-differentiated in the older cohort but converge by the younger cohort as men’s penalties intensify. Chapter 4 extends the embedding framework to joint work–family trajectories and decomposes the gender wage gap, showing that a substantial portion of the gap can be understood as a trajectory wage gap, with the trajectory mechanism becoming increasingly consequential among Hispanic workers in the early millennial cohort.Together, these chapters argue that trajectories are not descriptive summaries of biographies but stratifying mechanisms in their own right—and that institutions do not stratify people at discrete moments; they stratify lives as they unfold.SociologyLabor economicsGender studiespubliceScholarship, University of Californiahttps://escholarship.org/uc/item/40k1g9n5etdoai:escholarship.org:ark:/13030/qt1fv959272026-05-21T06:31:45Zqt1fv95927Bayesian Longitudinal Models for the Progression of Alzheimer's DiseaseBirchfield, Jesse WilliamWeiss, Robert E2026-05-14Alzheimer’s disease (AD) progression manifests in the thinning first of the entorhinal cortex (EC) and then of other cortical regions. The AD Neuroimaging Initiative (ADNI) conducted longitudinal cognitive assessments and MRI brain scans of 800 subjects in 3 diagnostic groups over 3 years. This dissertation develops Bayesian hierarchical multivariate longitudinal models to describe the evolution of cortical thickness in ADNI subjects. Chapter 1 gives an overview. Chapter 2 argues that a multivariate longitudinal model is superior to a univariate model with time-varying covariates for describing the dependence of time-varying quantities. Eight Bayesian multivariate random-intercept-and-slope (RIAS) models for assessment score and EC thickness are compared by cross-validation and a novel fit statistic. Through the conditional distribution of one element of a multivariate normal random vector given the others, the deviation from the group mean of a subject’s cortical thickness trajectory predicts the deviation from the group mean of the cognitive score trajectory. Chapter 3 presents a multivariate RIAS model for the trajectories of 16 cortical regions. Exploiting the symmetry and proportionality of the brain, the proposed model decomposes random intercepts and slopes into subject, hemisphere, and region components, for a reduced number of covariance parameters that grows linearly instead of quadratically. Chapter 4 shows that for bivariate distributions with elliptical contours (EC), standard deviation and correlation correspond to transformations of the unit circle; for bivariate distributions that are linear transformations of independent components (IC), standard deviation and correlation correspond to transformations of the unit square. The bivariate normal (BVN) uniquely possesses both EC and IC. Different bivariate generalizations of the normal to the t distribution are compared: the bivariate t (BVT), with elliptical contours; the non-elliptically contoured t, with marginal t distributions; and the t linear combination (TLC) distribution, a novel proposal with IC. The TLC has flexibility to model different kurtosis in different directions and a simple density function enabling efficient maximum likelihood estimation. A simulation and case study of the ADNI dataset investigate the relative performance of the BVN, BVT, and TLC, showing that the TLC is a viable model for multivariate data with outliers. StatisticsNeurosciencesBiostatisticsBioengineeringapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1fv95927https://escholarship.org/content/qt1fv95927/qt1fv95927.pdfetdoai:escholarship.org:ark:/13030/qt4x3154rj2026-05-21T06:31:41Zqt4x3154rjModeling NBA Offensive Efficiency Through the Implementation of Markov ChainsCraig, RobertSchoenberg, Frederic P2026-05-18Offensive efficiency in the NBA is examined by modeling possessions as sequences of actions across different areas of the court rather than focusing solely on final shot outcomes. A Markov chain framework is developed using possession-level data from the Oklahoma City Thunder during the 2025 NBA Finals, where transition probabilities are used to estimate expected values across court zones. Data consisted of more than 5,500 manually collected possession-level actions spanning the seven-game series. Results indicate meaningful differences in efficiency across court locations. Additional differences in efficiency were observed when segmented by game outcome and possession length, while standard error estimates provided insight into the stability of the resulting estimates.StatisticsPhysiologySports managementData VisualizationMarkov ChainNBA Analyticsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4x3154rjhttps://escholarship.org/content/qt4x3154rj/qt4x3154rj.pdfetdoai:escholarship.org:ark:/13030/qt3xr996152026-05-21T06:31:36Zqt3xr99615Evaluating Offline Reinforcement Learning for Vasopressor Management in the ICUChun, AllenZhu, Yuhua2026-05-13This thesis presents an analysis of offline reinforcement learning (RL) methods for vasopressor management in the intensive care unit (ICU) using retrospective clinical data. Vasopressor administration is defined as a sequential decision-making problem, and the problem is formulated as a finite-horizon Markov Decision Process (MDP). The research examines three different approaches: Behavior Cloning (BC), Conservative Q-Learning (CQL), and Batch-Constrained Q-Learning (BCQ).
Policy behavior is evaluated using mean arterial pressure (MAP). All three techniques yield clinically plausible policies that increase the usage of vasopressors in the presence of lower MAP values and reduce their usage in the presence of higher MAP values. BC approximates clinician behavior fairly accurately, CQL offers comparable performance but with more regularization to prevent overestimation, and BCQ yields systematic and conservative divergences. In addition to this, the study explores off-policy evaluation through the application of Weighted Importance Sampling (WIS) and Fitted Q-Evaluation (FQE), which further reveal substantial variability and overlapping confidence intervals across methods. These findings suggest that, while empirical evaluation remains challenging, offline reinforcement learning provides a solid foundation for future exploration in clinical decision support systems.StatisticsMedicineArtificial intelligenceapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3xr99615https://escholarship.org/content/qt3xr99615/qt3xr99615.pdfetdoai:escholarship.org:ark:/13030/qt54n7r5xb2026-05-21T06:31:31Zqt54n7r5xbUnveiling Dissonance: Media Dynamics and Public Perceptions in the United States and ChinaLiang, SiyuHazlett, Chad JSteinert-Threlkeld, Zachary2026-05-13This dissertation investigates the dynamics of media influence on public attitudes toward China in contemporary U.S.–China relations, where domestic and international perceptions diverge sharply. Chapter 2 examines how the Chinese government utilizes foreign influencers to make propaganda persuasive. Using survey experiments in China and the United States, it shows that foreign influencers improve China’s image among American audiences but not among Chinese audiences, highlighting how authoritarian messaging can be more effective abroad than at home. This chapter is co-authored with Lachlan McNamee and published in Comparative Political Studies. Chapter 3 analyzes how media portrayals of China as a threat shape foreign policy attitudes and domestic racial perceptions. Combining text analysis with survey and experimental evidence, it shows that “China as a threat” increases support for hawkish policies and heightens anti-Asian resentment regardless of the target of threat. Chapter 4 develops a methodological approach to political text analysis by evaluating cross-domain transfer learning across U.S. news outlets.Political scienceFilm studiesEthnic studiesAsian American studiesapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/54n7r5xbhttps://escholarship.org/content/qt54n7r5xb/qt54n7r5xb.pdfetdoai:escholarship.org:ark:/13030/qt06h8216k2026-05-21T06:31:27Zqt06h8216kWorking Together: Labor and Solidarity in 20th and 21st Century Black French NarrativeBarger, Alexa RaeThomas, Dominic R2026-05-13“Working Together: Labor and Solidarity in 20th and 21st Century Black French Narrative” considers how artists have depicted laboring life and solidarity in France for African and Afro-Caribbean workers, with a particular focus on post-World War II economic developments. This dissertation uses literary and cultural history methods to compare fiction and nonfiction writings, films, television series, and graphic novels. I specifically examine how these works use history, particularly in the ways that artists have linked colonial history with present class relations. Through this connection, these artists have also linked sexist and racist oppression to their experiences of economic exploitation, noting how these experiences both intersect and reproduce one another into the present. I therefore emphasize how workers, and working artists, have communicated these historical connections in the effort to forge solidarity beyond their workplaces and immediate communities.Each chapter of “Working Together” considers a particular historical and social context as experienced by Black French workers. The Introduction contains a “mini-chapter” examining the place of history in the film La Noire de… and the short story “Lettres de France” by Ousmane Sembène, as well as in the novel Pluie et vent sur Télumée-Miracle by Simone Schwarz-Bart. Chapter 1 offers a close reading of three films on working life and worker power by filmmakers Med Hondo, Sidney Sokhona, and Sarah Maldoror. Chapter 2 provides a transhistorical analysis of the histories of the BUMIDOM migration scheme and the Mé 67 massacre in Pointe-à-Pitre, as described by three French Guadeloupean and Caribbean writers. Chapter 3 compares contemporary film and literary representations of precarious workers, especially the undocumented or sans-papiers. I conclude that, studied together, these works represent a corpus-in-motion: of people working together, in the present continuous, to create and maintain sites of solidarity.French literatureCaribbean literatureBlack studiesEconomicsFilm studiesapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/06h8216khttps://escholarship.org/content/qt06h8216k/qt06h8216k.pdfetdoai:escholarship.org:ark:/13030/qt6qs1f2272026-05-21T05:03:09Zqt6qs1f227The Social Rights Imaginary of the Contemporary Postcolonial NovelStambler, Arielle HannaGoyal, YogitaRothberg, Michael P2024-01-01This dissertation examines how twenty-first-century postcolonial fictions remake the liberal language of human rights to contest economic violence. A moral lingua franca for narrating violence and redress, human rights discourse typically deploys individual stories of suffering to evoke readerly empathy and galvanize ethical action. But its focus on the state as exclusive perpetrator and the self-possessed individual as the standard of personhood means it habitually misses economic violence—the structural denial from racialized subjects of rights to housing, healthcare, employment, education, food, and other tenets of material citizenship. By contrast, I argue that a growing group of contemporary postcolonial fictions—which I name “social rights novels”—helps us to rethink the relationship between human rights and economic violence. Included in this group are works by NoViolet Bulawayo, Tsitsi Dangarembga, Imbolo Mbue, Ishmael Beah, Nicole Dennis-Benn, Jonathan Escoffery, Marlon James, and Edwidge Danticat. The genre exposes economic violence as a product of global racial capitalism and ongoing financial colonialism. It identifies and contests forms of predation overlooked by the dominant, rights-based imaginary—from the pollution and dispossession of Niger Delta communities by multinational oil companies, to the redirection of Caribbean state wealth toward international debt service and away from social welfare programming, to the marginalization of the economic refugee struggling to survive in the Global North. What holds the genre together across these diverse narrative sites is how it both marshals and destabilizes the language of human rights. To make this language usable, the social rights novel stretches it to a breaking point—something that postcolonial fiction has long done with colonial cultural materials. By breaking and remaking human rights in this way, the social rights novel builds a new narrative framework for imagining economic justice. Specifically, these works challenge the logic of neoliberal austerity, under which material provision is not a matter of rights but a market prerogative, a personal responsibility, or a problem for humanitarian charity. English literatureAfricaCaribbeanEconomic JusticeHuman RightsNovelPostcolonialenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6qs1f227https://escholarship.org/content/qt6qs1f227/qt6qs1f227.pdfetdoai:escholarship.org:ark:/13030/qt6p2024dp2026-05-21T05:02:54Zqt6p2024dpAdvancing noninvasive cancer diagnosis and prognosis through cell-free DNA analysisHu, RanZhou, Xianghong Jasmine2025-01-01Cell-free DNA (cfDNA) is a powerful biomarker for liquid biopsy and has attracted substantial research attention in the development of precision healthcare. As a mixture of short DNA fragments released from various healthy and cancerous tissues, cfDNA holds great promise for capturing disease signals noninvasively, but also presents significant computational challenges. The heterogeneity of cancer-related genomic and epigenomic signals, the low fraction of tumor-derived cfDNA, limited sample sizes relative to the high dimensionality of genome-wide biomarkers, and the lack of specialized, user-friendly analytical tools all hinder the broader clinical application of cfDNA. This dissertation aims to address these key barriers to the effective use of cfDNA in cancer research through three primary contributions. First, we identify a large panel of cfDNA methylation biomarkers and develop a survival risk score, methRisk, which improves the noninvasive prognostication of hepatocellular carcinoma (HCC) beyond existing clinical biomarkers. Second, we present a multimodal machine learning framework that leverages transfer learning and fusion strategies to integrate cfDNA methylation profiles with imaging data, enhancing the accuracy of noninvasive lung cancer diagnosis. Third, we introduce two user-friendly software tools, cfSNV Docker and cfTools R/Bioconductor package, which provide accessible, streamlined workflows for mutation detection and tissue deconvolution from raw or standard preprocessed cfDNA sequencing data. Collectively, this work explores the broad potential of cfDNA applications in cancer diagnosis, prognosis, and monitoring, advancing its utility and adoption in clinical research.BioinformaticsOncologyArtificial intelligencecancercell-free DNAcomputational methodliquid biopsymachine learningmethylationenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6p2024dphttps://escholarship.org/content/qt6p2024dp/qt6p2024dp.pdfetdoai:escholarship.org:ark:/13030/qt6kr118k92026-05-20T06:31:09Zqt6kr118k9Metabolic Atlas of Early Human Cortex Identifies Regulators of Cell Fate TransitionsMil, JessenyaBhaduri, Aparna2026-05-18The cerebral cortex, the outermost layer of the brain, is responsible for higher-level cognition functions such as language, perception, decision-making, and motor planning. Understanding the molecular mechanisms underlying the formation of this critical brain region will further enhance our knowledge of the cognitive functions that differentiate humans from other species. Extensive studies have examined both intrinsic factors, such as genes and transcription factors, and extrinsic factors, such as interactions with other brain regions, that contribute to cortical formation and development. However, despite these molecular and cellular insights, there remains limited understanding of how metabolic regulation impacts cortical development. While metabolic changes from stem cells to neurons are relatively well understood, the cerebral cortex comprises a vast diversity of cell types with distinct gene expression profiles, which may rely on equally diverse metabolic programs to support their functions. Although metabolism has traditionally been associated with cellular energy production, emerging studies highlight its additional roles in redox regulation, cell signaling, gene regulation, and ultimately cell fate specification.
In this study, we generated a metabolic atlas of early human cortical development, spanning developmental stage from the first trimester to the mid-second trimester. Using human primary cortical tissue (HPCT) samples, we characterized glycolytic and oxidative phosphorylation activity. In addition, we performed targeted liquid chromatography-mass spectrometry metabolomics, incorporating 13C isotopic labeling of glucose and glutamine to further investigate carbon metabolism and pathway utilization in these cells. Through these analyses, we identified increased activity of the pentose phosphate pathway (PPP), along with enhanced incorporation of glucose-derived 13C.
To investigate the functional role of the PPP during cortical development, we utilized cortical organoids, a three-dimensional cell culture model, and performed both pharmacological inhibition and genetic knockdown of two key PPP enzymes: glucose-6-phosphate dehydrogenase (G6PD) and transketolase (TKT). These perturbations disrupted metabolic properties, and we assessed their effects on cell-type composition using single-nuclei/cell RNA sequencing (sn/scRNA-seq). Through this integrative approach, we found that the PPP regulates the abundance of outer radial glia (oRG) and inhibitory neurons, two cell types essential during developmental period 4. We further examined which branch of the PPP contributes most to this regulation and found that ribose-5-phosphate, an intermediate shared between the oxidative and non-oxidative branches, was sufficient to restore cell-type composition to levels observed under unperturbed conditions.
Overall, we present a metabolic atlas of early human cortical development and identify the PPP as a key metabolic pathway regulating the generation of oRGs and inhibitory neurons. Our findings reveal a previously unappreciated role for metabolism, specifically the PPP, in cell-type specification within the developing cortex.Molecular biologyCellular biologyDevelopmental biologyNeurosciencescell fatecell metabolismcortical organoidsglycolysisneurodevelopmentpentose phosphate pathwayapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6kr118k9https://escholarship.org/content/qt6kr118k9/qt6kr118k9.pdfetdoai:escholarship.org:ark:/13030/qt7s76g6hq2026-05-20T06:31:03Zqt7s76g6hqCashing in on HIV Prevention: An Exploratory Mixed Methods Study Assessing the Preferences and Acceptability of a Pre-Exposure Prophylaxis-Based Conditional Cash Transfer among Black and Latino Men Who Have Sex with Men in Los Angeles CountyTrujillo, Dillon WesleyPrelip, Michael L2026-05-15Black and Latino men who have sex with men (MSM) face disproportionately high rates of HIV in the United States, yet uptake of pre-exposure prophylaxis (PrEP), a highly effective HIV prevention medication, remains critically low in these communities. Paying people money or rewards to take PrEP, is a strategy called conditional cash transfers (CCTs), and they have shown promise in HIV prevention globally, but no research has asked Black and Latino MSM what kind of CCT interventions they would actually want, or whether their sexual health history and life circumstances shape those preferences.Data were drawn from the Incentives and Prevention Study (TIPS), a pilot study conducted in Los Angeles County with 133 Black and Latino MSM. Three interconnected studies are presented. Study One used in-depth interviews with 20 participants to explore how Black and Latino MSM felt about the idea of a PrEP-based CCT, whether it felt worth it, burdensome, or likely to work. Studies Two and Three used a discrete choice experiment (DCE), a method in which participants repeatedly chose between hypothetical CCT intervention designs to reveal which features mattered most, examining whether participant’s PrEP use status and housing stability shaped those preferences.Study One found broad support for PrEP-based CCTs and identified specific burdens to participating in a PrEP-based CCT such as stigma, access barriers, and medical mistrust that varied by race/ethnicity and PrEP experience. Study Two found that Black and Latino MSM regardless of whether they were currently using PrEP or not strongly preferred higher payment amounts, more frequent payments, and cash over gift cards, with PrEP type playing a minimal role in their choices. The one stand out difference was that current PrEP users placed a stronger preference for higher payment amounts compared to non-PrEP users, suggesting that Black and Latino MSM already engaged in HIV prevention may respond more strongly to financial incentives to further sustain their engagement with HIV prevention. Study Three found that housing stability did not change these preferences, with Black and Latino MSM in both stable and unstable housing wanting the same program design.These findings challenge a common assumption in HIV prevention — that programs must be extensively tailored by subgroups to be effective. The data show that when it comes to CCT intervention design for increasing PrEP use, Black and Latino MSM want the same things regardless of their PrEP history or housing situation. What requires consideration and tailoring is the implementation itself — accessible locations to participate, flexible hours, transportation support, and culturally grounded outreach. This dissertation is among the first to rigorously explore CCT intervention acceptability and examine CCT design preferences among this population, and its findings provide direct evidence how to design CCT interventions that improve PrEP use. That evidence is essential for HIV prevention organizations, programs, and local jurisdictions considering utilizing financial incentives as a strategy to improve HIV prevention outcomes. To be effective, these programs must be financially meaningful, accessible, and built with the community at the center. Limitations include a relatively small sample from Los Angeles County, which constrains the generalizability to other geographic settings and broader communities at risk for HIV. The sample size also limits the statistical power for the quantitative analyses, meaning that some real differences in CCT design preference across subgroups may exist but were too small to detect with the number of participants enrolled. Therefore, these findings should be interpreted as initial and exploratory evidence.Public healthGender studiesLatin American studiesBlack studiesConditional cash transfersDiscrete choice experimentHIV preventionMen who have sex with menPre-exposure prophylaxisThematic Analysisapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7s76g6hqhttps://escholarship.org/content/qt7s76g6hq/qt7s76g6hq.pdfetdoai:escholarship.org:ark:/13030/qt1f17481v2026-05-20T06:30:58Zqt1f17481vEssays in Labor EconomicsInguanzo Gonzalez, Jose AntonioBailey, Martha J2026-05-15This dissertation comprises three essays in labor economics that study how workers, firms, and local economies respond to large economic shocks and policy changes. In Chapter 1, co-authored with Daniela Puggioni, I study the effects of the 2019 minimum wage reform in Mexico’s northern border and examine how wage under-reporting shapes firms’ responses to large increases in the minimum wage. Using administrative and survey data, I document evidence that wage under-reporting declined substantially after the reform, helping explain the relatively small employment effects observed following the policy change. In Chapter 2, also co-authored with Daniela Puggioni, we study how firms geographically reallocate workers across establishments in response to place-based policies. Focusing on multi-establishment firms affected by the same minimum wage reform, we document substantial within-firm worker reallocation across regions following the policy change. In Chapter 3, co-authored with Benjamin Pirie, we study the effects of post-pandemic gentrification in Mexico City driven by the influx of remote workers and short-term rental activity. Using geographically granular Airbnb and business establishment data, we document positive effects on local employment and business creation in neighborhoods more exposed to these demand shocks.Public policyLabor economicsEconomicsEconomic theoryInformalityLabor EconomicsMinimum WagesPlace-Based PoliciesPublic Economicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1f17481vhttps://escholarship.org/content/qt1f17481v/qt1f17481v.pdfetdoai:escholarship.org:ark:/13030/qt1ds9h76z2026-05-20T06:30:48Zqt1ds9h76zGeneral Theory of Beautiful Pairs in Continuous LogicJiang, ChuyinYe, JinheNeeman, Itay2026-05-13This dissertation develops a systematic theory of beautiful pairs in the framework of continuous logic. The goal is to extend the classical theory from the discrete setting to metric structures and to identify the structural conditions governing beauty transfer in this context.At an abstract level, we introduce a general framework for beautiful pairs relative to classes of stably embedded pairs and establish their existence and elementality via amalgamation and extension properties. A central result shows that if a continuous theory T has the non-finite cover property (NFCP), then saturation implies beauty; that is, sufficiently saturated pairs are automatically beautiful. This yields a general mechanism for beauty transfer in continuous logic, extending the classical implication known in first-order logic [Poi83].We further propose a metric analogue of elimination of the quantifier ∃ ∞, for which no standard formulation previously existed. We compare two natural candidate definitions and show that the formulation based on definability aligns with the general theory, in particular satisfying that NFCP implies elimination of ∃ ∞. In contrast, an alternative formulation based on entropy numbers fails even in well-behaved settings such as atomless probability algebras.The general theory is then applied to several central examples of continuous model theory: algebraically closed metric valued fields, atomless probability algebras, and infinite dimensional Hilbert spaces. These results extend key connections between NFCP, definability, and structural transfer phenomena from classical model theory to the continuous setting, while highlighting new phenomena specific to metric structures. Several open problems are formulated concerning the precise relationship between NFCP, beauty transfer, and elimination of ∃ ∞ in continuous logic.MathematicsTheoretical mathematicsApplied mathematicsBeautiful PairsContinuous LogicModel TheoryStabilityapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1ds9h76zhttps://escholarship.org/content/qt1ds9h76z/qt1ds9h76z.pdfetdoai:escholarship.org:ark:/13030/qt4b3520bf2026-05-17T05:02:23Zqt4b3520bfThe Water’s Edge: Empire, Race, and the Global History of Oakland, California, 1848-1980Klein, Andrew Nelson CarverKelley, Robin D.G.2024-01-01This dissertation explores historical intersections between capitalism, race-making, environmental change, and U.S. foreign relations from the vantage of Oakland’s waterfront. It tracks the evolution of this geostrategic coastline since it emerged as a coal-powered nexus of railroads, steamships, and military installations during American colonization. Revising the story of Oakland’s development from a global perspective, my project documents how local boosters, businessmen, and bureaucrats forged intimate ties with far-flung territories administered by U.S. troops and landowners. Combining social and environmental history with methods from Black studies, I map the transformation of Oakland’s fertile estuary into an infilled hub of postindustrial commerce, a vital node of military logistics, and a focal point for racialized labor migration. Simultaneously, this dissertation reveals how, over the long twentieth century, Black, Indigenous, Chinese, and other working-class Oaklanders found footholds on the harbor, often repurposing its imperial routes for dramatically different ends. Drawing on archives and oral histories in Cuba, Hawai‘i, and across the continental United States, I trace Oakland’s tradition of radical internationalism, most visible in the antiwar and Black Power movements of the sixties, to multi-generational struggles over the governance of coastal land. I argue that Oakland’s waterfront both bridged the continental and overseas expansion of an ascendant world power and came to serve as a proving ground for alternative forms of globalization. “The Water’s Edge” therefore offers a critical reappraisal of the generally place-bound history of North American urbanism and far-reaching lessons for building just cities in a present defined by global environmental crises.American historycapitalismempireenvironmentglobal historyOaklandraceenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4b3520bfhttps://escholarship.org/content/qt4b3520bf/qt4b3520bf.pdfetdoai:escholarship.org:ark:/13030/qt22v3c88w2026-05-17T05:01:45Zqt22v3c88wHealth in Context: Patterns of Social Risk Factors and Associated Health OutcomesPereira, NicoleZhu, Xi2024-01-01Among the various factors that shape health, people’s circumstances, and the environments they live in hold profound influence. Areas with poor quality or overcrowded housing, few options for nutritious food, limited space for physical activity, and high levels of pollution are often areas deprived of monetary resources. These broader factors, termed the social determinants of health (SDoH), impact health and well-being across the life course. Addressing these factors has become part of the purview of the healthcare system efforts, amid a growing recognition that illness and poor health outcomes frequently stem from disparities and inequities rooted in social determinants, such as poverty, education, housing, access to nutritious food, and social support networks. Increasingly, healthcare systems are investing in initiatives to collect data on various social risk factors affecting health outcomes. However, the extent to which this data is used todevelop more holistic interventions that address multiple simultaneous social risk factors that influence individual health outcomes is limited. Moreover, current approaches do not leverage existing data regarding the community context to better understand the lifelong implications of social risk, which may begin in early childhood and persist into adulthood, affecting both social circumstances and individuals’ interactions with the healthcare system. Furthermore, while the healthcare system plays a crucial role in ensuring access to care, the extent to which broader social determinants can limit access remains an area of ongoing work. The three studies in this dissertation use data on social risk factors to better understand the broader set of these social determinants and their associations with health outcomes. These studies reveal patterns at both the individual and community levels, highlighting how social risk tends to cluster and how early life experiences influence where people live in adulthood for those with a history of childhood adversity. These findings emphasize the need for approaches that combine health care system efforts with community-based initiatives to better address health-related social needs throughout the life-course.Public healthenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/22v3c88whttps://escholarship.org/content/qt22v3c88w/qt22v3c88w.pdfetdoai:escholarship.org:ark:/13030/qt217144342026-05-17T05:01:41Zqt21714434Memory Machines: Mnemonics, Poetry, and Computing in Nineteenth-Century BritainSherrill, Jessica RoseGrossman, Jonathan H.2024-01-01This study asserts that nineteenth-century mnemonics, known as ‘artificial memory systems’ until the twentieth century, served as one of the cornerstones for the invention of modern computing. Mnemonics systems rejected the associationist conceptualization of an intertwined mental hierarchy of ideas. As I show, Victorian mnemonics systems instead conceptualized a new form of memory akin to random access memory; they developed systems in print that perform as algorithms; and they invented universal and executable languages before programming. The roots of this project lie in my discovery that, unlike their predecessors, Victorian mnemonic systems were realized as autonomous memory machines. “Autonomy” here is defined not by physical externality from humans, but rather by a random-access recall process that consists in a printed system worked partly by the mind. The story that flows from this insight that Victorian mnemonics devised an autonomous memory system revises the standard accounts of technology historians and digital scholars and challenges the consensus that mnemonics usage declined in the eighteenth century to reposition Victorian mnemonics as an exciting new dimension of the history of computing. English literatureHistory of ComputingMemoryMnemonicsPhilosophyRomantic LiteratureVictorian Literatureenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/21714434https://escholarship.org/content/qt21714434/qt21714434.pdfetdoai:escholarship.org:ark:/13030/qt4k03k56f2026-05-15T06:31:53Zqt4k03k56fStructural Characterization of Protocells and Confinement-Driven Evolution of Virtual Circular GenomesChoi, JongsukChen, Irene2026-05-12The origin of life likely required systems that could store information, maintain individuality, and evolve. Among the proposed models, RNA-containing lipid vesicles are a useful protocell system because RNA can carry genetic information and also perform catalytic functions, while vesicles provide compartmentalization. However, it is still unclear how confinement inside vesicles affects RNA structure, organization, and evolution. In this work, I explore the heterogeneity of protocell systems, the interaction between RaiA–Group II intron RNA and POPC vesicles, and how the topology and length of virtual circular genomes (VCGs) affect encapsulation in oleic acid (OA) vesicles. First, I use cryo-EM to examine how heterogeneity among protocells can be directly captured at the level of individual vesicles and to study basic RNA–membrane interactions outside of vesicles. Preliminary results suggest that RaiA–Group II intron RNA does not strongly associate with POPC vesicle membranes before encapsulation under a given buffer condition. Second, I investigate how protocellular confinement may affect the encapsulation of VCG constructs with different sizes and topologies. Overall, this work helps us understand how protocell confinement could influence early evolution, while also showing that cryo-EM can capture the heterogeneity of protocells and can be used as a useful tool to study interactions between RNA and vesicles.Chemical engineeringBiophysicsBiochemistryGeneticsCryoEMEncapsulationLiposomeRNA worldThe origin of lifeVirtual circular genomeapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4k03k56fhttps://escholarship.org/content/qt4k03k56f/qt4k03k56f.pdfetdoai:escholarship.org:ark:/13030/qt2wm625042026-05-15T06:31:48Zqt2wm62504Statistical Methods for Complex Biomedical Data: From Bayesian Changepoint Detection to Machine Learning ApplicationsLiu, VincentWu, Yingnian2026-05-12This dissertation develops and applies advanced statistical and machine learning methods to address challenges in biomedical data analysis. The work spans six interconnected studies that contribute methodological innovations and practical applications across epidemiological surveillance, longitudinal modeling, clinical prediction, and precision medicine.Chapter 1 presents efficient MCMC algorithms for Bayesian changepoint detection in overdispersed autoregressive count data, with applications to COVID-19 surveillance. Chapter 2 introduces penalized fractional polynomials via mixed models for flexible non-linear longitudinal data analysis. Chapter 3 compares machine learning models for hospital readmission prediction. Chapter 4 optimizes blood glucose predictions using advanced forecasting techniques. Chapter 5 develops a generative latent protocol model for sepsis treatment in ICU settings. Chapter 6 presents an explainable machine learning model for hypertension screening in Type 1 diabetes patients using continuous glucose monitoring data.Together, these studies demonstrate the power of combining classical statistical theory with modern computational methods to solve pressing problems in biomedical research and clinical practice.StatisticsEndocrinologyMedicineArtificial intelligenceBioinformaticsDiabetesMachine LearningMCMCReinforcement LearningSepsisStatisticsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2wm62504https://escholarship.org/content/qt2wm62504/qt2wm62504.pdfetdoai:escholarship.org:ark:/13030/qt3720j9pm2026-05-15T06:31:43Zqt3720j9pmDiscrete time mixture cure models for analyzing multi-dose vaccination outcomes as recurrent events in stepped wedge trialsZhou, YixuanCrespi, Catherine M.2026-05-12In intervention studies aimed at increasing vaccine uptake, receipt is often modeled as a binary outcome. However, this approach is not well suited for vaccines that require multiple doses, where uptake occurs as a sequence of related events rather than a single endpoint. The challenge is further compounded in designs with a dynamic cohort and time-varying intervention exposure, such as stepped wedge trials, where aligning exposure status with the timing of each dose can be complex. In addition, a subset of the target population may be inherently non-responsive to the intervention; failure to account for this group can lead to biased estimates of intervention effects.To address these limitations, this dissertation develops a discrete time mixture cure model for recurrent events. Multiple vaccine doses are treated as recurrent events within individuals, with dependence accommodated through a shared frailty term. The discrete time formulation partitions the follow-up of each individual into intervals, enabling alignment of intervention exposure with the timing of each dose and accommodating time-varying interventions and covariates. The model further incorporates a cure fraction to account for a latent subgroup of individuals who will never initiate or complete vaccination. Model parameters are estimated within a Bayesian framework. The modeling approach yields estimates of intervention effects on dose-specific uptake (e.g., odds or hazard ratios) while simultaneously estimating the latent cure proportion. By explicitly accounting for the cure fraction, the model enables estimation of intervention effects among individuals who are susceptible to vaccination, providing a more interpretable and less biased assessment of intervention impact. The proposed model is applied to data from a cluster randomized stepped wedge trial evaluating two interventions to promote human papillomavirus (HPV) vaccination among adolescents. Completion of the HPV vaccine series requires two or three doses, which are represented as recurrent events. Clusters correspond to clinics, each of which began in the usual care condition and transitioned to different intervention conditions at different time points. The study used an open cohort design, with patients entering and exiting throughout the study period, further motivating the need for a discrete time, time-varying modeling framework. The results provided evidence for the effectiveness of both interventions as well as the presence of a latent subgroup that would likely never receive HPV vaccination.These findings demonstrate the value of modeling vaccine uptake as a multi-stage process rather than a single endpoint. The proposed framework provides a flexible and interpretable approach for evaluating interventions in complex longitudinal settings and can be applied to other multi-dose or sequential health behaviors.BiostatisticsPublic healthapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3720j9pmhttps://escholarship.org/content/qt3720j9pm/qt3720j9pm.pdfetdoai:escholarship.org:ark:/13030/qt1wd358dd2026-05-15T06:31:38Zqt1wd358ddThe Vision and Disillusionment of Confederate Settlement in Southeastern Brazil: A Case Study of Santa Bárbara D’oeste and AmericanaKHYM, DO rBell, Stephen2026-05-12This study investigates the post–Civil War migration of former Confederates to Brazil as a case of voluntary exile shaped by political defeat, racial ideology, and the psychological aftermath of war. Rather than viewing the movement simply as an economic search for opportunity, the project situates it within broader global patterns in which defeated populations confront the loss of status, the collapse of familiar social orders, and the pressures imposed by victorious regimes. For many Confederates, the transition to a society moving toward racial equality proved incompatible with their worldview, prompting thousands to leave the United States for Brazil.The research traces both the material and emotional dimensions of this migration. Settlers faced the practical challenges common to nineteenth-century agricultural colonization: new environments, unfamiliar crops, and the need to reconstruct livelihoods. Yet the study argues that the deeper struggle was psychological: migrants grappled with a form of homesickness intensified by the realization that the antebellum South they longed for no longer existed. This sense of irretrievable loss contributed to the high rate of return migration, as many found that Brazil could not substitute for a homeland transformed beyond recognition. A smaller cohort remained and established communities that became part of Brazil’s diverse immigrant landscape. Drawing on archival sources, personal correspondence, oral histories, and material culture, the study examines how their descendants inherited and reshaped the emotional legacy of exile. Central to this process was the Campo cemetery, which emerged as a symbolic locus of memory, belonging, and identity formation. Through commemorative practices, festivals, and ongoing negotiations over contested symbols, including the Confederate battle flag, the community constructed a sense of “home” rooted not in the United States but in the cultural space they created in Brazil. By analyzing the interplay of migration, memory, and identity across generations, the study demonstrates how the Confederate diaspora transformed an act of political flight into a long-term process of cultural reinvention.GeographyHistoryLatin American studiesBrazilCivil WarConfederatesDiasporaImmigrationSlaveryapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1wd358ddhttps://escholarship.org/content/qt1wd358dd/qt1wd358dd.pdfetdoai:escholarship.org:ark:/13030/qt0qk2b3sm2026-05-15T06:31:33Zqt0qk2b3smMitochondrial control of innate and adaptive immunityBall, AndréaDivakaruni, Ajit S2026-05-12The immune system rapidly identifies and eliminates foreign pathogens. As part of this response, immune cells modulate activity of their mitochondria, organelles responsible for cellular ATP generation and signaling. This thesis focuses on mitochondrial remodeling upon activation in two distinct types of immune cells – macrophages and T cells. In macrophages, mitochondria are thought to shift away from energy metabolism and towards accumulating signals that augment their function. However, through rigorous examination and several orthogonal approaches, I have determined that macrophages do not require inhibition of mitochondrial ATP production to acquire a pro-inflammatory state. I have also examined the role of mitochondrial metabolism in chimeric antigen receptor (CAR) T cells, a promising cancer immunotherapy. Since metabolic reprogramming accompanies T cell activation, I determined whether early metabolic biomarkers exist that can predict CAR-T cell efficacy and durability. I developed a method to address this question yet did not discover any metabolic markers that could predict CAR-T cell efficacy. Overall, this research expanded our understanding of the extent to which metabolism can predict and determine immune cell function.ImmunologyBiochemistryPharmacologyMolecular biologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0qk2b3smhttps://escholarship.org/content/qt0qk2b3sm/qt0qk2b3sm.pdfetdoai:escholarship.org:ark:/13030/qt3js9371g2026-05-15T06:31:28Zqt3js9371gFrom Lineage Architecture to Immune Landscape: Frameworks for Rational Therapeutic Targeting in GlioblastomaFazzari, ElisaBhaduri, Aparna2026-05-12Glioblastoma (GBM) remains refractory to all current therapies, including immune checkpoint inhibitors. This dissertation addresses how transcriptionally defined cellular heterogeneity in GBM is functionally organized to sustain tumor growth, and how that organization can be modeled and therapeutically exploited. Three complementary studies are presented.Chapter 2 constructs a lineage-resolved map of progenitor organization in primary GBM. Using the CellTagging combinatorial barcoding system in the human organoid tumor transplant (HOTT) model, we profiled 235,155 malignant cells from nine patient tumors and identified 10,880 clones. Clonal relationships organize into five reproducible Tracks, each anchored by a distinct progenitor population. No single progenitor generates all tumor cell types; instead, multiple non-redundant progenitors with complementary fate potentials collectively sustain tumor heterogeneity. Combined inhibition of LGALS1 and CDK4, selected by their complementary Track distributions, reduces tumor burden more effectively than either agent alone.Chapter 3 characterizes the neurovascular progenitor (NVP), which exhibits the highest cross-lineage clonal connectivity of any progenitor in the Chapter 2 dataset. Meta-atlas analysis across seven published GBM cohorts establishes NVP as a consistent, tumor-intrinsic cell type co-expressing neural progenitor and perivascular programs. Direct clonal tracing in HOTT demonstrates that individual NVP cells generate both neural-like and mesenchymal progeny. In vivo ablation of NVP-associated programs in a mouse GBM model prolongs survival.Chapter 4 introduces iHOTT (immune-HOTT), an autologous co-culture platform integrating patient-derived GBM cells and matched peripheral blood mononuclear cells within human cortical organoids. Single-cell RNA sequencing confirms preservation of major tumor and immune cell types and recapitulates tumor-immune interactions observed in patient tumors. Treatment with the anti-PD-1 checkpoint inhibitor pembrolizumab produces immune cell-type shifts and altered cell-cell interaction patterns mirroring those in pembrolizumab-treated patient tumors. TCR sequencing reveals expansion of stem-like CD4 T-cell clonotypes with patient-specific repertoires, consistent with the individualized clonal landscapes that likely underlie the limited clinical efficacy of checkpoint blockade in GBM.Together, these studies establish a functional map of GBM progenitor organization, identify a cross-axis lineage intermediary with disproportionate influence on tumor composition, and introduce a physiologically relevant human platform for studying autologous tumor-immune interactions and personalized immunotherapy responses.NeurosciencesCellular biologyBiochemistryImmunologyGlioblastomaLineage tracingOrganoidProgenitorTumor ImmunologyTumor stem cellapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3js9371ghttps://escholarship.org/content/qt3js9371g/qt3js9371g.pdfetdoai:escholarship.org:ark:/13030/qt1t92g0th2026-05-15T06:31:24Zqt1t92g0thDomination, determination, and decomposition: an analytic triptychHu, NicholasNeedell, DeannaVișan, Monica2026-05-11In this dissertation, we investigate three separate problems in harmonic analysis, partial differential equations, and numerical analysis.
The first concerns an important family of inequalities in analysis known as the Brascamp–Lieb inequalities, which subsume several inequalities significant in their own right, including Hölder’s inequality, the Loomis–Whitney inequality, and Young’s inequality. Several variants and extensions of these inequalities have been developed, some of which have proved to be very useful in Fourier restriction theory. We formulate Brascamp–Lieb inequalities for algebraic objects known as quiver representations and establish necessary and sufficient conditions for such inequalities. We also show that, unlike in classical Brascamp–Lieb inequalities, Gaussians do not saturate certain quiver Brascamp–Lieb inequalities.
The second considers equations modelling wave-like behaviour whose solutions exhibit a phenomenon known as scattering. The problem of deducing properties of these equations from how their solutions scatter has been extensively studied and can be conceived of as the determination of the properties of a physical object from the way it interacts with waves. We study the scattering behaviour of a semilinear wave equation and employ the method of deconvolution developed by Killip, Murphy, and Vișan to determine more general nonlinearities under significantly weaker conditions than those previously assumed by Sá Barreto, Uhlmann, and Wang.
The third addresses the alternating least squares (ALS/AltLS) method, a widely used algorithm for computing the CP decomposition of a tensor. This decomposition expresses a tensor as a sum of simpler tensors and is applied in numerous sciences to reveal patterns and features of multidimensional data. We analyze the theoretical convergence of the CP-AltLS method for so-called orthogonally and incoherently decomposable tensors and establish quantitative, explicit, and precise local convergence theorems for these tensors. In contrast to existing convergence results, our arguments are constructive, more direct, and less technical; and extend to non-orthogonal decompositions while remaining applicable to tensors of arbitrary rank. We also perform numerical experiments confirming our theorized rates of convergence.MathematicsApplied mathematicsharmonic analysisnumerical analysisnumerical linear algebrapartial differential equationsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1t92g0thhttps://escholarship.org/content/qt1t92g0th/qt1t92g0th.pdfetdoai:escholarship.org:ark:/13030/qt0tf767n82026-05-15T05:01:29Zqt0tf767n8Redefining Preclinical Testing for Cervical Disc Replacements: A Focus on Implant StabilityWahbeh, Jenna MarieSangiorgio Barbarie, SophiaKasko, Andrea M2025-01-01Degenerative disc disease of the cervical spine is a highly prevalent condition, commonly requiring surgical intervention. While anterior cervical discectomy and fusion (ACDF) has long been the surgical standard of care, its limitations, including degeneration of the adjacent segments and complete loss of motion, have prompted the development of alternative solutions. Notably, cervical disc replacements (CDRs) were introduced to preserve the range of motion and reduce the likelihood of adjacent segment degeneration. However, despite their increase in popularity, clinical outcomes have indicated that mechanical failure of these devices can lead to early revision surgery.Based on the available clinical data, one of the most frequently reported mechanical causes for CDR removal is loosening and/or migration. In large joint arthroplasty, it is typically reported that relative cyclic motions between the bone-implant interface need to be below 40–150um to achieve successful long-term fixation. Failure to meet this threshold can lead to migration of the implant over time, resulting in an increase likelihood of implant failure. Specifically, migration- related failures are the leading cause of cervical device revisions cases, with 84% of CDR revisions and 96% of all adverse events being removed due to migration as reported in the FDA adverse event reporting database. Moreover, specific devices vary substantially in the reported rates of migration-related reasons for failure. Interestingly, these devices can vary widely in design features that could affect bony fixation and stability.
Unfortunately, current preclinical testing may not be adequate in addressing implant fixation with relatively few biomechanical studies evaluating CDR fixation in the literature. In contrast, studies of loosening and migration have formed the cornerstone for preclinical testing of arthroplasty implants in the hip and knee. These studies have successfully predicted the clinical performance of joint replacements. Typically, investigations of migration in large joint literature utilizes composite long bones, which have been made commercially available. However, no such model exists for the cervical spine. The absence of a validated biomechanical testing model may have led to this lack of migration studies over the last two decades for CDR, representing a major deficiency in the current field of spine arthroplasty.
Therefore, the overall objective of this dissertation is to develop comprehensive testing methodology to evaluate cervical device fixation and initial implant stability, through the fabrication of an in vitro biomimetic model and analysis of current devices for the relative contributions of specific design features to the overall stability of the device. Accordingly, this dissertation is the first step in developing a protocol to systematically assess fixation of CDRs.
Chapter 1: Retrieval Analysis
A retrieval analysis conducted as the first component of this dissertation established a clinical record of migration-related failures in current cervical disc replacements. Specifically, analysis of explanted devices revealed a significantly higher incidence of anterior migration in a ball-and-socket design compared to a fixed-core counterpart. These findings highlighted the role of implant design features in influencing fixation and stability. This clinical insight motivated the development of a preclinical testing platform to systematically assess the biomechanical behavior of CDRs under physiologic loading conditions.
Chapter 2: Creation of a 3D-Printed Model
To address the lack of established preclinical models for evaluating cervical implant fixation, a novel 3D-printed cervical vertebral body model was developed and validated. This biomimetic model featured a nonhomogeneous cancellous core designed to replicate the ultrastructure and material properties of human cervical bone. A range of lattice architectures and material densities were explored to produce a tunable and cost-effective platform for biomechanical testing. Uniaxial compression and shear testing confirmed that the model could replicate the load response of cervical bone, though limitations in replicating viscoelastic and ductile properties remain.
Chapter 3: Comparison of a 3D Model and Polyurethane Foam Model
The 3D-printed model created in chapter 2 was then benchmarked against a rigid polyurethane foam model, commonly used to simulate cancellous bone in long bone models, to assess their respective abilities to predict clinical performance. Through micromotion testing of multiple CDR designs the rigid polyurethane foam model was more sensitive in detecting differences in clinical observed migration and micromotion differences observed in retrieval data in chapter 1, suggesting it may be better suited for distinguishing clinical performance outcomes across varying implant designs. However, the 3D-printed model exhibited superior consistency and repeatability across trials. These findings underscore a trade-off between sensitivity and reliability in model selection and suggest that each platform offers distinct advantages. The foam model may be more appropriate for comparative micromotion testing across devices, while the 3D-printed model provides a robust and reproducible framework for future development of more accurate testing models.
Chapter 4: Micromotion Testing of Five Different Cervical Device Designs
Five distinct cervical disc replacement designs were evaluated using the validated testing model under both singular and combined loading conditions. Device design characteristics such as keel height, articulation type, ball-and-socket contact arc, and ball coverage angle were measured for each device and motions were assessed as a function of each feature. The results demonstrated that larger keel heights, smaller ball contact arcs, and greater ball coverage angles significantly reduced micromotion at the bone-implant interface. These findings suggest that specific implant geometries play a critical role in achieving primary stability and minimizing the risk of migration, which could inform future device design and regulatory approval pathways.
This dissertation addresses a critical clinical need in cervical spine arthroplasty literature by developing a methodology for preclinical testing to evaluate the contribution of implant design features to overall device stability. By integrating clinical retrieval data with in vitro testing, this work establishes a comprehensive protocol for assessing fixation-related design features in CDRs. The insights gained offer a roadmap for improving implant design, predicting long-term clinical performance, and refining preclinical testing standards, ultimately contributing to safer and more reliable cervical spine arthroplasty solutions.BioengineeringAdditive ManufacturingCervical Disc ReplacementFixationMicromotionMigrationRetrieval Analysisenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0tf767n8https://escholarship.org/content/qt0tf767n8/qt0tf767n8.pdfetdoai:escholarship.org:ark:/13030/qt12m258vw2026-05-12T06:30:25Zqt12m258vwStatistical Methods for Mapping Gene Regulation with Functional AssaysXue, Albert ShaoyangPimentel, Harold JSankararaman, Sriram2026-05-07The advent of new functional assays give researchers new ability to connect genetics to function. In turn, careful and specialized computational methods can use these data to ask questions about genetic regulation and architecture. In my first project I developed dotears, a method for learning causal gene regulatory networks using Perturb-seq data, with guarantees of statistical consistency under mild assumptions as well as improved performance in simulations and real Perturb-seq data. In my second project I developed keju, a Bayesian hierarchical model that estimates transcription rate and differential activity in Massively Parallel Reporter Assay data. keju improves inference by tying uncertainty estimation to assay design, with better power and calibration than previous methods. Together, these methods use functional assay data to better characterize causal gene-gene regulation and the impacts of genetic variation on transcription in varied contexts.BioinformaticsBiostatisticsGeneticsbioinformaticsfunctional genomicsgeneticsstatisticsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/12m258vwhttps://escholarship.org/content/qt12m258vw/qt12m258vw.pdfetdoai:escholarship.org:ark:/13030/qt9ps2j9rv2026-05-12T05:03:19Zqt9ps2j9rvOxygen sensation in skin-penetrating, human-infective nematodesWalsh, BreannaHallem, Elissa A2025-01-01 To maintain successful host infections, skin-penetrating parasitic nematodes detect sensory cues – such as heat, odorants, and carbon dioxide – using specialized neurons. While traversing intra-host and extra-host settings, skin-penetrating nematodes confront changing ambient oxygen (O2) concentrations that range from atmospheric (~21%) to near-anerobic. We hypothesize that skin-penetrating nematodes use ambient O2 as a sensory cue to glean information about the environment and mediate behaviors necessary for parasitism. However, neuronal O2 sensation has yet to be described in any parasitic nematode. Here, we provide the first evidence of O2-evoked motile behaviors in skin-penetrating nematodes and demonstrate that these behaviors are distinct from those displayed in non-parasitic Caenorhabditis elegans nematodes. In the human-infective, skin-penetrating nematode Strongyloides stercoralis, we identified four candidate O2 sensors, each of which are homologs to the O2-sensing soluble guanylate cyclases (sGCs) found in C. elegans. Notably, homologs to a specific subset of C. elegans sGCs that detect downshifts in O2 concentration were absent in all tested species of skin-penetrating nematodes; these sGCs are required for C. elegans dauers to perform an O2-downshift-associated pause response.
To further characterize the molecular and neuronal basis of O2 sensation, we took advantage of the ever-expanding genetic toolkit for S. stercoralis, as Strongyloides species are uniquely genetically tractable among skin-penetrating nematodes. We devised a method to generate stable transgenic lines of S. stercoralis and expanded this methodology to produce stable, CRISPR/Cas9-mutagenized homozygous gene knockout lines by CRISPR/Cas9-targeted mutagenesis. Using this methodology, we discovered that the sGC Ss-GCY-35 is required for O2-evoked behaviors in S. stercoralis. Host-seeking S. stercoralis third-stage infective larvae (iL3s) normally crawl rapidly at 21% O2; genetic knockout of Ss-gcy-35 renders iL3s immobile. We next identified Ss-URX sensory neurons and used molecular homology, neuronal silencing, and calcium imaging to characterize these neurons at O2-sensing. Altogether, these results suggest that S. stercoralis employs neuronal O2 sensation to evoke behaviors associated with parasitism. Moreover, chemotherapeutic targeting of the O2-sensing molecular machinery may provide a new avenue for anthelmintic design.ParasitologyMolecular biologyNeurosciencesenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9ps2j9rvhttps://escholarship.org/content/qt9ps2j9rv/qt9ps2j9rv.pdfetdoai:escholarship.org:ark:/13030/qt21g8158r2026-05-09T05:01:47Zqt21g8158rMapping human skeletal muscle development to guide in vitro myogenesisChien, Peggie JanePyle, April D2024-01-01Skeletal muscle is one of the most regenerative tissues in the human body. Skeletal muscle progenitor cells (SMPCs) contribute to developmental myogenesis, and skeletal muscle stem cells (satellite cells, SCs) contribute to postnatal muscle homeostasis and regeneration. Differentiating human pluripotent stem cells (hPSCs) into SCs is a valuable resource for developing cell replacement therapies for muscle wasting diseases. However, current hPSC directed myogenic differentiation protocols result in immature, embryonic-like SMPCs that do not engraft and promote muscle regeneration as efficiently as postnatal SCs. Because it is unclear how SMPCs and SCs molecularly differ, it is not known how to mature SMPCs into SCs. Chapter 2 provides an overview on the current understandings of muscle development and the state of the field in generating directed differentiation myogenic cultures from hPSCs. In chapter 3, we found that although SMPCs and SCs from across development have many functional similarities in their roles in muscle formation, profiling them from both in vivo and in vitro hPSC directed differentiations using single-cell RNA-sequencing has revealed that they are transcriptionally distinct. However, it was unclear what regulated their differences in cell states. The chromatin landscape has a direct role in dictating the state of a cell, particularly through modulations in accessibility at regulatory regions which mediate transcription factor (TF) binding and control gene expression. In chapter 4, we evaluated differences in chromatin accessibility using single-nucleus ATAC-sequencing in tissue-derived SMPCs and SCs and hPSC-derived SMPCs (hPSC-SMPCs). We identified several TF candidates as potential regulators of development from embryonic SMPCs to postnatal SCs. For example, nuclear factor I family members were found to have roles in establishing stage-specific enhancers in the maturation from embryonic to fetal SMPCs. We also found that reduction of HMGA1 expression during directed differentiation promotes maturation of hPSC-SMPCs, and thus we identified HMGA1 as a TF that maintains SMPC immaturity. Collectively, this work sheds light on the transcriptional differences between muscle progenitor and stem cell states and the gene regulatory mechanisms that mediate these differences. It provides a comprehensive, multiomic view on the progression of human myogenesis in single-cell and single-nucleus resolution. These findings enhance our understanding of how to promote the regenerative potential of hPSC-derived muscle cells and enable the development of improved cell therapies for muscle diseases. Molecular biologyCellular biologyDevelopmental biologyenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/21g8158rhttps://escholarship.org/content/qt21g8158r/qt21g8158r.pdfetdoai:escholarship.org:ark:/13030/qt0s41c1kq2026-05-09T05:01:19Zqt0s41c1kqFundamental Study on the Arc Welding of Dissimilar Aluminum Alloys with Nano-Treated FillerMurali, NarayananLi, Xiaochun2024-01-01Fusion welding is an important fabrication process used on a variety of engineered structures to create strong and permanent joints. Today, there is a growing call for structural lightweighting and sustainability, and the incorporation of light alloys like aluminum can reduce emissions, improve fuel economy, and contribute to closed-loop manufacturing. However, there is a glaring incompatibility between fusion welding and aluminum alloy joining. More specifically, marrying two dissimilar aluminum alloys has historically proven to be difficult, and current methods come with a penalty to either structural weight or the joint’s design freedom. First, the weldability of dissimilar aluminum alloys is low, especially when one or both are prone to hot cracking. This stems from issues involving the solidification of the alloy, which can result in the formation of defects such as cracks during solidification. Second, the potential for post-weld heat treatment is low, as the chemistry differences between the weld and the base metals vary. Third, dissimilar welds often suffer from residual stresses after solidification. Adverse effects like distortion can result from this, and conventional methods to alleviate residual stress become difficult to implement.
Recently, nano-treating (NT) technology has been utilized to address these problems and improve the fusion welding of dissimilar aluminum alloys. Nano-treating is the addition of a low concentration of refractory nanoparticles into metals and alloys to elicit changes in processability, microstructure, and properties. Previous research into nano-treating strongly suggests a high potential for the technology to address the presently posed challenges.
In this dissertation, weldability of dissimilar aluminum alloys was first investigated by fabricating joints using high-strength grades of wrought alloys and nano-treated ER5183 filler with approximately 1 vol.% TiC nanoparticles. Specific alloy combinations were AA2024, AA6061, or AA7075 (similar welds) along with AA2024 to AA5083 or AA2024 to AA7075 (dissimilar welds), with comparisons drawn to welds made with commercial ER5356 filler when possible. Joints created using commercial filler displayed large dendritic microstructures, continuous secondary phase networks, and a tendency for defect formation while joints with nano-treated fillers displayed refined, equiaxial, non-dendritic grains, dispersed secondary phases, and a notable elimination of harmful defects. High joint quality was quantified using microhardness and tensile testing. This study showed a successful extension of nano-treating technology to dissimilar welding of high-strength aluminum alloys.
The second study focused on the natural aging behavior of dissimilar high-strength aluminum alloy joints made between AA6061 and AA7075 (with nano-treated AA6061 or AA7075 fillers) or AA2024 and AA7075 (with nano-treated AA2024 or AA7075 fillers). Short-term data over eight to ten weeks consisted of weekly microhardness measurements, while long-term data was collected after over a year of natural aging. It was noted that all dissimilar joints showed increases of over 30% in microhardness over the short term. In particular, joints created with 7075-NT filler increased by over 40% in weld metal microhardness in the short term, and moreover, continued to increase over the long term. Tensile tests confirmed improvements in strength after aging. Computational phase diagrams and microstructural studies suggest differences in the behavior of specific strengthening precipitates owing to the variance in compositions. The study shows the promise of nano-treating in improving dissimilar weld joint strength without the need for heat treatment. Additional investigation on the welding of AA2024 with nano-treated ER2319 filler confirmed improvements in microhardness during natural aging, but tensile testing showed improvements only as welded compared to commercial ER2319, necessitating deeper analysis of this system.
The third study evaluated the reduction of residual stress using nano-treating technology. Experiments related to angular distortion, outward thermal flux from the weld metal, in-situ strain, and microstructure were conducted to understand the effect of nano-treating on residual stress development in fusion welds of high-strength alloys AA2024, AA6061, and AA7075. Macroscopic observations noted significant reductions in average deflection and axial strain, while the inclusion of nanoparticles altered the release of heat during weld solidification. A theoretical mechanism based on the experimental results and established nano-treating effects is proposed to elucidate the nanoparticle-enabled control of residual stress by reducing constraints on solidification-induced shrinkage.
In summary, this dissertation extends the use of nano-treating technology to dissimilar welding of high-strength wrought aluminum alloys to expand the capabilities of fusion welding and build upon previously established research cementing the fabricability of high-strength aluminum alloys with nano-treating technology. Furthermore, it provides guidance on dissimilar joint design in terms of potential improvements to strength while circumventing limitations regarding heat treatment. Finally, this work proposes a new way of combatting a long-standing problem in welding residual stress using nano-treating to improve the process of fusion welding aluminum alloys.
Materials Scienceenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0s41c1kqhttps://escholarship.org/content/qt0s41c1kq/qt0s41c1kq.pdfetdoai:escholarship.org:ark:/13030/qt9q98p88z2026-05-08T06:31:02Zqt9q98p88zDiscrimination and Social Determinants of Mental Healthcare Access Among Racial and Ethnic Minority Cancer Survivors: A Cross-Sectional StudyWEN, YI-PINGChoi, Kristen R.Brauer, Eden R.2026-05-05ancer survivors are twice as likely to experience psychological distress, and those from racial and ethnic minority groups report higher levels of psychological distress but are less likely to seek mental healthcare compared to non-Hispanic White survivors. They also report more financial difficulties, which are correlated with increased mental distress. Discrimination is recognized as a key factor influencing individuals’ mental health and their willingness to seek help. However, the specific impact of discrimination on different racial and ethnic minority groups, how it influences their use of mental healthcare, and how cultural and social environmental factors interact with discrimination to shape survivors' unmet mental health needs are still not well understood, especially for those who struggle to afford mental healthcare services. This cross-sectional study used surveys and electronic health records from the All of Us Research Program. Adults aged 18 and older, racial and ethnic minorities, who have a cancer history, and who responded to surveys on discrimination and mental healthcare access were included. This study identified distinct patterns of discrimination by race and ethnicity. Black American survivors experienced higher levels of discrimination in everyday life and in healthcare. In contrast, Asian American survivors reported higher levels of overt everyday discrimination. Everyday discrimination increased mental healthcare utilization among Black American and Hispanic American survivors, whereas healthcare discrimination served as a barrier to mental health service use. Discrimination, especially in healthcare, increased survivors’ cost-related unmet mental healthcare needs. Besides discrimination, food insecurity and poor housing quality were ranked as the most important contributors to survivors' cost-related unmet mental healthcare needs.These findings highlight the need for anti-discrimination measures targeting both healthcare providers and systems. Additionally, strategies to reduce social barriers should comprehensively address multiple social domains, including food and housing. Assessments of discrimination and barriers to mental healthcare must account for the social and cultural contexts in which experiences are interpreted. Interventions to enhance survivors’ mental health and reduce unmet mental healthcare needs related to costs should be tailored to individuals’ sociodemographic and cultural characteristics. Additionally, efforts to support survivors’ mental health should focus on increasing the use of community-based mental health services.Health sciencesNursingOncologyMental healthcancer survivorsdiscriminationmachine learningmental healthcare accessracial and ethnic minoritysocial determinants of healthpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9q98p88zetdoai:escholarship.org:ark:/13030/qt78b9j9wx2026-05-08T06:30:57Zqt78b9j9wxUnderstanding Medical Staff Experiences and Perspectives on Child Language Brokers Translating their Medical Information for Family MembersTorres, Ines MelissaBailey, Alison L2026-05-06The purpose of this study was to gather information about medical and auxiliary staff experiences and perspectives on translation services alongside children's language brokering in medical settings. Medical staff often want to help the families they serve; however, limited resources can make this difficult. Not every staff member may be aware of children's language brokering and its effects (both positive and negative). Helping staff understand how they can work together with families can make the medical experience less stressful for everyone involved. An online questionnaire and one-on-one interviews were used to collect data. A total of 24 participants completed the questionnaire, and 4 agreed to a one-on-one Zoom interview. This study found that medical and auxiliary staff face various obstacles, including limited staff availability, limited technology (e.g., iPads), technological difficulties, and a lack of support for specific languages, when they need translation services to assist patients and their families, despite translation services being available. The interviewed participants were able to share more personal experiences and thoughts, such as under certain circumstances, it may be appropriate for older children to translate. Overall, this study found that translation services and children's language brokering in medical settings are complex and several factors must be considered. Some implications can be tentatively drawn from this study, the management of translation services, and how familiar staff are with the effects of children's language brokering within medical settings.LanguageBilingual educationIndividual & family studieschild language brokerlanguage brokeringtranslatingpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/78b9j9wxetdoai:escholarship.org:ark:/13030/qt4pj1g04w2026-05-08T06:30:52Zqt4pj1g04wThe Role of Microbiome-derived Metabolites In Cardiovascular DiseaseCheng, Jenny TLusis, Aldons J.Yang, Xia2026-05-05Dietary intake is one of the greatest environmental exposures that can influence metabolism and shape gut microbial composition. Nutrient metabolism, leading to the generation of biologically active metabolites, is among the many functions of the gut microbiome that establish it as an important modulator of host physiology and health. While the majority of gut microbes together function to maintain homeostasis, certain gut microbiota-derived metabolites have been increasingly recognized as either protective or contributing factors in the development and progression of various cardiometabolic phenotypes. We first aimed to examine trimethylamine-N-oxide (TMAO), a metabolite derived from dietary choline through the action of gut flora, in promoting atherosclerosis progression in a cell type-specific manner. We next aimed to study indole-3-propionic acid (IPA), a microbiota-derived metabolite synthesized by the species Clostridium sporogenes from dietary tryptophan, in protecting against abdominal aortic aneurysm (AAA). Collectively, our studies provide fundamental insights into the gut microbiome-dependent metabolites IPA and TMAO in AAA and atherosclerosis, respectively. We endeavor to establish proof-of-concept for clinical applications of our results, including noninvasive therapeutics and diagnostics. PhysiologyMedicineCellular biologyMolecular biologyAbdominal Aortic AneurysmAtherosclerosisMetaboliteMicrobiomeSingle Cell TranscriptomicsVascular Smooth Muscle CellspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4pj1g04wmultimediaoai:escholarship.org:ark:/13030/qt0gk166hz2026-05-08T06:30:46Zqt0gk166hzAn Investigation of Voter Turnout in American ElectionsStraus, Graham PaleyVavreck, Lynn2026-05-05The United States electorate tends to be older, more educated, and wealthier than the general population, patterns that are remarkably consistent across elections. How do cross-sectional findings such as these translate to within-individual changes? This dissertation examines how, as individuals’ lives change and evolve, voter turnout does and does not change. Chapter 1 focuses on income and turnout. I measure earnings with administrative salary records and show that voter turnout does not increase substantially even with large income changes. Chapter 2 uses data from decedent cases in probate court to understand how inheriting wealth changes participation. Chapter 3 focuses on health and turnout, showing with electronic health records that major illness significantly depresses turnout but convenience voting may mitigate the effect.Political sciencePublic administrationPublic policypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0gk166hzetdoai:escholarship.org:ark:/13030/qt79g733xp2026-05-08T06:30:41Zqt79g733xpRNA Variants as Biological IndicatorsHuang, ElaineXiao, Xinshu2026-05-05RNA transcripts are marked with single-nucleotide variants (SNVs), which can have genetic origins (i.e. arising from DNA), or be deposited co-/post-transcriptionally. These non-genetic SNVs often arise from RNA editing, a regulatory process in which RNA sequences are altered through substitution of nucleotides. Regardless of their origins, the presence and pattern of RNA variants can yield informative biological insights. In this work, we leverage RNA variants identified using high-throughput sequencing data to decipher gene regulatory mechanisms and disease-associated transcripts.We first demonstrate that genetic variants exhibiting allele-specific expression patterns in RNA can be utilized as an indicator for gene regulation mechanism. Gene expression is modulated jointly by transcriptional regulation and messenger RNA stability, yet the latter is often overlooked in studies on genetic variants. Leveraging metabolic labeling data (Bru/BruChase-seq) and a new computational pipeline, RNA tracker, we categorize genes as allele-specific RNA stability (asRS) or allele-specific RNA transcription events (asRT). Extensive characterization of stability-regulating variants revealed notable contribution of these genes to immune system related processes. This work highlights RNA stability as a critical, yet understudied mechanism linking genetic variation and disease.Next, we explored the usage of A-to-I editing as a marker for the age of gene transcripts. Standard transcriptomics captures steady-state abundance, obscuring the kinetics of the RNA lifecycle. We demonstrate that the progressive accumulation of endogenous adenosine-to-inosine editing events serves as a molecular timestamp to estimate RNA age. We present MEMORIA, a computational toolkit that leverages this ‘molecular clock’ to infer absolute decay and synthesis rates from single-timepoint long-read RNA-seq data, obviating the need for metabolic labeling.Finally, we leveraged a large compendium of brain-specific A-to-I editing sites as a basis for identifying double-stranded RNAs (dsRNAs). It has been hypothesized that by altering the structure of dsRNAs, RNA editing suppresses the activity of dsRNA sensors which mediate innate immune response. As such, RNA editing is highly relevant to neurodegenerative disorders such as Alzheimer’s Disease (AD), in which dysregulation of innate immune pathways contributes to disease pathogenesis. This work yields novel insights regarding the landscape surrounding immune dysregulation and RNA editing in AD.BioinformaticsGeneticsCellular biologyBiochemistrygenetic variantsRNARNA editingRNA-seqpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/79g733xpmultimediaoai:escholarship.org:ark:/13030/qt6b98j4jq2026-05-05T06:48:01Zqt6b98j4jqUnraveling the Impact of Biosimilar Market Entry on the Market Competition, Utilization, and Financial Implications in the U.S.Liu, XiaoyuShih, Ya-Chen Tina2026-04-302025 marks the 15th year anniversary of the Biologics Price Competition and Innovation Act. It is imperative to evaluate whether biosimilars have achieved the desired policy outcomes. This three-paper doctoral dissertation comprehensively examined the impact of biosimilar entry into the U.S. biologics market from three aspects: market competition, utilization, and financial implications. Drawing on three economic models, the first paper investigated the evolution of prices and market shares for biosimilars and reference products, as well as the determinants underlying these patterns. This paper found that the extended Stackelberg game model is the best model for the overall biosimilar market as well as the submarket where the premium pricing strategy was employed by the branded firm. The Bertrand model with product differentiation model is the best model for the submarket where the aggressive pricing strategy was used.The second paper examined treatment initiation with biosimilars and switching from reference products to biosimilars, and analyzed associated factors using patient-drug-level claims data. Results showed that about 40% of patients initiated the treatment using biosimilars instead of the reference product while about 5% of patients who started the treatment using biosimilars later switched to biosimilars. This chapter identified age, geographic location of primary beneficiary's residence of the U.S., and types of insurance as significant predictors of patient initiating the treatment with a biosimilar. Whether the patient started the reference product before the first biosimilar market entry and the number of biosimilars available were found to be the two significant predictors of the switching from the reference product to biosimilars in most product lines.The third paper estimated and compared mean monthly payor costs and patient out-of-pocket costs associated with 1) exclusively using the reference product; 2) switching from the reference product to biosimilars; 3) exclusively using biosimilars; and 4) switching from biosimilars to the reference product. This chapter found that biosimilars rendered statistically significant savings to payors both in the overall analysis and subgroup analysis across all product lines. However, with respect to OOP costs, biosimilars did not necessarily reduce financial burden for patients and the findings varied by product lines.Public healthHealth sciencesEpidemiologyBiosimilarHealth policyMarket competitionPharmaceutical economicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6b98j4jqhttps://escholarship.org/content/qt6b98j4jq/qt6b98j4jq.pdfetdoai:escholarship.org:ark:/13030/qt8xn5b66h2026-05-02T05:03:49Zqt8xn5b66hImmigration Status, Work Illness and Worker’s Compensation in California: Silicosis in Stone Countertop WorkersFazio, Jane CPonce, Ninez ABustamante, Arturo Vargas2025-01-01Foreign-born workers in the United States, particularly Hispanic/Latino/a individuals, are overrepresented in physically demanding and hazardous occupations and are known to have higher rates of occupational injury and illness. Workers who suffer injury or illness due to their work are generally entitled to claim worker’s compensation benefits for medical costs and lost wages due to illness/injury. However, injury rates are likely underreported or claims denied, especially among foreign-born workers due to individual and systemic barriers of a complex and cumbersome system. A specific example of this is the recently emerging epidemic of silicosis, a chronic and often fatal occupational lung disease among workers in California that disproportionately affects Hispanic/Latino/a immigrants. This dissertation examines the intersection of occupational health disparities, workplace safety, and financial consequences of work-related illness and injury, first, in general, among a population-based sample of California workers, then in-depth in the case of engineered stone countertop workers. Chapter 1 introduces the topic and provides background on work-related illness and injury, workers compensation, and the emergence of engineered stone associated silicosis. Chapter 2 presents the first study which uses data from the California Work and Health Survey to assess the relationship between foreign-born status and work-related injury. While foreign-born workers did not have higher overall injury rates, they did have higher odds of having multiple work-related illnesses or injuries. Mediation analysis showed that among Hispanic/Latino/a and Asian foreign-born workers, sorting into higher-risk physically demanding jobs accounted for some of the effect of potentially greater risk of work-related illness/injury. Worker’s compensation benefit acquisition after a work-related injury was overall low (18%), although did not differ by foreign-born status. Chapter 3 presents the second study which examines the specific case of stone countertop workers in Los Angeles County using in-depth, qualitative interviews with 20 workers. It revealed widespread unsafe work practices, inadequate respiratory protection, and a lack of formal safety training. Fear of retaliation prevented workers from reporting hazards, while employer reluctance to invest in protective measures further exacerbated risks. Chapter 4 presents the third paper which In analyzes hospitalization cost of silicosis-related hospitalizations in California among stone countertop fabricators from 2006-2022. The total estimated cost of hospitalizations for 37 unique individuals was 3.8 million dollars, with public insurance covering 71%, and worker’s compensation covering 5%. These findings highlight a major cost shift from employers to public dollars for a preventable occupational lung disease. This dissertation provides further evidence that foreign-born Hispanic/Latino/a workers often take higher risk physically demanding jobs that increase their propensity for serious and cumulative work-related illnesses or injuries. This disparity is highlighted among engineered stone countertop workers, who lack appropriate workplace protections and access to worker’s compensation benefits. Chapter 5 concludes that there is opportunity for improvement in protecting foreign-born and racial/ethnic minority workers from workplace illness and injury and increasing access to worker’s compensation benefits overall. Public healthOccupational safetyOccupational Lung DiseaseSilicosisWorkers compensationenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8xn5b66hhttps://escholarship.org/content/qt8xn5b66h/qt8xn5b66h.pdfetdoai:escholarship.org:ark:/13030/qt79h2d3p42026-04-30T06:34:59Zqt79h2d3p4Taming Religion, Managing Meaning: Religion, Secularization, and the Problem of Meaning in PoliticsCampbell, Joshua WilliamPagden, Anthony R.Parasher, Tejas2026-04-27This dissertation investigates the fraught and often-ambiguous relationship between politics and substantive human meaning – considered here broadly as a framework within which individual human actions are situated and that suggests lives worth living, a proper order of human affairs, the appropriate relations between individuals or social/political positionalities, and/or the larger significance of human endeavor, suffering, etc. This project claims that discussions of the relation of meaning to politics are today trapped in a paradigm oriented around the liberal principles of secularism and public neutrality and whether these principles render liberal politics “meaningless” and thus uniquely susceptible to irruptions of meaningful politics from exclusivist nationalism, far-right populism, and religious fundamentalism. By engaging with three key participants in the German secularization debate (1922–1976), Carl Schmitt, Karl Löwith, and Eric Voegelin, and one late entrant into the debate, Jürgen Habermas, I seek to move beyond this limiting paradigm and recover a broader set of theoretical reflections on both the necessity and dangers of substantive human meaning within political communities. Through readings of these four figures, this dissertation demonstrates that concerns and contestations around the relation of meaning to politics, rather than being unique to liberalism, are a persistent and inescapable feature of political life – and that every political form, liberalism included, inevitably develops a substantive meaningful core, as well as techniques by which to manage alternate frameworks of meaning that threaten the unity of the political community. Ultimately, this dissertation argues for placing questions of meaning at the center of political thought and discourse and suggests that not only may (post)secular questions of religion and politics be more usefully framed as questions of meaning in politics, but also that politics itself may be usefully defined as the management of meaning.Political scienceHistoryReligionCarl SchmittCritical SecularismEric VoegelinfascismGerman secularization debatemeaningapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/79h2d3p4https://escholarship.org/content/qt79h2d3p4/qt79h2d3p4.pdfetdoai:escholarship.org:ark:/13030/qt9jm060wd2026-04-30T06:34:55Zqt9jm060wdThe Influence of Menstrual Cycle Phase, Ovarian Hormones, and Premenstrual Symptom Severity on Alcohol and Cigarette UseHenry, BrittanyRay, Lara A2026-04-27Ovarian hormones, estradiol and progesterone, have been identified as a female-specific biological factor that influences the consumption of substances of abuse, such as alcohol and cigarettes. Animal models show an enhancing effect of estradiol on alcohol and cigarette use, whereas progesterone has been demonstrated to protect against alcohol and cigarette use. Many clinical studies have evaluated the effect of menstrual cycle phases on alcohol and cigarette use as a proxy for ovarian hormone levels. However, fewer clinical studies have investigated the association of ovarian hormones with alcohol and cigarette use, and it is unknown whether ovarian hormones are differentially associated with alcohol and cigarette use. To translate preclinical findings, further investigations of the effect of menstrual cycle phases and ovarian hormones on alcohol and cigarettes in clinical populations are necessary. Additionally, sensitivity to ovarian hormone fluctuations is a hypothesized etiology of premenstrual syndrome (PMS) and premenstrual dysphoric disorder (PMDD). PMS and PMDD, which are characterized by physical, mood, and behavioral symptoms, are associated with the development of substance use disorders (SUDs). Yet the literature remains mixed on whether PMS and PMDD affect alcohol use and cigarette use. This dissertation seeks to extend the literature on the associations of ovarian hormones and menstrual cycle phase with alcohol and cigarette use, as well as investigate the associations of PMS and PMDD with alcohol and cigarette use in female heavy drinking smokers (Study 1). Next, it examines group differences in the severity of premenstrual symptoms experienced in the week prior to menstruation onset and menstrual symptoms experienced during menstruation between women with and without alcohol use disorder (AUD) (Study 2). Additionally, this dissertation investigates the associations of premenstrual and menstrual symptom severity with alcohol use in women with and without AUD (Study 2). Study 1 consists of an investigation into the associations of menstrual cycle phase, PMS severity, and ovarian hormones with alcohol and cigarette use. Data was utilized from a 12-week randomized clinical trial for drinking reduction and smoking cessation in a sample of naturally cycling female heavy drinking smokers (N=26). Participants reported their recent alcohol and cigarette use, the dates of their last three menstrual cycles, and the severity of premenstrual symptoms in the week before their last period. Participants also provided saliva samples for assays of ovarian hormones. Multilevel modeling was used to test the associations of menstrual cycle phase, PMS severity, and ovarian hormones on drinking and smoking outcomes. Additionally, analyses probed for interactive effects of weekend status with menstrual cycle phase, premenstrual symptom severity, and ovarian hormones. Ovarian hormones, measured by progesterone-to-estradiol ratio, demonstrated a weekend-dependent negative association with alcohol use, such that the probability of any drinking on the weekend compared to weekdays increased at lower progesterone-to-estradiol ratios. PMS severity demonstrated a weekend-dependent positive association with high-risk alcohol use, such that the probability of heavy drinking on the weekend compared to the weekdays increased with greater PMS severity. Overall, this study supports prior literature on the protective effect of progesterone against alcohol use and the positive association between PMS severity and heavy alcohol use in a population of female heavy drinking smokers. Moreover, these results suggest that weekend status may moderate the associations of ovarian hormones, PMS severity, and alcohol use, highlighting the need for further study into how dynamic hormonal and emotional states interact with weekend status when investigating alcohol use in women. Study 2 explored group differences in premenstrual and menstrual symptom severity and examined the associations of premenstrual and menstrual symptom severity with alcohol use in women with and without AUD. Regularly cycling women with and without AUD (N=42; 20 AUD, 22 Control) reported their past-month alcohol use as well as their premenstrual and menstrual symptom severities in the week prior to and during their most recent period, respectively. Independent t-tests compared premenstrual and menstrual symptom severity between women with and without AUD. Multiple linear regressions tested the interaction between group (AUD vs. control) and symptom severity on past-month alcohol use. Women with AUD reported greater total premenstrual symptom severity, premenstrual pain, menstrual pain, and menstrual water retention severity compared to women without AUD. Additionally, menstrual autonomic reaction symptoms (dizziness, fainting, nausea, hot flashes, and cold sweats) demonstrated a negative association with past-month alcohol intake only in the AUD group, such that women with lower menstrual autonomic reaction symptom severity reported greater past-month alcohol use than women with higher autonomic reaction symptom severity in the AUD group. Altogether, these results extend the existing literature by demonstrating group differences in premenstrual and menstrual symptom severity in clinically distinct populations of female drinkers and illustrate how symptom clusters are differentially associated with alcohol use. The successful completion of these studies has provided additional clinical evidence of ovarian hormones, PMS severity, and menstrual symptom severity as factors that affect female alcohol use. Results indicated that progesterone-to-estradiol ratios were negatively associated with alcohol use, and PMS severity was positively associated with heavy alcohol use in a clinically unique population of female heavy drinking smokers. Further, the associations of ovarian hormones and PMS severity with alcohol use were moderated by weekend status. Additionally, women with AUD reported greater total PMS severity, premenstrual pain, menstrual pain, and water retention severity than women without AUD. Lastly, menstrual symptoms related to autonomic function were associated with reduced alcohol consumption only in women with AUD. Altogether, these findings provide additional clinical support of the protective effect of progesterone against alcohol use and further characterize the positive association between PMS severity and high-risk alcohol use in female heavy drinking smokers. In addition, these findings highlight the differences in premenstrual and menstrual symptom severity and demonstrate how symptom clusters are differentially associated with alcohol use in women with and without AUD. Collectively, results from these studies may be used to inform future experimental studies of ovarian hormones, PMS, and menstrual symptom severity for their impact on the female experience of alcohol and cigarette use.NeurosciencesEndocrinologyPsychologyWomens studiesalcoholcigarettesmenstrual cycleovarian hormonesPMS/PMDDapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9jm060wdhttps://escholarship.org/content/qt9jm060wd/qt9jm060wd.pdfetdoai:escholarship.org:ark:/13030/qt3zs3v8sw2026-04-30T05:02:19Zqt3zs3v8swMulti-omics Approach of Assessing the Gut Microbiome and Its Relationship with Pesticide Exposure and Parkinson’s Disease in CaliforniaZhang, KerenRitz, Beate R2024-01-01Parkinson disease (PD), one of most common neurodegenerative disorders in the United States and worldwide, continues to affect the elderly population. Medication and surgical intervention can alleviate the motor symptoms of PD and maintain some quality of life for the patient; however, progression is inevitable, and the etiology and a definitive cure of PD remain elusive. The current understanding is that PD is a multi-factorial disease attributed to multiple risk factors including genetic and environmental exposures. Our group has long demonstrated connections between ambient pesticides exposure and its interactions with genetic susceptibility and PD risk. Recent developments in omics research opened up exciting avenues for researchers to investigate metabolic and gut bacterial changes in PD patients and provide novel insights into the PD pathogenesis and opportunities for treatment. In this dissertation, we connect ambient exposure to organophosphorus pesticides (OPs), the gut microbiome, and the serum metabolomics in PD patients and community controls. All study participants are residing in Freso, Tulare and Kern counties of California were heavy agricultural pesticides use is common. Our aims are to gain a deeper understanding about the interplay between pesticide exposure, gut bacteria, and human metabolism in PD.First, we investigated the differences in gut bacterial composition and predicted metabolic function in PD patients and controls. For 96 PD patients and 74 controls, microbiome data were obtained with 16S rRNA gene sequencing of fecal samples. These were then analyzed for microbial diversity, taxa abundance, and predicted functional pathways in association with PD. We also examined the bacterial composition and predicted function associated with PD-specific features (disease duration, motor subtypes, L-Dopa daily dose, and motor function). We found that PD patients’ gut microbiome showed lower species diversity (p = 0.04) and were compositionally different (p = 0.002) compared to controls but had a higher abundance in three phyla (Proteobacteria, Verrucomicrobiota, Actinobacteria) and five genera (Akkermansia, Enterococcus, Hungatella, and two Ruminococcaceae) controlling for sex, race, age, and sequencing platform. Also, 35 Metacyc pathways were predicted to be differentially expressed in PD patients including biosynthesis, compound degradation/utilization/assimilation, generation of metabolites and energy, and glycan pathways. Additionally, the postural instability gait dysfunction subtype was associated with three phyla and the NAD biosynthesis pathway. PD duration was associated with the Synergistota phylum, six genera, and the aromatic compound degradation pathways. Two genera were associated with motor function.
We then focused on the perturbations of gut microbiota in response to long term ambient OP exposure. Similarly, the gut bacterial abundance and the predicted metagenome of 190 participants were obtained from 16S rRNA gene sequencing of fecal samples. Ambient long-term OP exposures were assessed based on pesticide application records combined with residential addresses via a geographic information system. We examined gut microbiome differences due to OP exposures, specifically differences in microbial diversity indices (Shannon diversity and Bray-Curtis dissimilarity), differential taxa abundance, and predicted Metacyc pathway abundance. Linear regression revealed no association between OP exposure and bacterial diversity after controlling for potential confounders. However, the predicted metagenes were sparser and less even among those highly exposed to OPs (p=0.02). Additionally, we found that the abundance of two families, 22 genera, and the predicted expression of 34 Metacyc pathways were associated with long-term OP exposure. These pathways included perturbed processes related to cellular respiration, increased biosynthesis and degradation of compounds related to bacterial wall structure, increased biosynthesis of RNA/DNA precursors, and decreased synthesis of Vitamin B1 and B6.
Lastly, we took a multi-omics approach to integrate the gut microbiome and serum metabolomic data collected from 113 PD patients and 46 controls. In addition to the microbiome data from 16s rRNA sequencing of fecal samples, high-resolution metabolomics analysis of serum samples was used to generate metabolomic profiles. Using principal component analysis of identified genera associated with PD, we constructed a summary PD-associated bacterial score using the first principal component. We then conducted partial least square (PLS) regression to identify metabolic features associated with the PD bacterial score. Pathway enrichment analysis was then performed on metabolic features selected from PLS. We identified 266 features and annotated 29 metabolic compounds as being associated with PD-related microbes at p<0.1. Pathway enrichment analysis indicated multiple perturbed pathways involving lipids metabolism including fatty acid activation and metabolism, linoleate metabolism, and glycerophospholipids metabolism, as well as carbohydrate metabolism such as hexose phosphorylation, starch and sucrose metabolism.
Our studies confirmed current literature on changes in the gut microbiome profile in PD patients compared to controls, and reported on some novel bacterial groups related to PD. In addition, we found evidence that the gut microbiome is associated with PD-specific features including disease duration and motor subtypes. Our findings of a disturbed gut microbiome in response to long-term OPs exposure provide evidence that the gut microbiome is a potential pathway for OPs causing an increased risk for PD. Linking the gut microbiome and serum metabolome, we found that gut microbiota associated with PD are involved in lipid metabolism and immune response in humans, adding information on the possible interplay between gut bacterial and human metabolism in PD. n conclusion, this dissertation enhances our understanding of risk factors contributing to Parkinson’s disease and provides evidence supporting the use of omics in investigating human disease, particularly for hypothesis validation and generation.
EpidemiologyEnvironmental EpidemiologyGut microbiomeMetabolomicsMulti-omicsParkinson’s diseasePesticide Exposureenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3zs3v8swhttps://escholarship.org/content/qt3zs3v8sw/qt3zs3v8sw.pdfetdoai:escholarship.org:ark:/13030/qt0263419c2026-04-27T06:30:35Zqt0263419cUnderstanding and Controlling Active Interphases across Battery SystemsLiu, BoLi, Yuzhang2026-04-23Electrochemical energy storage is essential for transportation electrification, grid-scale storage, and broader energy sustainability. Achieving these goals requires battery chemistry that is not only high-energy and fast charging, but also safe, durable, and practical under realistic operating conditions. In many next-generation batteries, these performance metrics are governed not simply by bulk materials, but by active interphases, where ion transport, electron transport, solvation, and parasitic reactions are tightly coupled. My dissertation centers on understanding and controlling active interphases across different battery systems, with a particular focus on two fundamental questions: how interfacial transport governs electrochemical performance, and how interfacial reactivity drives degradation and can be mitigated.
The first part of this dissertation focuses on quantifying and regulating interfacial transport. In lithium metal batteries, I developed a separator-free Cu|SEI|Li platform that treats the solid electrolyte interphase (SEI) as a functional solid-state electrolyte and enables direct quantification of its ionic conductivity, electronic conductivity, and ionic transference number. This work established the SEI cT number as a quantitative figure of merit linking SEI transport properties to battery performance and demonstrated room-temperature lithium plating and stripping in a separator-free Cu|SEI|Li architecture. I then extended this transport-centered perspective from buried nanoscale interphases to cathode architectures by designing a laser-scribed nano-vanadium oxide material with fast ion/electron transport pathways and predominantly surface-controlled Faradaic storage, enabling high capacity, ultrafast kinetics, and strong cycling stability in aqueous zinc batteries.
The second part of this dissertation focuses on understanding and mitigating interfacial reactivity. In aqueous zinc batteries, I discovered that zinc metal anodes can lose 12–37% of their capacity after only 24 hours of calendar aging. By using titration gas chromatography, I distinguished corrosion losses from electrically disconnected dead Zn and identified dead Zn as the dominant contributor to capacity loss during rest. Building on this mechanistic insight, I developed an ultra-dilute ZnSO4-based electrolyte that significantly improves both calendar-aging stability and cycling performance. This work showed that solvated water molecules, rather than free water, are the primary drivers of zinc corrosion, providing a molecular-level basis for electrolyte design to suppress parasitic reactivity while preserving battery cycling performance.
Together, these studies establish a broader framework for linking interfacial structure and chemistry to electrochemical performance across different battery systems. By quantitatively characterizing transport-governing interphases, engineering fast electrode surfaces, uncovering hidden degradation during calendar aging, and designing electrolytes that suppress parasitic interfacial reactions, this dissertation shows that active interphases are not merely sources of limitation, but opportunities for control. I hope these insights contribute to a more general interfacial science for electrochemical energy storage and provide guiding principles for the design of next-generation batteries that are more efficient, stable, and practical.Chemical engineeringElectromagneticsElectrical engineeringapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0263419chttps://escholarship.org/content/qt0263419c/qt0263419c.pdfetdoai:escholarship.org:ark:/13030/qt0v64q8sh2026-04-21T06:30:44Zqt0v64q8shA CFD-Based Digital Twin Framework for Transient Molybdenum Pentachloride Transport in an Experimental Atomic Layer Deposition ProcessALGHAMDI, ABDULRAHMANChristofides, Panagiotis D.2026-04-16A physics-based digital twin framework is developed for transient molybdenum pentachloride (MoCl$_5$) transport in an optically-accessible, experimental atomic layer deposition (ALD) reactor at the National Institute of Standards and Technology (NIST). The framework is anchored to high-speed absorption-imaging measurements reported by NIST and is implemented using a three-dimensional, time-resolved computational fluid dynamics (CFD) model that resolves momentum, heat, and species transport during pulsed precursor delivery. Experimentally measured, time-dependent absorbance data are used to inform the inlet precursor waveform, enabling direct comparison of normalized transport behavior between simulation and experiment in the absence of calibrated absolute concentrations. The digital twin of the reactor gas delivery system is validated against multiple experimental observables, including inlet velocity, precursor buildup and decay dynamics, residence time, spatial plume structure, and wafer chuck-level transport behavior, for representative operating conditions. The model reproduces the dominant temporal and spatial trends observed experimentally, including plume symmetry, transport delays, and purge dynamics, while maintaining physically consistent flow scaling near the wafer surface. Image-based comparisons further confirm that the CFD modeling framework captures the transient evolution and symmetry of MoCl$_5$ plumes under both low- and high-flow conditions. Beyond validation, a parametric study is conducted in which flow rate, chamber pressure, and injection duration are varied using a unified model configuration, including evaluation of an intermediate 300~SCCM condition to assess consistency between baseline cases. The results demonstrate that the model responds smoothly and predictably across the operating space, with wafer chuck-level velocity governed primarily by mass flow rate and precursor exposure transitioning from transport-limited to injection-duration–limited behavior for long pulses. Together, these results establish the present modeling framework as an experiment-informed, reusable digital twin suitable for rapid evaluation of ALD operating conditions and future extension to reaction-coupled modeling and process optimization.EngineeringPhysicsChemical engineeringapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0v64q8shhttps://escholarship.org/content/qt0v64q8sh/qt0v64q8sh.pdfetdoai:escholarship.org:ark:/13030/qt5v92m19x2026-04-18T06:30:36Zqt5v92m19xRedirecting Zinc Calendar Aging toward Interphase Maturation for High Post-Aging ReversibilityLI, YIBOHe, Ximin XH2026-03-20Calendar aging in aqueous Zn batteries is traditionally viewed as a simple corrosion issue. However, we demonstrate that for rechargeable systems, the fundamental challenge lies in the progressive loss of electrochemical reversibility—the inability to recover previously deposited Zn after rest. Here, we reconceptualize Zn calendar aging as a reversibility-retention problem and show that aging severely compromises post-rest stripping/plating efficiency.Using a continuous aging protocol, we find that conventional polyacrylamide (PAM) gels maintain only ~55% aging efficiency, while non-fluorinated carboxylate-containing PAM gels drop to ~45%. In striking contrast, a trifluoromethyl methacrylic acid (TFMAA)-based gel achieves a peak efficiency of 94% at an optimal 12 wt% concentration. Under identical conditions, the TFMAA system recovers ~0.72 mAh from an initial 0.785 mAh deposit, significantly outperforming control systems. Mechanistically, we show that the fluorinated-carboxylate coupling in TFMAA does more than suppress parasitic reactions; it reconfigures Zn ion coordination and drives interfacial evolution toward a functionally matured interphase. This robust interphase regulates Zn deposition into a highly reversible mode, preserving electrochemical accessibility even after 24-hour rest periods. Our work reframes the understanding of Zn aging and introduces a novel interfacial design principle for aging-tolerant aqueous metal batteries.EngineeringEnergyInorganic chemistrypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5v92m19xetdoai:escholarship.org:ark:/13030/qt5nv4k26g2026-04-17T06:30:26Zqt5nv4k26gThe Big Move: Timing and Predictors of Residential Transitions among Autistic Adults with and without Intellectual DisabilitySinger, HannahLord, Catherine2026-04-14Moving out of the family home is a priority for many adults with autism and other neurodevelopmental disabilities (NDDs; Scheeren et al., 2023). Research suggests autistic adults live in the family home longer than neurotypical peers, but few studies differentiate residential outcomes by intellectual disability (ID) status. This is a critical gap, as co-occurring ID can affect whether an individual seeks supported living or an independent residence.This study examined residential outcomes among 139 adults diagnosed with autism or other NDDs in childhood, recruited from three sites across the United States. Verbal IQ scores were used to operationalize co-occurring ID and to categorize participants into two separate groups for analyses: more cognitively able (MA; n = 64; verbal IQ ≥ 70) or less cognitively able (LA; n = 75, verbal IQ < 70).Kaplan-Meier survival analyses revealed that 64% of MA participants moved out of the family home by age 32 compared to 36% of LA participants (mean transition ages = 26.7, 30.3 years, respectively). Cox proportional hazards models revealed that higher hyperactivity and higher household income predicted earlier-aged transitions in LA participants. For MA participants, college educational attainment predicted earlier-aged residential transitions, and greater autism features and internalizing symptoms predicted later-aged transitions.Findings help characterize the different pathways to supported and independent living, while underscoring the unifying need for equitable, individualized transition planning services for autistic adults and adults with NDDs across ability levels.PsychologyDevelopmental psychologyNeurosciencesAutismpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5nv4k26getdoai:escholarship.org:ark:/13030/qt4br3b0ft2026-04-11T06:31:05Zqt4br3b0ftScaling-critical Nonlinear Dispersive DecayKowalski, Matthew JohnVisan, MonicaKillip, Rowan2026-04-08We investigate the energy-critical nonlinear Schr¨odinger equation, the generalized Korteweg– de Vries equation, and the energy-critical nonlinear wave equation: respectively,where u(t, x) is a function in spacetime Rt × R d x for d as above.For each of these models (assuming k ≥ 8 for (∗∗)), we prove that solutions exhibit pointwise-in-time dispersive decay, requiring only that initial data lie in a scaling-critical space. In particular, global, scattering solutions will decay at the same rate as the underlying linear model. In addition, for the model (∗), we demonstrate dispersive decay for the final-state problem, assuming only that scattering data lie in a scaling-critical space. Finally, for the model (∗∗) in the case 4 ≤ k < 8 (including the mass-critical k = 4) we prove dispersive decay with the additional assumption that initial data lie in a near-scaling-critical space.[Equation omitted].MathematicsTheoretical mathematicsdispersive decayKorteweg--de Vriespartial differential equationsSchrodinger equationwave equationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4br3b0fthttps://escholarship.org/content/qt4br3b0ft/qt4br3b0ft.pdfetdoai:escholarship.org:ark:/13030/qt3ch433fj2026-03-27T06:31:21Zqt3ch433fjPassive Acoustic Sensing of Operational State in Distributed Water Treatment SystemsAguilar, Christian BernardoVarghese, George2026-03-20This thesis presents the design, implementation, and operation of a data ingestion and visualization system built to support live monitoring and long-term research analysis needs of three distributed water treatment and desalination (DWTD) systems serving small rural communities in the Salinas Valley of California. This system, which has operated continuously for six years, has collected over one billion time-series data points, including sensor readings, valve positions, and programmable logic control (PLC) states. This operational data was combined with over 30,000 audio recordings of the systems’ pumps and actuators to develop inference models to reconstruct treatment system state from passive acoustic monitoring. Built on embeddings from a large pretrained neural network, a neural multi-label prediction model trained on the dataset achieved an F1 score of 0.557 in determining four individual pump states across a single site, and over .85 F1 score on multiple individual pumps. Experiments demonstrated that using a low-dimensional representation of continuous system sensors (such as flow, pressure, and conductivity readings) as an auxiliary learning target improved the performance of the discrete state prediction model. Finally, a regression model built on the same embedding backbone was trained to infer multiple flow sensor readings from audio clips, predicting flow sensor readings with an aggregate $R^2>0.80$ in matched training and evaluation domains, with some sensors nearing perfect reconstruction with $R^2>0.98$. These results suggest that passive acoustic sensing combined with pretrained audio embeddings could provide a viable and low-cost solution for reconstructing the operational state of remote water treatment systems.Computer scienceAcousticsEnvironmental engineeringStatisticsAcoustic SensingCyberinfrastructurePretrained ModelsReverse Osmosisapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3ch433fjhttps://escholarship.org/content/qt3ch433fj/qt3ch433fj.pdfetdoai:escholarship.org:ark:/13030/qt9cj332022026-03-26T06:32:54Zqt9cj33202Advancing Integrated Data Systems for Health Equity: Building Towards a Cross-Sector Learning Health System ApproachLiu, PatrickBui, Alex Anh-Tuan2026-03-20Health is shaped by social and structural conditions that span multiple domains – health, mental health, housing, social services, and the criminal legal system among others – yet public-serving agencies operating across these domains are organized around narrow mandates, misaligned incentives, and fragmented information systems that limit cross-sector visibility and coordinated response. For individuals with serious mental illness (SMI), these structural disconnections are acutely consequential: the transition from jail to community is a high-risk period in which disconnection from care, housing instability, and reincarceration reinforce one another in ways that routine population-level metrics fail to reflect. The National Academy of Medicine envisions learning health systems in which the evidence is embedded into care delivery and new knowledge is continuously generated from practice – yet this vision has been largely confined to clinical settings. This dissertation contributes to extending that vision across sectors, using linked administrative data from Los Angeles County to apply three analytical approaches for cross-sector evidence generation in the context of individuals with SMI exiting carceral settings. Statistical process control analyses revealed engagement with mental health services within 90 days of jail release remained low across the study period – and was consistently exceeded by rates of reincarceration. Causal inference analyses estimated that early post-incarceration SNAP enrollment among Medicaid-enrolled individuals was associated with increased mental health service engagement and reduced reincarceration. Unsupervised clustering of event histories using deep learning approaches identified distinct post-release trajectory patterns differing in timing, intensity, and sector engagement. Together, these approaches offer a transferable analytical pipeline for jurisdictions with similar data infrastructure.Public healthMental healthHealth sciencesEpidemiologyadministrative datadeep learninghealth equityintegrated data systemslearning health systemserious mental illnesspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9cj33202etdoai:escholarship.org:ark:/13030/qt8sv7c3mc2026-03-26T06:32:49Zqt8sv7c3mcDyslexia Model Implementation: A Retrospective Examination of the Experiences of School-Based Personnel and University ResearchersPedroza, VeronicaKasari, Connie L2026-03-20This study used a qualitative case study design to follow up on the implementation of a completed research project (TEDI). While TEDI examined screening-based reading interventions in three different elementary schools, this study sought to examine three questions around implementing school-based interventions for students with dyslexia: (1) What do researchers need to consider with designing school-based studies? (2) What structural and internal changes do schools need support in making to accommodate school based intervention? and (3) How can future school-based research accommodate the needs of both researchers and schools? Through surveys and interviews of five personnel from the three schools and a focus group with the three researchers involved with TEDI, this study offers insight into participants' experiences during and after the TEDI study. These experiences highlighted ways in which schools benefited from participating and had a successful implementation. These successes occurred despite schools and researchers facing the challenges of Covid-19, among other expected and unexpected challenges. This study also highlighted some pitfalls that can occur even with invested school partners, particularly overwhelm, communication gaps, and missing opportunities to provide flexibility. This study affirms the importance and benefit of school-research partnerships and suggests additional pathways for future researchers to strengthen their collaboration by including schools in dialogue on research design and necessary adaptations.EducationSpecial educationHigher educationHigher education administrationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8sv7c3mchttps://escholarship.org/content/qt8sv7c3mc/qt8sv7c3mc.pdfetdoai:escholarship.org:ark:/13030/qt1qb334fz2026-03-26T06:32:45Zqt1qb334fzPsychoanalysis in a Time of Pandemic and Global Unrest: How Psychoanalysts Confront Censorship in Response to a Transforming WorldBerwald, MarisaHart, Laurie KHollan, Douglas2026-03-20This dissertation constructs a speculative ethnography of psychoanalysis as a field structured around secrecy, censorship, and the repression of its own racial and collective origins. Part One situates the discipline within its Jewish and diasporic roots, mapping its various historical entanglements with social and political upheavals from fin-de-siècle Vienna to its fragmentation and dissemination during World War II. Drawing on textual analysis, ethnography, and personal interlocutors, I examine how the discipline’s early focus on group and racial life became a “secretized secret,” structuring acceptable forms of knowledge and delineating the boundaries of the field.Part Two considers psychoanalysis during the Covid-19 pandemic, when analysts were forced to contend with the uncanniness of collective existence and the limits of individual-centered practice. Ethnographic analysis of the professional activities of psychoanalysts in virtuality, including reading groups, town halls, and institutional events, reveals how professional communities attempted to stabilize anxiety through regulation of shared discourse and group identifications, often reproducing racial, political, and generational hierarchies while obscuring deeper psychic disturbances. I examine temporal disruptions caused by the pandemic, showing how analysts navigated a flattened, trauma-inflected “real” time and a stalled après-coup moment of interpretation through reconstructed narratives to manage collective uncertainty.Part Three explores progressive political discourses across intimate, public, institutional, and mass-level settings. In intimate publics, analysts articulated precarious, shifting psychic realities in smaller peer and support groups. Public forums, such as town halls and online listservs, mediated anxiety through performative discourse and provisional consensus, often obscuring deeper tensions. In institutional settings, discussions of curriculum and governance both regulated and formalized responses to social crises, while mass-level dynamics revealed how collective political narratives stabilized anxiety and reconstructed temporal and racial imaginaries within the discipline. Across these levels, progressive discourse obscured inherited knowledge of racialized psychic life, producing melancholic attachments to ideals of racial equality that may foreclose recognition of enduring structural and intergenerational losses. Overall, this dissertation demonstrates how psychoanalysis’ history of secrecy and censorship continues to shape its responses to social and collective crises, revealing the interplay of unconscious processes, institutional dynamics, and emergent forms of group life during a moment of global disruption.Cultural anthropologyPsychologySocial psychologyGlobalizationMass MovementsPandemicPoliticsPsychoanalysisPsychologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1qb334fzhttps://escholarship.org/content/qt1qb334fz/qt1qb334fz.pdfetdoai:escholarship.org:ark:/13030/qt14w2197g2026-03-26T06:32:39Zqt14w2197gPláticas, Stories, and Place-Making: Indigenous Knowledge and Transborder Meshworks in Mixtec CommunitiesGarcia, Reno CBoj Lopez, Floridalma2026-03-20This thesis examines how Indigenous Mixtec knowledge is sustained across the U.S.-Mexico border under conditions of displacement, colonialism, and limited access to culturally responsive healthcare. It addresses how migration and border regimes threaten continuity of traditional healing practices, language, and intergenerational knowledge. Using a qualitative, decolonial methodology grounded in pláticas and convivencia, this study draws on relational conversations with two Mixtec women to document storytelling, medicinal practices, and linguistic transmission as lived and embodied epistemologies. The findings demonstrate that these practices form transborder meshworks through which knowledge, language, and memory circulate across borders. Finally, the transborder meshwork not only sustains Indigenous epistemologies in diaspora but also operate as networks through which place-making unfolds across shifting geographies, producing Indigenous futurity.GeographyLatin American studiesNative studiesEpistemologyIndigenous epistemologiesMedicinal plant knowkedgeMixtec migrationPlace-makingStorytellingTransborder meshworksapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/14w2197ghttps://escholarship.org/content/qt14w2197g/qt14w2197g.pdfetdoai:escholarship.org:ark:/13030/qt4nz906jd2026-03-26T06:32:35Zqt4nz906jdThe Field-Lab Toggle: Accessible Applications and Analysis of Aerobic Biodegradation of 1,4-Dioxane and Chlorinated Volatile Organic CompoundsKwok, Ivy Yin-MeiMahendra, Shaily2026-03-20Environmental engineering and environmental microbiology have played instrumental roles in helping protect planetary and human health. Thus, it is important that as these impactful fields grow, their aspects are intentional from discovery to implementation. Overall, the goal of this doctoral research was to help advance environmental engineering research, with a focus on 1,4-dioxane and chlorinated volatile organic compounds, so that it can be (1) more affordably conducted in the laboratory; (2) successfully implemented in the field; and (3) accessible to learn by all those who wish to, as it is an interdisciplinary branch of knowledge with the power to affect everyone.This research explored biodegradation and natural attenuation of 1,4-dioxane and chlorinated volatile organic compounds (CVOCs), which commonly co-occur in contaminated groundwater and are toxic to human and ecological health. One aerobic microorganism particularly known for its ability to degrade and assimilate 1,4-dioxane is Pseudonocardia dioxanivorans CB1190, which was tested for its ability to assimilate the CVOC, cis-1,2-dichloroethene (cDCE). The culture, grown on [U-13C]-labeled glucose before exposure to unlabeled cDCE, showed subsequent unlabeling of metabolites relevant to energy generation, revealing it indeed incorporates cDCE into its biomass. This labeled-glucose method, which reduced the time and cost of custom synthesizing stable isotope-labeled-cDCE, can also be applied to other compounds of interest to increase accessibility for metabolomics studies.Presently, most studies that have employed metabolomics to explore bioremediation look more externally at metabolites that are parent compound byproducts and less commonly at the internal metabolome of organisms responsible for this biodegradation. Here, identifiable incorporation of 1,4-dioxane into microorganisms’ central metabolic pathways and amino acid synthesis processes confirmed that metabolomics can be a valuable tool to validate 1,4-dioxane assimilation and even metabolic biodegradation of other contaminants, whether in the field via integrated passive samplers or in the laboratory via microcosms and pure culture studies.Thirdly, these metabolomics findings were intersected with metagenomics and molecular biomarkers to construct a free, user-friendly predictive model for evaluating the feasibility of natural attenuation of 1,4-dioxane in the field. This model framework synthesized training datasets of contaminant concentrations, molecular biomarkers, metabolites, relevant biodegrading organisms, and water quality parameters. Upon replacement of relevant biomarkers, the model can likely be used to predict biodegradation of other contaminants of interest.Lastly, stepping out of the laboratory and into the classroom, this dissertation also explores effect of conference attendance on undergraduate students’ learning of interdisciplinary topics, specifically those related to environmental science and engineering. The results showed that attending conferences or related networking events had an influential impact on students engaged in environmental topics. Post-attendance, they demonstrated greater interest in the lucrativeness and logistics of other fields, as well as how transfer of knowledge occurs between environmental and environmental-adjacent fields.Collectively, the findings of this research would help interdisciplinary teams of microbiologists and environmental engineering professionals understand the role of microorganisms capable of degrading contaminant mixtures and select environmentally-relevant approaches to utilize their capabilities, as well as sustain the momentum for future generations of students to learn about this meaningful field.Environmental engineeringMicrobiologyCivil engineeringpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4nz906jdetdoai:escholarship.org:ark:/13030/qt4sx7f3tq2026-03-26T06:32:30Zqt4sx7f3tqInsights into the mechanisms supporting emotional memory processing during sleepCabrera, YeseniaPoe, Gina R2026-03-20Adaptations to stress involve integrated systems and processes, including the functions of the sympathetic branch of the autonomic nervous system (SNS) to enable the organism to quickly learn about, fight, or flee the stressor. Sleep affects diverse processes, including learning about the nature of stressors and memory of those stressors so they may be successfully avoided or dealt with in the future. The sympathetic nervous system, particularly as controlled by the noradrenergic locus coeruleus, undergoes extensive changes across the sleep/wake cycle. REM sleep, in particular, is critical to the consolidation of associative memories such as the relationship of context to fearful stimuli. Existing studies of the relationship between sleep and stress have incompletely measured whether the sleep states and traits prior to or after stress exposure led to adaptive or maladaptive learning outcomes. The research and theoretical construct of this dissertation addresses whether the sympathetic nervous system activity during REM sleep provides opportunity to continue to adaptively learn the contexts of fear as opposed to safety. The findings support a multiscale model in which sleep serves as a coordinating state for emotional memory processing, linking neural activity and autonomic regulation even in the absence of overt behavioral differences. Rather than reflecting a simple output of prior experience, sleep appears to provide a dynamic substrate through which emotional memories are reorganized and integrated. These results refine current models of sleep-dependent emotional processing and highlight the importance of examining memory across interacting biological systems.NeurosciencesMental healthCognitive psychologyemotionPTSDsleepstressapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4sx7f3tqhttps://escholarship.org/content/qt4sx7f3tq/qt4sx7f3tq.pdfetdoai:escholarship.org:ark:/13030/qt7vc7s7gj2026-03-26T06:32:25Zqt7vc7s7gjHoodology: Tracing the Relational Aesthetics of Hip Hop and Corridos in South LACantero, Bryan IzacCarpio, Genevieve2026-03-20This master’s thesis examines working-class Black and Brown cultural politics in South LA, a historically Black neighborhood that has undergone significant transformations since the late 1980s, becoming an increasingly Latinx space. I ask: In what ways do aesthetic expressions emerging from South LA—particularly among Black and Brown working-class youth—respond to and resist systems of racial containment and urban transformation? To create an interdisciplinary foundation for this project, I draw on scholarship from cultural politics, relational race studies, and historical work on South LA. Through ethnographic observation, analyses of music and videos by Fuerza Regida and Buddy, and close readings of Born x Raised streetwear campaigns, I develop a critical account of Black and Brown cultural politics in South LA. I frame this account through what I call Hoodology, a relational theory of working-class aesthetics grounded in the lived experiences and cultural productions of Black and Brown youth in South Los Angeles. A framework of Hoodology allows me to argue that Corridos and Hip Hop operate as both a response to material conditions and a means of reshaping social space. Fuerza Regida and Buddy not only speak to experiences of displacement, surveillance, and marginalization but also model new ways of being and belonging in the city. The study of these artists show us the power of Hood sensibilities— that is the sonic, visual, and spatial practices that emerge in resistance to urban erasure and displacementsEthnic studiesCultural anthropologyMusicLatin American studiesAestheticsBuddyCorridosFuerza RegidaHip hopHoodologySouth LAapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7vc7s7gjhttps://escholarship.org/content/qt7vc7s7gj/qt7vc7s7gj.pdfetdoai:escholarship.org:ark:/13030/qt0cr8k5282026-03-26T06:32:20Zqt0cr8k528Applications of carbonate clumped isotope paleothermometry in Plio-Pleistocene paleosols from the Afar region of Eastern AfricaUpadhyay, DeepshikhaTripati, Aradhna K2026-03-20Over the past two decades, clumped isotope geochemistry has become increasingly utilized as a method for reconstructing past environmental conditions. This proxy relies upon the bonding of heavy isotopes in carbonate minerals, and how bond ordering varies with temperature. The focus of this Ph.D. dissertation is split between methodological and applied research in reference to clumped isotopes. Best practices for clumped isotope standardization are developed in Chapter 1, then applied to investigate links between climate change and early human evolution in eastern Africa throughout Chapters 2 & 3. Chapter 1 evaluates a carbonate-based standardization protocol testing data comparability across multiple instrument configurations using multi-year datasets, with the three goals of refining previously published data from 2006 to the present, suggesting best practices, and improving interlaboratory comparisons. Results show that a specific series of different instrument components, sample types, and statistical analyses produce consistent outcomes when instrument drift is properly characterized. Chapter 2 utilizes terrestrial paleosol carbonates to reconstruct Plio-Pleistocene hydroclimate conditions, with the goal of statistically correlating global and regional climate events with the extinction of Australopithecus afarensis (commonly known as “Lucy”) and the emergence of early Homo at the Hadar UNESCO site. Clumped isotope analyses reveal three intervals of fluctuating temperature and aridity conditions from ~2.40 and 3.40 Ma, including a major cooling event between ~2.65 - 2.90 Ma that may have contributed to faunal turnover. Chapter 3 supplements Chapter 2 with an analysis of lacustrine paleosol carbonates from the Northern Awash Basin, located ~25 km NNE of the Hadar UNESCO site, and considers soil structure differences between the two sites. This final study documents a notable C₃–C₄ vegetation transition in the Northern Awash Basin earlier than the equivalent transition at Hadar, indicating regional microclimate variability within the Afar Triangle may be linked to broader climatic and evolutionary shifts.GeochemistryGeologyPaleoclimate scienceGeophysicsClimate changeapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0cr8k528https://escholarship.org/content/qt0cr8k528/qt0cr8k528.pdfetdoai:escholarship.org:ark:/13030/qt01h1d8vz2026-03-26T06:32:15Zqt01h1d8vzLearning-Based Transformations for Computational Imaging and Diffractive Optical ProcessingYang, XilinOzcan, Aydogan2026-03-20This dissertation explores learning-based transformations for computational imaging and diffractive optical processing. The central premise of this work is that deep learning can be used to convert optically encoded measurements into representations that are more useful for visualization, analysis, and information processing. By integrating physical image formation with data-driven inference, the methods presented here extend the capabilities of conventional optical systems across microscopy, histology, and diffractive optics.The dissertation first presents a virtual refocusing framework for fluorescence microscopy that combines an engineered point-spread function with neural-network-based reconstruction to recover refocused image information from a single encoded measurement. It then introduces learning-based methods for histological image transformation, including virtual stain transfer and the generation of diagnostically relevant tissue contrast from label-free autofluorescence microscopy. These approaches demonstrate that tissue images acquired in one domain can be computationally transformed into alternative representations without repeated chemical staining or destructive sample preparation. The dissertation also presents an automated method for labeling surgical resection margins in histological whole-slide images, extending learning-based transformation from image generation to semantic tissue interpretation.In addition, this work investigates learned diffractive optical systems for complex-valued linear transformations and optical image encryption under spatially incoherent illumination, demonstrating that information processing can be performed directly in passive optical hardware. Finally, a snapshot multispectral imaging framework is developed in which wavelength-dependent optical encoding is paired with neural-network-based reconstruction, highlighting the power of optical-computational co-design.Overall, this dissertation shows that learning-based transformation provides a unifying framework for computational microscopy, intelligent interpretation of the images, and diffractive optical processing. By coupling physical encoding with learned reconstruction and interpretation, this work contributes to the development of more capable and intelligent optical systems.EngineeringComputer engineeringOpticsElectrical engineeringpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/01h1d8vzetdoai:escholarship.org:ark:/13030/qt2925z8352026-03-26T06:32:11Zqt2925z835Inline Coagulation and Mini Hydrocyclone Separation as a Pretreatment Step for Microfiltration of Secondary Treated WastewaterMorales, Jose FelicianoCohen, Yoram2026-03-20Advanced water purification facilities rely on secondary-treated municipal wastewater (STMW) as a potable reuse feedwater source, but its fine colloidal particulate fraction accelerates downstream membrane fouling. This fine fraction persists through biological treatment, increasing transmembrane pressure, shortening membrane service life, and requiring more frequent chemical cleaning. Conventional pretreatment by coagulation, sedimentation, and granular media filtration is effective for coarser solids but poorly suited to the submicron particles characteristic of secondary effluents. Compact, high-rate alternatives are therefore needed to remove this fine fraction ahead of reverse osmosis without the large footprint of conventional trains.This study evaluated an integrated inline pretreatment train combining FeCl₃ coagulation in a helical coil tube flocculator (HCTF), mini hydrocyclone (MHC) separation, and batch microfiltration (MF). Feedwater was STMW collected from the Groundwater Replenishment System at the Advanced Water Purification Facility in Fountain Valley, California. Jar testing first identified 5 mg/L Fe(III) as the near-optimal coagulant dose, achieving a fractional turbidity reduction of 86.5% after quiescent settling. Inline experiments varied Fe(III) dose (3, 5, 7, and 10 mg/L), coil Reynolds number (Re = 4,217–7,590), and coil length (5.5, 10, and 15 m). Filtrate turbidity was measured across MF pore sizes of 0.45, 1.2, and 5.0 µm to assess downstream filterability. Fe(III) dose was the dominant determinant of both HCTF effluent turbidity and hydrocyclone performance. The 5 mg/L dose at Re = 5,903 with the 15 m coil produced the highest hydrocyclone fractional turbidity reduction of approximately 28% at Ac/g = 150.4. Batch microfiltration at this condition yielded the lowest filtrate turbidities across all pore sizes, ranging from 0.13 to 0.35 NTU—consistently below the untreated STMW baseline. Both underdosed (3 mg/L) and overdosed (10 mg/L) conditions yielded turbidity reductions below 10%, defining the practical dose boundaries of the system. These results show that jointly optimizing Fe(III) dose and Reynolds number within the HCTF–MHC train can meaningfully reduce the fine-particle burden reaching downstream membranes.Environmental engineeringChemical engineeringEnvironmental scienceWater resources managementcoagulationhydrocyclonemembrane foulingmicrofiltrationwastewater treatmentwater reusepubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2925z835etdoai:escholarship.org:ark:/13030/qt275158qv2026-03-26T06:32:06Zqt275158qvInquiry as Catalytic Interruption: Distributed Leadership and Organizational Learning in a Research Practice Partnership between a California Community College and the University of CaliforniaOliveira, KristineGomez, Louis2026-03-20This dissertation examines how distributed leadership within a Research Practice Partnership (RPP) can support organizational learning and equity-focused change in a community college context. Focusing on the Learning Together Initiative, a collaboration between Crescent Valley Community College (CVCC) and the University of California, Los Angeles (UCLA), the study investigates how faculty participation in shared inquiry interacts with institutional routines, governance structures, and leadership practices to shape whether learning becomes visible, sustained, and actionable at the organizational level. Particular attention is given to equity gaps affecting African American/Black and Latine students.
Using a qualitative focus case study design, the research draws on participant observation, semi-structured interviews, and document analysis to examine three research questions: (1) how organizational learning becomes visible through inquiry-related routines and practices; (2) how shared governance structures and faculty routines support or constrain engagement in equity-focused inquiry; and (3) how leadership practices condition the movement of inquiry-generated insight into institutional processes. Rather than treating leadership or learning as attributes of individuals, the study examines how learning is produced or stalled through patterned interactions, authorization pathways, and institutional conditions.
The analysis is grounded in four intersecting theoretical frameworks: distributed leadership (Spillane, 2006), learning organizations (Senge, 1990), contextualized learning (Gutiérrez, 2008), and organizational routines theory (Feldman & Pentland, 2003). Together, these frameworks support an interpretation of leadership as practice and learning as a collective, structurally contingent accomplishment. Findings demonstrate that inquiry alone does not constitute organizational learning; instead, learning occurred only when inquiry-generated insights were taken up, routinized, and authorized through shared governance processes. Where alignment among routines, governance pathways, leadership practices, and institutional capacity was absent, learning remained partial, localized, or ephemeral.
This study contributes to the literature on RPPs and organizational learning in higher education by offering a structural account of how learning moves–or fails to move–through shared governance systems. It highlights the role of faculty, often positioned in mid-level leadership roles, not as drivers of change but participants in practices conditioning institutional learning. By distinguishing inquiry-generated insight from institutional learning, the study reframes equity-oriented change as a contingent organizational achievement rather than an automatic outcome of collaboration.Educational leadershipEducational administrationHigher educationAfrican American studiesapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/275158qvhttps://escholarship.org/content/qt275158qv/qt275158qv.pdfetdoai:escholarship.org:ark:/13030/qt2dj0p9fk2026-03-26T06:31:42Zqt2dj0p9fkFrom Historical Witness to Mythic Reconstruction: Cross-Cultural Translation of National Memory in Contemporary CompositionLi, ShirunyuKrouse, IanDanielpour, Richard D2026-03-20This dissertation examines how meaning is produced in text-based concert works through the interaction of text, voice, and sonic medium. Rather than centering on genre or explicit thematic content, the study investigates how different modes of vocal delivery and mediation shape the listener’s perception of meaning across acoustic and electroacoustic contexts. By focusing on compositional strategies rather than ideological intent, the dissertation proposes a flexible framework for understanding how text functions within contemporary musical practice. Drawing on historical and theoretical perspectives, the study identifies four defining dimensions that recur across text-based works: (1) text as a semantic catalyst; (2) vocal mode and perceived authority; (3) medium as a temporal and experiential frame; and (4) the composer’s role as mediator through intimacy distance. Together, these dimensions offer an analytical lens through which diverse approaches to texted music can be examined without reliance on stylistic, generic, or programmatic categorization. The analytical chapters focus on three contrasting works: Aaron Copland’s Lincoln Portrait (1942), John Adams’s The Wound-Dresser (1989), and Raising the Red Flag (2018) by Xuesi Xu. Each case study explores how text is framed and mediated through compositional choices involving vocal delivery, musical structure, and sonic medium. Copland’s use of spoken narration foregrounds public address and collective resonance; Adams’s setting of Walt Whitman’s poetry emphasizes intimacy, restraint, and personal witness; and Xu’s electroacoustic work recontextualizes historical speech through recording, fragmentation, and electronic mediation, highlighting distance and reflection. The dissertation concludes with an examination of The Legend of Hu-Lu Kids by me, presented as a creative application of the proposed framework. This chapter reflects on compositional decision-making in relation to text, voice, and medium, demonstrating how the identified dimensions can inform contemporary creative practice without prescribing specific expressive or thematic outcomes. Through the integration of theoretical inquiry, analytical study, and creative reflection, this project offers a transferable framework for understanding text-based concert music as a site of mediation, perception, and meaning-making in the 20th and 21st centuries.MusicMusical compositionPerforming artsMusic theoryapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2dj0p9fkhttps://escholarship.org/content/qt2dj0p9fk/qt2dj0p9fk.pdfetdoai:escholarship.org:ark:/13030/qt7138g17s2026-03-26T06:31:37Zqt7138g17sAssociation Between Purchase of Organic Food and Hypertension in a Nationally Representative Sample of US AdultsChoi, ChristopherNianogo, Roch A2026-03-19Background: An increasing demand for organic food has risen due to perceived health benefits. Current evidence for health effects of organic food is limited.Objective: To evaluate the association between organic food purchase, a proxy for organic food consumption, and hypertension in a nationally representative population of the US.Methods: This was a cross-sectional study that included 9173 participants aged 18 and older from the National Health and Nutrition Examination Survey 2007-2010. Organic food purchase and frequency were obtained from survey questionnaires. Hypertension was defined as having either a systolic BP ≥ 130 mm Hg/ diastolic BP ≥ 80 mm Hg, currently taking antihypertensive medication, or self-reported diagnosis of hypertension. We used multivariable iii logistic regression with sample weights and adjustment of potential confounders to assess associations (adjusted odd ratio, aOR, and 95% confidence intervals, CI) between organic food purchase and hypertension status. Results: Findings suggest a 11% decrease in odds of hypertension (aOR = 0.89, 95% CI: 0.75– 1.06) among those who purchase organic food compared to those who did not. Lower odds of hypertension were observed across all categories of organic food purchasing frequency, with 13% lower among rarely purchasing organic food (aOR = 0.87, 95% CI: 0.67–1.14), 9% lower (aOR = 0.91, 95% CI: 0.71–1.16) among sometimes purchasing organic food, and 17% lower (aOR = 0.83, 95% CI: 0.55–1.27) among always or mostly purchasing organic food, as compared to those who never purchased organic food. Conclusion: Our findings suggest that organic food purchase, a proxy for organic food consumption may be associated with lower odds of hypertension, though majority of estimates were imprecise. These findings may either reflect the true benefits of organic food consumption including lower pesticides amount and higher nutrient content, or reflect health seeking behaviors among health conscious, healthy and highly educated individuals.EpidemiologyPublic healthNutritionBiostatisticsblood pressurehypertensionNHANESorganic foodpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7138g17setdoai:escholarship.org:ark:/13030/qt8pz030t02026-03-26T06:31:32Zqt8pz030t048V-to-1V Backside Power Delivery for Si-IF Wafer-Scale Systems : Architecture, Transformer Design and SimulationLEE, NAMKANGIyer, Subramanian S2026-03-20Wafer-scale integration (WSI) using the Silicon Interconnect Fabric (Si-IF) enables heterogeneous assembly of thousands of bare dies on a single 300 mm wafer, supporting up to 50 kW of total power consumption. The power density of modern high-performance compute chips is rapidly approaching 1W/mm^2, and supplying tens of kilo-amperes at ∼1 V from a 48 V supply through conventional peripheral distribution leads to prohibitive IR drop, routing congestion, and poor scalability. Meeting this demand requires backside power delivery modules that simultaneously achieve a system-level conversion efficiency exceeding 85% and a power density on the order of 1W/mm^2—targets that neither existing PCB-based converters nor MEMS-based microtransformers have demonstrated. This thesis proposes a 4×12:1 stacked microtransformer architecture for Si-IF backside power delivery. Through a system-level efficiency budget and loss allocation analysis, the standalone transformer efficiency requirement is established at ≥95%. To meet this target while satisfying the thermal, magnetic, and spatial constraints of the wafer-scale environment, the 48:1 conversion is partitioned into four series-input, parallel-output modules, each designed as a 12:1 interleaved solenoid microtransformer with a MnZn ferrite core. The key design parameters and operating frequency are systematically derived from the physical boundaries imposed by skin-depth effects, core saturation, and thermal dissipation limits. Three-dimensional finite element simulation in Ansys Maxwell validates the proposed design. The extracted coupling coefficient of k = 0.986 and a maximum power transfer efficiency exceeding 99.9% confirm that the electromagnetic structure does not impose a fundamental efficiency bottleneck. Under the nominal operating condition, the standalone transformer achieves an efficiency of 95.47% at 4 MHz within a 5.25 mm^2 footprint, with a lateral heat flux of 0.69 W/cm^2—well within the 1W/cm^2 forced-air cooling limit. A higherload simulation further demonstrates that the architecture can sustain 1.949W per module at 0.90W/cm^2 , projecting to a system power density of 1.49W/mm^2. These results establish a concrete simulation-based foundation for a scalable, high-efficiency Si-IF backside power delivery network.Electrical engineeringElectromagneticsMaterials science48V-to-1V Step Down ConversionBackside Power DeliveryElectromagnetic SimulationSilicon Interconnect FabricStacked MicrotransformerWafer-Scale Integrationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8pz030t0https://escholarship.org/content/qt8pz030t0/qt8pz030t0.pdfetdoai:escholarship.org:ark:/13030/qt3qh1c99h2026-03-26T06:31:27Zqt3qh1c99hImproving Inference Efficiency of Hybrid State Space Models through Speculative DecodingWu, YangchaoSoatto, Stefano2026-03-20Speculative decoding has emerged as a promising approach to accelerate autoregressive generation by exploiting hardware concurrency, yet existing techniques are primarily designed for Transformer architectures and do not directly extend to state space models (SSMs) due to their latent Markov states and distinct computational structure. This thesis develops scalable speculative decoding algorithms for SSMs and hybrid architectures combining SSM and Transformer blocks. We introduce methods that enable efficient backtracking and verification in SSMs, as well as the first tree-based speculative decoding framework tailored to SSMs and hybrid models, leveraging structured state transitions to support parallel token generation with minimal overhead. Combined with hardware-aware implementations, these approaches achieve substantial inference speedups across multiple benchmarks and improve utilization under variable workloads. Together, these contributions establish speculative decoding as a practical and scalable acceleration strategy for state space and hybrid models, providing a foundation for future high-throughput language model inference.Computer scienceArtificial intelligenceComputer engineeringStatisticsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3qh1c99hhttps://escholarship.org/content/qt3qh1c99h/qt3qh1c99h.pdfetdoai:escholarship.org:ark:/13030/qt4r90s0d62026-03-26T06:31:23Zqt4r90s0d6All Things Return to Spring (wanwu guichun 万物归春): Xiangsheng as a Spatial Production of Tianqiao (1900-1966)ZHUANG, YUGoldman, Andrea S.2026-03-19This thesis examines the historical relationship between xiangsheng (cross-talk comedy) and the space of Tianqiao in Beijing from approximately 1900 to 1966. Rather than repeating the conventional understanding of xiangsheng as a linguistic or literary art form (or as pop culture), it argues that xiangsheng must be construed as an everyday practice produced through specific spatial formations. Using performers’ memoirs, oral histories, documents from the Beijing Municipal Archives, contemporary newspapers, and recollections from later periods, this thesis reconstructs the social ecology of Tianqiao on paper, thereby demonstrating how Tianqiao performers, in order to eke out a living through words, developed a set of techniques closely tied to the district’s distinctive spatial formation. The divergent trajectories of two amusement parks—the New World Amusement Park and the South City Amusement Park—opened in the greater Tianqiao area further illustrate the futile Republican-era efforts to modernize Tianqiao and the South City. Despite how municipal authorities and elite observers often depicted the area as a chaotic world, Tianqiao prospered. After 1949, Tianqiao did not immediately disappear; instead, it continued to serve as a final refuge for performers and some urban laborers, sustained by the recurring social relations that had long structured Beijing’s urban life. Drawing on Henri Lefebvre’s concept of the production of space and Pierre Bourdieu’s notion of social fields and convertible capital, the thesis shows how xiangsheng materialized through the everyday spatial practices of performers and audiences embedded in Tianqiao, as well as the broader population of Beijing urbanites who wandered into the area. Instead of treating Tianqiao as a community without hierarchy, the thesis explains how the community produced and maintained its own internal hierarchies, which replicated the larger social structure of Beijing society. By situating xiangsheng within a specific spatial formation, this thesis contributes to broader discussions of urban culture and everyday life in Beijing by arguing that entertainment and cultural practices were not merely reflections of urban social hierarchy but active participants in producing it. Tianqiao, therefore, was not merely the birthplace of xiangsheng but the spatial formation that made the art form historically possible.HistoryLanguageLinguisticsEast Asian studiesBeijingXiangshengapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4r90s0d6https://escholarship.org/content/qt4r90s0d6/qt4r90s0d6.pdfetdoai:escholarship.org:ark:/13030/qt3gc4w40h2026-03-26T06:31:18Zqt3gc4w40hGranular Distortions in Supply Chains and Aggregate OutcomesMartner, AntonioHopenhayn, Hugo2026-03-19This dissertation studies how firm and product-level distortions propagate through supply chains and shape aggregate outcomes, including welfare and total factor productivity.
Chapter 1, co-authored with Luca Lorenzini, examines the aggregate welfare consequences of price discrimination in supply chains. We make progress on three fronts. First, using product-level firm-to-firm transaction data in which we observe prices and quantities for each product–buyer–seller triple, we document how pricing works in the population of firms: unit prices systematically decline with quantities and firms price differently across buyers. Second, we build a structural general-equilibrium model of supply chains in which firms both price discriminate and are price discriminated against, allowing us to compare pricing regimes and assess their aggregate welfare effects. Third, we bring the model to the data and find that price discrimination matters quantitatively through two forces: it improves allocative efficiency relative to uniform pricing, and it distorts the distribution of profits and firm entry with ambiguous welfare effects. In our quantitative results the allocative-efficiency gains dominate — realistic price discrimination in supply chains reduces the aggregate cost of market power relative to a world with uniform prices.
Chapter 2, co-authored with Yasutaka Koike-Mori, studies how multiproduct firms affect resource misallocation within production networks. Using Chilean administrative data, we derive sufficient statistics that capture misallocation under joint production. We show that changes in allocative efficiency drive observed TFP growth and that multiproduct firms engaging in joint production are central to understanding granular resource misallocation and hence aggregate TFP dynamics in distorted economies.
Chapter 3, co-authored with Federico Huneeus and Yasutaka Koike-Mori, presents an aggregation result that structurally dissects the drivers of aggregate TFP across arbitrary parts of the economy. In a general equilibrium framework with production networks, distortions on all inputs, and international trade, aggregate TFP decomposes into technology and reallocation contributions of any partition of the economy, distinguishing downstream and upstream propagation. The latter captures a new income redistribution channel absent from prior work. Applying the framework to two decades of Chilean firm-to-firm tax data, aggregate TFP growth between 2005 and 2022 is driven almost entirely by resource reallocation, were both upstream and downstream propagation play a quantitatively relevant role.EconomicsStatisticsBusiness administrationEconomic theoryapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3gc4w40hhttps://escholarship.org/content/qt3gc4w40h/qt3gc4w40h.pdfetdoai:escholarship.org:ark:/13030/qt8438495g2026-03-26T06:31:14Zqt8438495gAtomistic Insights into Reactive Sorption and Surface Restructuring on Metal and Metal Oxide CatalystsJiang, DavidSautet, Philippe PS2026-03-18Understanding how catalyst structure influences chemical reactivity is essential for design of materials for chemical transformation and environmental remediation. At the atomic scale, catalytic activity depends not only on the intrinsic properties of a material but also on the local coordination, electronic structure, and dynamic restructuring of surface atoms under reaction conditions. Computational modelling, particularly density functional theory (DFT), provides a framework for probing these factors and elucidating reaction mechanisms that are difficult to access experimentally. This dissertation employs atomistic simulations, combined with in-situ experimental observations, to investigate how surface structure and restructuring influence reactive sorption and catalytic processes on metal and metal oxide systems.The first part of this work examines the reactive sorption of hydrogen sulfide on CuO surfaces. DFT calculations reveal that the reactivity of CuO is strongly dependent on surface termination, with low-coordinated oxygen atoms serving as key active sites that facilitate H₂S dissociation and the subsequent formation of water and CuS. The analysis further shows that progressive surface sulfidation modifies the stability of surface species and alters the adsorption behavior of H₂S, highlighting the dynamic interplay between surface composition and reaction energetics.The second study explores the degradation of sulfur mustard and its simulant, 2-chloroethyl-ethyl sulfide (2-CEES), on single metal atoms supported on anatase TiO₂. Atomistic modelling demonstrates that isolated Pd, Pt, and Ir atoms can promote dehydrochlorination pathways through cooperative interactions between the metal center and the oxide support. In particular, Pd single atoms facilitate C–H activation while the TiO₂ surface stabilizes the resulting H and Cl fragments, illustrating a hybrid catalytic–reactive sorption mechanism for detoxification of vesicant agents.The latter part of the dissertation focuses on the dynamic restructuring of metal surfaces under reactive environments. Using machine-learning-accelerated atomistic simulations in combination with high-pressure scanning tunneling microscopy and ambient-pressure X-ray photoelectron spectroscopy, the structural evolution of Pt nanoclusters on Pt(100) surfaces is investigated under CO pressure. The results reveal that adsorbate interactions can drive substantial changes in cluster size and morphology, with the equilibrium cluster size emerging from a balance between the energetic cost of metal atom rearrangement and stabilization through CO adsorption.Finally, a previously unrecognized restructuring mechanism on Pd surfaces is identified. In-situ microscopy and neural-network-accelerated simulations show that CO adsorption can induce the formation of vacancy pools on Pd(111) terraces through adsorbate-assisted displacement of surface atoms. These concave defect structures expose metal atoms with distinct coordination environments and demonstrate that adsorbates can generate entirely new catalytic sites through dynamic restructuring of the metal surface.Taken together, the results presented in this dissertation demonstrate how atomic-scale structure, surface coordination, and adsorbate interactions govern catalytic reactivity and restructuring on metal and metal oxide catalysts. By combining atomistic modelling with experimental observations, this work provides insight into the mechanisms by which catalytic surfaces evolve under reaction conditions and highlights the importance of considering dynamic structural changes in the design of next-generation catalytic materials.Computational chemistryChemical engineeringMaterials sciencePhysical chemistryMolecular chemistryAb Initio SimulationsComputational CatalysisMachine LearningMaterial DesignSurface ModellingThermocatalysisapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8438495ghttps://escholarship.org/content/qt8438495g/qt8438495g.pdfetdoai:escholarship.org:ark:/13030/qt3717w5362026-03-26T06:31:04Zqt3717w536Topics in Scattering AmplitudesGopalka, TusharBern, ZviGutperle, Michael2026-03-18This dissertation is a synthesis of work completed during my Ph.D., related to aspects of Scattering Amplitudes and Topics in S-matrix Bootstrap. In Chapter 1, we follow the paper written in collaboration with Enrico Herrmann where we build the integrand for the two loop four particle form factor of the stress-tensor operator in maximally supersymmetric Yang-Mills theory. This was motivated by the surprising finding of where they found an antipodal duality between the six particle maximally helicity violating (MHV) on shell amplitude and the three particle MHV form factor of the chiral stress-tensor supermultiplet operator in planar N = 4 SYM. The antipode refers to a special operation that exchanges derivatives with discontinuities. A natural question to follow is whether such a duality can be extended to higher point form factors as well, and our work is a significant step in that direction. We employ a variant of generalized Unitarity known as Prescriptive Unitarity which allowed us to construct the basis integrands, and the coefficients was found through matching the residues of the integrands with the the field theory cuts computed through supersymmetric state sums. This procedure was pivotal in constructing the four dimensional integrand of the planar two loop four point MHV form factor of the chiral stress-tensor supermultiplet in N = 4 SYM theory. In Chapter 2 and 3, we follow the work done in where we study the analytic structure of the Black Hole S-matrix. This work is motivated by the remarkable achievements of the S-matrix Bootstrap program, in which after imposing conditions for the UV theory such as Causality, Unitarity, and Crossing symmetry, one employs techniques like the dispersion relations to bound Wilson coefficients of the low energy Effective Field Theory. However, despite the large success of such techniques, the S-matrix Bootstrap studies have been limited to gapped QFT’s on flat spacetimes which obey good symmetry properties. Here we try to take the first step towards extending this to other settings by studying the analyticity of the S-matrix for classical scattering on a curved background for an open system that breaks boost and translation symmetries. We find that causality ensures analyticity of the retarded Green’s function on the UHP in frequency domain but the S-matrix develops a branch cut along the entire UHP imaginary axis. The central result is to show how the analyticity of the potential in complex position space leads to analyticity (up to isolated poles arising due to QNM’s) of the S-matrix in complex frequency space.In Chapter 3, after having established the relation between analyticity of the potential and the S-matrix, we move on to example cases. We start by studying the Poschl-Teller potential which serves as a good toy model as it’s wave equation can be solved analytically. After this, we move on to the Schwarzschild BH case where due to the gravitational force being long range, one has to deal with logarithmic IR divergences. To tackle the IR issues, we had to go from the radial to the tortoise coordinates. This resulted in good fall-off behavior for the potential, leading to well defined asymptotically free scattering states. We find that the potential can be extended to the full complex plane with branch cuts only on the imaginary axis. Given this, we proposed that the retarded Green’s function is analytic in the UHP and has a branch cut only in the LHP imaginary axis due to tail effects. However, the S-matrix has a branch cut on the entire imaginary axis due to Stokes phenomenon. To confirm our results, we look at analytic solutions for the Regge-Wheeler wave equation in different limits and find a similar conclusion. PhysicsNuclear physicsTheoretical physicsParticle physicsBootstrapHigh energy physicsScattering Amplitudesapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3717w536https://escholarship.org/content/qt3717w536/qt3717w536.pdfetdoai:escholarship.org:ark:/13030/qt47n4v91w2026-03-26T05:02:06Zqt47n4v91wDevelopment and Characterization of a Conductive Microneedle Patch for Cardiac RepairLee, Albert YuhengAnnabi, Nasim2024-01-01Myocardial infarctions (heart attacks) are the most common cause of cardiac damage globally, leading to the significant health consequences and even death. Biomaterials, such as hydrogels, can serve as a better method to manage cardiac damage. However, an exploration of the current biomaterial and microneedle solutions shows a lack of directed drug delivery capabilities or conductivity. Given the density and conductivity of human cardiac tissue, these aspects are critical in improving the effectiveness of any biomaterial solution. Introduced in this thesis is the combination of both elements into a conductive hydrogel-based microneedle patch. We utilized a new, naturally derived biomaterial, tannic acid:PEDOT (poly(3,4-ethylenedioxythiophene), also known as TADOT. We then combined this with gelatin methacryoyl (GelMA) hydrogel to develop a biomaterial-based microneedle platform aimed to solve both issues. The formation of a tannic acid complex with PEDOT to form TADOT allows the hydrogel to be conductive, while the GelMA provided sufficient mechanical strength. With the anti-inflammatory characteristics of tannic acid, and the biocompatible nature of gelatin, the material is designed for better contribution to cardiac healing. We were able to engineer a microneedle patch based on TADOT-GelMA into microneedles which could penetrate porcine cardiac tissue, proving the potential for this biomaterial as a platform for cardiac repair applications. The conductive and mechanical characteristics of the TADOT-GelMA microneedles can allow it to serve as a delivery platform to be combined with other cardiac repair solutions.Chemical engineeringBioengineeringMaterials ScienceCardiac repairHydrogelMicroneedleMyocardial InfarctionTannic acidenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/47n4v91whttps://escholarship.org/content/qt47n4v91w/qt47n4v91w.pdfetdoai:escholarship.org:ark:/13030/qt2207257c2026-03-25T13:40:31Zqt2207257cGuayla Nation: Unyielding Tigrinya Music, Dance and Identity in EritreaStory, Dexter Gordon BryanKeyes, Cheryl L.2025-01-01The cornerstone of this dissertation is guayla, a remarkable popular music of the small northeast African nation of Eritrea, its diaspora and northern Ethiopia. Indigenous to Eritrea’s largest ethnic group, the Tigrinya, who make up 50% of the country’s total population of roughly 3-4 million, guayla arose out of traditional music-making and local customs, many of which are still performed today. Guayla has a highly distinctive sound that features soaring vocals, call-and-response singing, and a signature cyclical “five-beat” rhythmic structure that sets everyone dancing at social and political events. Ravinder Rena states that guayla “is [a] powerful and meticulous form of art that brings the emotional integration among the masses. The song is a driving force of an Eritrean society” (Rena 2009). Guayla is particularly present on Eritrean social media and YouTube channels and is a powerful marker of Tigrinya ethnic identity and Eritrean nationhood among the substantial diasporic communities located in North America, Europe, Saudi Arabia, and Australia. Lastly, secular guayla music contrasts with both the prevalence of ancient liturgical music of the Orthodox Tewahedo church which occupies an important place in the cultural history of the country, and the richness of the music of the other eight nationally recognized ethnic groups: Afar, B’dawiyet, Bilen, Kunama, Nara, Rashaida, Saho, and Tigre. The doctoral dissertation “Guayla Nation: Unyielding Tigrinya Music, Dance and Identity in Eritrea” draws from observations, interviews, videos, writings, ephemera, archives, and online testimonials to provide a preliminary ethnographic and historical exploration of Eritrea’s most popular and ubiquitous music legacy.MusicAfrican studiesHistoryEritreaEthiopiaGuaylaMusicTigrinyaTraditionalenpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2207257cetdoai:escholarship.org:ark:/13030/qt6vn881032026-03-25T06:32:59Zqt6vn88103Bioprinted Organoids for High-Throughput Multimodal Cancer Treatment EvaluationLin, LudaSoragni, Alice2026-03-20Organoids are self-organized multicellular structures formed within three-dimensional (3D) extracellular matrices (ECMs) and are increasingly used in basic research, drug discovery, and precision medicine. However, manual organoid seeding is labor-intensive, prone to operator-induced variability, and limits scalability. In addition, there is a growing need for organoid models that recapitulate complex tumor microenvironments while remaining compatible with high-throughput workflows. Here, we describe the development of a bioprinted, organoid-based high-throughput screening platform designed to address these challenges. We introduce PlateGenerator, a customizable software tool that enables automated bioprinting of diverse organoid architectures through spreadsheet-defined parameters that are converted into G-code commands. We apply this platform to head and neck cancer organoids to establish a high-throughput drug screening workflow integrating automated drug treatment, radiotherapy, and machine-learning–based brightfield image analysis. This enables quantitative assessment of organoid growth and invasion beyond bulk viability measurements, revealing both inhibitory and unexpected pro-invasive drug effects. Finally, we develop concentric double-ring organoid models incorporating distinct ECM compartments and coculture with endothelial cells and pericytes to study angiogenesis-related processes. Overall, this work establishes an organoid-based high-throughput platform for drug screening with applications in basic research, drug discovery, and functional precision medicine.Molecular biologyCellular biologyMedicineOncologybioprintingcancerhigh-throughput screeningorganoidpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6vn88103multimediaoai:escholarship.org:ark:/13030/qt0qh1g6632026-03-25T06:32:54Zqt0qh1g663Solar-driven Kinetically Controlled Gas Reforming of Alkanes and Carbon DioxideJeevaretanam, BarathanSpearrin, R. Mitchell2026-03-20Gas reforming is a vital process that converts naturally available gas resources into useful and high value materials. In an effort to minimize emissions associated with endothermic gas reforming processes, this dissertation presents the advancement of a solar-driven reforming method. Direct concentrated solar-thermal energy is leveraged to provide the energy necessary for three reforming processes: (1) methane pyrolysis for production of hydrogen and graphite, (2) dry reforming of biogas for production of syngas and graphite, and (3) ethane pyrolysis for synthesis of ethylene and graphite. A high flux solar simulator and reactor system at UCLA is used to perform parametric analyses of the respective reforming processes by varying a range of process parameters that includes feedstock flow rate, reaction pressure, incident solar flux, and feedstock composition. The dissertation also presents the development of a mid-infrared laser absorption spectroscopy sensing method that is used to monitor process yields via measurements of key exhaust products during the reforming reactions. Thermochemical modeling of the process anchored by in situ temperature measurements of the extreme environment of the reaction zone in the solar-thermal reactor is also detailed. The potential for scale-up of the laboratory-developed process is highlighted by a first- of-its-kind field demonstration of direct solar-thermal methane pyrolysis in Casa Grande, Arizona. Prospective future work includes further characterization of the reaction zone of the solar-thermal reactor, reforming of alternate feedstocks, and higher fidelity modeling.Mechanical engineeringMaterials scienceAlternative energyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0qh1g663https://escholarship.org/content/qt0qh1g663/qt0qh1g663.pdfetdoai:escholarship.org:ark:/13030/qt4q14k7xf2026-03-25T06:32:49Zqt4q14k7xfA Modeling Framework for Architecture-Level Exploration of High-Bandwidth Memory SystemsLEE, CHANHEEGupta, Puneet2026-03-20High Bandwidth Memory (HBM) has become a critical technology for modern high-performance computing systems due to the rapidly increasing memory bandwidth demand in data-intensive workloads such as artificial intelligence and large-scale data processing. By vertically stacking multiple DRAM dies using through-silicon vias (TSVs), HBM significantly improves memory bandwidth and energy efficiency compared to conventional DRAM architectures. However, the increasing complexity of stacked memory structures introduces new challenges in early-stage design exploration, including accurate modeling of memory die organization, TSV allocation, thermal constraints, and power delivery.In this thesis, we propose a flexible modeling framework for architecture-level exploration of HBM-based memory systems. The proposed framework models key physical and architectural components of HBM, including memory die structure, peripheral circuits, base die logic, and TSV interconnects. In addition, the framework incorporates thermal TSV allocation and energy-efficiency estimation to capture important design trade-offs in stacked memory architectures. By leveraging configurable architectural parameters, the model enables rapid exploration of design choices across different HBM generations.Using the proposed framework, we analyze the architectural scaling trends of HBM2, HBM3, and emerging HBM4 designs. We further conduct multiple case studies to evaluate the impact of thermal constraints, TSV allocation strategies, and power delivery requirements on stacked memory systems. Experimental results demonstrate that the proposed model can effectively capture the design trade-offs among area, power, thermal constraints, and memory bandwidth in modern HBM architectures.The proposed framework provides a useful tool for early-stage design exploration of next-generation HBM systems and enables systematic evaluation of architectural trade-offs in future 3D-stacked memory technologiesElectrical engineeringComputer engineeringArchitectureArchitectural engineering3D-stacked memoryArchitecture-level modelingHBMHigh-bandwidth memoryMemory architectureapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4q14k7xfhttps://escholarship.org/content/qt4q14k7xf/qt4q14k7xf.pdfetdoai:escholarship.org:ark:/13030/qt9v99m7t42026-03-25T06:32:42Zqt9v99m7t4The Development and Application of Proteomic Approaches to Assess Consequences of Amino Acid Modifications Within the ProteomeShikwana, FlowreenBackus, Keriann M2026-03-20A central challenge of biochemistry is determining the consequences of modifications to proteinaceous amino acids as they pertain to protein function and broader cellular function. Mass spectrometry (MS)-based proteomics enables proteome-wide identification and quantification of modified peptides either through chemical modifications to amino acids such as covalent probe addition or even genetic modifications such as single amino acid variants (SAAVs). Chemical modifications are often assayed through chemoproteomic workflows in order to identify specific ligandable sites by covalent probes designed to be reactive towards certain amino acids, such as cysteine residues in the case of cysteine chemoproteomics. While chemoproteomic workflows are great tools to discover targetable sites by many different types of covalent probes, the scope for which the direct and indirect effects of liganding specific cysteines contribute to reported modes-of-action remains under-characterized. Knowledge of such effects are especially important for prioritizing compounds for follow up studies when performing high throughput screens with multiple scout fragments. Similarly, when using MS-based proteomics for the study of genetic modifications to amino acids, proteogenomic workflows often only address the identification of modifications such as SAAVs and their relative abundance within cells but fail to stratify or establish functional significance of each of these variants. In order to determine functional significance, follow up biochemical studies are often needed which is a time-intensive and low-throughput process. In this work, we first address improving the throughput of cysteine chemoproteomics through the adaptation of the single-pot solid-phase-enhanced sample preparation (SP3) workflow paired with our isotopic capture reagents for cysteine chemoproteomic profiling. We scaled the workflow to accommodate a 96-well plate sample preparation and in-plate peptide enrichment, along with identifying low-cost magnetic beads capable of comparable cysteine coverage, which allowed us to profile 12,265 and identify 2633 ligandable cysteines. We also demonstrated its use for screening multiple compounds, including atropisomers, at several concentrations to better determine unique cysteine and protein targets of such molecules. Next, we investigated a panel of cysteine reactive fragments in order to delineate observed degrader effects on our protein of interest, the SARS-CoV-2 non-structural protein 14 (nsp14), from proteome-wide proteostasis dysregulation. We found that direct covalent modification, ubiquitylation of nsp14, as well as the inactivation of protein disulfide isomerases (PDIs) 3 and 6 are necessary for nsp14 depletion. In addition, we implicated cysteine-reactive electrophiles, used at screening concentrations, in generalized ubiquitylation, proteasome activation, widespread aggregation, and the formation of stress granules upon cysteine-reactive electrophile screening. These unexpected effects on cellular proteostasis led us to develop a framework for identifying wide ranging cellular effects that should be monitored alongside screens which use protein depletion as a readout for identifying cysteine-reactive degrader compounds. Turning to SAAVs, we sought to use thermal proteome profiling (TPP) as a high-throughput proteomic methodology to stratify variants of functional significance as read out by shifts in thermal stability. We first established the use of TPP to characterize alterations in thermal stability between wild-type (WT) and variant proteoforms using B-Raf and its more well-known SAAVs as a model system for this platform. We validated the use of TPP both at the protein and peptide level for assaying variant thermal stability first within an overexpression system, then within a pooled B-Raf variant cell line. Finally, we extended our variant TPP methodology to quantify 77 endogenous variants in Molt-4 leukemia cells through proteogenomic workflows and identify stabilizing and destabilizing SAAVs.BiochemistryCellular biologyMolecular biologyGeneticschemoproteomicscysteineproteogenomicproteomicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9v99m7t4https://escholarship.org/content/qt9v99m7t4/qt9v99m7t4.pdfetdoai:escholarship.org:ark:/13030/qt0m55p5tc2026-03-25T06:32:29Zqt0m55p5tcAutonomy in Immunization: Factors Associated with Support for Adolescent Self-Consent in a National Dyadic StudyMoore, JadeArah, Onyebuchi2026-03-20Background: Although vaccinations have been proven of their effectiveness and safety, due to an increase in parental vaccine hesitancy, routine vaccine uptake among children and adolescents has started to decline in the United States. Implementing policies permitting self-consent to adolescents may be a potential way to combat declining vaccination rates among adolescents and provide adolescents more medical autonomy to make decisions regarding their physical and mental health. However, there is little known about the attitudes of adolescents and their parents towards adolescent self-consent policies in the U.S. This study investigated attitudes regarding adolescent self-consent among parents and their adolescents and characterized who would most likely support or not support such self-consent for routine vaccinations.
Methods: We conducted a nationally representative non-probability cross-sectional survey among 764 parent-adolescent dyads in the United States between October 2023- January 2024. Parents were selected from an existing YouGov online panel. Self-reported support for adolescent self-consent was assessed among parents and their adolescents with univariable and multivariable logistic regression models, with and without adjustment for race/ethnicity, religion, parental education and marital status, household income, and political party affiliation.
Results: After data quality review, a total of 729 parent–adolescent dyads were included in the analysis. About 45% of parents and 51% of adolescents supported adolescent self-consent to routine vaccines. Support for self-consent was associated with race/ethnicity, religion, and political party affiliation, with similar results for COVID-19 vaccination support. Main characteristics of parents that were more likely to support adolescent self-consent for routine vaccinations were either younger, in a married or legal relationship, or identified as a Democrat. For adolescents, those who provided their support were more likely to identify as Agnostic or a Democratic and have already received routine vaccinations in the past.
Conclusions: Public support for adolescent self-consent for vaccinations among parents and their adolescent is mixed. Having slightly more adolescents than parents support adolescent self-consent may suggest a greater emphasis on medical autonomy and access for adolescents. These results highlight a need for cautious policy consideration and targeted communication and stakeholder involvement around such policies.EpidemiologyPublic healthImmunologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0m55p5tchttps://escholarship.org/content/qt0m55p5tc/qt0m55p5tc.pdfetdoai:escholarship.org:ark:/13030/qt9564v3k62026-03-25T06:32:24Zqt9564v3k6An Exploratory cis-eQTL Analysis of Brown Adipose Tissue as a Cold Adaptation Response in Peruvian AndeansYoung, Emma AnnaBigham, Abigail W2026-03-20Andeans have thrived in the extreme conditions 50°F to -40°F during austral winter) of the Altiplano for the last 10,000 years. While human adaptations to high-altitude hypoxia have long been studied in this region, cold adaptation has not received similar interest. To systematically investigate Peruvian Andean adaptation to cold climate, we performed a cis-eQTL analysis on 58 participant-matched DNA and RNA pairs followed by selection analysis. Our findings revealed several pathways under enhanced gene expression amongst Peruvian An-deans related to cold-adaptation physiology including mitochondrial transcription, positive regulation of adipose tissue development, fatty acid homeostasis, energy homeostasis, and negative regulation of lipid catabolic process. This research quantifies the capacity for cold adaptation in a cold, high-altitude residing population, and explores the genetic underpinnings of cold adaptation through the identification of functional variants.GeneticsCellular biologyPhysical anthropologyDevelopmental biologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9564v3k6https://escholarship.org/content/qt9564v3k6/qt9564v3k6.pdfetdoai:escholarship.org:ark:/13030/qt072909j72026-03-25T06:32:13Zqt072909j7Optimization for High-Level SynthesisPouget, StephaneCong, Jason2026-03-20Field-Programmable Gate Arrays (FPGAs) have become an essential computing substrate for domains requiring high performance and energy efficiency, such as machine learning, signal processing, and scientific computing. Their fine-grained parallelism and hardware configurability enable tailored accelerators that outperform conventional CPUs and GPUs under strict power budgets.High-Level Synthesis (HLS) has emerged as a key enabler for FPGA-based acceleration by allowing developers to describe hardware in high-level languages (e.g., C/C++ or domain-specific abstractions) rather than low-level register-transfer level (RTL) code. In principle, HLS improves programmer productivity, shortens development cycles, and makes FPGA design accessible to a broader community by providing a compiler-driven path from software-like descriptions to hardware implementations. FPGAs are particularly attractive targets for HLS because they offer fine-grained parallelism, deep pipelining, and reconfigurable microarchitectures that can be tailored to a workload. As a result, FPGA accelerators can deliver high performance and strong energy efficiency under strict power budgets, which is valuable in domains such as machine learning, signal processing, and scientific computing. Despite these advantages, programming FPGAs effectively remains challenging. Current HLS tools still rely heavily on manual optimization through pragmas, source-level code transformations, and explicit management of on-chip memory and dataflow. Achieving high Quality of Results (QoR) therefore often requires deep hardware expertise and lengthy design iterations, limiting the practical adoption of FPGAs by software developers.This dissertation introduces a unified compilation methodology that automates FPGA design optimization from high-level source code to system-level hardware generation. The proposed approach combines analytical modeling, optimization theory, and dataflow synthesis to address the multiple stages of the HLS process within a single, coherent flow. At the kernel level, the method employs non-linear optimization guided by analytical latency and resource models to explore the pragma design space efficiently, identifying configurations that balance performance and hardware utilization without exhaustive synthesis. The same modeling foundation enables a unified treatment of pragma insertion and code transformation, jointly optimizing unrolling, tiling, and pipelining to capture key trade-offs in latency and parallelism.At the system level, the methodology introduces a dataflow-oriented compilation model that organizes kernels into streaming pipelines. This model manages task communication, shared buffering, and overlapping of computation and I/O to maximize concurrency while maintaining resource balance across Super Logic Regions (SLRs). To further improve scalability, a reuse-aware synthesis strategy extends the flow to cyclic and cross-kernel designs, allowing multiple kernels to share compute units, on-chip memory, and communication channels without sacrificing performance.Together, these contributions form an end-to-end optimization and synthesis framework that bridges the gap between algorithmic code and efficient, reusable hardware. Across a diverse set of benchmarks — including linear algebra, convolutional, and graph-based applications — the proposed techniques achieve throughput and resource efficiency comparable to hand-optimized implementations while reducing design-space exploration time by up to an order of magnitude. This work advances the HLS design automation frontier , demonstrating how analytical optimization and dataflow reasoning can jointly enable scalable, system-level FPGA compilation for emerging computational workloads.Computer scienceComputer engineeringAutomotive engineeringElectrical engineeringHigh-level SynthesisNonapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/072909j7https://escholarship.org/content/qt072909j7/qt072909j7.pdfetdoai:escholarship.org:ark:/13030/qt566947b72026-03-25T06:32:07Zqt566947b7Light-induced phase transition kinetics and electronic symmetry breaking probed by time-resolved second harmonic generationCarbin, James TylerKogar, Anshul2026-03-20In this dissertation, I present work completed during my PhD on ultrafast light-matter interactions in quantum materials. I begin with an introduction to ultrafast phenomena in cooperative systems, placing an emphasis on time-dependent Ginzburg-Landau (TDGL) theory. I then introduce the foundations of time-resolved second harmonic generation (tr-SHG), the technique that is used in all of the work presented here. In the first part of my results, I investigate a photoinduced phase transition in Ca3Ru2O7. By supplementing tr-SHG measurements with numerical simulations, I reveal underlying percolative and glassy phase transition kinetics. In the last chapter, I investigate a purely electronic polarization in Cr2O3 induced by sub-gap light in which the polar direction is controlled by the light polarization.PhysicsApplied physicsStatistical physicsCondensed matter physicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/566947b7https://escholarship.org/content/qt566947b7/qt566947b7.pdfetdoai:escholarship.org:ark:/13030/qt3zw1z52g2026-03-25T06:32:01Zqt3zw1z52gA Theoretical Investigation of Copper Zinc Catalyst Structural Evolution during Methanol SynthesisIssac Paul, GeorgeSautet, Philippe2026-03-20Methanol synthesis from CO2 hydrogenation is a key catalytic route for sustainable fuel and chemical production. Industrial catalysts are typically based on Cu/ZnO/Al2O3, yet the structural role of Zn under reaction conditions is not yet fully understood. In particular, the dynamic interaction of Zn with Cu surfaces and reaction intermediates may strongly influence catalyst structure and function.In this work, we use theoretical calculations to investigate the structural evolution of Cu-Zn surfaces relevant to methanol synthesis, focusing on crystalline Cu(111) and stepped Cu(533) facets. We examine the stability and preferred location of Zn in the presence and absence of OH adsorbates, which are important surface intermediates under methanol synthesis conditions. Our results show that, in the absence of OH, Zn preferentially remains alloyed within the Cu surface or subsurface. In contrast, OH adsorption drives Zn segregation from the alloyed state to the surface, where Zn preferentially forms adatom chains.These results demonstrate that adsorbate-induced restructuring can substantially alter the local Cu-Zn environment under reaction conditions. The findings provide atomistic insight into the dynamic nature of Cu-Zn catalysts and highlight the importance of considering surface restructuring when identifying active sites for CO2 hydrogenation to methanol.Chemical engineeringPhysical chemistryComputational chemistryCatalysisCu/ZnODFTMethanolTheoretical Chemistryapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3zw1z52ghttps://escholarship.org/content/qt3zw1z52g/qt3zw1z52g.pdfetdoai:escholarship.org:ark:/13030/qt5v41111k2026-03-25T06:31:56Zqt5v41111kDesign and Manufacturing of a Hexapod Utilizing Proprioceptive ActuatorsParres, Federico TonatiuhHong, Dennis2026-03-20Hexapods are well suited to serve as platforms for future space exploration missions. However, due to the inherent complexity of multi-legged systems, simpler platforms which utilize hopping or wheeled robots are often preferred. While less complex, wheeled platforms lack the ability for precise manipulation of objects, and hopping systems are difficult to control with accuracy. Detailed exploration of small body objects in deep space requires precise locomotion, adaptability to variations in terrain, and the ability to manipulate objects of interest. Conventional platforms used for space exploration are limited in these capabilities.
This thesis presents the development of TOCATL, a hexapod designed as an experimental platform to advance multi-legged systems while addressing concerns of complexity through robustness and serviceability. The robot was developed to utilize modern additive manufacturing techniques, user-focused design philosophy, and structurally robust components
TOCATL is a 16 kg hexapod, consisting entirely of additively manufactured structural components with a hip-to-hip diameter of 600 mm, and a leg length of 825 mm when measured from hip to foot. Each of its six legs is actuated by 3 proprioceptive quasi-direct drive actuators capable of adjusting stiffness and damping properties in real time. Its long limbs and force-controlled actuators allow it to adapt to terrain variations despite its simple open loop control method. The system is designed for durability, capable of sustaining falls, overturning, and electrical faults without permanent structural damage, while also incorporating features that reduce assembly errors and improve serviceability.
To evaluate performance, TOCATL in both outdoor and lab settings. The robot demonstrated a payload capacity exceeding 31.8 kg, and achieved stable locomotion utilizing wave, ripple, and tripod gaits across a range of step heights and stride lengths. While capable of traversing obstacles up to 100 mm in height, performance was limited on terrain requiring body heights greater than 300 mm or larger step heights, primarily due to actuator thermal constraints.RoboticsEngineeringMechanical engineeringMaterials science3D PrintingAdditive ManufacturingHexapodProprioceptiveRobotTOCATLapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5v41111khttps://escholarship.org/content/qt5v41111k/qt5v41111k.pdfetdoai:escholarship.org:ark:/13030/qt8qj4x59t2026-03-25T06:31:51Zqt8qj4x59tSome Adventures in Microstructure and Battery Kinetics: Understanding (de)Lithiation Mechanisms Towards Fast-Charging Mixed Transition Metal Oxide Host MaterialsPe, DavidTolbert, Sarah H.2026-03-20Energy storage materials remain an enormous subject of scientific and engineering endeavors. Among energy storage devices, Li-ion batteries are ubiquitous and both applied and fundamental efforts continue to go towards improving fast-charging capacities and understanding the mechanisms that govern kinetics. In this dissertation, I characterized the connection between battery (dis)charging kinetics and structural (de)lithiation mechanisms in mixed transition metal oxide insertion host materials. Through sol-gel methods, I synthesized tunnel structure oxides: the distorted rutile (Mo,W)O2 family as a model system for microstructural variation and tetragonal Nb0.5Mo0.5O2 as a fast-charging lithium-ion anode material. Through a combination of materials characterization (XRD, SEM-EDS, TEM), electrochemical testing (GCD, CV, GITT, galvanostatic entropic potential measurements), and synchrotron experiments (total scattering/PDF, operando XRD and XANES), I related dynamic host structures with electrochemical kinetics. I showed a progression from slow to fast kinetics in Lix(Mo,W)O2 materials with decreasing amounts of phase separation. I determined multi-variate process conditions for the reproducible formation of different microstructures in this class of materials. We used these insights to develop a promising anode material LixNb0.5Mo0.5O2 and showed that optimized synthesis conditions improve high-rate capacities by over three times. When charging in 3 min (20C) a respectable 62% of theoretical single lithium insertion capacity (131/ 212 mA h g −1) was achieved. Lastly, I related entropic potential profiles to (de)lithiation kinetics across these mixed metal materials. I contextualized these observations with existing theory and my structural characterization from previous chapters. I show that damped guest-ion ordering is correlated with enhanced rate capability and can be encouraged through substitutional disorder in a stoichiometric- and atom-specific way. I showed an increasing progression in the magnitude of ion ordering as a function of phase separation. Overall, I found that microstructural variation in mixed metal Li-ion insertion materials can considerably alter (de)lithiation mechanisms, including first-order phase transitions, along with rate capabilities. These findings provide new structure-property relationships and inform material process considerations for realizing fast ionic (de)insertion in mixed metal host systems.ChemistryMaterials scienceEnergybatterieselectrochemistryion orderingkineticsmicrostructurephase transitionsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8qj4x59thttps://escholarship.org/content/qt8qj4x59t/qt8qj4x59t.pdfetdoai:escholarship.org:ark:/13030/qt7q11m29t2026-03-25T06:31:47Zqt7q11m29tAdditively Manufactured Functional Architected Materials for Ultrasound Transduction and Force SensingLu, HaotianZheng, Xiaoyu2026-03-20Advanced functional devices increasingly rely on complex three-dimensional architectures to precisely control the transmission and transduction of mechanical, electrical, and acoustic energy. However, conventional fabrication techniques impose severe geometric constraints, limiting the achievable device performance and functionality. This dissertation explores how architected structures, enabled by advanced additive manufacturing, can be used to design and fabricate high-performance piezoelectric and multi-functional devices with tailored force and energy pathways.A customized projection stereolithography platform, integrated with an extrusion-assisted resin spreading mechanism, is developed to enable the fabrication of high-viscosity functional materials with complex geometries. Using this system, we address a fundamental challenge in additively manufacturing piezoelectric ceramics: the trade-off between printability and functional performance. A novel photosensitive resin formulation with extremely high ceramic loading (up to 50 vol% PZT) is developed, enabling liquid-phase sintering to achieve high-density ceramics with excellent electromechanical properties. The resulting process enables high-resolution fabrication of dense PZT structures with significantly reduced defects such as porosity, cracking, and shape distortion.The process–structure–property relationships of architected piezoelectric ceramics are systematically investigated, demonstrating that three-dimensional geometry plays a critical role in governing electromechanical response and device-level performance. By breaking the geometric limitations of traditional ceramic processing, this work enables the rational design of piezoelectric elements that are inaccessible through conventional manufacturing routes.Building upon this fabrication capability, the dissertation further extends to device-level demonstrations, including miniaturized ultrasound transducers and multi-axis force sensors. In therapeutic ultrasound applications, architected piezoelectric structures are designed to enhance acoustic performance under high-power operating conditions relevant to cavitation-enabled drug delivery. In the realm of sensing, a mechanically architected force sensor is developed to achieve intrinsic decoupling of three-axis forces. Notably, the decoupling mechanism arises from structural design rather than the sensing material itself, allowing the architecture to be compatible with piezoelectric, resistive, and other transduction mechanisms. The sensor architecture is inherently scalable, providing a tunable working range and load-bearing capacity without compromising decoupling performance.Overall, this work establishes architected functional structures as a versatile and material-agnostic platform for next-generation sensing and actuation systems, with applications in therapeutic ultrasound, robotics, and intelligent mechanical systems.Mechanical engineeringElectrical engineeringMaterials scienceAerospace engineering3D printingPiezoelectricsTactile sensorUltrasoundapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7q11m29thttps://escholarship.org/content/qt7q11m29t/qt7q11m29t.pdfetdoai:escholarship.org:ark:/13030/qt276753242026-03-24T06:33:42Zqt27675324Machine Learning for Portable Variant Genomics: Cross-Build Harmonization and Structural Variant ImputationWang, NicholasZaitlen, NoahGoldstein, Andrew2026-03-19Reference genomes and genotyping technologies are foundational to human genomics, yet both evolve rapidly, producing incompatible coordinate systems and heterogeneous data modalities that hinder reproducible analyses at scale. This dissertation advances a unifying framework for representation-robust variant genomics, addressing two major sources of inconsistency: (i) genome build differences that alter read alignment and variant detection, and (ii) limited availability of whole-genome sequencing for comprehensive structural variant (SV) genotyping in large cohorts.First, I quantify the impact of reference genome build on germline and somatic variant calling by analyzing tumor–normal whole genomes across GRCh37 and GRCh38. Build discordance is modest for germline SNPs but substantial for somatic variation and SVs, and targeted resequencing confirms that build-discordant variants are not simply algorithmic false positives. I then introduce StableLift, a machine-learning method that predicts whether individual variants are consistently represented across builds using caller-, site-, and annotation-derived features, enabling computationally efficient mitigation of cross-build artifacts. Second, I present SNP2SV, a portable machine-learning approach to impute germline SV genotypes from local SNP and indel patterns, enabling SV analyses in cohorts lacking whole-genome sequencing. Trained on the high-coverage 1000 Genomes reference panel, SNP2SV produces accurate models for tens of thousands of well-represented SVs and outperforms a state-of-the-art haplotype-based imputation method in an independent validation cohort. Collectively, these contributions provide complementary methods to stabilize variant interpretations across genome builds and expand structural variant discovery to data types more widely available in existing biobanks. This work supports more reliable cross-study integration and increases power for association studies by reducing representation-driven error and missingness.BioinformaticsArtificial intelligenceGeneticspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/27675324etdoai:escholarship.org:ark:/13030/qt97b3n4mf2026-03-24T06:33:37Zqt97b3n4mfCharacterizing Aging through Untargeted Metabolomics with High-Dimensional Cerebrospinal Fluid and Blood Serum DataLee, CalvinOphoff, Roel2026-03-19Aging is a complex biological process marked by gradual physiological decline and increased vulnerability to disease. Metabolomics, which captures the dynamic output of cellular processes, offers a powerful lens for characterizing aging at the molecular level. This study leveraged untargeted metabolomic profiling across cerebrospinal fluid (CSF) and blood serum to investigate aging-related signatures using high-dimensional data from 407 individuals aged 18 to 64. By integrating data from three distinct platforms and targeting primary metabolites, biogenic amines, and lipids, I assessed metabolic changes in both central and peripheral compartments.To overcome the challenges posed by high dimensionality, noise, and missing data, I evaluated a comprehensive suite of machine learning methods, including penalized regression (LASSO, Ridge, Elastic Net), ensemble learning (Random Forest, XGBoost, LightGBM), kernel-based models (Support Vector Regression), and deep learning approaches (Autoencoders). Our findings demonstrate that Random Forest achieves the lowest average root mean squared error (RMSE), highlighting its robustness in noisy, small-sample contexts. Moreover, I employed Sparse Multiple Canonical Correlation Network Analysis (SMCCNet 2.0) to identify coordinated aging-related metabolite networks across CSF and serum. Notably, CSF-derived models outperformed serum-based ones in predictive accuracy, emphasizing the brain’s central role in aging. Metabolites such as quinic acid and anserine emerged as key predictors. Our integrative approach bridges central and systemic aging signatures and underscores the promise of metabolomics for biomarker discovery, risk stratification, and precision aging strategies. Overall, these results support the feasibility of combining advanced statistical modeling with multi-biofluid metabolomics to uncover robust, interpretable insights into the biology of human aging.BioinformaticsCellular biologyMolecular biologyAgingapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/97b3n4mfhttps://escholarship.org/content/qt97b3n4mf/qt97b3n4mf.pdfetdoai:escholarship.org:ark:/13030/qt25r5w6tx2026-03-24T06:33:32Zqt25r5w6txDesign, Synthesis, and Study of Internal Dynamics in Crystalline Molecular Machines in the Solid-State: An Emphasis on Nuclear Magnetic Resonance as an Analytical ToolChavez, RobertoGarcia-Garibay, Miguel A.2026-03-19The main aim of this dissertation is to expand the current knowledge on the condensed phase known as amphidynamic crystals. These special materials are solids that have been engineered to incorporate moving parts that can achieve molecular motion in the range of a liquid or a gas while retaining the high phase order characteristic of a crystal. The seminal work presented by my research group and others in the field is showcased and further expanded by the contributions presented in this thesis regarding the synthetic methodology and study of these materials, as well as collaborations to help characterize the dynamic processes displayed by them.Chapter one serves as an introduction to artificial molecular machines in the solid state. Key definitions and general concepts illustrate and provide context to support the main thesis of this work. What is a Machine? What are the current known Molecular Machines? How can we design and synthesize Crystalline Molecular Machines? Answers to these questions can be found in this chapter. Chapter two explores the design of a synthetic logic for the unbound growth of 3D shape-persistent fractal dendrimers as motifs for nanoscale crystalline molecular rotors. Taking nature as an inspiration, I have developed the synthetic logic to grow large fractal-like synthetic elements with structural stability to use as motifs within crystalline molecular machines. The recursive synthesis methodology mainly employs four design elements: high-symmetry, self-similarity, dimensionality, and shape-persistence. The project focuses on the synthesis of the largest solid-state molecular rotor within a series explored by our research group historically. The recursive synthesis method is tested and successfully employed to access a 3D shape-persistent dendrimer molecular rotor. Chapter three focuses on solid-state nuclear magnetic resonance (ssNMR) for the characterization of physical processes in the solid-state. We studied the complex dynamics of a cucurbit[7]uril-based (CB[7]) supramolecular rotor inside a crystal. Crystal structure determination is used to guide the design of a model that help explain the non-conventional dynamics observed empirically. Computer simulations, and a line shape analysis of the 2H spin echo ssNMR data is carried out. Finally, chapter four expands the boundaries of quantitative NMR (qNMR) as an analytical tool for chemists. ChemisTwin, a software application developed by MilliporeSigma, allows for automated qualitative/quantitative analysis of NMR data. We build calibration curves to determine the precision and accuracy of the software as it calculates yields and produces comprehensive reports on commercially available chemicals. We compare the results to traditional qNMR techniques such as use of an internal standard for sample quantification.Organic chemistryPhysical chemistryMaterials scienceAnalytical chemistryanalyticaldendrimerfractalsupramolecularsynthesisapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/25r5w6txhttps://escholarship.org/content/qt25r5w6tx/qt25r5w6tx.pdfetdoai:escholarship.org:ark:/13030/qt73h5j3042026-03-24T06:33:27Zqt73h5j304Engineering Soft Materials Across Scales: From Molecular Interactions to Stimuli-Responsive MotionLiu, ZixiaoHe, Ximin2026-03-19Soft materials offer unusual opportunities for engineering because their behavior is shaped by events spanning many length scales. Molecular-level interactions govern how polymer networks absorb solvent, respond to ions, and reorganize under external stimuli, while mesoscopic structures and device-scale geometries determine how those materials carry load, deform anisotropically, and produce motion. Establishing predictive links across these scales is therefore essential for moving soft materials beyond empirical trial-and-error and toward rational design. This dissertation addresses that challenge by studying how soft materials can be engineered from molecular interactions to macroscopic function, with implications for tunable hydrogels, anisotropic soft matter, and autonomous stimuli-responsive actuators.
This dissertation begins with an introductory chapter that outlines the central objective of engineering soft materials across scales. Chapter 2 develops a theoretical framework that connects salt-mediated molecular interactions to macroscopic hydrogel behavior, showing how elasticity, mixing, ion hydration, hydrogen bonding, Born solvation, and direct ion-polymer binding collectively govern swelling and mechanical response. Chapter 3 then examines how mesoscopic architecture controls bulk properties by fabricating porous PVA hydrogels through bidirectional ice templating and relating lamellar alignment to tunable anisotropic mechanical performance using experiments, thermal simulations, and micromechanical modeling. Chapter 4 shifts to stimuli-responsive motion and establishes a coupled photo-thermo-mechanical model for photothermal self-excited oscillators, revealing the feedback mechanism that sustains oscillation and identifying how operating conditions, material properties, and geometry influence oscillation onset and performance.Materials scienceMechanicsRoboticspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/73h5j304etdoai:escholarship.org:ark:/13030/qt27p1g1c02026-03-24T06:33:22Zqt27p1g1c0Mechanistic Discovery with Virtual B CellHuang, HelenHoffmann, Alexander2026-03-19Vaccination relies on an evolutionary process in which antigen-reactive B cells are selected and expanded while autoreactive cells are eliminated, yet vaccine optimization remains largely empirical. In this dissertation, I develop and apply virtual B cell models to translate diverse immunological measurements into causal, testable mechanisms across scales, linking intracellular signaling dynamics, population dynamics, and tissue-scale organization. First, I resolve a regulatory paradox in B-cell proliferative expansion: although NFκB cRel is required for division, high cRel both activates and terminates proliferation program early. I show that a cRel-mediated incoherent feedforward mechanism controlling the cell-cycle driver cMyc reproduces the experimentally observed faster-but-fewer division phenotype in cRel-overexpressing B cells. Second, I build and experimentally test a multi-scale ODE model to integrate BCR and CD40 inputs to B-cell, showing that these signals differentially engage death and proliferation programs and that the precise timing of their stimulation can switch their combined effects from synergistic to antagonistic, revealing a temporal proofreading mechanism tuning positive versus negative selection. Third, I examine whether antibody repertoire phylogenies can serve as retrospective records of fate kinetics despite sampling, sequencing, and reconstruction error. Finally, to extend the virtual-cell perspective to virtual tissue, I present a method review of an iterative, prior-knowledge-guided workflow for analyzing imaging-based spatial transcriptomics, while the underlying analyses of post-vaccination lymph node data are ongoing. Together, these contributions lay a foundation for more predictive and mechanistically interpretable models of humoral immune response that can inform precision vaccination strategies.Systematic biologyImmunologyBioinformaticsCellular biologyAffinity maturationB-cell signalingMechanistic modelingPrecision vaccinationSpatial transcriptomicsSystems immunologypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/27p1g1c0etdoai:escholarship.org:ark:/13030/qt00k5988t2026-03-24T06:33:17Zqt00k5988tRegulating Supramolecular Microphase Separation via Backbone Steric Hindrance for Tough and Dynamic HydrogelsZhao, HaoboHe, Ximin2026-03-18Hydrogels with phase-separated structures hold great promise for biological and electrical applications due to their robust yet tunable mechanical performances. Supramolecular hydrogels typically rely on phase-separated motifs for mechanical toughness, yet how the steric hindrance of the soft-matrix backbone governs this supramolecular assembly remains unexplored. Here, I challenge the conventional assumption that increased backbone hydrophobicity monotonically promotes hydrophobic aggregation. By systematically introducing α-methyl groups (methacrylamide, MAM) into UPy-functionalized polyacrylamide hydrogels under strong citrate-mediated salting-out, I uncover a non-monotonic structural evolution dictated by competing noncovalent interactions. Small-angle X-ray scattering (SAXS) reveals that at low MAM contents (0–5%), the system thermodynamically prefers dense interchain hydrogen-bonded "zippers" over bulky UPy π−π stacking. At 10% MAM, tailored steric hindrance weakens these zippers, shifting the thermodynamic balance to trigger distinct UPy microphase separation. Conversely, at 15% MAM, excessive backbone rigidity kinetically traps the network, triggering a giant dynamic shift toward a frustrated glassy state that completely suppresses UPy assembly. This profound thermodynamic-to-kinetic transition dictates macroscopic viscoelasticity, providing a new paradigm for designing dynamically tunable, multiscale soft materials.Physical chemistryPolymer chemistryMaterials scienceThermodynamicsMolecular chemistryHydrogelsMethyl modificationStructure-property relationshipSupramolecular interactionspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/00k5988tetdoai:escholarship.org:ark:/13030/qt7wh3s3942026-03-24T06:33:12Zqt7wh3s394Lower-Eyelid Magnetoelastic Film for Eye-Movement Event Recognition and Digital ControlREN, YANYang, LinChen, Jun2026-03-18In a highly digitalized society, people with severe motor impairments often lack an intuitive, reliable, and convenient interface for essential tasks such as text entry, interface navigation, and device control. Mainstream eye-based approaches, including electrooculography (EOG) and camera/infrared tracking, are limited by electrode-interface drift and moisture sensitivity, or by strong dependence on lighting and occlusion, making long-term daily use challenging. This thesis presents a lower-eyelid–mounted, soft magnetoelastic, self-powered eye-control system that converts subtle eyelid deformations induced by eye movements into electrical signals and uses a convolutional neural network to decode five classes of events (up, down, left, right, and blink) in real time, achieving overall accuracies above 96%. To reduce false activations from spontaneous single blinks, a double-blink “select/confirm” command is introduced to improve triggering reliability without substantially compromising accuracy. End-to-end feasibility is demonstrated through a virtual keyboard workflow, showing that eyelid micro-motions can be translated into general control commands for low-burden digital interaction.BioengineeringElectrical engineeringMaterials scienceOphthalmologyEye-movement event recognitionMagnetoelastic compositeSoft MaterialspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7wh3s394etdoai:escholarship.org:ark:/13030/qt8r65494d2026-03-24T06:33:07Zqt8r65494dBuilding World Models with Spatial IntelligenceZhou, ShijieKadambi, Achuta2026-03-18The rapid ascent of Large Language Models (LLMs) and Vision-Language Models (VLMs) has revolutionized semantic understanding, yet these systems remain “spectators”—observing the world through 2D projections without true physical grounding. This dissertation, Building World Models with Spatial Intelligence, addresses this critical gap by establishing a blueprint for AI systems that possess Spatial Intelligence: the ability to perceive, imagine, interact with, and reason about the 3D world. We posit that achieving this requires a symbiotic architecture that combines the vast semantic knowledge of 2D foundation models with the geometric precision of explicit 3D/4D representations, specifically Gaussian Splatting.The research is presented through four core contributions corresponding to the essential faculties of a World Model. First, we introduce Feature 3DGS, a framework for Perception that transforms 2D observations into a semantic, editable, and promotable 3D scene representation. By distilling high-dimensional features from 2D foundation models (e.g., SAM, CLIP) into explicit 3D Gaussian fields, this approach enables spatially grounded multimodal world modeling. Second, to address scenarios where direct 2D observations are absent, we present DreamScene360, an engine for Imagination that generates immersive, globally consistent 3D environments from natural language descriptions by lifting synthesized 360 degree panoramas into explicit 3D Gaussians. Third, recognizing that the true physical world is inherently dynamic rather than static, we advance towards a more generalized world modeling framework for Interaction. We introduce Feature4X, which transforms monocular videos into a dynamic, multimodal, and interactive 4D world via a unified spatiotemporal latent feature field. This explicit representation equips VLM-driven agents with the spatial and temporal grounding needed to perform complex tasks such as 4D scene editing and spatiotemporal Visual Question Answering (VQA). However, whether frontier AI is truly capable of solving complex, high-level cognitive tasks within the real, dynamic physical world remains an open question. To investigate this, we finally propose VLM4D, a rigorous benchmark for Reasoning that exposes the fundamental limitations of current VLMs in understanding dynamic physical interactions, driving future advancements in physical AI. Together, these works demonstrate a path toward building World Models that do not just recognize patterns in pixels, but understand the geometry and dynamics of the physical world, laying the groundwork for the next generation of Spatial Computing and Embodied AI.Artificial intelligenceComputer scienceInformation technologyRoboticsComputer engineeringapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8r65494dhttps://escholarship.org/content/qt8r65494d/qt8r65494d.pdfetdoai:escholarship.org:ark:/13030/qt9k11f3b42026-03-24T05:03:34Zqt9k11f3b4Microneedle Fabrication: Drug Loaded Microneedles & Bistable Adhesive Polymer based MicroneedlesMitchell, Karina DLi, Song2025-01-01Microneedles offer a minimally invasive and efficient platform for transdermaldrug delivery, disease detection, and biosensing. This study explores two novel
microneedle-based technologies. The first project investigates the use of polyvinyl
alcohol (PVA) microneedles loaded with oxygen-doped microparticles and α-
Galactosylceramide (α-GalCer) nanoparticles to treat melanoma tumors.
Characterization confirmed successful particle synthesis, encapsulation, and controlled
release, while mechanical testing demonstrated structural integrity for effective skin
penetration. In vitro biocompatibility studies revealed no cytotoxic effects, and in vivo
assessments verified skin penetration efficiency. The second project explores the
feasibility of fabricating microneedles using a BAP, a material that transitions from rigid
to soft at body temperature. Morphological analysis confirmed the successful formation
of sharp, well-defined microneedles. Although specific applications remain
undetermined, the material’s unique properties make it a promising candidate for
biosensing and other innovations. These studies highlight the versatility of microneedles
and pave the way for further research into potential applications.Bioengineeringalpha-galactosylceramideBistable PolymersDrug Loaded MicroneedlesMicroneedlesmicroparticlesPolyvinyl Alcoholenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9k11f3b4https://escholarship.org/content/qt9k11f3b4/qt9k11f3b4.pdfetdoai:escholarship.org:ark:/13030/qt5543r5xv2026-03-24T05:02:34Zqt5543r5xvEngineering 3D-printed electroconductive ROS-scavenging nanoparticles embedded cardiac patchZHOU, FANGAnnabi, Nasim N.A.2025-01-01Myocardial infarction (MI) is a leading cause of morbidity and mortality worldwide, necessitating advanced therapeutic strategies. This study reports the development of a novel electroconductive hydrogel system incorporating polymeric nanoparticles (NPs) composed of methacrylated choline and dopamine. These NPs provide conductivity, antioxidant properties, and adhesive characteristics, essential for cardiac tissue engineering. The hydrogel, based on gelatin methacrylamide (GelMA), was synthesized through a blue-light-induced photocrosslinking method. Various physicochemical characterizations, including Fourier transform infrared spectroscopy, nuclear magnetic resonance, scanning electron microscopy, and transmission electron microscopy, confirmed the successful formation of NPs and their integration into the GelMA matrix. Mechanical testing demonstrated enhanced tensile strength, elasticity, and adhesion properties of composite hydrogels compared to the control GelMA, aligning with the requirements for cardiac tissue applications. Additionally, the hydrogel exhibited significant antioxidative potential by scavenging reactive oxygen species, crucial for reducing inflammation post-MI. The bioink formulation facilitated the successful 3D printing of structured hydrogel scaffolds, promoting cell adhesion, proliferation, and viability. This study introduces a promising conductive hydrogel system for myocardial repair, offering a multifunctional platform for future regenerative medicine applications.Engineeringenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5543r5xvhttps://escholarship.org/content/qt5543r5xv/qt5543r5xv.pdfetdoai:escholarship.org:ark:/13030/qt19f3f02w2026-03-23T06:33:17Zqt19f3f02wThe Impact of Sample Based Virtual Instruments in Music Composition EducationDuran Zarate, Carlos AlbertoKrouse, Ian2026-03-20This dissertation examines how sample-based virtual instruments (commonly referred to as sample libraries) reshape contemporary composition pedagogy and musical agency. As composition increasingly unfolds within digital audio workstations, software functions simultaneously as instrument, studio, and interface, altering how composers encounter sound, labor, and orchestration. Situating sample libraries within the histories of digital sampling and media-industry restructuring, the study argues that these tools transform recorded performance into modular, commodified assets that mediate many composers’ first sustained encounters with orchestral sound. To clarify their aesthetic and structural differences, the dissertation develops a dual-axis taxonomy that maps libraries according to production logic and correlates it with the aesthetic practices of the 20th and 21st centuries.The project also identifies two central tensions in sample-library-mediated composition: reification, in which abstract and embodied labor is obscured by tangible commodities (presets and interfaces), and phenomenological rupture, the separation of sound from its physical source. The dissertation positions them as sites for reflective engagement and advances the concept of critical musical literacy as a framework for integrating commercial technologies without reducing composition to software operation. The final chapter considers emerging generative artificial intelligence systems and proposes a model of instruction that equips composers to act deliberately and critically within increasingly automated creative environments.MusicPerforming arts educationFilm studiesImpactMusic CompositionMusic EducationOrchestrationPedagogySample librariesapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/19f3f02whttps://escholarship.org/content/qt19f3f02w/qt19f3f02w.pdfetdoai:escholarship.org:ark:/13030/qt3kt8d04t2026-03-23T06:33:12Zqt3kt8d04tPhysics-Informed Methods for Generative Modeling and DynamicsXie, TianyiTerzopoulos, Demetri DT2026-03-20AI-driven content generation has transformed creative industries ranging from entertainment and gaming to education. Yet most generative models learn purely from data, lacking an intrinsic understanding of physics. The resulting outputs frequently exhibit unrealistic structures, implausible dynamics, and nonphysical artifacts, limiting their use in applications that demand physical consistency. This dissertation tackles this fundamental limitation by integrating physics-based simulation into generative modeling, enhancing the plausibility, stability, and coherence of synthesized content.
We begin with the foundations of physics-based simulation, developing a robust and efficient method for coupling deformable solids and fluids in a unified Lagrangian framework, achieving a penetration-free guarantee while significantly improving computational efficiency over prior monolithic approaches. Building on differentiable simulation, we then tackle the problem of self-supportability in generative 3D asset creation, embedding standability constraints under gravity, contact, and friction directly into text-to-3D optimization pipelines, and validating the results through real-world fabrication. Moving beyond static assets, we address the challenge of endowing multi-view reconstructions with physically grounded dynamics, integrating Newtonian continuum mechanics into the 3D Gaussian representation to synthesize novel 4D motions across diverse material types, and further extending this to real-time immersive interaction in virtual reality through efficient deformable body simulation and two-level mesh embedding. Finally, we explore the synergy between physics-based simulation and video foundation models for animation and interaction generation. We address the problem of generating physically plausible cartoon animations from static illustrations by coupling image-space deformable simulation with guided video diffusion. We then tackle the inverse problem of recovering physical parameters and forces from generated videos via differentiable simulation-based tracking, enabling faithful reproduction of complex 3D character dynamics. Extending this paradigm to humanoid loco-manipulation, we address the challenge of scalable 4D human-object interaction generation and physically plausible skill learning, producing a large-scale dataset of diverse interaction sequences and learning generalizable humanoid control policies through reinforcement learning in physics simulation. Together, these contributions demonstrate that embedding physical priors into generative pipelines yields content that is not only visually compelling but also physically consistent, opening new avenues for computer graphics, embodied AI, and robotics.Computer scienceArtificial intelligenceInformation technology3D/4D Content GenerationGenerative AIPhysics-Based SimulationpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3kt8d04tetdoai:escholarship.org:ark:/13030/qt9124m40k2026-03-23T06:33:07Zqt9124m40kSolar-Thermal Synthesized Graphitic Materials for Electronics CoolingKIL, MIN JONGFisher, Timothy2026-03-20Thermal management is increasingly a performance and reliability limiter for high-power-density electronics, where localized hot spots accelerate degradation and constrain power scaling. This challenge is amplified in the emerging computing infrastructure: global data centers are expected to consume approximately 682 TWh of electricity in 2026, and a substantial fraction of this demand is associated with cooling and environmental control. As device and package power densities increase, effective cooling requires both lateral heat spread to reduce the peak junction temperature and low-resistance thermal interfaces to efficiently couple heat into external heat sinks. Accordingly, this dissertation focuses on heat spreaders and thermal interface materials (TIMs) based on graphitic carbon. Conventional synthesis routes, such as chemical vapor deposition, require long process times, complex procedures, and transfer/assembly steps that introduce defects and interfacial resistance. To overcome these limitations, a rapid and green manufacturing pathway is developed using a concentrated solar thermal (CST) power. A custom CST 10 kWe xenon light source; approximately 3cm peak heated region; up to 4.4 MWm-2 delivers short-duration flashes (tens of seconds) that directly convert organic precursors to highly graphitic carbon on the target substrate. In a methane environment, the same solar-thermal platform enables the decomposition of methane and the deposition of graphitic carbon, linking the thermal-management material synthesis with a methane-to-solid-carbon approach. Three material platforms are demonstrated: (1) graphitic heat spreaders formed on a printed-circuit-board-type organic laminate (ASTRA MT77), (2) flexible heat spreaders derived from polyimide films, and (3) compliant graphite TIMs fabricated on carbon-fiber veils. Process parameters (irradiation duration, off-focal position, and optical power) are optimized to achieve thick (few to hundreds of micrometers), uniform, conformal graphitic layers over large conversion areas. Graphitization is verified by Raman spectroscopy, XRD is used to verify crystal structures, and field-emission SEM quantifies graphite thickness and morphology. Heat spreading is evaluated via a modified room-temperature Angstrom's method with infrared thermography during operation of a GaN device bonded to the graphitized substrate, while TIM performance is quantified using a one-dimensional reference-bar method to measure total thermal resistances. Collectively, the results establish CST as a scalable route to fast, low-chemical, and package-integrated graphitic heat spreaders and TIMs for next-generation electronics.Mechanical engineeringMaterials scienceElectrical engineeringpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9124m40ketdoai:escholarship.org:ark:/13030/qt4273r6132026-03-23T06:33:02Zqt4273r613Research on Adversarial Attacks against Image-prompt-based Text-to-Image GenerationYu, PengyueTian, Yuan2026-03-19Retrieval-augmented image generation is designed to improve text-to-image generation for rare, previously unseen, or highly specific visual concepts by retrieving external reference images at inference time. In these systems, the retrieved images are not used only as background information for a user or a reasoning module. Instead, they are passed directly into the image generation model through image-prompt mechanisms such as IP-Adapter, which converts a reference image into visual features that guide the final output. This design improves generation quality, but it also creates a new security risk: a malicious image placed in the retrieval database may influence both which image is selected and what the model ultimately generates.This thesis studies whether a single poisoned image can attack both stages of such a pipeline. We propose a joint attack framework that optimizes one imperceptible perturbation for two objectives. First, the perturbation increases the chance that the poisoned image is retrieved for either a specific query or a broader concept category. Second, once retrieved, the same image acts as a malicious visual prompt that pushes the text-to-image model toward attacker-chosen content, even when the user’s text prompt is benign.We evaluate this attack on the Stanford Cars and Stanford Dogs datasets using five retrieval encoders, including CLIP and DINO variants, and multiple generation backbones, including Stable Diffusion and FLUX. The results show high attack success rates for retrieval manipulation and strong effectiveness in inserting malicious visual content during generation. Together, these findings show that retrieval-augmented image generation systems are vulnerable at both the retrieval and visual-conditioning stages. Future defenses must secure both components jointly rather than treating them as separate problems.Computer scienceArtificial intelligenceInformation technologyadversarial attackimage promptretrieval-augmented generationtext-to-image generationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4273r613https://escholarship.org/content/qt4273r613/qt4273r613.pdfetdoai:escholarship.org:ark:/13030/qt2kx8d3d12026-03-23T06:32:57Zqt2kx8d3d1Photosensitizer-dependent Oxidation and Capture by Amine as a Proximity Labeling Method for Investigating Cholesterol BiologyBecker, Andrew PalmerBackus, Keriann M2026-03-20The physical interactions of proteins with other biomolecules govern the fundamental processes of cellular adaptation and homeostasis. Within this landscape, protein-lipid interactions—particularly those involving cholesterol—occupy a privileged role in modulating membrane fluidity and coordinating signaling. Proximity labeling (PL), where cellular proteins in spatial proximity to a “bait” biomolecule are covalently tagged to allow enrichment and identification, coupled with proteomic analysis enables the proteome-wide detection and quantification of proteins interacting with the bait. This work details the development and application of Photosensitizer-dependent Oxidation and Capture by Amine (POCA), a singlet oxygen-based photocatalytic PL platform designed to map the intracellular and surface interactomes of proteins and lipids. I first demonstrate POCA using HaloTag fusion proteins to map the nuclear pore complex and capture the cholesterol-dependent interactome and state-dependent oligomerization of the cholesterol transport protein Aster-B/GRAMD1B. POCA was then adapted for lipid-directed labeling, which identified both known and unprecedented cholesterol-binding proteins, ultimately revealing the ER membrane complex as a cholesterol-responsive assembly that undergoes thermal stabilization and transcript induction upon cholesterol manipulation. Building on these technical foundations, I applied POCA to identify proteins associated with accessible and sequestered cholesterol using photosensitizer-bioconjugates of the respective ‘sensor’ proteins—ALOD4 and OlyA—targeted to the cell surface. Our analyses had revealed that athero-prone oscillatory disturbed flow (DF) significantly increases the abundance of accessible cholesterol in the plasma membrane; I used POCA to identify the proteins associated with accessible and sequestered cholesterol in this system. I then integrated proteogenomic analysis to identify changes in protein stability in ECs as a result of athero-prone DF. These findings demonstrate that the accessibility of membrane cholesterol regulates endothelial signaling and lay the groundwork for future investigations using this approach.Organic chemistryChemistryCellular biologyBiochemistryCholesterolPhotosenstizerproteomicsproximity labelingSinglet oxygenpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2kx8d3d1etdoai:escholarship.org:ark:/13030/qt6514d7bj2026-03-23T06:32:52Zqt6514d7bjSaccharomyces Cerevisiae and Shewanella Oneidensis under Electrochemical Stress for Applications in Commercial Waste ManagementLopez, Fernando AndresDuan, XiangfengHuang, Yu2026-03-20Move towards electrification creates massive strain on the existing electric grid. Implementing sustainable sources of energy like wind, solar, hydrogen, and batteries can provide sufficient power, but associated capital costs are too great for widescale adoption. Therefore, it is imperative that new power supplies integrate to existing infrastructure to minimize initial capital investments and generate useful electricity as a product.Microbial fuel cells (MFCs) are a form of bioelectrochemical system that fits this profile due to the conversion of organic waste streams to electrical energy using electroactive bacteria biocatalysts. Despites its conversion capabilities, MFCs face critical barriers in commercialization: material longevity, limited power output, limited substrate conversion. Therefore, the research mission addressed herein is to develop a scalable, practical, affordable method of waste conversion to useful byproducts using yeast species Saccharomyces Cerevisiae and electroactive bacteria Shewanella Oneidensis.In the first project I utilize a drop casting method to develop engineering living materials on carbon paper to be used in an electrochemical flow-cell. The goal of this project is to develop a cost-effective alternative to traditional MFCs: I developed a functional biomaterial that uses an interkingdom relationship with yeasts and bacteria to digest a wide variety of substrates, and a bias-free alternative to biofilm formation on highly functional bacterial cellulose.In the second project I demonstrate how elevated electrical potential can be sufficient stimulation to induce catabolic change in S. Oneidensis based on cyclic voltammetry curves. Electrical stimulation was induced on wildtype S. Oneidensis strains and its mutant counterparts ΔMtrC / ΔOmcA to elucidate electron transfer mechanisms.The final project of this dissertation is to elucidate electron transfer mechanisms in S. Cerevisiae and S. Oneidensis under chemical exposure of LiOH. The goal herein is to develop a foundational understanding of how these species react and interact under harsh chemical environments. Lastly, I created a lab-made symbiotic culture of bacteria and yeast (SCOBY) using store-bought kombucha to create an S. Oneidensis inoculated biomaterial for future applications in wastewater treatment.Materials scienceMicrobiologyChemistryCellular biologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6514d7bjhttps://escholarship.org/content/qt6514d7bj/qt6514d7bj.pdfetdoai:escholarship.org:ark:/13030/qt5v84d00g2026-03-23T06:32:47Zqt5v84d00gVoices from the War: Complicating the Founding Myth of the Second Republic of Costa RicaSotillo, Luis AntonioRomero, Robert Chao2026-03-20This thesis examines the 1948 Costa Rican Civil War in an effort to challenge the dominant narrative of Costa Rican exceptionalism. Drawing on archival research, oral histories,
and family testimonies, it argues that Costa Rica’s “Second Republic” founded by José Figueres
Ferrer was built upon selective remembering, historical erasure, and unacknowledged U.S.
intervention. The analysis traces the rise of the Communist Party and its unique “comunismo a la
tica,” which, through an unlikely alliance with the catholic church and President Calderón
Guardia, enacted sweeping social reforms in the 1940s. However, this coalition was dismantled
by the rising tide of anti-communism and red-baiting propaganda that framed legitimate
domestic politics as a foreign threat. After the annulment of the 1948 election, Figueres launched
an armed insurrection where evidence reveals that U.S. diplomatic pressure, selective arms
embargoes against the government, and tolerance of rebel supply lines from Guatemala
decisively tilted the conflict in Figueres’ favor. Testimonies from ordinary Costa Ricans at the
time further complicate the heroic narrative of “good” revolutionaries triumphing over “evil”
communists, revealing a war marked by displacement, dehumanization, and the erasure of
inconvenient truths. By recovering marginalized voices and documenting U.S. involvement, this
thesis demonstrates that Costa Rica’s celebrated identity was not inevitable but forged through
political struggle and historical omission. The abolition of the military in 1949, while
remarkable, has served to obscure the violence and foreign intervention that accompanied the
nation’s reformation.Ethnic studiesPolitical scienceInternational relations1948Civil WarCommunistsCosta RicaFigueresSecond Republicapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5v84d00ghttps://escholarship.org/content/qt5v84d00g/qt5v84d00g.pdfetdoai:escholarship.org:ark:/13030/qt2qm1r70x2026-03-23T06:32:43Zqt2qm1r70xSequential Learning and Inference of Causal Graphical ModelsZhao, YijiaZhou, Qing2026-03-19Many problems involve systems in which causal relationships among variables are represented by causal graphs. Because experimental data are often costly, learning procedures must combine statistical inference with sequential decision making. This dissertation develops methods for learning and inference of causal graphical models under sequential data collection, integrating observational and interventional information to support both optimal decision making and causal structure discovery. The analysis is conducted under linear structural equation models with Gaussian errors, which provide a direct link between causal effects and regression estimation.
The first part of the dissertation (Chapters 2 and 3) studies sequential identification of the intervention with the largest causal effect on a reward variable in an unknown graph. Interventions are treated as actions, and the objective is to determine which intervention produces the greatest effect on the reward. We develop upper-confidence-bound–based algorithms that exploit potential causal relationships among variables to improve learning efficiency relative to standard bandit methods. Under causal sufficiency, observational data are incorporated to construct valid confidence bounds for causal effects, leading to improved regret guarantees. The framework is then extended to settings with latent confounders, where some causal effects may not be identifiable through adjustment. The resulting method distinguishes identifiable and non-identifiable interventions and provides regret bounds that reflect this distinction. Theoretical analysis establishes finite-sample regret guarantees, and simulation studies demonstrate improved learning efficiency compared with existing methods.
The second part of the dissertation (Chapter 4) studies adaptive causal structure learning. Observational data determine a directed acyclic graph only up to its Markov equivalence class, and interventions are required to resolve the remaining uncertainty. We propose a sequential experimental design procedure that selects interventions based on uncertainty in edge orientation and updates the graph using statistical evidence. Theoretical analysis shows that the proposed procedure recovers the true graph almost surely, and numerical experiments demonstrate that it requires fewer interventions than sequential-random and complete-random baseline strategies.MathematicsStatisticsApplied mathematicspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2qm1r70xetdoai:escholarship.org:ark:/13030/qt3qw2743c2026-03-23T06:32:38Zqt3qw2743cRobust and Practical Beamforming for Millimeter-Wave Joint Communication and SensingOlson, Dominic KleinschmidtRoberts, Ian P2026-03-20As cellular networks continue to evolve toward the sixth-generation (6G) of wireless, joint communication and sensing (JCAS) has emerged as a promising research direction that aims to equip these communication networks with high-resolution sensing capabilities. Opportunities for JCAS are particularly ripe in systems operating at millimeter-wave (mmWave) frequencies, as they can offer both high range and angular resolution, courtesy of wide bandwidths and many antennas. Recent research aims to realize JCAS on such systems through a variety of innovative beamforming strategies, spanning compressive sensing, clutter rejection, interference cancellation, and others. Due to hardware imperfections, however, implementing these beamforming techniques on actual mmWave transceivers with high precision can be considerably difficult, but this has largely been overlooked in the majority of prior work on JCAS. In light of this, the objectives of this thesis are twofold. The first aim is to highlight the detrimental effects of these hardware imperfections on JCAS beamforming techniques, if not accounted for. The second aim is to propose methods to overcome these hardware imperfections through measurement and characterization of mmWave transceivers. To accomplish these objectives, extensive measurements are collected with a 60 GHz phased array platform using a custom signal processing pipeline. Then, this platform is used to experimentally evaluate two techniques proposed herein for physically realizing desired beams while accounting for hardware nonidealities. These experimental results indicate that the proposed techniques yield beams that tend to offer higher main lobe gain, lower side lobe levels, greater null depth, and reduced clutter, when compared to beams that neglect hardware impairments. Ultimately, the contributions of this thesis can be used to improve the translation of existing JCAS techniques to practice and can influence future research that involves high-precision beamforming on real-world transceivers.Electrical engineeringComputer engineeringTechnical communication6GBeamformingJCASMillimeter-WavePhased ArrayWirelessapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3qw2743chttps://escholarship.org/content/qt3qw2743c/qt3qw2743c.pdfetdoai:escholarship.org:ark:/13030/qt63w095dm2026-03-23T06:32:33Zqt63w095dmComputational Modeling of Laser Wakefield Acceleration with Spatiotemporally Structured Laser PulsesPierce, JacobMori, Warren B.2026-03-20Plasma-based acceleration (PBA) refers to a class of advanced accelerator concepts in which the electromagnetic fields sustained in a plasma wave are used to accelerate charged particle beams. The strength of these fields can be orders of magnitude higher than those achievable in conventional radio-frequency accelerators. Thus, PBA offers the prospect of dramatically reducing the accelerator size and cost for a given beam energy. This makes PBA attractive for a variety of accelerator-based applications including high-energy collider physics, microscopy using free electron lasers, radiotherapy for cancer treatment, and ion implantation in semiconductors. Laser wakefield acceleration (LWFA), in which an intense laser pulse drives the plasma wave, is one of two concepts of interest for PBA. In this dissertation, new results on LWFA and new capabilities for its simulation are presented through a combination of theory and particle-in-cell computer simulations.In LWFA, the acceleration length and the total energy gain for an accelerating beam are critical system parameters. Lu et al. provided estimates for how these parameters scale in the nonlinear, self-guided regime as a function of basic quantities such as the laser wavelength, laser power, and plasma density. It was assumed that the laser is self-guided over a length greater than the length over which the beam dephases with respect to the wake, but it was recognized that this assumption may break down for sufficiently low plasma density. New results on this physics are presented in this dissertation. Using the quasistatic particle-in-cell (PIC) code QPAD, a parameter scan of simulations is carried out with a fixed laser vector potential, a laser profile which scales with the plasma skin depth c/ωp, and a varying ratio of the laser frequency ω0 to the plasma frequency ωp. It is found that the Lu et al. scaling laws are accurate for densities above a threshold value, below which the self-guiding length is less than the dephasing length and scales as Lguide ≈ 25zR where zR is the Rayleigh range. This results in similar scalings for the acceleration length Lacc ≈ 25zR and energy gain ∆E ≈ 25zR(ωp/c)mec 2 for sufficiently low density. The results indicate that near-term 10 PW lasers may be able to generate 30-GeV electron beams in a single 2-m plasma stage. They also inform longer-term design considerations for LWFA in the context of a linear collider.Spatiotemporally structured laser pulses, which feature coupled spacetime-dependent properties, have been proposed to enable or enhance a variety of laser-plasma interactions, including LWFA. Many of these methods require an accurate understanding of the linear propagation of the pulse. Previous work on linear pulse propagation has mostly considered the transverse or longitudinal evolution of the laser pulse separately, but these must be considered simultaneously to describe spatiotemporally structured pulses. To address this need, theory is developed to describe the linear evolution of the full spatiotemporal structure of a laser pulse in a linear dielectric. The approach uses the dispersion relation to derive integral expressions for the evolution of the pulse. Taylor expansion of the dispersion relation leads to systematic estimates for length scales over which spatiotemporal coupling emerges for a laser pulse with an envelope which is initially separable transversely and longitudinally. These are derived in vacuum and in cold, uniform plasma. The results are also described from the standpoint of single photons propagating within the pulse. Simulations confirm accuracy of the integral formulation and its predictions for the length scales over which spatiotemporal coupling emerges.Exploration of applications of spatiotemporally structured laser pulses requires an understanding of the kinds of spatiotemporal structure that can exist in nature. It is also desirable to be able to describe a given kind of spatiotemporal structure with a mathematical model that captures its essential features. In this dissertation, a general formalism describing spatiotemporally structured laser pulses is introduced which meets both of these demands. This concept of arbitrarily structured laser (ASTRL) pulses employs a superposition of prescribed pulses to create a desired field structure. The ASTRL formalism is capable of describing a variety of kinds of spatiotemporal structure, including pulses with an evolving focal point, spot size, orbital angular momentum, or polarization. Several examples are presented to illustrate the versatility of ASTRL pulses to address a broad range of laser-based applications, including LWFA, inertial confinement fusion, nanophotonics, and attosecond physics.Laser wakefield acceleration (LWFA) may enable the next generation of TeV-scale lepton colliders. Current designs are based on the use of multiple LWFA stages to overcome limitations on the energy gain achievable in a single stage. The use of stages, however, introduces challenges such as alignment, adiabatic matching between stages, and a lower average accelerating gradient. Flying focus pulses, a specific type of spatiotemporally structured pulse, have been proposed to circumvent dephasing. This would reduce both the number of stages and the total accelerator length required for a target energy by enabling operation at higher plasma densities. Adding to these concepts, LWFA driven by a discrete flying focus based on the ASTRL formalism is proposed. A sequence of laser pulses with staggered focal points and delays drives a plasma wave in which an electron beam experiences a near-constant accelerating gradient over distances beyond those attainable with a conventional pulse. Simulations with non-optimized parameters demonstrate that a discrete flying focus with a total energy of 150 J can transfer 40 GeV per electron to a 50-pC beam in a single 30-cm stage, corresponding to 50 dephasing lengths. Extension of the concept to positron acceleration through the use of a discrete set of higher-order Laguerre-Gaussian pulses is also presented.Fully relativistic particle-in-cell (PIC) simulations are crucial for understanding a variety of processes in plasma physics, including LWFA. Modern supercomputers based on graphics processing units (GPUs) offer the potential to perform PIC simulations of unprecedented scale, but require robust and feature-rich codes that can fully leverage their computational resources. To address this demand, GPU acceleration was implemented in the PIC code OSIRIS. An overview of the algorithm, which features a CUDA extension to the underlying Fortran architecture, is given. Detailed performance benchmarks for thermal plasmas are presented, which demonstrate excellent weak scaling on NERSC’s Perlmutter supercomputer and high levels of absolute performance. The robustness of the code to model a variety of physical systems is demonstrated via simulations of Weibel filamentation and laser-wakefield acceleration run with dynamic load balancing. Finally, measurements and analysis of energy consumption indicate that the GPU algorithm is up to 14 times faster and 7 times more energy efficient than the optimized CPU algorithm on a node-to-node basis. The described development addresses the PIC simulation community’s computational demands both by contributing a robust and performant GPU-accelerated PIC code and by providing insight into efficient use of GPU hardware.Plasma physicsNanoscienceFlying focusLaser wakefield accelerationStructured lightapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/63w095dmhttps://escholarship.org/content/qt63w095dm/qt63w095dm.pdfetdoai:escholarship.org:ark:/13030/qt96c3k5912026-03-23T06:32:28Zqt96c3k591Variance Reduction Methods for Ratio Metrics in Online ExperimentsLi, LingyiWu, Yingnian2026-03-20Online experimentation, most commonly implemented as A/B testing, evaluates product changes by randomly assigning users to different versions of a system and comparing their performance on key business metrics. Many commonly used metrics are ratio metrics, such as click-through rate (CTR). To improve statistical sensitivity, variance reduction (VR) methods are widely adopted in practice. However, ratio metrics are nonlinear functions of unit-level outcomes and often exhibit substantial heterogeneity in unit-level contributions. These features limit the applicability and performance of classical variance reduction methods developed for mean outcomes.
This thesis first provides a review of VR methods for ratio metrics and then proposes a stratification-based VR method designed for settings with heterogeneous unit-level contributions. Simulation results show that the proposed method achieves strong variance reduction while remaining unbiased. Overall, this work provides a clear and practical framework for improving statistical efficiency in large-scale online experiments.StatisticsApplied mathematicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/96c3k591https://escholarship.org/content/qt96c3k591/qt96c3k591.pdfetdoai:escholarship.org:ark:/13030/qt2t2152tr2026-03-23T06:32:23Zqt2t2152trQueer Metropolis: Lesbian Gay Bisexual Transgender Plus Placemaking in Mexico CityChica, ChristinaHunter, Marcus2026-03-19Mexico City’s global transformation, increasing LGBT+ visibility, a major political transition, and COVID-19 led me to ask the following questions: (1) How has Mexico City’s transformation into a global city influenced LGBT+ placemaking? (2) How has COVID-19 impacted LGBT+ experiences, relationships, and place projects? (3) How are LGBT+ people queering the map of Mexico City and shaping its geographies? To answer these questions, Queer Metropolis investigates the relationship between urban change and LGBT+ placemaking with particular emphasis on the pre and post COVID-19 landscape for LGBT+ people and organizations. I identify proactive and reactive strategies queer placemakers utilize in adapting to dynamic needs, conflicts, and change emerging from shifting interpersonal relationships, political economy, and crisis. Queer Metropolis paints a vivid qualitative and quantitative portrait of LGBT+ placemaking marshaling ethnography, oral history interviews, archival materials, and an original survey distributed in May 2020 to LGBT+ Mexico City residents.
I connect sociology and geography with interdisciplinary work in gender, sexuality, and ethnic studies as well as disaster studies to bridge insights across them and ground my contribution. I incorporate a connected understanding of space, time, contingency, and luck to intervene in the relationship between structure and agency by theorizing action through placemaking. I call this dynamic, relational theory of action through placemaking, creative destiny—the process of people called by their specific spatial, temporal, and embodied relationships to respond to dynamic needs by creating material and symbolic places. My research revealed that the creative destiny of the collectives I studied was shaped by a pre-existing, social-ethical tension between acting and decision-making through a central figure or vision on the one hand and acting and decision-making through mutual participation and growth on the other. The interplay between this ongoing tension and adapting to COVID-19 led to a diverse array of organizational politics, outcomes, and placemaking projects.SociologyLGBTQ studiesLatin American studiesSexualityCOVID-19Gender and Sexuality StudiesLGBTQMexico CityPlacemakingUrban StudiespubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2t2152tretdoai:escholarship.org:ark:/13030/qt5dh012x32026-03-23T06:32:19Zqt5dh012x3Representation Learning for Image-based Autonomous Driving PerceptionLeng, ZiyangZhou, Bolei BZ2026-03-20Recent advancements in image-based Bird's Eye View (BEV) and 3D occupancy perception have significantly improved the ability of autonomous vehicles to understand their surrounding environments from camera inputs alone. By projecting multi-view camera features into a unified representation, these methods have achieved strong performance on core perception tasks such as 3D object detection, semantic segmentation, and occupancy prediction. Despite this progress, current methods still face fundamental limitations in the effectiveness and efficiency perspective: the learned feature representations in BEV models remain underexplored, and temporal information is insufficiently and inefficiently exploited for volumetric scene understanding. This thesis focuses on representation learning for image-based autonomous driving perception, addressing these challenges through two complementary contributions.The first part of this thesis proposes BEVCon, a contrastive learning framework designed to improve BEV perception by enhancing the quality of learned representations. BEVCon introduces two modules: an instance feature contrast module that refines BEV features through dense contrastive learning in BEV space, and a perspective regional contrast module that provides direct supervision to the image backbone using region-specific features. By jointly optimizing contrastive objectives alongside detection losses, BEVCon achieves consistent performance gains across multiple state-of-the-art BEV architectures on the nuScenes benchmark, with up to 2.4% mAP improvement over competitive baselines. The second part of this thesis presents ST-Occ, a scene-level occupancy representation learning framework that effectively exploits spatiotemporal information for 3D occupancy prediction. ST-Occ introduces a unified temporal modeling paradigm featuring a spatiotemporal memory bank constructed under scene-centered coordinates and a memory attention mechanism with uncertainty and dynamic awareness. This design enables efficient and robust temporal fusion, surpassing state-of-the-art methods by 3 mIoU on the Occ3D benchmark, reducing temporal inconsistency by 29% while achieving best efficiency across training and inference. Together, these two works demonstrate the critical role of representation learning from a spatial and temporal perspective, which advances image-based perception for autonomous driving.Computer scienceArtificial intelligenceRoboticsAutonomous DrivingRepresentation Learningapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5dh012x3https://escholarship.org/content/qt5dh012x3/qt5dh012x3.pdfetdoai:escholarship.org:ark:/13030/qt2b31d3f12026-03-23T06:32:14Zqt2b31d3f1Fundamental Study on Laser Additive Manufacturing of Nano-Treated High Strength Aluminum AlloysZheng, TianqiLi, Xiaochun2026-03-20Wrought aluminum alloys are central to lightweight structural design in aerospace,automotive, and consumer electronics, yet their adoption in laser powder bed fusion (LPBF) additive manufacturing remains limited by poor processability, hot cracking, defect sensitivity, and insufficient thermal and long-term mechanical stability. This dissertation develops and validates nano-treating—the introduction of low volume percent ceramic nanoparticles into wrought aluminum alloys—as a unifying materials–process strategy to enable LPBF fabrication of high-strength aluminum alloys and to improve performance under service-relevant conditions. Guided by a framework that links materials/microstructure, processing, properties, and performance, this thesis combines fundamental alloy studies with process innovation and mechanical reliability testing.First, a controlled casting platform is used to systematically investigate nanoparticle effects in Al-Si-Mg-Cu alloys, revealing composition-dependent interactions between nanoparticles and alloying elements that govern microstructure refinement, precipitation response, and mechanical behavior. Building on these mechanistic insights, nano-treated powders are then applied to LPBF of high-strength wrought systems. For AA6061, process optimization yields dense builds with robust tensile properties, while post-print heat treatment further increases strength. For AA7075— traditionally considered “unprintable” by LPBF—nano-treating enables crack-free fabrication and achieves a step change in mechanical performance, including ultra-high strength after heat treatment while retaining meaningful ductility.The dissertation then focuses on the two critical barriers to structural deployment of LPBFaluminum: thermal stability (creep) and long-term mechanical stability (fatigue), using nanotreated AA2024 (thereafter 2024NT) as a model alloy. Elevated-temperature tensile and creep testing demonstrate that LPBF 2024NT exhibits substantially increased strength and reduced minimum creep rates relative to conventional wrought counterparts, consistent with nanoparticle enabled stabilization of microstructure and resistance to thermally activated deformation. Mechanistically, microstructural analyses link these gains to nanoparticle-stabilized fine precipitates and refined grains that resist coarsening and act as persistent obstacles to dislocation motion, delaying thermally activated deformation. Under high-cycle fatigue loading, 2024NT demonstrates exceptional fatigue performance: in the non- hot isostatic pressing (HIP) condition, its fatigue strength already matches wrought 2024, while after HIP it reaches ~200 MPa and approaches 50% of yield strength, surpassing both wrought 2024 and previously reported LPBF 2xxx aluminum alloys. Fractographic and microstructural analyses reveal that singular TiC nanoparticles and TiC pseudo-clusters act synergistically to enhance fatigue resistance by promoting tortuous crack propagation, deflecting and locally arresting advancing cracks, and inhibiting crack initiation near pores. Building on these observations, this dissertation further elucidates a theory framework describing how nano-treating mitigates porosity sensitivity through the combined effects of crack-path tortuosity and local high-modulus pseudo-cluster barriers, thereby explaining the unusually high pore tolerance and superior long-term fatigue durability achieved in LPBF nano-treated aluminum alloy.Collectively, this thesis establishes nano-treating as a scalable platform to unlock LPBF of wrought aluminum alloys by simultaneously improving printability, mechanical properties, and reliability. The results provide mechanistic understanding and practical processing guidelines for translating high-strength wrought alloys into additively manufactured components with enhanced creep resistance and fatigue performance for demanding lightweight applications.Mechanical engineeringMaterials scienceNanosciencepubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2b31d3f1etdoai:escholarship.org:ark:/13030/qt2gs892sb2026-03-23T06:32:09Zqt2gs892sbSubscriber Identity Module-Based Mini Systems and ApplicationsDing, BoyanLu, Songwu2026-03-20Subscriber Identity Module (SIM), including eSIM (embedded SIM), is ubiquitous as an essential part of widely-deployed 5G connected devices. The SIM possesses rich capabilities beyond its conventional use, including autonomous on-card processing, secure storage, and a standardized interface for device control and information retrieval. Despite the potential of SIM to uniquely benefit the device and user applications, its usage today are still largely confined to the traditional network authentication and limited application-specific or operator-centric functions. We identify three roadblocks hindering the development of practical SIM-based mini systems, including (1) interaction with users and applications, (2) limitations of SIM hardware and interface, and (3) the ability of verification on commercial SIM and network.
This dissertation introduces SIM-Based Mini Systems, a systematic approach to building practical SIM-based systems for a wide range of user applications. Based on the design principles of exploring the flexibility within the standard and seeking innovation from both inside and outside the SIM, we address the roadblocks by applying the following ideas in the design, including a thin system layer, device-SIM cooperation, and verification on operational 5G network. The effectiveness of the design principles and ideas are demonstrated by two practical applications. First, we develop a SIM-based filesystem to provide resilient and user-private storage for applications while supporting standardized file system access APIs, resulting in 1.27x--3.90x speedup in filesystem operations when compared to a traditional solution. Second, we create a SIM solution to address the communication requirements for the drone delivery scenario. It improves the reachability and timeliness in drone communication, reducing the disruption duration by a maximum of 5.41x and increases the deployment density by 4.08x.Computer scienceComputer engineeringElectrical engineering5G NetworkMobile SystemsSIMapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2gs892sbhttps://escholarship.org/content/qt2gs892sb/qt2gs892sb.pdfetdoai:escholarship.org:ark:/13030/qt7dc421fx2026-03-23T06:32:04Zqt7dc421fxNew Frontier: Architecture and Service, 1893-1966Hou, Chi-ChiaOsman, Michael2026-03-20Beginning in the 1890s, with the exhaustion of “unoccupied” lands in the American West, the closing of the frontier became a prevailing framework for reconsidering the mission of American land-grant universities. Without free land to sustain the Jeffersonian ideal of a democratic republic, the vision of individuals securing property, exercising exclusive rights, cultivating moral character, and contributing to the wealth of the nation appeared increasingly baseless. In response, the nation’s political imagination were taken up by a professional class of agricultural scientists, land economists, home economists, and urban planners. They sought new ways to scrutinize, manage, and redistribute property. Their effort to reconfigure the imbrication of subjecthood, wealth, and ownership did not simply extend the scientific and industrial logics that had shaped nineteenth-century development. Rather than endorsing the unbounded expansion of mechanized production, they questioned its limits and turned to the cultural and economic conditions under which a discourse of service could guide capitalist determinations.
At this historical juncture, the problem of settlement was no longer governed solely by the rational management of natural resources, but by the unstable and interacting relation between production and demand. An emerging economic and service-oriented discourse redirected disciplinary attention toward the usefulness and desirability of output in relation to others—toward demand itself. Unlike the inquiry into the relative stability of natural conditions, demand appeared as a subjective, fluctuating, and indeterminate force, shaped by individual and collective contingencies. Its analysis required new methods capable of grasping variability and change, while also casting doubt on the tenets of industrialism—production for its own sake—exposing the possibility that labor and capital might be expended without corresponding need.
Settlement, in turn, was recast as a continual process of recalibration among land, labor, and market, guided by forms of economic reasoning grounded in measurement, comparison, prediction, and risk. These statistical practices rendered property intelligible within relational systems of evaluation and decision-making, where physical assets of disparate material forms were made commensurable and subject to coordinated management. Property, as such, became increasingly embedded within broader economic, legal, and ideological formations. Within this domain, professional expertise intruded upon the bounds of private ownership, complicating the moral framework that had once anchored property in absolute personal responsibility.
Over the following half century, this reconfiguration of property extended beyond rural settlement. It informed corporate expansion, pedagogical formation, urban planning, and broader techniques of economic management. A professional managerial class, equipped with tools of measurement and forecast, reproduced the national imaginary of a “new frontier”—not as open land, but as a continuous process of calculation and adjustment. In this formulation, ownership remained central to national identity, but it was no longer defined solely by the interiority of possession. Instead, it was constituted through relational systems in which one’s property was continually evaluated in relation to that of others.ArchitectureHistoryManagementDemandManagementPropertyRiskServiceSettlementapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7dc421fxhttps://escholarship.org/content/qt7dc421fx/qt7dc421fx.pdfetdoai:escholarship.org:ark:/13030/qt2832x0zc2026-03-23T06:31:59Zqt2832x0zcThe Role of HAP40 in Biology and in Huntington’s DiseaseDionisio, Leonardo EstebanYang, Xiangdong William2026-03-20Huntington-Associated Protein 40kDA (HAP40), encoded by the F8A gene on the X-Chromosome, is an important and obligate partner of the huntingtin gene. Although it has been identified as a strong 1:1 binding partner, it’s role in both modulating huntingtin (HTT), in normal biology, and in the context of modifying Huntington’s Disease (HD) is unknown. Here I leveraged the generation of novel conditional mouse models of F8a to understand HAP40’s role in the cortex and striatum, both in WT and in the context of HD. Through neuropathology, transcriptomics, behavior, proteomics, and morphological profiling, I have observed minimal phenotypic changes when HAP40 is depleted in the striatum of both WT and HD mice; however, there is robust neurodegeneration and atrophy in the postnatal cortex of F8a cortically deficient mice. These phenotypes are age-dependent, progressive, cell-type selective, and is not phenocopied by the effects seen in normal HTT cortically deficient mice. HAP40 therefore plays an important role in the postnatal maturation and survival of cortical pyramidal neurons, and its relationship with HTT could suggest a key time window and potential mechanism underlying its essential function in mammalian cortical development.NeurosciencesBehavioral psychologyDevelopmental biologyGeneticspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2832x0zcetdoai:escholarship.org:ark:/13030/qt1mk3r7gs2026-03-23T06:31:54Zqt1mk3r7gsSeismic Functional Response of Highway Bridges in California with RC and NiTi SMA-ECC ColumnsCetinkaya, Mustafa YavuzTaciroglu, ErtugrulZhang, Jian2026-03-19Modern ordinary reinforced concrete (RC) highway bridges in high seismic regions can experience significant physical damage during rare but strong earthquakes. In such events, post earthquake serviceability is often severely compromised, largely due to damage concentrated in conventional RC columns. To explore the use of a novel bridge column design that incorporates Nickel-Titanium Shape Memory Alloy (NiTi SMA) and Engineered Cementitious Composite (ECC)—referred to as NiTi SMA-ECC columns, in place of conventional RC columns to enhance seismic performance and post-earthquake serviceability, this study is a comprehensive evaluation of the seismic performance of typical California bridge classes featuring both RC and NiTi SMA ECC columns.
A seismic performance assessment framework was developed to quantify post earthquake serviceability and expected recovery time (RT) as a function of earthquake intensity. This framework was applied to four representative California box girder bridge classes featuring RC and NiTi SMA ECC columns. The findings of the study are not limited to bridges in California and are generally applicable to other reinforced concrete bridges sharing similar characteristics. Analytical bridge models incorporating material and geometric uncertainties were subjected to a large ground motion set. Bridge component responses were statistically characterized to develop bridge failure fragilities and component level demand models. These statistical models were combined through a Monte Carlo simulation approach to estimate expected post earthquake RT for each bridge class as a function of intensity measure (IM). The resulting RT curves show that NiTi SMA ECC columns consistently reduce recovery times at high IM levels due to substantial reductions in column failures. A bracket design for abutments was also proposed to mitigate the increased likelihood of superstructure unseating associated with the greater flexibility of NiTi SMA ECC columns.
Critical IM levels at which bridges with NiTi SMA ECC columns provide shorter recovery times than their RC counterparts were identified with confidence levels. Finally, regional recovery time loss assessments were conducted for a 75 year design life across 1,000 locations in California using the expected RT curves developed for each bridge class. Geographic regions and site conditions where NiTi SMA ECC columns yield lower lifetime recovery time loss than RC columns were identified for each bridge class.
The influence of longer recovery times at low IM levels—stemming from the greater flexibility of NiTi SMA ECC columns—on cumulative recovery time loss was highlighted. An alternative design strategy involving modest adjustments to abutment stiffness was demonstrated to mitigate this low IM disadvantage for one bridge class.
The findings of this research are envisioned to provide a technical basis for departments of transportation (DOTs) in high seismic regions when they consider future column design choices for improving post earthquake functional recovery of highway bridges.Civil engineeringMaterials scienceEnvironmental engineeringEngineered Cementitious Composite (ECC)Nickel Titanium Shape Memory Alloy (NiTi SMA)Recovery TimeSeismic ResilienceSeismic RiskSelf-centering and low-damage columnspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1mk3r7gsetdoai:escholarship.org:ark:/13030/qt105050xf2026-03-23T06:31:50Zqt105050xfBayesian Artificial Intelligence for Complex Dependent DataZhou, Daniel F.Banerjee, Sudipto2026-03-20This dissertation develops novel methodology for transfer learning under the Bayesian statistical framework. We approach transfer learning from two different angles: by training on existing interpretable statistical models with the original data restructured to make training feasible, and by training on synthetic data generated by an existing model to learn the posterior distribution function. Beginning with the first approach, we take multiple solutions generated from computer models of physical or mechanistic systems and compile them as data to train our statistical model. If individual solutions of the data consist of a large number of points, it is segmented into smaller sets of points to make it feasible. The trained model may then be used to seek optimal values of unknown parameters from observational field data under an inverse problem framework. In short, this setup trains on segmented data and is then applied to larger non-segmented data to acquire parameter estimates. We apply this approach to population dynamics governed by the Lotka-Volterra equations and SIR output with spatial dependencies inspired by the heat equation.
The second approach trains a machine learning model on computer generated data from a prespecified statistical model, with the goal to output posterior samples given different realizations of data. It is also called amortized inference or amortized Bayesian inference, because training is expected to take a considerable amount of time, but each run of the trained model is short, so that the time cost averages out over the number of runs. We apply this approach to fitness data acquired from wearable devices, segmenting the data over blocks of time steps and training a separate model over each, with prior parameters for each segment adjusted for the time step in the sequence. We apply this using the BayesFlow method inspired by normalizing flows to facilitate posterior sampling and demonstrate the scalability of this approach for arbitrarily large time segments.
We close by considering extensions of the Markovian models to graphical structures, with the goal to utilize more flexible modeling. We flesh out the procedure for directed acyclic graphs (DAGs) and discuss to additional graphical structures that may be processed to form DAGs.BiostatisticsApplied mathematicsArtificial intelligenceAmortized inferenceBayesian transfer learningGaussian process regressionGraphical modelsUncertainty quantificationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/105050xfhttps://escholarship.org/content/qt105050xf/qt105050xf.pdfetdoai:escholarship.org:ark:/13030/qt9hx5p2222026-03-23T06:31:36Zqt9hx5p222Mixed Human-Autonomous Agent Collaboration in Non-Dyadic Teams: Coordination Across the Perception-to-Action Pipelinede Gortari Briseno, JulianSrivastava, Mani B.2026-03-19As robotic agents increasingly operate alongside humans in real-world domains, effective collaboration requires more than individual autonomy. While much prior work in human–robot interaction has focused on dyadic settings, real deployments commonly involve non-dyadic teams composed of multiple humans and autonomous agents operating under uncertainty, partial observability, and time pressure. In such teams, coordination failures often arise from misalignment in how teammates perceive the environment, reason about plans, communicate intent, and execute actions.
In this dissertation, we study mixed human–autonomous collaboration in non-dyadic teams by examining coordination across sensing, decision-making, communication, and action. We introduce a unifying perspective that treats coordination as a team-wide process spanning the full decision loop rather than as an isolated capability. To support this perspective, we develop an agent architecture that enables autonomous teammates to participate in collaborative decision-making with humans under uncertainty in these settings.
To test our architecture, we develop a realistic embodied simulation environment that supports heterogeneous teams of humans and autonomous agents performing time-critical hazard response tasks under partial observability. Using this environment, we conduct a series of empirical studies analyzing how team strategy and leadership structure influence coordination, performance, and human experience in non-dyadic human–robot teams. Our results show that leadership and communication structure play a central role in maintaining alignment as teams scale, and that autonomous leadership can improve performance when appropriately integrated into team interaction.
Finally, we translate these insights into the design of a robot coordination architecture intended for deployment in real-world non-dyadic teams. The proposed system integrates planning, communication, and mediation mechanisms to support alignment among multiple human teammates during task execution. By grounding the architecture in the coordination challenges identified through earlier empirical studies, this work outlines design principles for autonomous agents that participate in human teams not only as task performers but also as coordination partners. Together, the contributions of this dissertation advance the understanding of human–autonomous teamwork beyond dyadic interaction and provide design principles for building autonomous teammates that support robust and scalable collaboration in complex team settings.Computer engineeringRoboticsArtificial intelligenceDistributed Autonomous SystemsHuman-Robot CollaborationLarge Language ModelsMulti-Agent SystemsNon-Dyadic TeamsRobot Coordination Architectureapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9hx5p222https://escholarship.org/content/qt9hx5p222/qt9hx5p222.pdfetdoai:escholarship.org:ark:/13030/qt8ps8m25d2026-03-23T06:31:31Zqt8ps8m25dCloud Join: Reformulating Wander Join for Sample-Efficient Cardinality EstimationYao, AlanWang, Remy2026-03-18Sampling-based approaches for cardinality estimation traditionally suffer when join predicates are particularly selective, requiring far too many samples to arrive at a reasonable estimate; the effect is exacerbated by the potentially exponential decrease in join probability that comes with a linearly increasing number of relations. To combat this, we introduce Cloud Join, a new sample-efficient method that reformulates today's state-of-the-art estimator, Wander Join, as the evolution of a Feynman-Kac flow. We empirically demonstrate on the Join-Order and Stats-CEB benchmarks that Cloud Join significantly outperforms Wander Join in cardinality estimation, owing to its faster convergence.Computer scienceStatisticsArtificial intelligencecardinality estimationdatabasesjoin ordersamplingpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8ps8m25dmultimediaoai:escholarship.org:ark:/13030/qt22z722q52026-03-23T06:31:27Zqt22z722q5Causal Discovery for Dependent Binary and Mixed DataChen, AlexZhou, Qing2026-03-18Causal discovery seeks to infer the underlying causal relationships among a set of variables from observational data, typically represented through a directed acyclic graph. The assumption of independence between observations (units) in a dataset is prevalent across various methodologies for learning causal graphical models. However, this assumption often finds itself in conflict with real-world data, posing challenges to accurate structure learning. First, we propose a de-correlation-based approach for causal graph learning on dependent binary data, where the local conditional distribution is defined by a latent utility model with dependent errors across units. We develop a pairwise maximum likelihood method to estimate the covariance matrix for the dependence among the units. Then, leveraging the estimated covariance matrix, we develop an EM-like iterative algorithm to generate and de-correlate samples of the latent utility variables, which serve as de-correlated data. Any standard causal discovery method can be applied on the de-correlated data to learn the underlying causal graph.
We then extend this framework to mixed discrete and continuous data, which introduces additional modeling and computational challenges. We assume a similar latent utility model but include a thresholding mechanism to accommodate multi-level discrete data. We use a pairwise maximum likelihood approach for mixed data to estimate a covariance matrix (dependence) among units and impute the latent continuous versions of the discrete data under an EM algorithm. De-correlation and causal graph estimation is used similarly to the binary case. Simulations on both random and real graphs show our approach significantly improves causal graph recovery over standard methods on the original data. Lastly, we apply both methods to a single-cell RNA-seq (scRNA-seq) dataset to infer a gene regulatory network (GRN) in embryonic stem cells and early differentiation. We demonstrate the improvement of our de-correlated causal discovery approaches over baseline methods in terms of test data likelihood comparison. High confidence regulatory interactions predicted by our method align well with biological literature.StatisticsComputer scienceArtificial intelligenceInformation scienceBinary DataCausal DiscoveryDependent DataMixed Dataapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/22z722q5https://escholarship.org/content/qt22z722q5/qt22z722q5.pdfetdoai:escholarship.org:ark:/13030/qt0tv135hj2026-03-23T06:31:22Zqt0tv135hjEnhancing Stretchability and Toughness of Phase-Changing Polymers by Incorporating EVA and Porous StructuresCao, ZhilinPei, Qibing2026-03-18Side-chain-crystalline phase-changing shape memory polymers enable sharp thermo-mechanical switching within a narrow temperature window. However, their crystalline rigid state is often brittle, limiting strain tolerance and load-bearing applicability in wearable scenarios. Here, phase-changing polymer films (PCFs) were prepared by copolymerizing stearyl acrylate (SA) with urethane diacrylate (UDA) and trimethylolpropane trimethacrylate (TMPTMA) to form a crosslinked side-chain-crystallizable network, followed by blending with ethylene vinyl acetate (EVA) to improve stretchability and toughness. The SA side-chain crystals remain the dominant switch, giving a melting transition near 45–46 °C. EVA transforms the brittle network with ~25% fracture strain into highly ductile films exceeding 1000% elongation while maintaining ~7–8 MPa stress, and cyclic tests show ~100% shape fixity ratio with shape recovery ratio ~90%. In addition, PCF foams were further produced by adding azodicarbonamide (ACD), exhibiting uniform 200–300 μm cells and >95% height recovery upon heating after repeated compression. Future work could focus on microscopic mechanism including co-crystallization and lamellar structures and extend the platform toward integrated heating and sensing with long-term fatigue and environmental stability assessment.Materials sciencePolymer chemistryChemical engineeringEthylene vinyl acetatePorous structureShape memory polymersSide-chain crystallizationpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0tv135hjetdoai:escholarship.org:ark:/13030/qt67x6v3qd2026-03-23T06:31:17Zqt67x6v3qdAn Empirical Analysis of Arbitrage and Insider Trading on Polymarket NBA MarketsYang, JiaxinCheng, Guang2026-03-18This thesis leverages the transparent, on-chain architecture of Polymarket to empirically study algorithmic arbitrage and insider trading within NBA prediction markets. Analysis of high-frequency limit order book snapshots reveals extreme microstructural efficiency; single-market mispricings resolve in a median of 3.614 seconds, while combinatorial arbitrage is economically bounded by shallow liquidity. To detect insider trading without regulatory labels, an unsupervised Isolation Forest model is trained on pre-resolution behavioral features and calibrated against a quantitative Pseudo-Ground Truth (PGT) Oracle. The model’s top 1.0% anomaly cohort captured a statistically significant 11.7% of aggregate market profits. These findings demonstrate that while structural inefficiencies on Polymarket are fleeting, distinct on-chain behavioral signatures effectively isolate insider trading. StatisticsFinanceEconomicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/67x6v3qdhttps://escholarship.org/content/qt67x6v3qd/qt67x6v3qd.pdfetdoai:escholarship.org:ark:/13030/qt2f7898np2026-03-23T06:31:09Zqt2f7898npFraud Detection with Asymmetric Costs in Online Talent Marketplaces: A Comparative Study of Machine Learning ApproachesMahalley, AnshumanCheng, Guang2026-03-17This study compares machine learning approaches for detecting cheating in online interviews from a production dataset using anonymized behavioral features and social network data from ∼272,000 candidates. The dataset presents severe class imbalance, a large volume of unlabeled observations with weak labels, and a sparse social graph with over 1.7 million edges. We engineer 36 predictive features – including missingness indicators and graph- derived metrics – and evaluate three approaches: an XGBoost baseline, a semi-supervised XGBoost extension with differentiated sample weighting, and a GraphSAGE neural network. Models are evaluated under a cost-based metric with asymmetric penalties, where missing a cheater ($600) is twice as costly as incorrectly blocking a legitimate candidate ($300). The XGBoost model achieved the best cost score, outperforming both the semi-supervised model and GraphSAGE. Threshold analysis reveals auto-blocking is only cost-effective above 96.77% confidence. Feature importance analysis shows that feature missingness patterns strongly correlate with cheating, and handcrafted graph features outperform end-to-end graph learning in sparse networks. These results highlight the continued effectiveness of gradient boosting on tabular fraud detection tasks and the importance of aligning model evaluation with real-world cost structures.StatisticsComputer scienceArtificial intelligenceInformation scienceapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2f7898nphttps://escholarship.org/content/qt2f7898np/qt2f7898np.pdfetdoai:escholarship.org:ark:/13030/qt777180bq2026-03-22T06:30:35Zqt777180bqSystematic Optimization of Air-Processed Perovskite Solar Cells via Modelling, Solvent Engineering, and Interface PassivationTong, Yiu ShunYang, Yang YY2026-03-16Perovskite solar cells (PSCs) have emerged as a promising photovoltaic technology due to their high power conversion efficiency and compatibility with solution-based processing. Among scalable deposition techniques, blade coating offers significant advantages in material utilization and industrial adaptability. However, the blade-coating process involves multiple interdependent parameters, and precise control of film crystallization under ambient conditions remains challenging, which hinders its transition from laboratory-scale fabrication to industrial production.In this study, a systematic optimization strategy for blade-coated perovskite solar cells was developed by integrating data-driven modelling, solvent engineering, and interfacial passivation. A novel Complex System Response (CSR) function was introduced to quantitatively correlate blade gap and coating speed with device efficiency. A second-order nonlinear response surface was constructed using experimental data, enabling prediction of the global extremum of device performance. The predicted optimal parameters were experimentally validated, demonstrating strong agreement between calculated and measured efficiencies. The optimized blade-coating conditions were determined to be a wet film thickness of 200 μm and a coating speed of 15 mm s⁻¹ in 9:1 2-ME:NMP mixed solvent. Subsequently, the precursor dispensing volume and solvent composition were systematically investigated. Photoluminescence (PL) and scanning electron microscopy (SEM) analyses revealed that a dispensing volume of 6 μL per 2cm2 yielded improved film uniformity and reduced pinhole density. Solvent engineering further demonstrated that a 2-methoxyethanol (2-ME) to N-methyl-2-pyrrolidone (NMP) ratio of 9:1 promoted enhanced crystallinity and reduced non-radiative recombination, as confirmed by PL and X-ray diffraction (XRD) measurements. Different surface passivation layers were then evaluated to further improve device performance. Among the tested materials, phenethylammonium chloride (PEACl) exhibited the most effective defect passivation, enabling a power conversion efficiency of 22.92% through suppressed trap-assisted recombination and improved interfacial charge transport. This work establishes a modelling-assisted and experimentally validated optimization framework for blade-coated perovskite solar cells. The methodology developed herein provides practical guidance for scalable fabrication and offers valuable insights for advancing perovskite photovoltaics toward industrial application.Materials scienceChemistryChemical engineeringAlternative energySystems scienceAmbient processingBlade coatingComplex system response modelingPerovskite solar cellsProcess optimizationSolvent engineeringapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/777180bqhttps://escholarship.org/content/qt777180bq/qt777180bq.pdfetdoai:escholarship.org:ark:/13030/qt9pk9n7kv2026-03-22T05:03:34Zqt9pk9n7kvNeural-Symbolic Methods for Knowledge Graph ReasoningCheng, KeweiSun, Yizhou2024-01-01Knowledge graph (KG) reasoning has been studied extensively due to its wide applications, which is addressed by two lines of research, i.e., traditional symbolic reasoning and modern neural networks-based techniques. Symbolic reasoning has been the most studied approach toward KG reasoning since the earliest days. This approach represents expert knowledge as statements in a logic-based representation language and implements reasoning as logical inference, primarily addressing high-level reasoning and cognitive processes. For instance, consider the logical rule \emph{$\text{SpeakLanguage}(x, y) \leftarrow \text{LiveIn}(x, z) \wedge \text{OfficialLanguage}(z, y)$}, along with the observed facts \emph{``LiveIn(Mina Miller, USA)''} and \emph{``OfficialLanguage(USA, English)''}, we can infer the new fact \emph{``SpeakLanguage(Mina Miller, English)''}. Symbolic reasoning approaches have demonstrated strong interpretability and generalizability due to the power of logical rules. Nevertheless, they encounter certain limitations when it comes to scaling up to vast datasets, managing ambiguity, and capturing the correlations between entities and relations. To illustrate, consider the case where both \emph{``USA''} and \emph{``United States''} denote the same country. Symbolic reasoning approaches treat them as distinct entities, thereby failing to automatically transfer the knowledge from the existing triple \emph{``LiveIn(Mina Miller, USA)''} to deduce the new fact \emph{``LiveIn(Mina Miller, United States)''}.Neural networks-based techniques have recently emerged as state-of-the-art methods in KG reasoning, owing to their successful applications. Unlike symbolic reasoning, these techniques aim to predict unseen triples by exploring the graph structure in KGs to capture the similarity of entities. Neural networks-based techniques have demonstrated good scalability and a strong ability to capture correlations between entities and relations. However, they fall short in modeling higher-order dependencies of KG relations. For instance, while neural networks-based techniques can recognize that \emph{``USA''} and \emph{``United States''} refer to the same country based on their similarity in embeddings, they cannot infer that \emph{``Mary Stilwell''} and \emph{``Mina Miller''} speak \emph{``English''} without leveraging logical rules.While symbolic reasoning and neural networks-based techniques are typically seen as distinct approaches toward achieving the ultimate goal of KG reasoning, they possess a natural potential to complement and enhance each other. Symbolic approaches offer significant interpretability and generalizability, leveraging logical rules to provide insights into inferred results and enabling easy generalization to unobserved objects. However, they struggle to handle large datasets and fail to capture correlations between entities and relations. Conversely, neural networks-based techniques address the limitations of symbolic approaches by exploring intrinsic similarities among triples, but they lack interpretability and generalizability. By integrating both techniques into a unified framework, neural-symbolic reasoning provides a more efficient, generalizable, and interpretable way to perform KG reasoning. In this thesis, we provide a comprehensive overview of techniques in integrating symbolic logical rules and neural networks for KG reasoning, with a particular focus on two specific tasks, KG completion and logical rule learning and propose our own solutions to address the limitations of existing works.Computer scienceKnowledge GraphNeural-SymbolicReasoningenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9pk9n7kvhttps://escholarship.org/content/qt9pk9n7kv/qt9pk9n7kv.pdfetdoai:escholarship.org:ark:/13030/qt97z226n42026-03-22T05:03:27Zqt97z226n4Political Violence, Exile, and Mobilization from Abroad: The Approval of Emigrant Political Rights in Argentina and Colombia (1970-1991)Diossa-Jimenez, Leydy JhohanaWaldinger, RogerMenjivar, Teresa C2024-01-01This dissertation analyzes the relationship between political violence, forced migration, and the role of exile organizations on emigrant political rights. It examines Argentina (1970-1991) and Colombia (1980-1991), the first Latin American countries to grant such rights to citizens abroad. Through a comparative analysis, this study illuminates how variations in political violence influenced emigrants' displacement, politicization, and transnational activities. A central theoretical contribution of this research lies in its sending-state-centric perspective, which highlights the importance of politicides in understanding the path-dependent interactions among political violence, international migration, and emigrant politics.By employing a comparative framework, this dissertation extends the scholarship on emigrant enfranchisement by examining Argentina and Colombia as pioneer and yet unexplored cases of emigrant political rights. It employs insights from disparate research domains to develop a novel perspective into the evolution of political organizations, the nature of exile, the dynamics of forced migration, and the impacts of transnational political engagements. The analysis challenges conventional narratives by recognizing the varied roles of political entrepreneurs and former violence specialists in emigrant politics, thereby questioning the assumption that cultural and economic capital uniformly lead to politicization among educated emigrants.Furthermore, this work introduces an analytical framework that conceptualizes political violence as a constitutive element of emigrant politics, advancing our understanding of the ways in which violence shapes exile experiences and influences the political engagement of emigrants in both sending and receiving countries. This violence-centric approach reveals the intricate interplay between domestic political conditions and the provision and demand for emigrant political rights, shedding light on the challenges and opportunities for political inclusion in the context of dual transitions to democracy and peace.In sum, this dissertation not only fills a significant gap in migration studies by systematically exploring the relationship between political violence and emigrant politics but also provides critical insights into the political processes affecting emigrant rights in hybrid democratic regimes. Through its detailed comparative analysis, the study contributes to a deeper comprehension of transnational political practices and their impact on the political landscape of sending states, offering a novel perspective on the interplay of violence, migration, authoritarianism, and democratization.This dissertation analyzes the relationship between political violence, forced migration, and the role of exile organizations on emigrant political rights. It examines Argentina (1970-1991) and Colombia (1980-1991), the first Latin American countries to grant such rights to citizens abroad. Through a comparative analysis, this study illuminates how variations in political violence influenced emigrants' displacement, politicization, and transnational activities. A central theoretical contribution of this research lies in its sending-state-centric perspective, which highlights the importance of politicides in understanding the path-dependent interactions among political violence, international migration, and emigrant politics.By employing a comparative framework, this dissertation extends the scholarship on emigrant enfranchisement by examining Argentina and Colombia as pioneer and yet unexplored cases of emigrant political rights. It employs insights from disparate research domains to develop a novel perspective into the evolution of political organizations, the nature of exile, the dynamics of forced migration, and the impacts of transnational political engagements. The analysis challenges conventional narratives by recognizing the varied roles of political entrepreneurs and former violence specialists in emigrant politics, thereby questioning the assumption that cultural and economic capital uniformly lead to politicization among educated emigrants.Furthermore, this work introduces an analytical framework that conceptualizes political violence as a constitutive element of emigrant politics, advancing our understanding of the ways in which violence shapes exile experiences and influences the political engagement of emigrants in both sending and receiving countries. This violence-centric approach reveals the intricate interplay between domestic political conditions and the provision and demand for emigrant political rights, shedding light on the challenges and opportunities for political inclusion in the context of dual transitions to democracy and peace.In sum, this dissertation not only fills a significant gap in migration studies by systematically exploring the relationship between political violence and emigrant politics but also provides critical insights into the political processes affecting emigrant rights in hybrid democratic regimes. Through its detailed comparative analysis, the study contributes to a deeper comprehension of transnational political practices and their impact on the political landscape of sending states, offering a novel perspective on the interplay of violence, migration, authoritarianism, and democratization.SociologyExileMigrationMobilizationPoliticideRightsViolenceenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/97z226n4https://escholarship.org/content/qt97z226n4/qt97z226n4.pdfetdoai:escholarship.org:ark:/13030/qt44s6r7282026-03-21T06:32:38Zqt44s6r728Post-Earthquake Repair of Reinforced Concrete Structural WallsRodriguez Sanchez, SantiagoWallace, John W2026-03-19The ability to repair reinforced concrete walls with modest damage following earthquake shaking is important because walls provide substantial lateral load strength and stiffness and because damage is likely to be concentrated at a limited number of locations. Accordingly, guidance supported by experimental evidence is required to enable practicing engineers to select optimal repair strategies. Relatively few experimental studies have been performed to address this issue; therefore, an experimental study was conducted to investigate the post-repair behavior of approximately one-third scale slender walls with rectangular and C-shaped cross sections. The study focused particularly on walls incorporating lap splices at the wall–foundation interface, non-weldable longitudinal reinforcement, large shear span ratios, and either special or ordinary boundary detailing. During the initial testing phase, Performance-Critical Damage, as defined by FEMA P-2335, including concrete core crushing, longitudinal bar buckling, longitudinal bar rupture, and splice failure, was induced through reversed cyclic loading. The walls were then repaired using various techniques and subsequently retested to evaluate their post-repair performance. All repair techniques were developed considering potential field limitations and primarily focused on restoring lateral strength and improving deformation capacity. Repairs involved the removal and replacement of damaged concrete and reinforcement, combined with techniques intended to improve confinement, including the addition of transverse reinforcement, the use of steel plates, or the application of CFRP wraps with anchors. One rectangular wall was repaired using only concrete replacement along with supplemental CFRP confinement, without replacing longitudinal or transverse reinforcement. The influence of applying axial load during the repair process was also examined. Test results indicate that the repaired walls exhibited overall performance comparable to the baseline specimens, with some repaired walls demonstrating approximately 30% greater deformation capacity. However, the initial stiffness was not fully recovered for any of the repaired specimens. Additionally, two CFRP-based retrofit strategies were evaluated for the rectangular walls after the benchmark specimen exhibited splice failure despite satisfying the special wall detailing requirements of ACI 318-14 Chapter 18. For the second rectangular wall, externally bonded CFRP wraps with supplemental anchors successfully resulted in a flexural failure above the splice. In contrast, vertical CFRP anchors installed on a single face of the third rectangular wall were insufficient to prevent splice failure.Civil engineeringPlate tectonicsEngineeringEnvironmental engineeringLap splice failurePerformance-Critical DamagePost-EarthquakeReinforced Concrete WallsRepairRetrofitapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/44s6r728https://escholarship.org/content/qt44s6r728/qt44s6r728.pdfetdoai:escholarship.org:ark:/13030/qt0164q1x62026-03-21T06:32:33Zqt0164q1x6Dependent Scattering in Colloidal Suspensions and Mesoporous Films and MonolithsMartinez, RicardoPilon, Laurent G2026-03-19Light scattering by nanoparticles plays critical role in a wide range of technologies and applications. In the food and pharmaceutical industries, it enables accurate optical characterization of nanoemulsions and colloidal suspensions. Light transfer is also important for design of solar absorbers and color filters where metallic nanoparticle embedded matrices are engineered to enhance solar absorption or filter colors. Light transfer also plays a key role for design of aerogels for transparent insulation of commercial building windows. Typically, light scattering by nanoparticles is handled by using the independent scattering approximation which assumes that the effective scattering properties of a suspension of particles can be obtained by the superposition of each individual particle's contribution. However, when the concentration of particles increases, their interparticle spacing becomes small and the scattered wave spherical wave of one particle may interact with the scattered waves of its nearest adjacent particles. This situation is known as dependent scattering and significantly alters the optical response of a suspension. This phenomenon is often over-looked or neglected which may result in malinformed design of nanoparticle suspensions, thin-films, and mesoporous monoliths.This dissertation aims to (i) developed and validate rigorous framework for simulating light transfer through thick suspensions and monoliths containing concentrated nanoparticles, (ii) establish criteria for which dependent scattering becomes significant in suspensions of randomly distributed particles, (iii) understand the impact of particle aggregation on light scattering and (iv) inform design of nanoparticle-based materials with desired optical properties for a range of applications. To do so, first the novel Radiative transfer with reciprocal transactions (R2T2) method based on the T-matrix method was developed and validated against experimental predictions of different nanoparticle suspensions and mesoporous monoliths. Second, the effect of particle concentration was determined by using the R2T2 method and its predictions were compared against popular numerical light transfer simulation techniques to determine when dependent scattering prevails. Third, the R2T2 method was used to understand the effect of particle aggregation by simulating light transfer through digital twins of fractal aggregates having a range of fractal dimension and porosity. Finally, the simulations and experiments presented in this work provided parametric studies to assist the design of nanoemulsions, colloidal suspensions, plasmonic light filters, and silica aerogels.The results demonstrate that dependent scattering prevails in nanoparticle suspensions having volume fractions as low as 1%. Furthermore, the results indicate that dependent scattering always prevails in mesoporous monoliths due to inherit particle surface contact even at low volume fraction. Importantly, the results suggests that fractal microstructure of aerogels can be tuned during synthesis to achieve a high transmittance at any particle volume fraction.Mechanical engineeringMaterials scienceOpticsaerogelscolloidal suspensionsdependent scatteringelectro magnetic scatteringR2T2 methodradiative transferapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0164q1x6https://escholarship.org/content/qt0164q1x6/qt0164q1x6.pdfetdoai:escholarship.org:ark:/13030/qt85b1t1xw2026-03-21T06:32:28Zqt85b1t1xwDeveloping A Magnetoelastic Energy Method at the Atomic ScaleMcIntosh, MatthewCarman, Gregory2026-03-19The study of magnetic phenomena at the nanoscale requires modeling frameworks that bridge the gap between computationally expensive quantum mechanical treatments and spatially averaged continuum micromagnetics. This dissertation presents the development of a fully coupled, atomistic magnetoelastic energy method implemented within a hybrid Molecular Dynamics–Atomic Spin Dynamics (MD–ASD) framework. By treating individual atoms as classical particles and magnetic moments as classical spin vectors subject to quantum-informed energetics, the model enables the direct coupling of atomic displacements and spin evolution. The framework is implemented as a custom pair potential in the LAMMPS software package, incorporating exchange, magnetocrystalline anisotropy, dipole–dipole interactions, and a novel atomistic formulation of cubic magnetoelastic energy. The model is validated against established micromagnetic solvers for BCC iron, demonstrating its capability to capture both linear and nonlinear magnetoelastic responses. Specifically, simulations of displacement-induced magnetic reorientation reveal significant deviations from linear elastic behavior. The results show that the nonlinear spin-rotation pathway drives a distinct mechanical response in transverse displacements and system stiffness, with the latter exhibiting a local minimum and maximums as the spins settle into new energy wells. These findings underscore the importance of atomistic resolution in describing strain-mediated magnetization dynamics in nanoscale multiferroic systems and provide a predictive tool for the design of next-generation spintronic and memory devices.Mechanical engineeringElectromagneticsMaterials scienceEnergyAtomic Spin DynamicsLAMMPSMagnetismMolecular DynamicsMultiferroicapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/85b1t1xwhttps://escholarship.org/content/qt85b1t1xw/qt85b1t1xw.pdfetdoai:escholarship.org:ark:/13030/qt98c6x5762026-03-21T05:03:24Zqt98c6x576Prenatal Exposure to Per- and Polyfluoroalkyl Substances (PFAS) and Risk of Autism Spectrum Disorder in the California Central Valley, 2004-2010Osborne, KatherineRitz, Beate2025-01-01There is mixed evidence regarding the association between prenatal exposure to per- andpolyfluoroalkyl substances (PFAS) and autism spectrum disorder (ASD). ASD cases (n = 294)
born in the California Central Valley from 2004 – 2010 were identified from California
Department of Developmental Services (DDS) records. Controls (n = 286) born in the California
Central Valley were identified from California birth records and were matched to cases by sex,
year of birth, and exposure to selected pesticides and air pollution. Three PFAS were measured
through high-resolution metabolomic profiling in >60% of maternal serum samples collected
mid-pregnancy from mothers who participated in the California Prenatal Expanded Alpha-
fetoprotein Screening Program: perfluorooctanoic acid (PFOA), perfluorohexanesulfonic acid
(PFHxS), and perfluorooctane sulfonate (PFOS). Adjusted odds ratios (aORs) and 95%
confidence intervals (CIs) were estimated using logistic regression to examine relationships
between individual PFAS and ASD. We also tested for non-linear associations between prenatal
PFAS exposure and ASD using restricted cubic spline regression models. Additionally, we
utilized a quantile-based g-computation approach to examine associations between a total PFAS
mixture and ASD. We founded an elevated aOR for ASD comparing the highest to the lowest
PFOA exposure quartile (aOR = 1.71; 95% CI: 0.96, 3.06), but the confidence interval included
the null. Additionally, for PFOA we saw a non-linear J-shaped exposure-outcome relationship in
a restricted cubic spline regression model. No apparent associations were found for the PFAS
mixture and ASD. Associations with several PFAS were modified by maternal education. For
example, among children of mothers with low levels of education (high school or less), the
highest PFOA quartile was strongly associated with ASD compared to the lowest exposure
quartile (aOR = 2.97; 95% CI: 1.43, 6.18), and such association was not seen among mothers
with higher education levels (aOR = 0.62; 95% CI: 0.24, 1.64). Using an untargeted
metabolomic profiling approach, we found that prenatal exposure to PFOA was associated with
ASD in children, particularly among mothers with lower education levels. Larger studies are
warranted to replicate our findings, and more research is necessary to elucidate the underlying
mechanisms driving this association.Epidemiologyenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/98c6x576https://escholarship.org/content/qt98c6x576/qt98c6x576.pdfetdoai:escholarship.org:ark:/13030/qt8tq7b1922026-03-21T05:03:21Zqt8tq7b192Locally Adaptive Statistical Models with Applications in Quantile Regression and Causal InferenceYe, SiweiMadrid Padilla, Oscar Hernan2025-01-01Local patterns, such as piecewise constant structures, frequently appear in real-world data. Many applications demand models capable of capturing these localized variations, where data may change abruptly or remain constant across different local regions. Statistical models such as fused lasso and nearest-neighbor algorithms are particularly effective in addressing this localized complexity. The fused lasso encourages sparsity in differences between adjacent observations over a graph structure, making it well-suited for identifying and estimating local patterns. Nearest-neighbor algorithms focus on nearby data points, offering a flexible and locally adaptive framework to capture varying structures within the data. This dissertation develops innovative statistical models that integrate fused lasso and nearest-neighbor algorithms to address research problems in quantile regression and causal inference, as demonstrated in the following projects. The first project introduces a non-parametric quantile regression framework using the $K$-nearest-neighbor fused lasso to provide robust and locally adaptive estimation of multivariate functions at different quantile levels. The second project presents a score-based approach for estimating heterogeneous treatment effects, combining propensity and prognostic scores with nearest-neighbor matching to stratify and estimate treatment effects over a two-dimensional grid. The third project proposes graph-based fused lasso models to estimate heterogeneous treatment effects over graph structures. These projects highlight the versatility and effectiveness of locally adaptive models that build on fused lasso and nearest-neighbor algorithms. Validated through simulation studies and real-world applications, these models offer valuable insight for fields such as economics, medicine, and social science.Statisticsenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8tq7b192https://escholarship.org/content/qt8tq7b192/qt8tq7b192.pdfetdoai:escholarship.org:ark:/13030/qt6bq219422026-03-21T05:02:49Zqt6bq21942Earwitnesses in America: Listening Literatures in the Nineteenth-Century U.S.Pledger, Jené MHyde, Carrie2025-01-01In the eighteenth and nineteenth century United States, the art of oratory and rhetoric was held in high esteem as a powerful force of culture, politics, and nation building. But to what extent did the impact of the spoken word in this period depend on the listening subject? To what degree were there common listening practices or cultural investments in listening? Earwitnesses in America recovers “listening literatures,” a term this dissertation uses to refer to a body of U.S. literature that engages significantly with listening as a behavior and contains rich depictions of aural experience. By analyzing the writings of William Apess, Frederick Douglass, Harriet Jacobs, and Charles Brockden Brown, this dissertation shows that authors often invoked listening in their writings to imagine forms of knowledge and attentions that could enable social reform. For these writers, the power of eloquence and the spoken words was inherently conditional and dependent on access to a listening audience. What really mattered was the right to be heard.American literatureenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6bq21942https://escholarship.org/content/qt6bq21942/qt6bq21942.pdfetdoai:escholarship.org:ark:/13030/qt5qc5700q2026-03-21T05:02:31Zqt5qc5700qEngineering Polymers for Future Bioelectronicsyan, yichenHe, Ximin X2025-01-01Bioelectronic devices hold tremendous promise, yet their progress is constrained by the materials properties as well as by conventional design concepts. In my research, I have developed new materials and device designs to improve the energy capture and storage and signal collection aspect of the device. Chapter 2 describes a tough, high-conductivity, anti-freezing electrolyte for impact-resistant flexible batteries that addresses the strength-diffusivity trade-off. Chapter 3 describes a strong, flexible, low-impedance electrode for robust and reusable in vitro devices that addresses the strength-conductivity-capacity trade-off. For in vivo interfacing with delicate tissues, a modulus-switching ultrasoft electrode was developed for shape-morphing devices. Finally, a direct print-on-skin EEG was proposed that addressed the stability and efficiency limitations of existing systems. In Chapter 4, a portable phototropism photovoltaic system for enhanced energy harvesting was developed that addresses the complexity of previous designs. Chapter 5 includes several future directions to advance the capabilities of bioelectronics.Materials Scienceenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5qc5700qhttps://escholarship.org/content/qt5qc5700q/qt5qc5700q.pdfetdoai:escholarship.org:ark:/13030/qt4gd4d7qw2026-03-21T05:02:17Zqt4gd4d7qwQuantitative Dynamic Contrast-Enhanced MRI towards Clinical Feasibility and AccessibilityWu, ChaoweiLi, Debiao2025-01-01Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a powerful modality for characterizing tissue vascularity and monitoring therapeutic response. Pharmacokinetic parameters estimated from quantitative DCE-MRI provide in-depth insight onto the characteristics of the microenvironment. However, broader clinical adoption is hindered by limitations such as low temporal resolution, protocol variability, and safety concerns associated with gadolinium-based contrast agents. This dissertation aims to advance quantitative DCE-MRI methods to address these challenges—thereby improving both feasibility and accessibility—through two primary developments: (1) a retrospective quantification approach for conventional DCE-MRI via pharmacokinetics-informed deep learning (DL), and (2) a prospective quantification approach for an advanced low-dose Multitasking DCE (LD-MT-DCE) technique.First, a retrospective quantification (RoQ) technique leveraging pharmacokinetics-informed DL is proposed to convert conventional multi-phase abdominal DCE-MRI into high-temporal-resolution datasets. By synthesizing additional time points and estimating pharmacokinetic parameters in standard Tofts model with strong agreement to reference values, this method mitigates the trade-off between spatial and temporal resolution. In studies of healthy controls and patients with pancreatic ductal adenocarcinoma or chronic pancreatitis, the RoQ approach achieved intraclass correlation coefficients of 0.84–0.94 and significantly differentiated normal from diseased tissues. By addressing low temporal resolution constraints, this work supports broader adoption of quantitative DCE-MRI for tumor assessment under standard clinical protocols.Second, the RoQ technique was integrated into a multi-phase DCE-MRI pipeline to improve the generalizability of predicting pathological complete response (pCR) at early-treatment stages. In retrospective analyses of over 1,000 patients from multiple clinical sites, the proposed method achieved higher accuracy (AUC-ROC up to 0.82) and greater consistency than existing strategies, particularly in external datasets. Through harmonized pharmacokinetic parameter estimation, this framework tackles a key barrier to robust treatment-outcome prediction across diverse imaging protocols.Finally, a novel DL-based parametric fitting was introduced to alleviate the challenge of low signal-to-noise ratio for abdominal low-dose Multitasking DCE (LD-MT-DCE), which alleviates gadolinium-related safety concerns by using only 20\% of the standard contrast dosage. Incorporating non-rigid motion-correction into iterative image reconstruction and employing DL-based parametric fitting, LD-MT-DCE addresses the challenges of low signal-to-noise ratio and more pronounced motion artifacts. In 11 volunteer studies, LD-MT-DCE achieved high reproducibility, demonstrating its potential to reduce contrast load while maintaining quantitative fidelity.Collectively, these contributions underscore the promise of quantitative DCE-MRI in routine clinical practice. By synergizing deep learning-driven parametric mapping, improved temporal resolution, and low-dose imaging, this dissertation paves the way for safer, more robust, and widely accessible DCE-MRI in cancer detection, staging, and treatment monitoring.BioengineeringDCE-MRIDeep LearningMRIpCRPharmacokinetic Mappingenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4gd4d7qwhttps://escholarship.org/content/qt4gd4d7qw/qt4gd4d7qw.pdfetdoai:escholarship.org:ark:/13030/qt3hw3s1wm2026-03-21T05:02:12Zqt3hw3s1wmProtein Nanocapsules: Modulators of the Immune SystemCao, ZhengLu, Yunfeng2024-01-01 Protein therapeutics show promising potential for treating a wide range of diseases associated with immune dysfunction, yet their broader application is limited by disadvantages such as poor stability, short half-life, and potential immunogenicity. To address these challenges, this dissertation presents the development of protein nanocapsules that encapsulate protein molecules within a thin layer of polymer, conferring protein therapeutics with enhanced stability, prolonged circulation half-life, and reduced immunogenicity. Specifically, this dissertation discusses the application of protein nanocapsules to modulate immune responses in two critical healthcare challenges: viral infection and cancer. The first part of this dissertation explores catalase nanocapsules to alleviated symptoms related to SARS-CoV-2 infection. These nanocapsules act by mitigating hyperinflammation and suppressing viral replication through reducing the levels of hydrogen peroxide-a key immunostimulatory molecule that escalates during viral infection. The efficacy of catalase nanocapsules has been validated in SARS-CoV-2 infection model using non-human primates, showcasing its potential as a novel therapeutic strategy.
The second part of this dissertation focuses on the design of lactate oxidase nanocapsules, which counteract tumor immunosuppression and enhance the efficacy of cancer immunotherapy. By targeting excessive lactate, a byproduct of tumor metabolism, lactate oxidase nanocapsules not only alleviate tumor immunosuppression but also produce hydrogen peroxide, which further recruits and activates immune cells in the tumor microenvironment. This dual-action mechanism improves anti-tumor immunity and boosts the efficacy of immune checkpoint blockade therapy in multiple solid tumor models.
The innovative development of nanocapsules of different protein therapeutics allows us to regulate immune responses, such as mitigating hyperinflammation or activating antitumor immunity, offering a promising strategy to manage diseases related to immune dysfunction.
Biomedical engineeringChemical engineeringBioengineeringCancer immunotherapyDrug deliveryHyperinflammationInfectious diseasesNanomedicineenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3hw3s1wmhttps://escholarship.org/content/qt3hw3s1wm/qt3hw3s1wm.pdfetdoai:escholarship.org:ark:/13030/qt00j2n2j12026-03-21T05:01:09Zqt00j2n2j1Unveiling Novel Regulators of Mechanical Phenotype in Breast and Ovarian Cancer Cells by High-throughput Deformability ScreeningAlvarez, Francisco JavierRowat, Amy C2024-01-01During cancer metastases, tumor cells migrate through the extracellular matrix (ECM) of connective tissues and traverse even smaller, sub-micron pores to spread to distant sites. The biophysical properties of cells, such as cellular deformability, are implicated in metastasis. In addition, tumor cells with mesenchymal phenotype tend to be more deformable than those with epithelial phenotype. Our current investigation of cellular mechanical phenotype or mechanotype builds on previously reported differences in acquired cisplatin-resistant ovarian adenocarcinoma cells. Our team screened the library of 1280 pharmacologically active compounds (LOPAC) to identify drugs that could reverse the inherent deformability of cisplatin-resistant cancer cells. We identified top hits that significantly decrease cell deformability of OVCAR5-cisplatin resistant (cisR) cells and analyze novel regulators of cellular deformability using high-throughput parallel microfiltration (PMF). We used Mergeomics, a bioinformatics approach to conduct meta-analysis across the molecular targets of the hits, their associated signaling pathways, and subnetwork neighbors, to predict regulators of cellular deformability, and validated these results experimentally. To assess cell deformability we used cellular filtration, where the percent retention represents the fraction of suspended cells retained over our micro-pore membrane filter following PMF. We confirmed the effects of key drivers in regulating cell deformability using pharmacologic and genetic manipulations. We also observed shared molecular mediators of cellular mechanotype, deformability, and invasion in breast and ovarian cancer cell lines, following treatment with another pharmacologically active agent, 4′-hydroxyacetophenone (4-HAP). 4-HAP is known to inhibit non-muscle myosin II (NM2), increasing cell stiffness and cortical tension. We discussed the functional interpretation of cellular deformability and overall cell mechanotype to obtain a mechanistic understanding of the mechanome and shared regulation of metastatic behaviors in breast and ovarian cancer cell lines. Taken together, our data revealed potential targets to modulate cellular mechanotype and highlights cell mechanical behaviors as a target for anti-cancer therapeutics.Cellular biologyMolecular biologyBiophysics4′-hydroxyacetophenonecellular deformabilitycellular mechanotypeepithelial-to-mesenchymal transitionHigh-throughput Parallel Microfiltration (PMF)metastatic behaviorsenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/00j2n2j1https://escholarship.org/content/qt00j2n2j1/qt00j2n2j1.pdfetdoai:escholarship.org:ark:/13030/qt8825v8ks2026-03-20T06:34:06Zqt8825v8ksImpact of Formin FH1 N-terminal DimerizationChristian, Bryan AndrewQuinlan, Margot E2026-03-18Formins are a class of actin nucleators that build unique cytoskeletal structures necessary for different cellular functions. Mammals express fifteen different formins divided into seven families. The C-terminal half of formins contain the formin homology 1 (FH1), FH2, and tail domains. The FH2 domain dimerizes to bind and processively travel with the barbed-end of actin filaments during elongation. The FH1 domain is intrinsically disordered and contains proline-rich motifs (PRMs) to bind profilin-actin monomers to add to the barbed-end where the FH2 domain is. Some formin families have been predicted to have a coiled-coil domain N-terminal to the FH1, suggesting that the FH1 could dimerize at its N-terminal end. This can have alternate effects on the FH1: 1) it can extend the FH1 to free any PRMs previously occluded due it its intrinsic nature, and 2) any PRMs that are close to the dimerization point—where the coiled-coil domain ends—can become occluded due the ends coming into proximity. We have preliminary data showing that the putative coiled-coil domain in the Drosophila melanogaster formin Fhod B hinders formin-mediated actin filament elongation when included with the C-terminal half. Additionally, simulated data from a paper currently in review show that including a dimerization domain close to the FH1, and the primary PRM, slows down elongation in all formins C-terminal halves tested. Here, we show that the putative coiled-coil domain in Fhod-B is not a canonical coiled-coil domain and may have other intramolecular interactions. AlphaFold-predicted structures show that the putative coiled-coil domain could form in an anti-parallel conformation, though it has low confidence of the coiled-coil sequence interacting in such way. We also show that FH1-N-terminal dimerization using GST does not hinder formin-mediated actin polymerization in a panel of formins, therefore suggesting that the Fhod-B putative coiled-coil may hindering formin activity through a different interaction altogether.BiochemistryOrganic chemistryCellular biologyActinCoiled-coilDimerizationFH1FhodForminapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8825v8kshttps://escholarship.org/content/qt8825v8ks/qt8825v8ks.pdfetdoai:escholarship.org:ark:/13030/qt0fb4d1rh2026-03-20T06:34:00Zqt0fb4d1rhPhysically Grounded World Modeling: Simulation, Generation, and EmbodimentChen, YunuoJiang, Chenfanfu2026-03-18Advances in physics-based simulation and data-driven generative modeling have significantly improved our ability to construct digital representations of the physical world. High-fidelity numerical solvers grounded in partial differential equations (PDEs) provide principled and interpretable models of physical phenomena, while modern diffusion-based generative models enable scalable and expressive synthesis of shape and motion. Despite their complementary strengths, these two paradigms have largely evolved in isolation. Simulation emphasizes physical correctness but often lacks scalability, whereas generative models excel at visual realism yet frequently neglect physical validity. As a result, current digital world models risk achieving improved visual appearance without the functional reliability required for downstream applications, such as robotics and embodied AI, where accurate physical reasoning is essential.
This dissertation argues that physically grounded world modeling requires the integration of three key components: principled simulation, physics-aware generation, and embodied validation through real-world interaction. Simulation and generation are treated not as isolated approaches but as complementary mechanisms for constructing scalable and physically-consistent digital representations. Embodiment serves as the bridge that anchors these representations to real-world validation of their physical behaviors, ensuring reliability for downstream deployment, interaction, and learning.
To realize this vision, the dissertation develops unified optimization-based simulation frameworks for complex solid systems, incorporates differentiable physical reasoning into 3D and video generative models to improve physical plausibility, and introduces a real-to-sim-to-real pipeline that aligns virtual twins with real sensory observations for robotic manipulation. Together, these contributions establish a coherent framework for building effective and scalable digital world models that maintain physical fidelity while remaining applicable to real-world interaction.Applied mathematicsComputer scienceRoboticsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0fb4d1rhhttps://escholarship.org/content/qt0fb4d1rh/qt0fb4d1rh.pdfetdoai:escholarship.org:ark:/13030/qt6gb7t7r32026-03-20T06:33:55Zqt6gb7t7r3Read-Only: Rethinking PhasehoodAgarwal, HashmitaKeine, Stefan2026-03-18This dissertation proposes a novel view of phases titled Read-Only, which is necessitated due to the possibility of certain cross-phasal syntactic dependencies in Hindi-Urdu, as well as the impossibility of other such dependencies. I observe that in Hindi-Urdu, phases are opaque to dependencies—like case assignment and nominal licensing—which require a spelled-out goal to be modified. On the other hand, phases are transparent to phi-agreement, wh-licensing, movement, and dependent case competition, where the goal is not ultimately altered in the process. While traditional views of phases either rule out all cross-phasal dependencies (Chomsky 2000, 2001) or permit all of them (Fox and Pesetsky 2005), Read-Only offers a nuanced perspective on these dependencies based on how a particular dependency affects the features (and relative order) of the goal in a phasal spell-out domain. This theory that bases the viability of a cross-phasal dependency on if it changes the goal derives the curious case, agreement, movement, and licensing facts in Hindi-Urdu in a principled manner.
Read-Only has three components: Visibility, Feature Immutability, and Cyclic Linearisation. When an element is set to Read-Only, all three of these apply to it simultaneously. The goal of Visibility is to ensure that a Read-Only element is not removed from the syntax, paving the way for cross-phasal dependencies. Feature Immutability permanently renders immutable all features of any element in a Read-Only domain, such that these features cannot be valued or changed, and no new features can otherwise be added to Read-Only elements—thereby filtering out cross-phasal dependencies requiring modification of a Read-Only element. Cyclic Linearisation (Fox and Pesetsky 2005) fixes the relative order of all elements in a Read-Only domain, requiring that the ordering generated in one Read-Only domain be respected in all further phases, and constraining movement.
Empirical evidence for a Read-Only view of phases comes from several case studies comparing different cross-phasal operations in Hindi-Urdu, all of which point to the same conclusion. First, accusative case assignment is compared with phi-agreement, where the latter cannot be cross-phasal, while the former can. Here, Visibility ensures that higher heads can inspect a Read-Only DP and initiate a cross-phasal dependency, while Feature Immutability regulates whether the dependency can actually obtain—it blocks accusative case assignment to an Read-Only element, but allows phi-agreement with the same element. Similar results are shown for impossible cross-phasal accusative case assignment compared with possible cross-phasal wh-licensing, as well as impossible cross-phasal ergative case assignment squared with possible cross-phasal phi-agreement and wh-licensing. The next case study on nominal licensing shows that this process cannot apply to a Read-Only element, since it—like case assignment—requires modifying the Read-Only element's features, violating Feature Immutability. Dependent case competition is also brought into the picture in another case study, where Feature Immutability blocks accusative case assignment to a Read-Only DP, but does not prohibit it from conditioning dative case on a DP in a higher domain. Lastly, Cyclic Linearisation is motivated through the possibility of movement out of a Read-Only domain (which complies with Feature Immutability and Visibility) in addition to movement before Read-Only, as well as through independent reflexes of successive-cyclicity.LinguisticsLanguageSociolinguisticsagreementcaseHindi-Urdumovementphase theorysyntaxapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6gb7t7r3https://escholarship.org/content/qt6gb7t7r3/qt6gb7t7r3.pdfetdoai:escholarship.org:ark:/13030/qt17h0s5s12026-03-20T06:33:49Zqt17h0s5s1The Relationship Between California’s Wildfires and the Counties’ Bush PoliciesLiang, JiayiSchoenberg, Frederic2026-03-18California has experienced increasingly frequent and severe wildfires in recent decades, of- ten attributed to a combination of climate change and land management practices. Public discussions sometimes claim that counties with stricter vegetation or “brush” policies experience lower wildfire risk, yet these claims are rarely evaluated using formal spatial–temporal statistical models.This thesis examines the relationship between wildfire occurrence in California and county- level brush policies using a Stoyan–Grabarnik (SG) objective framework for spatial–temporal point processes. The analysis combines wildfire ignition records from the U.S. Forest Service with daily temperature data from Google Earth Engine, precipitation observations from NOAA stations, and county-level brush policy indicators collected from California county fire agency sources. After cleaning and merging the datasets, the modeling sample contains 35,500 wildfire ignition records between 1987 and 2021, along with climate covariates and two policy indicators: whether a county requires defensible-space inspections at the time of property sale (INSPE REQ FOR SALES) and whether local vegetation ordinances extend beyond minimum state Public Resources Code requirements (ORDINANCE BEYOND PRC).California is discretized into a 10 km by 10 km grid using the California Albers projection, and time is partitioned into four- and six-month intervals. Within each space–time cell, wildfire counts are modeled using an SG-optimized intensity proxy that incorporates baseline spatial fire activity, time since the last fire, temperature, precipitation, and policy indicators.Comparisons of models with and without brush policy variables show that adding the policy indicators produces almost no improvement in the SG objective function. In contrast, climate-related variables and baseline spatial intensity consistently explain variation in wildfire occurrence. Precipitation enters the models with a positive coefficient, which is clarified by a daily analysis showing that the busiest wildfire days are disproportionately associated with non-zero precipitation at nearby stations, consistent with storm systems that produce both lightning ignitions and rainfall.Overall, the results suggest that large-scale climate conditions, historical spatial fire patterns, and fuel accumulation dynamics play a much larger role in explaining wildfire occurrence than the coarse county-level brush policy indicators used here. These findings highlight the importance of incorporating richer climate, fuel, and policy implementation data when evaluating wildfire mitigation strategies.StatisticsGeographyEnvironmental scienceAmerican studiesBush PoliciesCA WildfiresStoyan--Grabarnikapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/17h0s5s1https://escholarship.org/content/qt17h0s5s1/qt17h0s5s1.pdfetdoai:escholarship.org:ark:/13030/qt8km8s0r62026-03-20T06:33:44Zqt8km8s0r6Peers, Play, and Place: Understanding Belonging and Intimacy Through Agentic Social PlayBigsby, MenissahGomez, Louis M2026-03-18Adolescence is a developmental period in which peer relationships play a critical role in emotional well-being, identity formation, and social development. This dissertation examines how agency in shared activities (ASP) contributes to adolescents’ peer relationship quality (PRQ), and how access to flexible low barrier community environments (ATP) can facilitate ASP.Drawing on theories from developmental psychology, play research, and the learning sciences, the dissertation introduces Agentic Social Play (ASP); shared, peer-directed activities in which adolescents shape the flow, tone, and purpose of their interactions. ASP emphasizes voluntary participation, flexibility, and intrinsic motivation, conditions that facilitate authenticity, emotional responsiveness, and sustained peer engagement. The dissertation also introduces Adolescents’ Third Places (ATP), defined as flexible, low-barrier community environments that adolescents can access independently and that plausibly support peer-directed interaction. Using data collected with the Middle Years Development Instrument (MDI), two studies examine these relationships. Study 1 explores the association between adolescents reporting greater engagement in ASP and reporting higher levels of peer belonging and intimacy. Study 2 examines whether access to ATP; measured using GIS-based walking network proximity to accessible community spaces is associated with greater participation in ASP. Both studies incorporate a covariate framework that distinguishes intrapersonal, interpersonal, and structural influences to clarify the scope and distinctness of the focal relationships.Results indicate that participation in ASP is positively associated with peer relationship quality and that access to ATP is associated with greater engagement in ASP. Together, these findings suggest that agency in shared activities represents a meaningful pathway through which peer relationships develop, and that structural access to flexible community spaces may facilitate such interaction. By linking relational processes with modifiable environmental conditions, this dissertation contributes to research on adolescent development and highlights how policy, design, and community planning can support opportunities for meaningful peer connection.EducationSocial psychologyDevelopmental psychologyCounseling psychologyAdolescent connectednessAgencyChild Friendly CitiesHanging OutPlayapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8km8s0r6https://escholarship.org/content/qt8km8s0r6/qt8km8s0r6.pdfetdoai:escholarship.org:ark:/13030/qt0594q70q2026-03-20T06:33:38Zqt0594q70qAsymmetric Drift-Orbit Bifurcation: Models and ObservationsKamaletdinov, SergeiAngelopoulos, Vassilis2026-03-18Understanding the dynamics of energetic electron fluxes in Earth’s radiation belts remains one of the key challenges in Heliophysics. The two most studied mechanisms contributing to these dynamics are (1) wave–particle resonant interactions, which can accelerate and pitch-angle scatter electrons, and (2) radial diffusion driven by ultra-low-frequency (ULF) waves. Yet, observations often reveal behaviors that cannot be fully explained by these two processes. In particular, they necessitate considering radial transport mechanisms that go beyond classical diffusion. Radial transport can arise even in the absence of waves, solely due to the topology of magnetic field lines. This mechanism is known as Drift-Orbit Bifurcation (DOB), which occurs on the dayside, where solar wind compression splits the equatorial magnetic field minimum into two off-equatorial minima, violating the second adiabatic invariant and enabling radial transport. This mechanism has been investigated for magnetic field configurations exhibiting north–south symmetry relative to the geomagnetic equator and east–west symmetry relative to the noon meridian. Although asymmetric magnetic field configurations occur in nature as a rule rather than an exception, studies quantifying the radial transport associated with these asymmetries—either through case studies or statistical analyses—are still lacking.This thesis presents a quantitative investigation of the role of magnetospheric asymmetry in drift-orbit bifurcation. Specifically, it addresses three key questions: (1) the importance of east–west and north–south magnetic field asymmetries in the scattering and radial transport of electrons undergoing drift-orbit bifurcation, (2) the radial transport arising from this asymmetric drift-orbit bifurcation, and (3) the energy range of electrons for which the effects of such transport are comparable to ULF-driven diffusion. To address these questions, we combine (1) empirical magnetospheric magnetic field models driven by realistic solar wind conditions, (2) observations from the THEMIS spacecraft, and (3) the theoretical framework of adiabatic invariant destruction during separatrix crossings. Including realistic magnetospheric asymmetries, driven by a nonzero IMF By and dipole tilt, leads to larger jumps in adiabatic invariants and enhances radial transport driven by drift-orbit bifurcation. The developed theoretical framework explains these large jumps as the so-called geometric jumps of adiabatic invariants, known in the theory of Hamiltonian systems with an asymmetric separatrix in phase space. Using empirical magnetic field models, these jumps are quantified under various solar wind conditions. Approximating these jumps via a model (lower-dimensional) Hamiltonian system, we study its long-term dynamics and find distinctly fast, non-diffusive transport occurring on timescales comparable to or shorter than those driven by ULF waves for electrons with energies above 0.1–1 MeV. Combining THEMIS observations of dayside magnetopause–outer radiation belt crossings with guiding center simulations under realistic (asymmetric) magnetic field configurations, we show that these large jumps can explain isolated enhancements in energetic electron fluxes occurring near the magnetopause. These findings establish the critical role of magnetic field asymmetry in particle radial transport, advancing our understanding of the mechanisms controlling energetic electron dynamics in the dayside magnetosphere.GeophysicsPlasma physicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0594q70qhttps://escholarship.org/content/qt0594q70q/qt0594q70q.pdfetdoai:escholarship.org:ark:/13030/qt3885g5qz2026-03-20T06:33:33Zqt3885g5qzEfficiency without Compromise: Rethinking Small Model's Roles for Better EffectivenessQin, ZongyueSun, Yizhou2026-03-18The ever-growing data and model scale have created a tension between efficiency and effectiveness. Model compression techniques such as pruning, distillation and quantization can significantly improve inference efficiency at the cost of downstream task performance. To go beyond this trade‑off, this thesis investigates lightweight models not merely as compressed versions of large models but as more diverse roles that accelerate both ML and non‑ML applications while preserving or even improving effectiveness. We explore three roles for lightweight models. (i) Lightweight drafters for large‑language-model inference: we design novel decoding algorithms that improve both inference speed and downstream task accuracy via using a small model to generate draft tokens. (ii) Lightweight pruners for large-scale search tasks: for HLS design automation and graph similarity search, we train lightweight models to efficiently and effectively prune unpromising candidates to facilitate downstram tasks. (iii) Lightweight teachers for MLP in link prediction: We show that by providing complementary signals, even simple heuristic methods can serve as good teachers for MLPs in link prediction task. Collectively, these contributions establish a new collaborative paradigm for lightweight models. Rather than approximating large systems, small models can reshape computational pipelines through drafting, pruning, and teaching. Across diverse domains, we show that such structured collaboration can mitigate, and in many cases overcome, the conventional efficiency–effectiveness trade-off. This thesis therefore provides both practical algorithms and conceptual principles for designing scalable, efficient, and high-quality machine learning systems.Computer scienceArtificial intelligenceInformation scienceEffectivenessEfficiencyMachine LearningSmall Modelsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3885g5qzhttps://escholarship.org/content/qt3885g5qz/qt3885g5qz.pdfetdoai:escholarship.org:ark:/13030/qt85p3b5z32026-03-20T06:33:28Zqt85p3b5z3Band-Engineered Molybdenum Disulfide–Molecular Hybrid Superlattices for High-Sensitivity Thermal SensingZhang, YuchengHuang, YuDuan, Xiangfeng2026-03-18Temperature sensing plays a fundamental role in modern technologies ranging from environmental monitoring and medical diagnostics to robotics and infrared imaging. Achieving high thermal sensitivity requires materials that exhibit a large temperature coefficient of resistance (TCR), allowing small temperature variations to be efficiently converted into measurable electrical signals. However, conventional thermal sensing materials typically exhibit limited TCR values due to intrinsic transport mechanisms in bulk semiconductors or metals.In this dissertation, I develop a band-engineered hybrid superlattice platform composed of two-dimensional MoS₂ nanosheets and molecular metal phthalocyanines (MPc). Through solution-phase molecular intercalation, MPc molecules are periodically inserted between MoS₂ layers to form an ordered organic–inorganic superlattice structure. This architecture introduces interfacial electronic barriers that fundamentally modify charge transport behavior. As a result, carrier transport becomes dominated by thermally activated hopping across the MoS₂/MPc interfaces, leading to a dramatic enhancement in temperature-dependent electrical response. Electrical transport measurements reveal colossal TCR values reaching up to −11 % K⁻¹ in MoS₂/NiPc superlattice thin films, significantly exceeding those of conventional metals, carbon materials, pristine transition-metal dichalcogenides, and most bulk semiconductors. Spectroscopic analysis and band-alignment studies further elucidate the underlying mechanism, demonstrating that the interfacial energy barriers and enhanced electron–phonon coupling play a critical role in amplifying the temperature dependence of carrier transport. Leveraging the solution processability and ultrathin nature of the superlattice films, scalable flexible temperature sensor arrays are demonstrated on polyimide substrates with excellent device uniformity. Furthermore, freestanding MoS₂/NiPc membranes enable fast and highly sensitive uncooled mid-infrared bolometers with response times below 40 ms and record-high resistance responsivity. These results establish molecular–2D hybrid superlattices as a new materials platform for ultrasensitive thermal sensing and open opportunities for next-generation flexible electronics and infrared detection technologies.Materials scienceApplied physicsNanotechnologyElectrical engineeringbolometerThermal sensepubliceScholarship, University of Californiahttps://escholarship.org/uc/item/85p3b5z3etdoai:escholarship.org:ark:/13030/qt842447xx2026-03-20T06:33:23Zqt842447xxLearning to Reason Across Modalities via Synthetic SupervisionBansal, HritikChang, Kai-WeiGrover, Aditya2026-03-18Foundation models have transformed artificial intelligence, yet they still falter on reasoning tasks that demand multi-step inference, temporal understanding, and real-world commonsense, capabilities essential for truly intelligent systems. This thesis diagnoses critical multimodal reasoning failures and presents a unifying solution: principled synthetic data supervision. We address three frontier challenges: (1) mathematical reasoning over visual contexts, where careful multi-stage data curation achieves state-of-the-art performance across model sizes; (2) semantic alignment in video-language models, where LLM-generated contrastive captions unlock stronger temporal and compositional reasoning; and (3) physical commonsense reasoning, where we benchmark and substantially advance video models' understanding of real-world plausibility. Collectively, this work establishes that data quality, not merely scale, is a primary driver of reasoning capability, charting a path toward breaking the data bottleneck that constrains the next generation of multimodal intelligence.Computer scienceComputer engineeringArtificial intelligenceapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/842447xxhttps://escholarship.org/content/qt842447xx/qt842447xx.pdfetdoai:escholarship.org:ark:/13030/qt5sh3w0xg2026-03-20T06:32:41Zqt5sh3w0xgPeptide-Peptoid Statistical Copolymers for Biomimetic MaterialsTan, Aryan Louise AnaboDeming, Timothy J.2026-03-16Peptide-peptoid copolymer hybrids are an emerging class of biomaterials that combine polypeptide and polypeptoid features. Polypeptides are commonly synthesized by ring-opening polymerization of amino acid N-carboxyanhydride (NCA) monomers, and polypeptoids can be prepared in a similar manner using N-substituted N-carboxyanhydride (NNCA) monomers. Homopolymerizations of these monomers are well understood, but the study of their statistical copolymerization has only recently been investigated. A quantitative method to analyze kinetics as well as initiators that can alter comonomer reactivities are both needed to control synthesis and properties of these materials. Controlled statistical copolymerization of NCA and NNCA monomers would enable the synthesis of peptide-peptoid copolymers with well-defined compositions and comonomer sequence distributions. Controlled sequence distributions of these copolymers will enable tunable properties not currently accessible in diblock copolymers using current initiators for statistical copolymerizations. Chapter 2 describes zerovalent and divalent nickel complexes capable of initiating NCA and NNCA statistical copolymerization with controlled sequence distributions. The kinetic model used to study these polymerizations allows reliable measurement of comonomer reactivity ratios for different initiators. We found that variation of donor ligands in nickel initiators can be used to significantly vary comonomer reactivities in several NCA and NNCA pairs. Chapter 3 describes use of these nickel-based initiators to prepare peptide-peptoid copolymers with tunable thermal properties for the development of degradable, processable thermoplastics. We found that bis(1,5-cyclooctadiene) zerovalent nickel (LnNiCOD) initiators were capable of depressing crystallization and melting points of poly(N-butyl glycine)s via controlled incorporation of leucine residues. Chapter 4 describes the preparation of polyion complex diblock copolypeptide hydrogels (DCHPIC) and incorporation of L-valine residues to enhance ?-sheet formation for improved mechanical strength.ChemistryBioengineeringStatisticsPolymer chemistrydiblock copolypeptide hydrogelsN-carboxyanhydridepolypeptidepolypeptoidstatistical copolymerizationtransition metal catalysisapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5sh3w0xghttps://escholarship.org/content/qt5sh3w0xg/qt5sh3w0xg.pdfetdoai:escholarship.org:ark:/13030/qt9t88s2z12026-03-20T06:32:36Zqt9t88s2z1Factors Impacting Prescribing of Novel Diabetes Drug Classes for Patients with Type 2 DiabetesTorres, HugoMangione, Carol2026-03-16Diabetes treatment has been revolutionized by the introduction of the new diabetes drug classes, glucagon-like peptide-1 receptor agonists (GLP-1RA) and sodium glucose transporter-2 inhibitors (SGLT2i). These drugs are the first to demonstrate improved cardiovascular and kidney disease outcomes in clinical trials while also reducing blood glucose and weight. However, uptake of the drugs in clinical practice has been slow, especially for individuals at higher risk of cardiovascular and renal complications. Also, ethnic and racial minority patients have been found to receive the drugs at lower rates than White patients in the early period after FDA approval of these drugs. This dissertation investigates the factors influencing the prescribing of GLP-1RA and SGLT2i medications in type 2 diabetes, with a focus on addressing disparities in their adoption. The study uses electronic medical record data from a large academic health system to analyze trends in prescribing from 2016 to 2023. Aim 1 follows a cohort longitudinally to determine how prescribing expanded and how individual, provider, and clinic factors impact prescribing over time. Aim 2 updates the literature by assessing disparities in prescribing using more recent data. Aim 3 assesses the rates of increase in prescribing of the GLP-1RA and SGLT2i drugs between racial and ethnic groups. The findings indicate a significant increase in GLP-1RA and SGLT2i utilization following the 2021 ADA guidelines, which prioritized their use in patients with cardiovascular and renal complications or obesity. Despite this overall increase, Asian patients experienced a slower rate of adoption compared to other racial and ethnic groups. Provider specialty was also found to be a significant factor, with endocrinologists demonstrating higher utilization rates than primary care physicians. The dissertation identifies a persistent challenge in achieving pharmacoequity and highlights the need for targeted interventions to address disparities in the utilization of these effective medications.Health sciencesHealth care managementPublic healthPharmacologyMedicineDiabetesGLP-1 RAHealth DisparitiesHealth Policyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9t88s2z1https://escholarship.org/content/qt9t88s2z1/qt9t88s2z1.pdfetdoai:escholarship.org:ark:/13030/qt5d84714h2026-03-19T06:37:53Zqt5d84714hModeling Organizational Engagement in Health Recruitment: A Mixed Methods Approach Using Descriptive Analysis, Clustering, and Spatial AnalysisTran, LaurenRitz, Beate2026-03-17Recruitment for community-based public health research often raises challenges related to organizational engagement and geographic disparities, particularly in rural and underrepresented minority populations. Ensuring that such groups are effectively recruited and engaged was one of the key goals of this study, as diverse and representative samples are essential for valid public health research. This study evaluates recruitment patterns and predictors across a multi-county region of Central California, where outreach to various community organizations was conducted to support a population-based study of neurological health, specifically Parkinson’s disease (PD) and related conditions, in older minority individuals with an emphasis on recruiting Latinos. Here, we describe data we collected on recruitment efforts for organizations and different means of recruitment in the three Central California counties targeted by this research study. Each organization was categorized by type, and its recruitment efforts and success were recorded. Clustering techniques grouped organizations by recruitment type, and spatial analysis examined county-level variation in recruitment success. Recruitment success varied by organization type, with hospitals and rehabilitation centers showing less collaboration, while churches, community groups, and outpatient providers showed more. There were geographic variations, with Kern County showing the highest recruitment success compared to Fresno and Tulare. Latino representation was lower among PD cases and controls, suggesting disparities in outreach effectiveness that may stem from barriers to engagement as well as delays in diagnosis and referral, which can prevent timely recruitment into research studies. This study demonstrates organizational and geographic patterns in community-based recruitment for research studies, with spatial context (such as county location) affecting recruitment success through differences in proximity to UCLA and population size. For example, Kern County’s closer proximity to UCLA may have facilitated communication and engagement, whereas smaller counties with fewer organizations or more geographically dispersed populations experiences lower recruitment. These results may inform more equitable and effective recruitment efforts in public health and epidemiologic research, particularly in underserved and rural populations.EpidemiologyPublic healthNeurosciencesapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5d84714hhttps://escholarship.org/content/qt5d84714h/qt5d84714h.pdfetdoai:escholarship.org:ark:/13030/qt0d00j0jf2026-03-19T06:37:48Zqt0d00j0jf2,3,7,8-tetrachlorodibenzo-p-dioxin in Vietnam: Exposure patterns and associations with birth outcomesMichel, Sophie Katharina Felicitasvon Ehrenstein, Ondine S2026-03-17Following the use of herbicides contaminated with TCDD (dioxin) by U.S. forces, reproductive health risks in Vietnam may be ongoing, through persistent environmental contamination and bioaccumulation in local foods and human tissues, as well as through lasting and heritable health effects. A database of all scientific English and Vietnamese-language reports of TCDD measurements in human tissues in Vietnam since 1961 was systematically collated. This data was used to estimate area-level TCDD exposure using a novel Bayesian areal kriging approach. These area-level TCDD exposure estimates were subsequently used to assess links with 2,048 individual live birth weights recorded as part of the 2006 and 2011 Multiple Indicator Cluster Surveys (MICS) in Vietnam, as well as associations with individual birth weight, the odds of preterm birth, and the odds of perinatal mortality, using data from 10,763 births at the Da Nang Hospital for Women and Children in 2015-2016, via Bayesian linear mean and logistic regression models. Sensitivity analyses assessing the impact of i) more informative Bayesian priors, ii) propagating the uncertainty from our TCDD estimation forward using a modular Bayesian inference framework, and iii) residual confounding, by using the generalized bias analysis framework, were undertaken. Tissue measurements from 8,325 persons reported in 58 key reports, sampled between 1970 and 2021, and largely consisting of blood samples, were identified, and used to create the Human TCDD Contamination in Vietnam Database (H-TCVD). Bayesian areal kriging results indicate increased TCDD exposure among residents of South Vietnam, particularly Dong Nai province, where the Bien Hoa airbase is located, and other areas known to have been subject to high-volume tactical herbicide sprays and/ or spills. Analyses using the MICS data suggested lower birth weight with higher area-level TCDD exposure, particularly among rural families. Using the Da Nang hospital data, analyses also indicated lower birth weight, as well as higher odds of preterm birth and possibly perinatal mortality with higher area-level TCDD exposure, again especially among rural residents. These findings highlight the need for continued remediation of known hotspots, monitoring for both ongoing TCDD exposure and adverse health effects, and targeted public and reproductive health programs.Public healthEnvironmental healthEpidemiologyBirth outcomesDioxinExposure modelingpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0d00j0jfetdoai:escholarship.org:ark:/13030/qt30s726hm2026-03-19T06:37:43Zqt30s726hmInformation-Efficient Representations: from Data to ModelLi, XinlinFragouli, Christina P2026-03-17Modern machine learning systems rely on heterogeneous multimodal data and large-scale foundation models with billions of parameters. While such scale enables remarkable performance, it also introduces fundamental inefficiencies in how information is represented, shared, and stored. This dissertation studies information-efficient representations from two complementary perspectives: redundancy in multimodal data and redundancy in over-parameterized models. The first part develops an information-theoretic framework for understanding the minimal complexity of shared structure in multimodal data. Through analytical and empirical studies of distributed detection, feature compression, and factorized representation learning, we demonstrate that multimodal observations admit substantial representational redundancy due to their inherent dependencies. These findings motivate a fundamental characterization of shared information among high-dimensional continuous sources. We introduce a new notion of Common Information Dimension (CID), which quantifies shared randomness in terms of dimensionality rather than bits as in existing notions. CID therefore provides a fundamental limit on the intrinsic dimensional complexity of shared representations in multi-modal systems. We establish structural properties of CID, derive explicit characterizations for jointly Gaussian sources, and prove rigorous connections between CID and traditional notions of common information, showing that CID characterizes the asymptotic behavior of approximate common information. The second part addresses model-side inefficiency in large language models through principled low-bit quantization. We introduce ICQuant, an outlier-aware quantization framework that leverages index coding principles to isolate and efficiently encode weight outliers. We further develop ScaleBITS, a sensitivity-aware mixed-precision quantization framework that balances nonuniform weight sensitivity without sacrificing hardware efficiency, and enables scalable bitwidth allocation under global memory constraints. These methods enable substantial memory and latency reduction while preserving model performance. Collectively, by advancing both foundational theory and practical algorithm design, this dissertation contributes to the broader goal of enabling more scalable and accessible deployment of multimodal and large-scale machine learning systems.Electrical engineeringComputer scienceArtificial intelligenceCommon Information DimensionEfficient Machine LearningInformation TheoryLLM QuantizationModel CompressionMultimodal Learningapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/30s726hmhttps://escholarship.org/content/qt30s726hm/qt30s726hm.pdfetdoai:escholarship.org:ark:/13030/qt6wt6m1tn2026-03-19T06:37:38Zqt6wt6m1tnSoft Magnetoelastic Bioelectronics for Human Biomechanics DecodingXu, JingChen, Jun2026-03-17Biomechanics represents the final layer of biological signaling. Biomechanical activities of tissues, such as blinking, muscle contraction, and joint movement, reflect the integrated output of neural activation, tissue properties, and structural constraints. Because biomechanics embodies assembled and system-level information, it provides a direct and information-rich representation of physiological state. Accurate capture of biomechanical activities is therefore essential for disease diagnosis and health assessment. However, reliable biomechanic measurement remains challenging. Conventional electrical and optical interfaces frequently suffer from motion artifacts and human biofluids, mechanical mismatch with soft tissues, and limited accuracy, which can lead to signal distortion and reduced long-term stability under dynamic conditions.This dissertation introduces soft magnetoelastic bioelectronics as a mechanically compliant strategy for biomechanics decoding. The core material consists of micromagnets embedded within elastomer matrices, forming a soft magnetoelastic coupling layer in which mechanical deformation modulates magnetic flux density. Time-varying magnetic flux is converted into electrical signals through electromagnetic induction, decoupling soft mechanical deformation from electrical readout and improving acquired biosignal quality. Built upon this unified magnetoelastic–induction framework, three major human mechanical systems are investigated: the muscular system, the integumentary system, and the skeletal system. For each system, device structures are engineered to match application-specific mechanical characteristics while preserving the same transduction principle. A textile-integrated platform enables muscle monitoring, an ultrathin on-eyelid interface captures blink biomechanics, and a finger-mounted system supports joint-driven underwater communication.Across these implementations, soft magnetoelastic materials demonstrate structural adaptability and consistent signal fidelity across both physiological and environmental contexts. The resulting platform supports applications spanning sensing, therapy, and energy, establishing soft magnetoelastic bioelectronics as a versatile approach for translating integrated biomechanical activities into stable and high- fidelity electrical outputs.BioengineeringBiomedical engineeringElectromagneticsBiomechanicspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6wt6m1tnetdoai:escholarship.org:ark:/13030/qt67p8j27t2026-03-19T06:37:30Zqt67p8j27tOOPrompt: Object-Oriented PromptingXu, TengyouChen, Xiang2026-03-16The emergence of large language models (LLMs) has given rise to a class of prompt-based interactive systems where users primarily express their input in natural language. However, composing a prompt as a linear text string becomes unwieldy in capturing users’ multi-faceted intents. We present Object-Oriented Prompting (OOPrompt), an emergent interaction paradigm that enables users to create, edit, iterate, and reuse prompts as structured, manipulable artifacts, unifying and generalizing several existing point systems. We first outlined a design space from existing work and built an early prototype, which we deployed as a probe in a formative study with 20 participants. Their feedback informed an expanded OOPrompt design space. We then developed the full OOPrompt prototype and conducted a validation study to further understand OOPrompt’s added values and trade-offs. We expect the OOPrompt design space to provide theoretical and empirical guidance to the design and engineering of prompt-based, LLM-enabled interactive systems.Computer engineeringElectrical engineeringComputer scienceDesign SpaceHuman-AI InteractionHuman-Computer InteractionLarge Language ModelsStructured PromptingpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/67p8j27tetdoai:escholarship.org:ark:/13030/qt5485g8vd2026-03-19T06:37:25Zqt5485g8vdLeer el movimiento: automóviles, motocicletas y aviones en la literatura mexicanaMartinez-Moron, Nylsavan Delden, Maarten H2026-03-17This dissertation explores the role of automobiles, airplanes, and motorcycles in a selection of Mexican literary works published from the second half of the 20th century to the present. This work demonstrates that motorized vehicles in a sample of novels, short stories, and chronicles function as means of organizing emotions and memory, of expressing their characters' identities, and of translating their presence into agency to navigate difficult or unforeseen situations. This work proves how motorized vehicles are central to the configuration of these texts and demonstrates that their presence, which transcends the anecdotal, the ornamental, or the accessory, is essential as a catalyst and organizer of literary texts.One core element of the theoretical analysis comes from Ottmar Ette, who states that one of the most fascinating aspects of travel literature lies in its omnipresent movements of understanding, which can be understood as movements of spatial comprehension. Ette suggests figures such as the circle, the pendulum, the line, the star, and the jump as forms that can capture the total essence of the journey or reflect a part of the narrated text, and can be used to identify and represent these movements of understanding in space. For the purposes of this dissertation, the selection of literary works is classified as travel literature, and Ette´s figures are used to attempt a spatial comprehension of the narrative and to propose an abstract representation of it.The first chapter explores the works of Federico Campbell and Ricardo Aguilar Melantzón, two writers from the northern Mexico-United States border region; the figures of the jump and the pendulum trajectories in their respective texts are explained. The kinetic aspect is addressed through the sociocultural dimension of being a border inhabitant and writer, focusing on how the presence of airplanes and motorcycles shapes and influences interactions with and conceptions of geography. The second chapter examines three authors: Cristina Rascón Castro and Daniel Espartaco Sánchez, both from northern Mexican cities, and Bárbara Jacobs, from Mexico City. The trajectories found are the circle, the line, and the star. The works are grouped around the theme of family relationships, in which the car serves as a space that facilitates and triggers emotions and invites recollection of memories. The third chapter looks at the works of Ana García Bergua, Daniel Saldaña París, and Adán Medellín, all from Mexico City. Their texts illustrate the trajectories of the star, the jump, and the circle, respectively. Automobiles and airplanes are presented as sanctuary spaces, where these means of transportation facilitate personal reflection and an opportunity to escape the situations that afflict their characters, offering a safe and multifunctional space where creativity allows for transformation and/or reinvention.The essence of individual mobility introduced by the bicycle at the beginning of the 20th century remains present in the literary pieces discussed here. Motorized vehicles still fulfill their essential function of transporting individuals from one place to another in these narratives, where the almost unnoticed but crucial role in daily life, of taking control of the time and space through the motorized mobility, is highlighted to render their relevance in terms of agency and freedom to make transcendental or necessary changes in the lives of the characters presented in these texts.Latin American literatureLanguageHispanic American studiesBorder RegionIdentityMemoryMexico-USSpaceTravel Literatureapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5485g8vdhttps://escholarship.org/content/qt5485g8vd/qt5485g8vd.pdfetdoai:escholarship.org:ark:/13030/qt7nz1j4s22026-03-19T06:37:20Zqt7nz1j4s2Towards Low-Level Vision in Adverse ConditionsZhang, HowardKadambi, Achuta2026-03-17Restoring and enhancing images is a central part of vision, both as a foundation upon which robust perception and understanding is built and as a tool for aesthetic fruition. Unfortunately, vision, especially at the pixel level, can become exceedingly difficult under harsh, adverse conditions. This thesis addresses these challenging conditions in two low-level vision tasks: weather removal and face restoration. For weather removal, while a majority of previous methods focused on synthetic weather patterns, we focus on the more complex task of removing real-world weather. These can manifest in a multitude of different ways depending on camera intrinsics, atmospheric pressure, or even geographic location. We overcome this challenge for rain removal by introducing time-multiplexed rain/clear image pairs and collecting a dataset capable of training a model that is able to bridge the sim2real performance gap. We then expand this to multiple weather conditions by introducing a semi-automatic weather collection pipeline based on light transport principles and the physical properties of weather. For face restoration, rather than focusing on typical degradation patterns, we target the extreme case where degradations are so strong that previous methods are unable to recover the high-frequency details that compose an individual's identity. We overcome this challenge by utilizing reference images with a novel attention-sharing architecture. Together, these methods constitute a basis for tackling harder and more difficult low-level vision scenarios through innovative dataset construction and novel architecture design.Computer scienceArtificial intelligenceComputer engineeringComputer VisionImage EnhancementImage GenerationImage RestorationLow-level VisionMachine Learningapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7nz1j4s2https://escholarship.org/content/qt7nz1j4s2/qt7nz1j4s2.pdfetdoai:escholarship.org:ark:/13030/qt4ws063zr2026-03-19T06:37:13Zqt4ws063zrMechanistic insights into how biomaterial properties regulate immune responses to microporous annealed particle hydrogelsShang, LilyScumpia, Philip ODi Carlo, Dino2026-03-17Biomaterials can actively regulate immune responses through their mechanical, chemical, and structural properties. This dissertation establishes design principles for engineering immune-instructive microporous annealed particle (MAP) hydrogels and applies these principles to vaccine enhancement and immune programming.Chapter 1 introduces the conceptual framework underlying immune-material interactions, summarizes how material stiffness, chemistry, and architecture influence innate and adaptive immunity, and motivates MAP hydrogels as a modular platform for immune instruction.Chapter 2 demonstrates that increasing MAP hydrogel stiffness enhances innate immune activation independent of antigen incorporation. Stiffer Vax-MAP formulations alter innate cell recruitment and transcriptional states, activating mechanosensitive pathways including Piezo1 and YAP/TAZ. Myeloid-specific knockout models confirm that these receptors mediate stiffness-dependent innate activation, and pharmacologic activation of each pathway amplifies T follicular helper and germinal center B cell responses. Together, these findings establish mechanoreceptor signaling as a tunable parameter for improving the efficacy of both biomaterial-based and conventional vaccines.Chapter 3 shows that hyaluronic acid (HA)-based MAP hydrogels elicit substantially greater immune cell infiltration than PEG-MAP or commercial non-porous HA fillers. HA chemistry enriches reparative macrophage populations and increases T cell recruitment, supported by single-cell RNA sequencing evidence of upregulated extracellular matrix sensing, chemotactic, and remodeling pathways. This immune activation is CD44-dependent, demonstrating that HA-CD44 interactions directly shape immune programming. HA-MAP thus synergizes HA’s biochemical activity with granular scaffold architecture to generate a more regenerative immune microenvironment.Across all chapters, this work establishes how stiffness, HA chemistry, and porosity can be strategically tuned to direct innate and adaptive immunity. These principles advance the rational design of next-generation biomaterial vaccines and immune-instructive scaffolds that leverage controlled immune instruction rather than immune evasion.BioengineeringBiomedical engineeringImmunologyBiomaterialsImmunologyMechanotransductionTissue EngineeringpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4ws063zretdoai:escholarship.org:ark:/13030/qt4w70957q2026-03-19T06:37:08Zqt4w70957qDesign and Characterization of Stable Organic Radical Passivators for Perovskite Solar CellsKundu, ArneshYang, Yang2026-03-17Within the development of perovskite solar technologies, the development of passivation materials for defect and interfacial engineering has been crucial for the prevention of energy losses and improving device efficiency. This work reported within this study describes the preliminary development of a new first-in-group class of intrinsically conductive interfacial materials. While the preliminary results of the first of these radical passivators were not promising, due to steric hindrance preventing interaction between the perovskite and the passivator, attempts were made to synthesize a new derivative designed to overcome this issue.In addition to the synthesis of the radical passivators, a new methodology was developed to synthesize aryl tert-butyl nitroxyl radicals from aryl bromides in a three-step one-pot reaction. This methodology, after optimization, showed replicable near-quantitative yields of over 90%. To date, it is one of the only high-yielding one-pot methodologies reported in literature for the synthesis of these types of radicals from aryl bromides or similar common functionalization handles.Overall, this thesis reports a new class of potentially promising passivation materials; and additionally provides a new methodology making aryl tert-butyl nitroxyl radicals highly accessible from a synthetic perspective.Materials scienceEnergyMechanical engineeringInterface EngineeringInterface PassivationPerovskite Solar CellsRenewable Energy TechnologiesStable Organic Radicalsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4w70957qhttps://escholarship.org/content/qt4w70957q/qt4w70957q.pdfetdoai:escholarship.org:ark:/13030/qt0km5r1gr2026-03-19T06:37:04Zqt0km5r1grMaterialism and Anthropology in the French Enlightenment: Charles de Brosses (1709-1777)Freeman, AaronTutino, Stefania2026-03-17This dissertation examines the intellectual world and work of the Enlightenment philosophe Charles de Brosses (1709-1777), demonstrating how the convergence of critical-historical scholarship and natural history in eighteenth-century France enabled new secular approaches to the study of humanity. A provincial magistrate and intimate friend of the great naturalist Georges-Louis Leclerc, Comte de Buffon, de Brosses produced two landmark treatises--the Traité de la formation méchanique des langues (1765) and Du culte des dieux fétiches (1760)--that represent a remarkably ambitious undertaking: to explain mind, language, and religion as products of nature without recourse to transcendence. Long recognized for his role in the early history of linguistics and comparative religion, de Brosses has nevertheless remained an entirely marginal figure in Enlightenment scholarship and has rarely been studied as a systematic thinker. Drawing on newly published correspondence, unpublished archival materials, and close textual analysis, this study reconstructs for the first time de Brosses’s commitment to an active, self-organizing conception of matter, biological epigenesis, and a material "internal sense" as the basis of cognition. These commitments, I argue, enabled de Brosses to explain language and religion as emergent products of embodied sensation, instinct, and analogy, rather than as effects of divine institution or immaterial reason. By establishing de Brosses as a significant figure in the history of Enlightenment materialism, it brings to light new continuities and contrasts between the early Enlightenment of the late-seventeenth century and the age of the Encyclopédie; illuminates the convergence of erudition and natural history in the formation of the secular human sciences; and offers a new perspective on eighteenth-century attempts to re-embed the mind in nature.European historyHistoryLinguisticspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0km5r1gretdoai:escholarship.org:ark:/13030/qt9jn8x24g2026-03-19T06:36:59Zqt9jn8x24gVariation in mammalian limb development and evolution: Macroevolutionary, transcriptomic, and cellular perspectives on the generation of variationHowenstine, Aidan OliverSears, Karen E2026-03-17Variation is a necessary component of evolution. Without phenotypic variation, natural selection has nothing to select from, and evolution slows. It is thus a fundamental goal of evolutionary biology to describe and understand the generation of the variability that selection requires. The generation of variation, or how a genotype is transcribed and translated to form differing phenotypes, is essentially the process of development. Investigating how development shapes the available variation for natural selection to act upon is therefore crucial to our understanding of the evolution of life.The system I leverage to investigate the generation of variation by development and its impact on evolution in this dissertation is the mammalian limb. The limb is an ideal system for this because it is readily investigated at multiple biological scales. Across mammals, the patterning of the limb is largely invariant; there is a single proximal element (stylopod), two medial elements (zeugopod), and a clutch of bones in the wrist and ankles that branch into the digits (autopod). This patterning is not only maintained across taxa, but also homologous between the fore- and hind limbs. These constituent elements form discrete, quantifiable morphologies that can be comparatively investigated across taxa and through deep time. The bones of the limb also serve specific functional roles in locomotion and interacting with the environment, providing a direct link to the selective pressures acting upon them. The limb has been valuable to evolutionary and developmental biology as a system through which we can understand how complex structures are initiated and patterned by the coordinated expression of genes and feedback between signaling centers; there is thus a rich body of literature dissecting limb and bone development that offers the foundation for interrogating the generation of variation within this system. In this dissertation, I investigate the generation and evolutionary relevance of variation in the mammal limb and its constituent parts at three levels: macroevolutionary, transcriptomic, and cellular.My first chapter addresses variation within populations and investigates the correlation between developmental and adult variation in the context of the macroevolutionary diversity of mammalian limbs. I leverage vast skeletal collections of adult and embryonic mice, opossums, bats, and adult gophers to compare the variation in limb element length of developing and adult specimens within populations. I investigate these patterns in the context of a recently published assessment of variation across mammal forelimbs, which identified a proximodistal gradient of increasing variability in the limb (Rothier et al., 2023). My findings highlight the complexity of evolution at these scales and the nuance required to identify patterns across them. I find the greatest levels of variation in the most distal elements of the limb, but not a consistent increase in variability along the proximodistal axis. I identify some coherence between embryonic and adult specimens, suggesting that development does bias the pool of variation present in an adult population, but not strictly so. Instead, it seems clear that the functional demands of different locomotory types may reshape the variation generated by development, but to different extents between different taxa. This chapter begins to reveal how developmental and adult variation may be linked and emphasizes the additional role of function in reshaping the landscape of variation between these scales.My second chapter addresses variation within the genetic architecture of early limb development, as measured by correlation networks constructed from transcriptomic data. I use Weighted Gene Correlation Network Analysis to create a consensus network of correlated gene expression across the early stages of bat, mouse, and opossum limb development (Langfelder & Horvath, 2008). The bat and the opossum offer systems with divergent morphology and developmental timing, respectively, allowing me to test how the pattern of interacting genes in each of these systems may be altered to generate their unique limb features. I find that the complex morphology of the bat forelimb and the delayed timing of the opossum hind limb are patterned by uniquely modular limb networks, relative to the mouse, bat hind limb, and opossum forelimb. I show that the compartmentalization of different components of development, like axial patterning and chondrogenesis, facilitates the development and evolution of unique limb phenotypes and developmental strategies across mammals. I also highlight several uninvestigated genes that are integral to the developmental network underlying limb development in the bat and opossum. Any of these candidates would be interesting to investigate within these systems, as they promise key coordinating roles in the unique differences of limb development across mammals. Taken together, this work demonstrates the power of a network-based approach to investigating differences in developmental pathways and architecture, particularly when applied to non-model, highly divergent systems.My third chapter addresses variation at the cellular scale of development, investigating the growth plate in developing bones to describe the variability in chondrocyte differentiation that may underlie the potential for extreme elongation of mammal long bones. Here, I leverage multiplexed in situ hybridization to quantify the sizes of each region of the growth plate through development and under selection. I find that the dynamics of the developing growth plate differ even between the two ends of a single bone, and that these differences in timing may account for discrepancies observed in the processes of limb bone elongation in mice selected for elongated limbs and bats. I show that at earlier developmental stages, the Longshanks population of mice, which exhibit elongated limbs after artificial selection, exhibit a similar increase in hypertrophic zone size to what has been documented previously in bats (K. E. Sears et al., 2006; Marchini & Rolian, 2018). Additionally, I find that this increase is associated with a reduced prehypertrophic zone, which is a key signaling center in the progression of normal endochondral development. This work highlights the importance of tracking development through time rather than relying on a single snapshot. Furthermore, by finding striking similarities between mice under selection for increased limb lengths and wild bats, my results continue to tie developmental variation to that which we see in populations and across taxa.Taken together, the three components of this dissertation offer perspective on how variability is translated from the genome to a population and to higher clades. I show how variability generated by development is shaped at the population level and propose that other factors within selection, like functional morphology and differences in developmental timing structure how that variation is selected from. I provide here a perspective that is only obtainable by leveraging and unifying multiple scales of evolutionary and developmental biology.Evolution & developmentCellular biologyBiologyDevelopmental biologyDevelopmentEvolutionLimbMammalVariationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9jn8x24ghttps://escholarship.org/content/qt9jn8x24g/qt9jn8x24g.pdfetdoai:escholarship.org:ark:/13030/qt4rg994k92026-03-19T06:36:53Zqt4rg994k9Sugarloaf: a 48 Volt to 1.8 Volt Gallium Nitride Direct Current to Direct Current Voltage ConverterYang, Qingyu BenIyer, Subramanian S2026-03-17Future wafer-scale computing systems require voltage regulators that can deliver power at sub-1V with multi-watt-per-mm² power density. Previously published converters have demonstrated >90 % efficiency for 48V to 1V regulation but remain limited in terms of areal power density, preventing integration into wafer scale systems. This work presents Sugarloaf, a 48V to 1.8V DC to DC two-stage GaN converter achieving 87.86% peak converter efficiency at 0.79 W/mm² and 82.6% full load efficiency at 1.56 W/mm². The converter employs GaN switches for a high-voltage open loop front-end that converts 48 V to 12 V at >95% full load efficiency. For the second stage, a closed loop two phase GaN buck topology is employed to further step down 12V to 1.8V at >85% full load efficiency for use by wafer-backside point-of-load distribution. This work details the prototype design, assembly, and characterization process. Compared with prior converters, Sugarloaf is uniquely positioned as a scalable modular solution for 48V to 1.8V DC-DC conversion at high power density and efficiency. This thesis demonstrates a key enabler of wafer scale systems by leveraging GaN switches, multi-stage topologies, and modular integration—providing a useful guide for designing & characterizing compact, high-efficiency converters for next-generation high performance computing.Electrical engineeringComputer engineeringComputer science48VAdvanced PackagingDC-DCGallium Nitride GaNVertical Power DeliveryVoltage Regulator ModulepubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4rg994k9multimediaoai:escholarship.org:ark:/13030/qt67k2k36h2026-03-19T06:36:48Zqt67k2k36hHippocampo-cortical coordination during sleep stage divergence and convergence in the human brainJang, RockellePoe, Gina R2026-03-17Sleep is important for a wide range of physiological processes and sleep disruption leads to impaired mood, poor cognition, and a host of pathological conditions ranging from cardiovascular disease to mental and neurological disorders. Sleep has been viewed as a homogeneous brain state, but animal research suggests different brain regions display distinct sleep states simultaneously. Prior to this dissertation, the occurrence of regional sleep across the night in human recordings has never been investigated. Studies that follow standard methods of sleep state determination are likely to miss hidden sleep stages with important implications for health and disease. I used scalp electroencephalography (EEG) and intracranial recordings (iEEG) from human subjects undergoing implantation of depth electrodes to score sleep independently in the posterior hippocampus and neocortex. I found strong evidence of regional sleep in the hippocampus and neocortex throughout the night. The hippocampus entered deeper stages of sleep prior to the neocortex and the amount of time spent in each state varied by region. About one third of the night was spent in regional sleep. Therefore, I found that regional sleep is indeed as prevalent in humans as it is in other animals. Remarkably, in every subject the majority of transitions were non-simultaneous. By analyzing the power spectral density (PSD) profiles from each recording site within each state, I found that regionally scored sleep resembled standard profiles in both the hippocampus and the neocortex. However, there were variations in spectral power in the neocortex when the hippocampus was in a different state, variations that could potentially be used to detect regional sleep noninvasively. The discovery of regional sleep necessitates development of automatic techniques that reliably determine hidden sleep stage divergence. Here I introduce a novel mathematical approach to understanding the region-region interactions during regional and global sleep. I hypothesized that a nonlinear mutual information-based approach to analyze the voltage recordings would be well suited for capturing the complexity of dynamic network interactions underlying hippocampo-cortical communication during sleep. I find mutual information is sufficient for differentiation of divergent and convergent sleep states, which lays the foundation for future work exploring the functional role of regional and global sleep in physiology and behavior.PhysiologyNeurosciencesMolecular biologyCognitive psychologyClinical psychologyEEGMutual informationSleepapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/67k2k36hhttps://escholarship.org/content/qt67k2k36h/qt67k2k36h.pdfetdoai:escholarship.org:ark:/13030/qt6x47f75p2026-03-19T06:36:43Zqt6x47f75pToward Agentic 5G SystemsXu, YifeiLu, Songwu2026-03-17The agentic paradigm offers a promising route to intelligent and adaptive wireless systems. This dissertation shows how agentic approaches can be made feasible in 5G. We make a case for infrastructure-level agents by developing FIRM, which senses the wireless channel and enables adaptive interference mitigation through standardized 5G mechanisms. We further develop DeepSpecs as a grounded, traceable human-machine interface that helps agents follow standards. At the application layer, we develop IoTGen and study IoT application generation as a tractable case for automating device-cloud interaction under wireless constraints. Overall, the dissertation demonstrates how agentic concepts can be realized across key layers of wireless systems.Computer scienceArtificial intelligenceComputer engineering3GPP5GAgentic SystemsCode GenerationInterference MitigationLLMapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6x47f75phttps://escholarship.org/content/qt6x47f75p/qt6x47f75p.pdfetdoai:escholarship.org:ark:/13030/qt7zg2v65f2026-03-19T05:02:48Zqt7zg2v65fUse of Paleoecological Proxies and Satellite-Derived Fire Measurements to Examine Vegetation and Fire Dynamics in California’s Coniferous ForestsNauman, Benjamin ColeMacdonald, Glen M2025-01-01In the past several decades, wildfires in California’s forests have increased in both size and severity. These changes are linked to a complex nexus of factors that include fire suppression practices, climatic changes, and exurban sprawl. To better contextualize these changes to vegetation and fire regimes, it is important to examine linkages between vegetation and wildfire during past climatic and human land use changes. Additionally, it is important to quantify the impact of modern interventions in coniferous forests to reduce the severity of subsequent fires in a time of dramatic change.Fossilized pollen preserved in lake sediments has proven useful in reconstruction of past ecosystem dynamics. Specifically, two sites, North Yolla Bolly and Big Lakes, had records of pollen and microscopic charcoal examined to better determine the response of vegetation and fire to both climatic shifts and human use of fire during the last 21000 years in Northern California. Records from both sites had pollen and charcoal dynamics that indicated increased drought-adapted vegetation during a warm period from 9500-5500 years before present. Big Lake had a charcoal and pollen response that signals greater Indigenous use of fire in the last several thousand years. Additionally, 20 records of macroscopic charcoal from the Sierra Nevada and North Coast were collated as a part of a meta-analysis. In general, these records indicate no signal of increased or decreased burning during notable warm or cool periods during the Holocene. Generally, sites located in both regions experienced a statistically significant monotonic rise in burning during the Holocene. Charcoal values increased at a greater rate in the last 4500 years, a trend that could also be linked to increasing Indigenous populations and use of fire by these groups in the forests of the state. Vegetative fuel reduction interventions can be examined using satellite-derived measurements to determine whether these interventions reduce the fire severity of subsequent burning. Fire severity data for all wildfires above 10 acres that burned coniferous forests in the state during 2011 to 2020 was compared with datasets of various categories of fuel reduction measures. Prescribed burning led to 33% reduction in fire severity, mechanical thinning was 12.4% lower, and reburning was 32.2% lower; all three interventions were statistically significant in reducing fire severity. Nearly all burned areas in this study (97.9%) did not receive mechanical thinning or prescribed burning before being burned in the study period, reducing the positive impact of these interventions. Generally, forested ecosystems in the state seem to have become dryer during past warm periods in the state, and there are signals of a human fingerprint on burning and vegetation dynamics in the late Holocene. Current management techniques reduce subsequent fire severity but are not implemented at scale.GeographyBotanyPaleoecologyCaliforniafire severityfuel treatmentpaleoecologyremote sensingwildfireenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7zg2v65fhttps://escholarship.org/content/qt7zg2v65f/qt7zg2v65f.pdfetdoai:escholarship.org:ark:/13030/qt3398t17x2026-03-18T06:36:14Zqt3398t17xSynthetic Data for Large Language Model Post-trainingDeng, YiheWang, Wei2026-03-16The development of Large Language Models (LLMs) has advanced significantly, with pre-training on extensive text corpora and subsequent post-training processes such as Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) enhancing their alignment with human values and tasks. Despite these strides, the reliance on human-annotated datasets for post-training presents challenges of scalability, cost, and domain-specific data scarcity.
My research focuses on synthetic data generation as a solution to the data bottleneck in LLM post-training. By leveraging the generative capabilities of LLMs, synthetic data can reduce dependency on human annotation while maintaining diversity and quality. The core focus includes three aspects of synthetic data for LLM post-training: preference data construction, instruction augmentation, and response generation.
This thesis outlines key findings from my PhD works. STIC introduces methods for self-training vision-language models using unlabeled image data to enhance reasoning and comprehension. GraphVis demonstrates the integration of visual knowledge graphs to improve instruction-based tasks. FlowDPO synthesizes reasoning traces with multiple agents. SRL leverages teacher reasoning traces to provide dense reward signals for the student model. DuoGuard proposes a two-player game to construct challenging synthetic data.Computer scienceComputer engineeringElectrical engineeringapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3398t17xhttps://escholarship.org/content/qt3398t17x/qt3398t17x.pdfetdoai:escholarship.org:ark:/13030/qt05z6p6wg2026-03-18T06:36:09Zqt05z6p6wgAI–Driven EV Power Management and Control: Integrated Forecasting and Bidirectional Converter ControlAhmadian, AmirhosseinGadh, Rajit2026-03-16Rapid EV adoption is increasing the penetration of EV charging load and the uncertainty in its temporal and magnitude characteristics. EV drivers differ in charging behavior in arrival time, dwell time, and energy need; therefore, utilities and distribution system operators, microgrid operators, and charging site/network operators need EV prediction capabilities to tackle this uncertainty. Increased EV penetration also creates an opportunity to utilize EVs as mobile energy storage systems, especially for medium- and heavy-duty EVs with large-capacity batteries. To utilize EVs as mobile energy storage systems, operators need to predict charging behavior and aggregated charger load to determine how much energy is available, when, where, and for how long. In addition, for operators to harness EV batteries at any time when EVs are plugged in, real-time control of EV batteries is also needed. EV onboard-charger power converters control power flow between the grid-side AC and battery-side DC and serve as the EV grid-connection point. Therefore, realizing EVs as mobile energy storage systems hinges on developing EV charging-demand prediction and EV power converter control capabilities.For EV charging-demand prediction, prior work has used statistical and machine-learning models at two scales: session-level prediction of stay/charging duration and delivered energy, and aggregate forecasting of charger load profiles. Session-level studies often cluster users and fit separate predictors for each cluster, producing cluster-specific outputs that are harder to reproduce and often do not generalize to other datasets. Aggregate-level studies often evaluate a limited set of forecasting models, which reduces insight into which models work best across horizons, sites, and charger groups.Moreover, prior work on EV power-converter control is largely model-based and tuned to predefined operating points. This dependence on accurate models can limit robustness as operating conditions deviate from the design point. Model-free reinforcement learning, on the other hand, can reduce reliance on closed-form models by training control actions through interaction in simulation across varied operating conditions and disturbances. However, reinforcement-learning control of power converters at sub-millisecond switching timescales remains limited. In addition, a single framework that can scale from light-duty to medium/heavy-duty power levels remains constrained.To address gaps in EV charging-demand prediction at the session level, this dissertation develops a unified two-stage neural-network framework trained end-to-end. The first stage uses session attributes, such as EV user identifier, session start time, and day of week, to predict session duration. The second stage uses the same session information, together with the predicted duration, to estimate delivered energy. The framework is trained as a single model on the full dataset, avoiding clustering users and fitting separate models per cluster. Using UCLA SMERC EV driver/user charging data collected over more than five years, the framework was trained on over 50k charging sessions from more than 341 EV drivers across multiple sites, time-of-year, charger levels, and operating conditions. The framework achieved 0.59% symmetric mean absolute percentage error for session-duration prediction and 0.36% for delivered-energy prediction, and maintained accuracy on held-out users not seen during training, showing generalization to new EV drivers. Conventional machine-learning baselines produced roughly 9–17% symmetric mean absolute percentage error on the same dataset, while the proposed framework reduced error by about one order of magnitude.Furthermore, at the aggregate level, UCLA SMERC aggregated sub-metered data with i over one million records were used to forecast total charging power at multiplexed charger groups (shared multi-port, mostly quad-port) at 15-min, 60-min, 4-h, and 24-h horizons. Four representative multiplexed charger groups were selected to cover distinct EV usage profiles. Forecasting performance was evaluated using statistical methods, tree-based models, deep sequence models, a Transformer baseline, and an ensemble. At the 15-min horizon, the lowest mean absolute error across models was 0.23–0.46 kW, about 4–8% of connector-rated power, with corresponding root-mean-square error of 0.46–1.20 kW, about 8–20%. At 15-min and 60-min horizons, statistical and tree-based models performed best across the four groups, while deep sequence, attention-based, and Transformer models became more competitive at 4-h and 24-h horizons.To address gaps in EV power converter control, this dissertation develops a model-free reinforcement-learning (RL) control framework for bidirectional onboard-charger power converters. The framework is formulated as a Markov decision process, where converter measurements define the states/observations, voltage-reference signals define the actions, and tracking performance defines the reward. RL agents/controllers were trained in simulation using detailed converter models; during training, the agent adjusts its actions at each time step to improve tracking of randomized four-quadrant active and reactive power setpoints. After training, the controller was validated at both light-duty (LD), up to 50 kVA, and medium/heavy-duty (MD/HD), up to 150 kVA, power levels.At the LD power level, the trained controller achieved steady-state active-power tracking error below 0.4% across four-quadrant operating conditions, with similar performance for reactive power. At the MD/HD power level, the trained controller was evaluated against a tuned PID baseline under step changes in commanded active and reactive power, with and without injected current-measurement noise. Two three-step setpoint schedules, with steps at t = {0, 10, 20} s over a 0–60 s window, were tested and then repeated under noise. Without noise, PID produced an overall accumulated squared active-power tracking error over 0–60 s on the order of 105–106 , while RL reduced it to the order of 102–103 . PID also produced an accumulated time-weighted absolute active-power tracking error on the order of 105 , while RL reduced it to the order of 102–103 . With noise, PID remained on the order of 105–106 in accumulated squared active-power error, while RL stayed on the order of 103 ; the time-weighted absolute error showed the same gap, with PID on the order of 105 versus RL on the order of 103 .Step-response results further show clear transient differences between RL and PID. Without noise, RL achieves finite active-power settling times after the final step in the last segment, on the order of 10−2–10−1 s, while PID often does not settle within the segment. With noise, RL still achieves finite settling times after the final step on the order of 10−1 s to a few seconds, whereas PID again frequently fails to meet the settling criterion. Peak deviations immediately after step changes are comparable in magnitude, but the key difference is that RL recovers and settles while PID often stays mismatched.Due to the large-capacity batteries of medium- and heavy-duty EVs and their strong potential for grid services, the controller was further validated in two microgrid operating modes. (i) In grid-connected mode, it remained robust under step-load changes and enabled load-following through vehicle-to-grid power support: a 120 kW local load was applied at t = 0 s, increased by 20 kW at t = 15 s, and the EV reference was updated at t = 25 s to supply the full 140 kW demand, with battery state of charge decreasing from 85.5% to 84.7% over 45 s. (ii) In islanded mode, it demonstrated decentralized droop-based active-power sharing and frequency regulation. Two EV inverters shared a 240 kW load at approximately 120 kW each, while frequency deviations were limited to +0.36 Hz and −0.31 Hz and settled within a ±0.1 Hz band by 5.4 s.Overall, this dissertation paves the way for grid and charging operators to leverage underutilized EV batteries that plug into the grid daily as mobile, reliable assets. Unified, high-accuracy EV prediction capabilities, coupled with smart real-time control of bidirectional onboard-charger power converters, support positioning EVs as mobile energy storage systems and reliable grid assets for ancillary services.Mechanical engineeringElectrical engineeringEnergyArtificial IntelligenceBidirectional Onboard ChargersCharging Demand ForecastingElectric Vehicle ChargingGrid ServicesReinforcement Learning ControlpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/05z6p6wgetdoai:escholarship.org:ark:/13030/qt31m5q1b52026-03-18T06:36:03Zqt31m5q1b5Nanozyme-Based Strategies for Improving Lateral-Flow ImmunoassaysLu, JiakunKamei, Daniel T.2026-03-16The emergence and rapid global spread of COVID-19 in late 2019 underscored the essential role of infectious disease testing in the surveillance and control of communicable diseases. Even though gold standard testing methods, such as the polymerase chain reaction (PCR) and the enzyme-linked immunosorbent assay (ELISA), can provide high sensitivity and specificity, they often require trained personnel and expensive equipment, frequently leading to delays in diagnosis and patient care. One diagnostic that can potentially resolve these issues is the lateral-flow immunoassay (LFA). While the LFA has been successful in the form of the over-the-counter pregnancy test and more recently the COVID antigen test, it still suffers from two main disadvantages that limit its potential to fully replace laboratory-based tests. These disadvantages are its low sensitivity and lack of quantitative readout, and this thesis focuses on addressing these limitations through different properties of nanozymes, which are inorganic nanoparticles with intrinsic catalytic capabilities.
Our lab previously integrated the peroxidase-like activity of nanozymes with biomarker preconcentration using an aqueous two-phase system (ATPS) to improve the sensitivity of the LFA. In this approach, our research group took advantage of the ability of an ATPS to partition the target Escherichia coli (E. coli) into a phase opposite that of the signal enhancement reagent 3,3’,5,5’-tetramethlybenzidine (TMB) while using paper to significantly accelerate the macroscopic phase separation process. Although successful, this previous work required a trial-and-error approach to identify the appropriate ATPS and signal enhancement reagent combination. Accordingly, in Chapter 2, we discuss our work in developing a molecular thermodynamic model to predict the partitioning behavior of signal enhancement reagents in ATPSs. Such a model can aid in the selection of optimal ATPS and signal enhancement reagent combinations as extreme partitioning of the signal enhancement reagent to the phase opposite that of the target molecule can lead to a reduction in the background signal as premature signal enhancement would be minimized. Moreover, such partitioning can also yield a higher intensity of the test line by preventing the signal enhancement reagent to flow ahead of the target molecule and be lost to the absorbent pad of the LFA. Our molecular thermodynamic model with no fitted parameters reasonably predicted the partitioning behavior of two different signal enhancement reagents in an ATPS, demonstrating the potential of using this model to guide the selection of the ATPS and signal enhancement reagent combination to reduce the time and resources associated with laboratory experiments.
To further improve LFA sensitivity using nanozymes, we applied a different approach in combining ATPS preconcentration with nanozyme-based signal enhancement in Chapter 3. Specifically, instead of using paper to automate preconcentration and signal enhancement, we employed a multistep approach with a wash buffer to ensure almost of the nanozymes could flow into the detection region while simultaneously removing components in serum that interfere with signal enhancement. This approach led to a 1000-fold improvement in the detection of the malaria biomarker Plasmodium lactate dehydrogenase (pLDH) in human serum compared to the conventional LFA, achieving a limit of detection in the range recommended by the World Health Organization.
In addition to improving sensitivity through the peroxidase-like activity of nanozymes, our lab has been working to develop a semi-quantitative LFA by using the catalase-like activity of nanozymes. Specifically, our research group previously combined the catalase-like activity of nanozymes with the anisotropic etching of gold nanorods to achieve a multicolor output that could be used for the semi-quantitative detection of digoxin, which is commonly used to treat heart failure and requires therapeutic drug monitoring due to its extremely narrow therapeutic window. Although this previous work was successful as a proof of concept, it required numerous liquid-handling steps and laboratory equipment. In Chapter 4, we describe our research in translating this technology to a fully paper-based diagnostic which required overcoming the challenges of attaining catalase activity directly on paper, achieving gold nanorod etching on paper, and designing and 3D-printing a device that could integrate all of these processes.
In the last chapter, we discuss our work in using a nanozyme with dual functionality. Specifically, a nanozyme with a magnetic core was conjugated to antibodies and used to bind and preconcentrate the target prior to flowing on an LFA and exhibiting its signal enhancement properties via its peroxidase-like activity. Additionally, this research represented our laboratory’s first venture into using an LFA to detect cancer. Although most cancer applications do not warrant the need for a rapid paper-based assay for detection, leptomeningeal disease (LMD) is the exception as patients diagnosed with LMD have an extremely short median survival time of 4 to 6 weeks. We therefore combined nanozyme preconcentration and signal enhancement with cell lysis and a barcode-style LFA to achieve semi-quantitative detection of cancer cells in artificial cerebrospinal fluid, and have also recently begun testing our diagnostic with patient samples obtained by our collaborator Dr. Won Kim and deidentified by the UCLA Translational Pathology Core Laboratory. In addition to using our paper-based diagnostic to detect LMD, our technology has potential for being used to determine if certain chemotherapy treatment plans are effective as it can achieve same-day results and thus be used to continue or modify treatment plans in the timeframe that is necessary for this devastating disease.BioengineeringBiomedical engineeringNanotechnologyImmunologypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/31m5q1b5etdoai:escholarship.org:ark:/13030/qt85p7h9rs2026-03-18T06:35:58Zqt85p7h9rsThe Vermicular Imagination in Eighteenth-Century Literature (1660-1760)Happe, MargueriteDeutsch, Helen E.2026-03-16This project argues that eighteenth-century authors used the language of the vermicular imaginary to represent the boundaries between human bodies and their nonhuman environment, using worms as a fleshly metonym for atoms or other invisible minutiae. Each chapter of the dissertation is a case study which examines the textual discourse generated around a specific worm in the years following the evolution of the microscope. Because those chapters take a particular worm species or figuration as their organizing principle, they collage genres, authors, and chronologies, assembling a bricolage of texts documenting cultural representations of creatures that still to this day resist easy definition or Linnean taxonomizing. From the chronically worm-infested bodies I present in Chapter One to the unusually-minded readers and thinkers examined in Chapter Three, my project centers bodies and minds which, through encounters with the nonhuman, reconsider the boundaries of disability and able-bodiedness.In Chapter One, I examine the heretofore undocumented extent of Grub Street’s collective, pressing fascination with the utility of worms as palimpsestic cultural symbols. Chapter Two traces the eighteenth-century figuration of silkworms from agricultural and husbandry manuals as aesthetically reformulated in popular poetry, essays, and periodicals. Chapter Three argues that the scientific shaping of insect knowledge and mobility, specifically in the figures of the maggot and bookworm, created poems and texts that invite the reader to actively consider the embodied effects of their mental activity.LiteratureModern literatureArt criticismapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/85p7h9rshttps://escholarship.org/content/qt85p7h9rs/qt85p7h9rs.pdfetdoai:escholarship.org:ark:/13030/qt404783p52026-03-18T06:35:53Zqt404783p5Multimodal Mapping of Anxiety Symptoms and Executive Function Using Resting-State Brain Signal Variability and TranscriptomicsSIGAR, PRIYANKA JAIPALUddin, Lucina Q2026-03-16Anxiety is a prevalent and impairing form of psychological distress that spans a continuum from subthreshold symptoms to diagnosable anxiety disorders and is associated with disruptions in daily functioning, neurocognitive performance, and executive function (EF). Brain signal variability (BSV), quantified from resting-state functional MRI (rs-fMRI) as moment-to-moment BOLD fluctuations, has emerged as a clinically informative index of intrinsic neural dynamics; however, its relationship to subthreshold anxiety, the cognitive mechanisms linking BSV to anxiety, and the molecular architecture supporting anxiety-related BSV patterns remain incompletely characterized. In this dissertation, four studies integrate rs-fMRI-derived BSV, dimensional anxiety symptom measures, EF phenotyping, and spatial transcriptomics to link macroscale neural dynamics to cognitive and molecular mechanisms relevant to anxiety. In the first study, we assessed associations between dimensional anxiety and BSV in youth and adult cohorts, and tested age-by-anxiety interactions, using rs-fMRI data from the Enhanced Nathan Kline Institute Rockland Sample (NKI-RS). We found that higher anxiety was consistently associated with increased BSV in both cohorts, with age-contingent shifts in the spatial distribution of effects and non-linear age-by-anxiety trajectories, including elevated BSV during mid-adulthood in higher-anxiety adults. In the second study, we assessed whether anxiety-related BSV effect maps correspond to spatial variation in cortical gene expression using the Allen Human Brain Atlas. We found that anxiety-related differences in BSV mapped onto distinct transcriptomic signatures, including pathways implicated in membrane transport, kinase/phosphatase regulation, receptor signaling, metabolic demand, cytoskeletal remodeling, and inflammatory lipid signaling, as well as signatures consistent with later-life constraints on intrinsic neural flexibility. In the third study, we assessed whether EF mediates associations between BSV and dimensional anxiety symptoms in adults by integrating BSV with behavioral measures of inhibitory control, processing speed, cognitive flexibility, and working memory. We found that elevated BSV within frontoparietal/central executive, dorsal attention, and default mode networks was associated with poorer inhibitory control, slower processing speed, and reduced cognitive flexibility, which in turn predicted greater anxiety severity and statistically accounted for the BSV–anxiety association. In the final study, we assessed the transcriptomic architecture underlying EF-mediated BSV–anxiety effects by mapping mediation-effect patterns to cortical gene-expression profiles. We found shared and domain-specific molecular signatures across EF-mediated maps, including pathways related to membrane excitability, lipid transport, cellular metabolism, cytoskeletal remodeling, myelin-dependent conduction, and synaptic trafficking and calcium-dependent plasticity. Together, these studies provide a multiscale framework in which dimensional anxiety is reflected in altered intrinsic neural variability, expressed through executive-control inefficiencies, and constrained by spatially organized molecular systems.Medical imagingCognitive psychologyMental healthAgingAgingAnxietyBrain Signal VariabilityCognitionExecutive functionTranscriptomicspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/404783p5etdoai:escholarship.org:ark:/13030/qt70c6j40s2026-03-18T06:35:47Zqt70c6j40sModeling and Estimation of Variable Productivity Models for Infectious DiseasesPhillips, SophieSchoenberg, Frederic R. Paik2026-03-13This dissertation develops statistical methods for estimating the reproduction rate and for forecasting infectious disease. We present: (1) direct, stable estimators for the productivity in Hawkes processes, with a comparison of their relative performance; (2) an application to COVID-19 case data demonstrating the utility of the estimated reproduction rate, which corresponds to the Hawkes productivity, as an early warning signal for surges; and (3) a framework for sharing information across outbreaks by fitting each wave separately with simple curves, borrowing strength from prior outbreaks to stabilize estimation and improve forecasts.StatisticsPublic healthEpidemiologyinfectious diseasepoint processreproduction rateapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/70c6j40shttps://escholarship.org/content/qt70c6j40s/qt70c6j40s.pdfetdoai:escholarship.org:ark:/13030/qt6v3442jc2026-03-18T05:02:41Zqt6v3442jcDeveloping a Transcriptomic Biomarker for Neurodegenerative DiseaseNGO, KATHIE JEANFogel, Brent L2025-01-01Neurodegenerative diseases (NDDs) are disorders caused by degeneration of neurons leading to damage to the nervous system, especially the brain. It affects about 15% of the people worldwide and is expected to increase with the aging population. The underlying causes of complex NDDs are not well understood due to complicated gene-environmental interactions which makes it difficult to accurately diagnose patients. Identifying genetic factors in these diseases is essential for early and accurate diagnosis, enabling patients access to timely interventions. In this dissertation, we developed a strategy to identify genetic factors associated with a complex NDD using environmental modifiers. We utilized genetic information from a cohort of well-documented patients that included data on disease progression and environmental exposures to identify rare variants associated with disease risk. We found rare variants that were highly associated between disease and exposure patterns. However, validation of these variants using functional studies is cumbersome and expensive in both time and cost. Therefore, we proposed to use a novel approach, transcriptional profiling, to validate variants in patients directly. To test our method, we started with a simple use case to identify disease-specific transcriptomic signatures from known patients and mouse models in a rare neuromuscular disorder. We demonstrated that disease-specific transcriptomic signatures could be derived and subsequently applied to validate a variant of unknown clinical significance in a test patient. We also demonstrated that for genes that are ubiquitously expressed, we can leverage accessible tissues such as whole blood to derive disease specific expression profiles for NDDs. We then applied our method to a relatively more prevalent heterogenous ataxic disease. We developed disease-specific biomarkers to validate patients suspected of having this disorder. We expect our transcriptional profiling method to provide a foundation for investigating more complex neurodegenerative diseases and to help identify potential therapeutic targets that could improve patient prognosis.BioinformaticsBiomarkerTranscriptional profilingenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6v3442jchttps://escholarship.org/content/qt6v3442jc/qt6v3442jc.pdfetdoai:escholarship.org:ark:/13030/qt5wq7q0gs2026-03-18T05:02:29Zqt5wq7q0gsEngineering armored chimeric antigen receptor (CAR)-T cells for the safe and efficacious treatment of solid tumorsClubb, Justin D.Chen, Yvonne Y2025-01-01Chimeric antigen receptor (CAR)-T cell therapy has shown impressive efficacy against deleterious hematological malignancies; however, CAR-T cell activity against solid tumors is curtailed by an immunosuppressive tumor microenvironment (TME) and a lack of targetable antigens homogeneously expressed by the malignant cells. To overcome these hurdles to effective immunotherapy, we and others have investigated CAR-T cells engineered to overexpress cytokines and other immune modulators. In addition to their ability to directly target and kill tumor cells, these “armored” CAR-T cells gain further functionality encoded in the armor, such TME reprogramming towards a more anti-tumor state, or autocrine cytokine stimulation to potentiate enhanced T-cell efficacy. In particular, macrophages constitute an abundant and immunosuppressive population within the tumor, yet they remain highly susceptible to polarization by molecules expressed by armored–CAR-T cells and have the potential to orchestrate endogenous anti-tumor immunity not restricted to the CAR-targeted antigen. In this dissertation, we evaluate CAR-T cells armed with novel armor combinations—intended to polarize macrophages towards an anti-tumoral phenotype—against orthotopic syngeneic solid tumors. After discovering CAR-T cells armored with interleukin (IL)-12 plus decoy resistant IL-18 (DR-18, CAR-12.DR18 T cells) are remarkably efficacious, we attempt to optimize the armor transgenes and their expression cassette to minimize cytokine leakiness or excessive expression, and present a novel solution to in vivo toxicity—a critical step for clinical translation—of pooling CAR-12.DR18 T cells with CAR-T cells engineered to overexpress an anti-VEGF scFv. Finally, we probe the mechanistic underpinnings of armored CAR-T cell efficacy and their effects on the TME, and demonstrate that pooled CAR-T cells are efficacious against antigen-heterogeneous glioma.OncologyImmunologyCellular biologyAdoptive cell therapyArmored CAR-T cellChimeric antigen receptor (CAR)-T cellGlioblastomaImmuno-oncologyImmunotherapyenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5wq7q0gshttps://escholarship.org/content/qt5wq7q0gs/qt5wq7q0gs.pdfetdoai:escholarship.org:ark:/13030/qt12f8k34s2026-03-17T06:32:08Zqt12f8k34sCapital in the Capitol: Congressional Trades Resemble Uninformed Retail TradingChen, HaotianHazlett, Chad2026-03-12Do elected officials exploit informational advantages for personal financial gain? This question has attracted heightened attention amid increased scrutiny of congressional stock trading, particularly following the COVID-19 pandemic. Prior research finds little evidence that legislators outperform the market, but existing studies rely on limited time periods and offer limited insight into the mechanisms underlying trade timing. We revisit this question by constructing a novel dataset covering the stock trading activity of all U.S. members of Congress and their immediate families from 2012 to 2023. We find that, on average, legislators' portfolios underperform or, at best, match market benchmarks after the STOCK Act. What explains this mediocre performance? We show that legislators' positions track financial professionals' recommendations, and that their timing largely reflects prevailing market sentiment, estimated from retail investors' social media posts, rather than anticipating future price changes. Together, these results suggest that legislators' financial behavior more closely resembles that of uninformed investors than that of strategic insiders.FinancePolitical scienceBusiness administrationEconomicspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/12f8k34setdoai:escholarship.org:ark:/13030/qt9nh5r9xp2026-03-17T06:32:03Zqt9nh5r9xpBiochemical Content Analysis of Individual Small Extracellular Vesicles using Surface-Enhanced Raman Spectroscopy-Based Biopsy for Cancer Diagnostics and TherapeuticsLiu, JunXie, Ya-Hong2026-03-12Cancer remains a leading cause of mortality worldwide, where patient outcomes depend strongly on early detection and rapid initiation of effective therapy. However, early cancer detection and effective targeted therapy remain critical challenges in modern medicine. Small extracellular vesicles (sEVs) or exosomes are nanoscale vesicles actively secreted by cells and carry molecular cargo reflective of their parental cell state, making them promising candidates for disease diagnosis and therapeutic delivery. However, conventional bulk averaged characterization techniques obscure vesicle-to-vesicle heterogeneity and often require destructive processing. This dissertation aims to overcome these limitations by enabling label-free, non-destructive, single-exosome biochemical analysis with high sensitivity and improved throughput via Surface-Enhanced Raman Spectroscopy (SERS) based single-vesicle analytical platform. A central technical contribution of this work is the batch fabrication of highly uniform gold nanopyramid arrays using sphere lithography combined with anisotropic KOH etching and metal transfer. Systematic process optimization substantially improved substrate uniformity and hotspot reproducibility at the wafer scale. To address the intrinsic non-quantitative nature of SERS arising from spatial variations in electromagnetic enhancement, a hybrid graphene–plasmonic platform was developed. Single-layer graphene functions as a chemically inert and biocompatible internal electromagnetic field reference, enabling deconvolution of local field effects from molecular density and allowing quantitative SERS analysis at the single-vesicle level. To mitigate the throughput bottleneck associated with random vesicle/hotspot overlap, surface functionalization strategies, including antibody-based immunocapture, were implemented. These approaches reduce salt precipitation, increase vesicle localization at plasmonic hotspots, and significantly enhance signal-to-noise ratios, enabling efficient Raman mapping. The resulting high-quality datasets are well suited for computational analysis. Given the high dimensionality and complexity of SERS spectra, both supervised and unsupervised machine learning methods were employed to classify and cluster single-vesicle spectral fingerprints, revealing disease-specific biochemical patterns. The diagnostic capability of the platform was demonstrated through gastric cancer and rectal cancer studies using clinical samples. For gastric cancer, sEVs isolated from tissue, blood, and saliva were analyzed, revealing that disease relevance and sample heterogeneity strongly influence diagnostic performance. Immunocapture targeting CLDN18.2 significantly improved specificity, producing clearer separation between cancer and healthy samples in latent-space analysis and improving prediction accuracy in blinded tests. For rectal cancer, serum-derived sEVs were classified using support-vector-machine models, achieving clinically meaningful discrimination with an area under the ROC curve of 0.84. Beyond diagnostics, this work extends single-vesicle SERS analysis to therapeutic applications, specifically the quantitative evaluation of drug loading in exosomes. By modulating osmotic conditions and employing graphene-calibrated SERS measurements, the loading of doxorubicin into individual sEVs was directly quantified. Distinct spectral markers enabled single-exosome assessment of drug content, revealing statistically significant differences across treatment conditions and demonstrating strong reproducibility. These results establish a framework for optimizing exosome-based drug delivery systems. In summary, this dissertation establishes a quantitative, scalable, and single exosome resolved SERS platform that overcomes long standing challenges in sensitivity, throughput, and reproducibility. The demonstrated applications in non-invasive cancer diagnostics and therapeutic monitoring, together with insights into exosome heterogeneity, highlight the platform’s potential impact. Future work will focus on multiplexed immunocapture, refined isolation of exosome subpopulations, optimized plasmonic substrate design, and in-vitro validation of drug delivery and cellular uptake.Cellular biologyOncologyAnalytical chemistryBiochemistryapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9nh5r9xphttps://escholarship.org/content/qt9nh5r9xp/qt9nh5r9xp.pdfetdoai:escholarship.org:ark:/13030/qt9xw9z7dw2026-03-17T06:31:58Zqt9xw9z7dwInvestigating local environment effects on the photonic interactions of cesium lead halide perovskite nanocrystalsGrishchenko, Alexandra YvetteEisler, Carissa N2026-03-12Precise control over light emission at the nanoscale is essential for advancing energy-efficient optoelectronic devices and emerging quantum photonic technologies. As semiconductor nanocrystals approach near unity photoluminescence quantum yield (PLQY), further performance improvements depend increasingly on directing emitted photons. Lead halide perovskite (CsPbX3 (X=halide)) (LHP) nanocrystals have unusually tunable angular emission due to their soft ionic lattice, which along with near-unity PLQY, defect tolerance, and facile emission wavelength tunability makes isolated LHP nanocrystals great candidates for photon qubit sources and close-packed films useful for light emitting diodes and solar energy technology. In this work, we investigate how the interplay of nanocrystal synthesis, surface chemistry, thin film assembly, and substrate interactions collectively govern transition dipole moment (TDM) orientation and thus angular light emission in CsPbBr3 nanocrystals.We developed size- and shape-controlled synthetic protocols under ambient conditions spanning strongly quantum-confined to unconfined (3.5 - 10 nm) regimes. Optimized purification strategies were critical for preserving colloidal stability and preventing aggregation-induced spectral shifts. By comparing ligand chemistries including oleic acid/oleylamine (OA/OAm), didodecyldimethylammonium bromide (DDAB), and zwitterionic lecithin, we balanced passivation, stability, and compatibility with ordered self-assembly. Using controlled deposition methods, we fabricated films with high density and coverage (>50%) of varied nanocrystal dimensions and oriented nanoplate assemblies. Angular emission was imaged via back focal plane fluorescence microscopy (BFPFM) and fit to an effective TDM angle using a three-layer model. Isotropic nanocrystal thin films exhibited nearly identical effective TDM angles (~40–41°) across size regimes, indicating that interparticle interactions in dense films dominate over size-mediated substrate effects. Anisotropic nanoplates demonstrated substantial tunability, with effective TDM angles varying from ~28° in face-down assemblies to ~46° in edge-up configurations. This work demonstrates that the angular emission of lead halide perovskite nanocrystals is a tunable property governed by nanocrystal synthesis, surface chemistry, packing motif, and substrate interaction. By integrating colloidal materials synthesis and film deposition with quantitative optical modeling, we establish a design framework for engineering transition dipole alignment and directional emission in next-generation optoelectronic and quantum photonic devices.Chemical engineeringEnergyPhysical chemistryNanotechnologyangular light emissiondipole alignmentlead halide perovskitephotonicssemiconductor nanocrystalthin filmapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9xw9z7dwhttps://escholarship.org/content/qt9xw9z7dw/qt9xw9z7dw.pdfetdoai:escholarship.org:ark:/13030/qt8wm0x4vx2026-03-17T06:31:52Zqt8wm0x4vxSeeing Small: Probing Visual Perception Limits of Vision-Language ModelsYuan, YuyangSrivastava, Mani2026-03-12Vision-Language Models (VLMs) achieve strong results on standard VQA benchmarks, yet it remains unclear how their performance is affected by low-level characteristic of target object. In this work, we take a controlled approach to diagnose visual perception limitations in current models. We formalize a patch-based input pipeline in which a scene is rendered on a fixed canvas, partitioned into non-overlapping patches, and encoded by a vision encoder into tokens for reasoning. Within this setting, we systematically examine three core factors that probe visual perception: (i) size sensitivity, analyzing how recognition varies as the target scales from tiny (few-token) to large (many-token); (ii) search complexity, evaluating robustness to distractors and background clutter; and (iii) alignment to patch grid, testing how the spatial alignment of small objects relative to the patch grid affects recognition. We construct a synthetic emoji-based dataset that precisely controls object size, placement, and distractors, and benchmark three state-of-the-art VLMs under identical conditions. Our experiments reveal that current models exhibit clear signs of perceptual weakness: accuracy drops by 60–70% from large to tiny objects, and declines by over 50% when a 14-pixel icon shifts from lying within a single patch to spanning multiple patches, indicating strong sensitivity to patch-grid alignment and exposing visual bottlenecks masked by aggregate benchmark metrics.Computer engineeringComputer scienceInformation scienceapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8wm0x4vxhttps://escholarship.org/content/qt8wm0x4vx/qt8wm0x4vx.pdfetdoai:escholarship.org:ark:/13030/qt7z3046c82026-03-17T06:31:47Zqt7z3046c8Evaluation of Vertical Seismic Effect Methods in American Society of Civil Engineers 7 Across the Continental United StatesPeng, ChengBozorgnia, Yousef2026-03-12Section 12.4.2 of the ASCE/SEI 7 (2022) provisions offer two alternative methods for estimating the seismic load effects due to vertical ground motions: one based on horizontal spectral acceleration at a short horizontal period, and the other on vertical spectral acceleration at a vertical structural period. This study examines the similarities and discrepancies between the two methods across the contiguous United States. A previous investigation, based on California sites, is generalized for the contiguous US covering Seismic Design Categories (SDCs) A through E and a full range of vertical structural period 0 to 10 seconds. Although structures with vertical periods approaching 10 s are unlikely in practice, this range is considered for completeness. Overall, 331,231 sites in the U.S. are analyzed in this study. A lognormal-based probabilistic model is developed to characterize the distribution of the ratio of the outcomes of the two methods across vertical period ranges for each SDC, thereby quantifying their relative differences. Results indicate that for sites located in the western U.S., and for vertical structural periods between 0.05 and 0.2 seconds, the approach based on vertical spectral acceleration generally results in higher vertical seismic load effects than that based on short-period horizontal spectral ordinates. For sites located in the central and eastern U.S., the short-period horizontal spectral acceleration approach consistently yields comparable or higher estimates of the vertical seismic load effect than that based on the vertical spectral acceleration approach.Civil engineeringEngineeringEnvironmental engineeringASCE 7vertical ground motionsvertical seismic effectsvertical spectraapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7z3046c8https://escholarship.org/content/qt7z3046c8/qt7z3046c8.pdfetdoai:escholarship.org:ark:/13030/qt5dg508xg2026-03-16T06:30:39Zqt5dg508xgAdvancing the Frontiers of Post-Quantum Public-Key CryptographyGhosal, RiddhiSahai, Amit2026-03-11Noisy linear algebraic assumptions with respect to random matrices, in particular Learning with Errors (LWE) and Alekhnovich Learning Parity with Noise (Alekhnovich LPN), are among the most investigated assumptions that imply post-quantum public-key encryption (PKE). They enjoy elegant mathematical structure. Indeed, efforts to build post-quantum PKE and advanced primitives have increasingly focused their attention on these two assumptions and their variants.Unfortunately, this increasing reliance on these two assumptions for building post-quantum cryptography leaves us vulnerable to potential quantum and classical attacks on Alekhnovich LPN and LWE. Quantum algorithms is a rapidly advancing area, and we must stay prepared for unexpected cryptanalytic breakthroughs. Therefore, we ask the following question In a world where both LWE and Alekhnovich LPN are broken, can there still exist noisy linear assumptions that remain plausibly quantum hard and imply PKE?To answer this question positively, in Part 1, we introduce two natural noisy-linear algebraic assumptions that are both with respect to random matrices, exactly like LWE and Alekhnovich LPN, but with different error distributions. Our error distribution combines aspects of both small norm and sparse error distributions. We design a PKE from these assumptions and give evidence that these assumptions are likely to still be secure even in a world where both the LWE and Alekhnovich LPN assumptions are simultaneously broken. We also study basic properties of these assumptions, and show that in the parameter settings we employ to build PKE, neither of them are ``lattice'' assumptions in the sense that we don't see a way to attack them using a lattice closest vector problem solver, except via NP-completeness reductions.One notable weakness in Part 1, is that it is still LPN-broken: namely, an oracle that breaks LPN with noise rate can break their NLA, for some . In Part 2, we ask: can this barrier be overcome? We answer this question positively, by introducing an NLA that is plausibly hard given an oracle that breaks even constant noise rate LPN, while still being plausibly hard given an oracle that breaks LWE.Specifically, we introduce a single new natural NLA and provide constructions of (i) public-key encryption, (ii) two-round maliciously secure oblivious transfer protocol, and (iii) circular-secure secret-key encryption, from this single assumption. We show that for a broad range of parameters including those used by our constructions, our assumption does not appear to fall within the class of “lattice” assumptions, that is, there seems to be no reduction to approximate shortest vector problem solvers, and, crucially, it is plausibly hard even given an oracle that breaks LPN for any noise density parameter.Computer scienceStatisticsMathematicsInformation technologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5dg508xghttps://escholarship.org/content/qt5dg508xg/qt5dg508xg.pdfetdoai:escholarship.org:ark:/13030/qt13q991gk2026-03-14T05:01:26Zqt13q991gkTopological Spintronics Based on Magnetic Skyrmions and Magnetic Topological InsulatorsTai, LixuanWang, Kang L2024-01-01Spintronics harnesses spin degrees of freedom for information storage and processing, offering notable advantages of non-volatility, low power consumption, and fast speed. Topology, as invariant geometric and physical properties under continuous deformation, endows spintronics with higher efficiency and robustness against external perturbations. Protected by real-space topology, magnetic skyrmions are swirling topological spin structures with particle-like properties and potential candidates for high-density, non-volatile storages. As an electrical readout of magnetic skyrmions, the topological Hall effect (THE) is a non-monotonic feature in the Hall signal. However, in the presence of an anomalous Hall effect (AHE), the THE can be easily confused with the non-monotonic co-existence of two AHEs, or artifact of “THE”. Here, we develop systematic methodologies for distinguishing between the two. Genuine THE occurs in the transition region of the AHE, while artifact of “THE” may occur well beyond the saturation of the “AHE component”. Minor loops of genuine THE with AHE are always within the full loop, while minor loops of artifact of “THE” may reveal a single loop that cannot fit into the “AHE component”. The temperature or gate dependence of artifact of “THE” may also be accompanied by a polarity change of the “AHE component”. Our methods may help future researchers ascertain genuine THE for applications of magnetic skyrmions.
Protected by k-space topology, magnetic topological insulators (MTI) can apply highly efficient spin-orbit torque (SOT) and manipulate the magnetization with their unique topological surface states. Here, we demonstrate efficient SOT switching of a hard MTI, V-doped (Bi,Sb)2Te3 (VBST) with a large coercive field that can prevent the influence of an external magnetic field. A giant switched anomalous Hall resistance of 9.2 kΩ is realized, among the largest of all SOT systems. The SOT switching current density can be reduced to 2.8×105 A/cm2. Moreover, as the Fermi level is moved away from the Dirac point by both gate and composition tuning, VBST exhibits a transition from edge-state-mediated to surface-state-mediated transport, thus enhancing the SOT effective field to 1.56±0.12 T/(106 A/cm2) and the interfacial charge-to-spin conversion efficiency to 3.9±0.3 nm-1. The findings establish VBST as an extraordinary candidate for energy-efficient magnetic memory devices.EngineeringPhysicsMaterials Sciencemagnetic skyrmionmagnetic topological insulatorspin-orbit torquespintronicstopological Hall effecttopological insulatorenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/13q991gkhttps://escholarship.org/content/qt13q991gk/qt13q991gk.pdfetdoai:escholarship.org:ark:/13030/qt0dd5w35x2026-03-13T06:31:58Zqt0dd5w35xA Generalized Latent Space Modeling Framework for Item Response DataLuo, JinwenJeon, Minjeong2026-03-10Item Response Theory (IRT) provides the theoretical basis for modern measurement, but its reliance on conditional independence is rarely fully met in practice, especially when educational and psychological assessments take place in highly interdependent contexts with complex interactions among items and persons. This dissertation argues that such violations can meaningfully signal the response process that can be explicitly modeled. Building upon the latent space item response modeling (LSIRM) approach introduced by Jeon et al., (2021), this dissertation develops the Generalized LSIRM framework, extending the original model to a broader class of outcome families and measurement contexts. The framework formally distinguishes between two levels of interaction: Person-Item (PI) interaction, which identifies neighborhoods of items, and Person-Category (PK) interaction, which localizes interaction to specific response steps or categories. Based on identified latent space visualized in an interaction map, this dissertation proposes and demonstrates a visualization-centered workflow, which allows researchers to examine the structural patterns of residual conditional dependence beyond global fit statistics. Using both simulation studies and empirical examples, the dissertation shows that the generalized framework accurately recovers the underlying latent structure across various types of data and assessment contexts. By elevating residual dependence from an error term to an inferential target, the framework provides a mechanism to strengthen validity arguments and make the previously hidden dynamics of the response process localizable, visualizable, and testable.Quantitative psychologyEducational tests & measurementsStatisticsEducationpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/0dd5w35xetdoai:escholarship.org:ark:/13030/qt1h29d4fh2026-03-13T06:31:54Zqt1h29d4fh"The novel and the transient": Queer Turns in SurrealismSpies, EmmaHornby, Louise2026-03-10This dissertation examines interwar surrealism as a mode of urban perception structured by estrangement, partial visibility, and spatial disorientation. Focusing on literary and visual works produced in Paris during the 1920s and 1930s, this project argues that surrealist modernity is best understood not as a unified avant-garde movement but as a set of spatial practices shaped by the built environment of the modern metropolis. Drawing on the work of Walter Benjamin and Sara Ahmed's queer phenomenology, this dissertation reads surrealist texts and images as experiments in seeing that foreground thresholds — windows, doorways, arcades, alleys, nocturnal streets — as sites where vision falters and meaning becomes destabilized.
Across readings of writers and artists including Louis Aragon, Djuna Barnes, André Breton, Claude Cahun, Germaine Krull, and Phillippe Soupault, this project reconsiders the place of female queerness within surrealism. While much surrealist scholarship has looked primarily at how women function as muses, symbols, or screens for male avant-garde desire, this dissertation centers sapphic and queer-coded figures as structuring forces within surrealist perceptual economies. These figures, often glimpsed partially, rendered fragmentary, or otherwise resistant narrative comprehension, do not function primarily as representational objects but as forces of perceptual disruption. Their opacity exposes the limits of dominant visual regimes — including those that govern avant-garde scopic practices — and unsettles assumptions about legibility, embodiment, eroticism, and spatial mastery in modern city life.
Methodologically, this dissertation develops a Benjaminian, queer-phenomenological approach that centers shock, fragmentation, the optical unconscious, and the loss of the aura as tools for analysis. Rather than treating queerness as a stable identity category, this project conceptualizes it as a spatial and perceptual condition that is simultaneously everywhere and nowhere within the surrealist city: diffuse, intermittent, and structurally embedded in moments of misalignment, interruption, and refusal. By recentering female queerness within the study of surrealist urban aesthetics, this dissertation demonstrates how surrealist practices generate queer effects that reconfigure how bodies, sexualities, and spaces are apprehended in interwar modernity.LiteratureArt historyLGBTQ studiesGender studiesModernismQueer StudiesSurrealismUrban Cultural StudiesVisual CultureWalter BenjaminpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1h29d4fhetdoai:escholarship.org:ark:/13030/qt5th2p5bv2026-03-13T06:31:49Zqt5th2p5bvInterpreting Othello-Playing Transformers via Sparse Dictionary LearningZhang, ZhiyuanWu, Yingnian YW2026-03-10Mechanistic interpretability aims to reverse-engineer the internal computations of neural networks into human-readable algorithms. We study a decoder-only Transformer trained to predict legal moves in 6 × 6 Othello from move sequences alone, achieving 99.62% top-k legal-move accuracy. We train two sparse dictionary learning models using end-to-end cross-entropy training: JumpReLU transcoders (replacing MLP sublayers) and JumpReLU sparse autoencoders (reconstructing residual streams), preserving top-k accuracy at 99.3–99.6% across all layers.To measure each feature’s interpretability, we fit decision trees over hand-crafted boolean game features and evaluate the F1 score. A clear interpretability trend emerges across depth: in transcoder Layer 0, 58.1% of features achieve F1 > 0.99, whereas this fraction declines to 1.8% in Layer 5. The SAE exhibits a similar but smoother trend, from 35.8% in Layer 0 to 6.8% in Layer 5. We conduct causal intervention experiments to confirm that these sparse features drive the model’s predictions rather than merely correlating with game state. Comparing random forests against single decision trees, we find that the primary bottleneck for unexplained features is feature engineering (13–20% of features lack adequate boolean descriptors) rather than classifier capacity (>70% are fully explained by a single tree). These results demonstrate that sparse dictionary learning can extract large numbers of unambiguous, human-readable rules from a Transformer trained in a well-controlled domain.StatisticsArtificial intelligenceapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5th2p5bvhttps://escholarship.org/content/qt5th2p5bv/qt5th2p5bv.pdfetdoai:escholarship.org:ark:/13030/qt53w923852026-03-13T06:30:59Zqt53w92385Building Generalizable Embodied AI Agents through Simulation, Foundation Models, and Human FeedbackPeng, ZhenghaoZhou, Bolei2026-03-05Embodied AI is crucial for robotics applications, spanning autonomous driving and delivery systems to service robots in healthcare and hospitality. Traditional AI training methods, including reinforcement learning (RL) and imitation learning (IL), face significant challenges: IL requires expensive human demonstrations and struggles with distribution shifts, while RL, typically trained in simulation, suffers from the simulation-to-reality gap caused by simulator artifacts and reward misspecification. Recently, the emergence of large-scale vision-language foundation models has opened new avenues for embodied decision-making, yet effectively integrating these models into real-time robotic systems introduces unique challenges around inference latency, action grounding, and self-reflective reasoning. This dissertation addresses these challenges through three complementary pillars: large-scale data-driven simulation, vision-language foundation models for embodied decision-making, and online human-in-the-loop learning.For the first pillar, we develop an integrated simulation ecosystem. ScenarioNet provides a unified, open-source platform for large-scale traffic scenario simulation that reconstructs real-world data from multiple datasets into interactive environments. We further enhance simulation realism through reinforcement learning fine-tuning of multi-agent behaviors. SceneStreamer advances scenario generation by formulating it as autoregressive next-token-group prediction, enabling unified generation of agent states, motions, and traffic signals with the ability to continuously inject new agents during simulation.For the second pillar, we investigate two complementary approaches for integrating vision-language models into embodied systems. Counterfactual VLA (CF-VLA) is a self-reflective vision-language-action model for autonomous driving that performs counterfactual reasoning on its own predicted actions, critiques them, and corrects unsafe plans before execution. We also propose a latency-resilient VLM-planner fusion framework for mobile robot navigation that bridges the temporal mismatch between fast local planners and slow but semantically powerful vision-language models through Score Fusion with geometric similarity and exponential staleness decay.For the third pillar, we develop online human-in-the-loop learning methods that align agent behaviors with human preferences through real-time interventions. Proxy Value Propagation (PVP) learns from active human involvement by propagating proxy values from human interventions to temporally related states, achieving efficient utilization of sparse human feedback. PVP4Real extends this approach to physical robots, demonstrating that mobile robots, including a legged quadruped and a wheeled delivery robot, can be trained from scratch within minutes using only online human interventions, without reward functions or pretraining.Our findings highlight three primary contributions: (1) large-scale, data-driven simulation frameworks significantly boost learning efficiency and scenario diversity for embodied AI training; (2) vision-language foundation models can effectively augment embodied decision-making through self-reflective reasoning and latency-resilient fusion, improving both trajectory accuracy and safety; and (3) human-in-the-loop learning aligns AI behaviors with human preferences and bridges the sim-to-real gap through continuous policy refinement and real-world robotic deployment. The broader impact of this work includes open-source simulation tools widely adopted by the research community, practical methods for deploying foundation models in real-time robotic systems, and a systematic methodology for training robots from human feedback in real-world settings. Together, these three pillars form a cohesive framework for building generalizable embodied AI agents that are scalable, intelligent, and human-aligned.Artificial intelligenceComputer engineeringRoboticsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/53w92385https://escholarship.org/content/qt53w92385/qt53w92385.pdfetdoai:escholarship.org:ark:/13030/qt2j36f9b12026-03-13T06:30:54Zqt2j36f9b1Deep Learning in Label-Free Sensing and Computational Microscopy: From Microbial Quantification to Virtual Histochemical Tissue StainingLi, YuzhuOzcan, Aydogan2026-03-05Since Antoni van Leeuwenhoek and Robert Hooke first revealed the microscopic world, optical microscopy has remained central to biological discovery and medical diagnosis. However, most biological specimens, from microbes to human tissue sections, are largely transparent and therefore provide limited contrast under standard brightfield illumination. For more than a century, this fundamental limitation has been addressed primarily through chemical labeling, for example, Hematoxylin and Eosin (H&E) to visualize tissue morphology or crystal violet to reveal viral plaques on the cell monolayers. Despite their effectiveness, these labeling procedures are often labor-intensive, time-consuming, costly, and can irreversibly alter or destroy the native state of the specimen. To reduce reliance on exogenous labels, a broad class of label-free imaging modalities has been developed, including phase contrast microscopy, quantitative phase imaging, Raman spectroscopy and so on. These techniques exploit intrinsic physical signatures such as refractive index variations and molecular vibrations, enabling noninvasive imaging and, in some cases, real-time monitoring of living systems. However, a key barrier prevents the widespread clinical translation of these label-free imaging techniques, which is they frequently lack the familiar visual cues used in routine biological and pathological interpretation, making them harder to read, standardize, and integrate into established diagnostic workflows. This thesis bridges that gap by developing AI-powered computational microscopy frameworks that translate label-free measurements into actionable quantitative and diagnostic information, spanning from rapid microbial quantification to virtual histological staining of unlabeled tissue sections. First, a field-portable, cost-effective, and lens-free sensing platform based on a large field-of-view thin film transistor (TFT) image sensor is introduced for early detection and classification of bacterial colonies. By combining time-lapse imaging with deep neural network analysis of spatiotemporal growth signatures, the system enables automated colony detection hours earlier than conventional culture-based readouts while maintaining high detection accuracy, supporting scalable and low-cost microbial screening without mechanical scanning or manual counting. Building on this label-free sensing paradigm, this thesis extends computational microscopy to virology by introducing a compact, cost-effective, and label-free live plaque assay for automated plaque detection and plaque-forming unit (PFU) quantification, eliminating the need for chemical staining and manual counting. Leveraging time-lapse lens-free holographic imaging and deep learning-based analysis, the proposed system identifies over 90% of total PFUs up to 48 hours earlier than conventional plaque assays, while achieving 100% specificity across multiple viral strains. This accelerated, automated workflow reduces experimental turnaround time and enables higher-throughput studies for vaccine development and antiviral drug discovery. Transitioning from microorganisms to clinical histopathology, this thesis further develops deep learning-based virtual staining methods that transform label-free tissue autofluorescence images into brightfield histochemically equivalent representations. Compared with conventional histochemical staining, virtual staining offers a rapid and reagent-free workflow with reduced cost and waste, consistent staining appearance, and preservation of the tissue for downstream analyses. Using lung and heart transplant biopsies as clinically-relevant test beds, the proposed virtual staining approach produces images that closely resemble their histochemically stained counterparts and supports diagnostic assessments of transplant rejection that are comparable to those made using traditional stained slides. The framework is further extended to autopsy tissue, where fixation-related degradation and processing artifacts can compromise chemical staining quality and reduce morphological definition. Compared with standard histochemical staining, the virtual staining model presented in this thesis enables high-quality virtual H&E image generation for lung autopsy samples, effectively restore nuclear, cytoplasmic and extracellular features that may be partially obscured or lost in conventional staining. Finally, because virtual staining models are generative and can exhibit failure modes that range from obvious artefacts to realistic hallucinations, this thesis introduces an autonomous quality and hallucination assessment framework that operates without access to histochemically stained ground truth. This method detects both technical artefacts and realistic hallucinations in virtually stained tissue images, achieving strong agreement with board-certified pathologists while identifying misleading cases that can evade human inspection, thereby providing a scalable quality assurance mechanism for deployment. Collectively, this thesis demonstrates that deep learning can substantially expand the capabilities of label-free optical sensing by improving detection sensitivity, enabling virtual molecular and histological contrast, and providing algorithmic safeguards for reliability. These advances establish an integrated computational imaging paradigm that connects physical measurement to data-driven interpretation, enabling rapid, scalable, and chemistry-free biomedical analysis from microorganisms to human tissue.Electrical engineeringOpticsComputer sciencepubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2j36f9b1etdoai:escholarship.org:ark:/13030/qt4dx424br2026-03-13T05:02:22Zqt4dx424brHotspots from Top to BottomBao, XiyuanLithgow-Bertelloni, Carolina R.2024-01-01The dynamical and chemical processes of the Earth’s interior are the critical driver of the evolution of various “spheres”, like the hydrosphere, atmosphere, and biosphere, and shape the surface where we live. While the interior of the Earth is largely inaccessible, the surface volcanism provides a unique window into the planet’s physical and chemical evolution. The majority of the volcanism on Earth is generated by decompression melting at the mid- ocean ridges, with a smaller proportion due to dehydration melting at subduction zones, and melting at intraplate volcanoes. Intraplate volcanism away from ridges (like Hawaii) and excessive melting anomalies near or at ridges (like Iceland) are not readily explained by plate tectonics. Such phenomena are also known as hotspots.Hotspots could be fed by active upwellings, or mantle plumes, a distinct scale of flow within mantle convection, separate from the plate-scale flow, and directly linked to the bot- tom thermal boundary layer. In particular, it has long been suggested that hotspots originate from the Large Low Shear Velocity Provinces (LLSVPs) at the core-mantle boundary (CMB), as imaged by seismic waves. However, the presence of mantle plumes under hotspot volcanoes extending to the CMB, and the possible link between LLSVPs and plumes have been the source of debate.In this thesis, I aim to address the link between hotspots and LLSVPs and their relation to the bottom thermal boundary layer by analyzing surface observations and performing analog experiments. By employing a multi-disciplinary approach, this research facilitates a comprehensive exploration from the Earth’s surface to its deep interior. This holistic investigation helps bridge the gap in our understanding of the geophysical and geochemical signals seen at the top thermal boundary layer, and their correlation to the dynamics and entrainment phenomena at the bottom thermal boundary layer.In Chapter 2, I use self-consistent thermodynamic calculations to infer the potential temperature beneath hotspots and mid-ocean ridges in the mantle from seismic tomography. By comparing the excess temperature of hotspots over ridges with buoyancy flux estimates and geochemical signals from hotspots, I have discovered not all hotspots are hot and fed by deep active upwellings; some are cold and require different dynamical mechanisms from those provided by classical plume theory. I have further explored the spatial pattern of global ridge temperature in Chapter 3, and showed the ridge temperature contains fingerprints of past mantle convection and plate tectonics, which can be used to predict the geographic distribution of ridges and disentangle the contribution of deep and shallow processes to temperature and geophysical, geochemical and geological variations observed at ridges.Chapter 2 and 3 serve to provide an observational lens with which I investigate the dynamical, morphological, and chemical relationship between the LLSVPs and plumes with laboratory experiments in later chapters. The working fluid (corn syrup) is heated from below, and cooled from the top, with Earth-like convective vigor. Among different geneses, I focus on two end-members of the LLSVPs: purely thermal plume clusters and an undeformable, fixed pile, or essentially Rayleigh-Benard convection without or with a 3-D-printed obstacle. Utilizing 4-D velocity acquisition 4, Lagrangian analysis 5, and adjoint reconstructions 7, I have quantitatively analyzed the flow in unprecedented detail (Chapters 6 and 8). In the simple Newtonian working fluid we use for our experiments (corn syrup with only has
temperature-dependent viscosity, I have identified a diverse array of plume behaviors that could explain a range of observations from neighboring hotspot tracks to the tree-structure of the LLSVPs and seismic velocity local minima in the lowermost mantle (cf. Chapter 6). Plumes do not seem to have site preference in the purely thermal case, while strong and stable plumes tend to rise around and from the rigid LLSVP (cf. Chapter 8). With a thorough material entrainment analysis of the two end-member experiments at different proximity to the LLSVP, the physical locations of reservoirs linked to the geochemical signals seen at hotspots are also inferred using the geographical (dis)correlation of these hotspots and the LLSVPs. This framework leverages the infinite resolution and unaltered physics of real-world experiments, offering a thorough workflow for 4-D viscous flow measurement that could be extended to other terrestrial planets and beyond. My results also open the door to quantitative comparison of laboratory experiments and numerical simulations of geodynamical time-dependent, multi-scale flow.
Geophysicsenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4dx424brhttps://escholarship.org/content/qt4dx424br/qt4dx424br.pdfetdoai:escholarship.org:ark:/13030/qt8bz2c55m2026-03-12T06:30:54Zqt8bz2c55mOlder Adults’ Memory and Susceptibility to Scams: Factors and InterventionsAlberts, Kylie O'BrienCastel, Alan D2026-03-06Young and older adults are often victimized by various forms of scams and fraud. However, few studies have investigated psychological factors that may make individuals more susceptible to scams. This dissertation explores the cognitive factors that could contribute to scam susceptibility in older adults, as well as potential interventions that could help young and older adults reduce the amount they fall victim to scams. Older adults’ trustworthiness, loneliness, confidence, and curiosity were examined as potential factors, as prior research has suggested that each may be linked to scam susceptibility. Associative memory deficits, gist-based false memory, and the reliance on schematic information in memory retrieval are all prevalent in older adults, and this dissertation aimed to investigate these further through their potential relationships to scams and fraud. In the present research, both younger and older adults participated in a scam susceptibility intervention for imposter email scams, with the hope that participants could improve their ability to detect scams. Overall, some factors, such as older adults exhibiting higher trust when viewing faces than young adults, were related to an increase in scam susceptibility (Chapter 2). However, in a few experiments, older adults performed similarly and, in some cases, better than young adults when engaging with tasks that require memory retrieval for verbatim details about offers and prices (Chapter 3) and memories that align with schemas (Chapter 4). Both young and older adults were also more cautious when viewing email scams after engaging with an interactive scam susceptibility intervention (Chapter 5). These findings have important implications for identifying factors that contribute to scam susceptibility and for designing interventions that help both young and older adults become more cautious when encountering scams, by emphasizing the cognitive processes involved in scam and fraud detection.Cognitive psychologyAgingExperimental psychologyDevelopmental psychologyAgingMemoryScams/Fraudapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8bz2c55mhttps://escholarship.org/content/qt8bz2c55m/qt8bz2c55m.pdfetdoai:escholarship.org:ark:/13030/qt6hq3c80n2026-03-10T06:31:20Zqt6hq3c80nOvercoming Therapeutic Resistance in EGFR-Driven Glioblastoma: From Targeted Inhibition to Molecular AdaptationOkobi, QuincyNathanson, David A2026-03-02Epidermal growth factor receptor (EGFR) is the most frequently altered oncogenic driver in glioblastoma (GBM), yet prior EGFR inhibitors have demonstrated limited clinical benefit owing to variant heterogeneity and inadequate central nervous system (CNS) exposure. We developed KTM-101, a highly selective, reversible EGFR tyrosine kinase inhibitor engineered for potency against GBM-relevant variants, including amplified wild-type EGFR, extracellular-domain mutations, and EGFRvIII, while achieving robust brain penetration. KTM-101 demonstrated favorable brain-to-plasma ratios across species, durably suppressed intracranial EGFR signaling, and significantly prolonged survival in orthotopic patient-derived xenograft (PDOX) models. In early-phase clinical evaluation, KTM-101 achieved plasma and intracranial exposures exceeding preclinical thresholds for pathway inhibition, with initial evidence of disease control.Across a randomized efficacy study of 28 PDOX models recapitulating GBM heterogeneity, therapeutic benefit correlated most strongly with EGFR amplification, establishing a biomarker framework for patient selection. Despite sustained target suppression, however, tumors ultimately progressed under continuous therapy. Using serial in vivo selection, we modeled acquired resistance and found that resistant tumors retained EGFR inhibition without target reactivation or loss of drug engagement. Instead, resistance emerged gradually and was characterized by transcriptional and proteomic rewiring toward alternative receptor tyrosine kinase signaling, most prominently involving PDGFRA. In select models, activating PDGFRA alterations reinforced this bypass state. Notably, resistance was reversible: tumor-derived cells remained sensitive ex vivo, and resistant tumors regained responsiveness outside the intracranial microenvironment, indicating a dynamic, context-dependent state.Molecular biologyOncologyPharmacologyPharmaceutical sciencesNeurosciencesBiochemistryBiomedical sciencesCancer BiologyDrug discoveryNeuroscienceapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6hq3c80nhttps://escholarship.org/content/qt6hq3c80n/qt6hq3c80n.pdfetdoai:escholarship.org:ark:/13030/qt4611j60n2026-03-10T06:31:10Zqt4611j60nThe development and evolution of visual cortical plasticity: molecules to cells to circuitsGorzek, RyanTrachtenberg, Joshua2026-02-26The neocortex, particularly primary sensory cortices, develops under a dual mandate: establish pathways that provide stable perception, but allow room for experience-dependent adaptation to the statistics of the environment. Here, I examine the manifestation of this motif on the molecular, cellular, and circuit levels in the primary visual cortex (V1). I begin by reframing the classical critical period as a constructive developmental process beginning at eye opening, where a plastic pathway from the ipsilateral eye develops alongside a genetically specified contralateral scaffold. Largely hard-wired thalamocortical and layer 4 circuits provide tuned information to layer 2/3 (L2/3), where receptive fields in the binocular pool undergo experience-dependent sharpening via cellular exchange. My data reveal that long-range intracortical projections from L2/3 of V1 to higher visual areas (HVAs) are not uniformly experience-dependent. Dark rearing preferentially reduces feedforward projections to area RL and AM/PM, while projections to LM remain intact. These changes are related to the functional integrity of these HVAs, which may impact downstream computations. Finally, using single-nucleus and spatial transcriptomics, I show that thalamorecipient and corticofugal populations are tightly conserved between mouse and the marsupial Monodelphis domestica, whereas intratelencephalic (IT) cells—especially in L2/3—exhibit substantial divergence in gene expression, manifold structure, and laminar organization. Across these analyses, the visual cortex exhibits plasticity in distinct aspects of its architecture, which emerge as conserved targets for both developmental refinement and evolutionary change.NeurosciencesMolecular biologyCellular biologyDevelopmental biologyColumnComparativeEvolutionNeocortexTranscriptomicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4611j60nhttps://escholarship.org/content/qt4611j60n/qt4611j60n.pdfetdoai:escholarship.org:ark:/13030/qt1j8315gm2026-03-10T06:31:05Zqt1j8315gmIdentifying Deepfake Audio with Statistical and Deep Learning MethodsWen, Derek HaozhouWu, Yingnian2026-02-26As deepfake audio becomes increasingly realistic, the need for reliable detection models is critical. This thesis evaluates the effectiveness of statistical versus deep learning methods for identifying deepfake audio in the for-norm release of the Fake-or-Real dataset. Statistical baselines are established using Logistic Regression and Random Forest models trained on handcrafted, non-learned acoustic features. The Random Forest model achieves a strong test accuracy of 0.815 and a test AUC of 0.888, showing that classical signal processing features remain competitive. Deep learning models are evaluated using log-Mel spectrogram inputs and convolutional architectures, including a lightweight CNN trained from scratch and a ResNet18 model fine-tuned for single-channel spectrograms. Threshold-based metrics are computed using a decision threshold selected on the validation set and then fixed for test evaluation on the held-out test set. Using this framework, the CNN attains a test accuracy of 0.745 with a test AUC of 0.882, while ResNet18 achieves the highest test AUC of 0.964 and a test AP of 0.963 but a lower test accuracy of 0.528 due to calibration mismatch between the validation and test splits. The results show that tree-based statistical baselines remain competitive in accuracy under fixed decision rules, while deeper architectures improve ranking performance and capture spectro-temporal cues that are not fully represented by handcrafted features, thereby highlighting the importance of calibration and threshold selection in deep learning models for reliable deployment.StatisticsComputer scienceMathematicsConvolutional Neural NetworksDeep LearningDeepfake Audio DetectionMachine LearningMel-Frequency Cepstral CoefficientsRandom Forestapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/1j8315gmhttps://escholarship.org/content/qt1j8315gm/qt1j8315gm.pdfetdoai:escholarship.org:ark:/13030/qt9187x1pv2026-03-09T06:31:25Zqt9187x1pvMaternal diseases, medication, vitamin use and the risk of childhood cancer among offspringDeng, ChuanjieRitz, Beate R2026-03-04Medication use during pregnancy has been increasing for decades, partially due to rising maternal ages and increasing prevalence of maternal comorbidities. Medication use in pregnancy can help to manage maternal chronic and infectious conditions and improve maternal health and birth outcomes, but it might also lead to severe adverse outcomes. Childhood cancer has been linked to several maternal conditions which might be an indication of pharmaceutical use. Prenatal vitamin intake and metabolism may influence carcinogenesis, but there are few epidemiological studies of retinoblastoma risk factors. Linking maternal exposures to pediatric cancer risk identifies potential prenatal risk factors of pediatric cancer, and provides information on safety of medication/supplement use in pregnancy and cancer prevention. In this dissertation, we investigated the association between maternal nitrosatable drug use, polypharmacy, thyroid diseases/medication use and vitamin metabolites and pediatric cancer risk among offspring to better understand biological mechanisms underlying pediatric cancer formation.We first investigated the link between exposure to nitrosatable drugs in pregnancy and childhood cancer risk among offspring. Using the Taiwan Maternal and Child Health Database (N = 2,090,806), we conducted a population-based cohort study of mother-child pairs with children born 2004-2015. Prescriptions of nitrosatable drugs were obtained from the National Health Insurance Program, and cancer cases were ascertained through linkage to the Cancer Registry. Cox proportional hazards regression models were applied to estimate both crude and adjusted hazard ratios (aHR) with 95% confidence intervals (CI). In total, we identified 3203 cancer cases. Offspring of mothers who used nitrosatable drugs had elevated risks of neuroblastoma, hepatoblastoma, and osteosarcoma. The strength of these associations varied with drug functional group and cancer type. We also investigated the relationship between maternal thyroid diseases and medication use and childhood cancer among offspring. We conducted a population-based case-control study using Danish registries. Cases (N=2521) diagnosed between 1973 and 2016 were ascertained from the Danish Cancer Registry, and controls (N=63014) were randomly selected from the Central Population Registry and matched on sex and date of birth. Information on diagnoses and prescriptions were obtained from the National Patient Register and the National Prescription Register, respectively. Conditional logistic regression was applied to estimate odds ratios (ORs) and 95% CIs. Goiter was associated with any cancer, leukemia, acute lymphoblastic leukemia, and lymphoma. There was no observed association between childhood cancer and maternal hypothyroidism or hyperthyroidism; however, these results were likely underpowered. No increased risk of cancer was observed for maternal thyroid medication use. Additionally, we investigated the association between concomitant use of different medications in pregnancy and childhood cancer using the same study design and the linkage of registries in Denmark. Different medications were defined according to distinct chemical substances based on the ATC 5th level. Polypharmacy was defined as at least five different medications prescribed in pregnancy. Conditional logistic regression models were applied to estimate ORs and corresponding 95% CIs. Among all women, antifungals and antibiotics were two most prescribed medications in pregnancy. Even though increasing numbers of concomitant use of medications in pregnancy were associated with a higher risk of neuroblastoma, risks of multiple childhood cancer types were elevated even when mothers were only exposed to a single medication in pregnancy, suggesting the need for caution with ever medication use in pregnancy. Antibiotics appeared to have an important role in these findings. Antifungal use may indicate an underlying immunosuppressed status and potential Epstein-Barr virus coinfection, which is related to a higher risk of Hodgkin lymphoma. Lastly, we investigated pre-diagnostic vitamin metabolite intensities and risk of retinoblastoma. This case-control study included 501 retinoblastoma cases and 899 controls matched by birth year (20:1 ratio). Vitamin feature intensities were identified from neonatal dried blood spots collected by the California Newborn Genetic Disease Screening Program (1983-2011); 10 outlier samples were excluded. Logistic regression estimated associations between vitamin quintiles and retinoblastoma risk, with stratification by maternal birthplace (U.S. vs. Mexico). Restricted cubic splines assessed dose-response relationships. Metabolites of vitamin B6 and nicotinamide adenine dinucleotide showed a differential abundance among children who were later diagnosed with retinoblastoma. This abundance varied by maternal place of birth (US vs. Mexico). In summary, our studies linked maternal diseases, medication and supplement use in pregnancy to pediatric cancer risk, to provide guidance on medication use in pregnancy, and thus provide information for pediatric cancer prevention.EpidemiologyOncologyObstetricschildhood cancerin‐utero exposurematernal medicationmaternal vitaminpopulation-based studypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9187x1pvetdoai:escholarship.org:ark:/13030/qt6m09w4n92026-03-09T06:30:45Zqt6m09w4n9Quantifying the Complexity of Kernel Densities in Item Response TheoryKim, Yun KyungWu, Yingnian2026-02-26This study focuses on an item response theory model with kernel density estimation (KDE-IRT) that represents the latent trait distribution nonparametrically. A longstanding challenge in integrating latent density estimation into IRT has been the inability to rigorously quantify the additional complexity it introduces into the model. Leveraging a recent development that defines the effective number of parameters for KDE, this study implements a model comparison approach that balances goodness-of-fit and parsimony to help enhance the model’s generalizability. Specifically, the tuning parameter of the KDE-IRT model, known as the bandwidth, is selected by minimizing a model selection criterion that accounts for the complexity of the estimated latent density. This approach also enables direct comparisons between the KDE-IRT model and the standard IRT model that assumes latent density normality, using model selection criteria that penalize complexity from both latent density and item parameter estimation. A simulation study verified the performance of bandwidth selection within the KDE-IRT model and the performance of model comparison between the KDE-IRT and standard IRT models across various data conditions. Empirical examples further illustrate the utility of the proposed method.StatisticsEducational tests & measurementspubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6m09w4n9etdoai:escholarship.org:ark:/13030/qt6hs634p82026-03-09T06:30:41Zqt6hs634p8Measuring and Modeling Partisan Geographic Segregation with Expected Kullback-Leibler DivergenceStraus, GrahamHandcock, Mark S2026-02-27Democrats often live with minimal exposure to Republicans and Republicans often live with minimal exposure to Democrats. Leading accounts of partisan segregation document intense and rising segregation both across and within counties. Existing approaches for measuring partisan segregation either drop nonpartisans altogether or model their partisanship. These approaches also use single scale measures of segregation, like the index of dissimilarity. In this thesis, I measure partisan segregation with national voter registration records and a multiscale approach—Expected Kullback-Leibler Divergence—finding stable and relatively low levels of within-county segregation. I further describe patterns in segregation with a spatiotemporal conditional autoregressive model.StatisticsPolitical sciencePublic administrationapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6hs634p8https://escholarship.org/content/qt6hs634p8/qt6hs634p8.pdfetdoai:escholarship.org:ark:/13030/qt5s1426mj2026-03-06T06:33:58Zqt5s1426mjCharacterizing roles for NFkB in B-lymphopoiesis and embryonic developmentAlonso, ValentinaHoffmann, Alexander2026-03-04Inflammation is essential for fighting infections, yet excessive inflammation is a hallmark of aging and contributes to many age-related diseases. The NFkB pathway is critical in maintaining this balance and becomes increasingly dysregulated with age. The research described here focuses on how dysregulated NFkB affects two different biological contexts. Chapter 1 describes the importance of dynamic NFkB regulation during B lymphopoiesis and how it’s dysregulation can cause severe B-lymphopenia. Further, this chapter connects a novel model of chronic NFkB to inflammaging. Chapter 2 highlights the importance of healthy NFkB signaling and how its imbalance can cause issues in development such as cardiac defects. Chapter 3 concludes the thesis by integrating these findings and discussing the broader impacts of chronic inflammation, NFkB dysregulation, and aging. Together, these studies contain a common theme: NFkB must be tightly controlled, as excessive signaling can negatively impact important development processes.ImmunologyCellular biologyDevelopmental biologyMolecular biologypubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5s1426mjetdoai:escholarship.org:ark:/13030/qt9t19r1sg2026-03-06T06:33:53Zqt9t19r1sgLight-Matter Interactions in Layered Hybrid SuperlatticesZhou, BoxuanHuang, Yu YH2026-03-04Superlattices, artificially structured solids composed of alternating layers with distinct compositions or electronic structures, have long served as a foundation for tailoring electrical, optical, and optoelectrical properties. Conventional epitaxial superlattices rely on covalent bonding and strict lattice matching between adjacent layers, which severely restricts the diversity of materials that can be combined. To overcome these constraints, layered hybrid superlattices (LHSLs) have recently emerged as a new class of designable solids composed of alternating two-dimensional (2D) atomic crystals and molecular intercalant inserted in their van der Waals gaps. LHSLs bridge the strengths of crystalline 2D layers, which exhibits long-range order, with the high-tunability of synthetic molecular systems that offer customizable structural topologies and diverse functionalities. This dissertation focuses on light–matter interactions in LHSPs, which bring unprecedented opportunities in photonics, nonlinear optics, and emergent quantum states.The first part of the dissertation developed an electrochemical intercalation method for synthesis of MoS2-CTAB hybrid superlattice (MoS2/CTAB) with tunable carrier density for enhanced photoelectric performance. Moreover, key characteristics of monolayer MoS2 are preserved in MoS2/CTAB including a direct bandgap, large exciton binding energy, and pronounced valley polarization. The second part demonstrates a solution-assembly approach for constructing MoS2-PVP hybrid superlattices (MoS2/PVP) by electrochemical exfoliation, surface ligand adsorption, and reassembling into superlattices. Building upon this, we report giant second-harmonic generation (SHG) from MoS2/PVP, yielding SHG efficiencies over 100-fold higher than pristine monolayers. In the final part, a solid-state ionomer interlayer strategy employing perfluorinated sulfonic-acid (Nafion) is developed to achieve thickness-scalable, luminescent LHSP membranes with record-high PL quantum yields, enhanced nonlinear responses, and exceptional environmental and mechanical stability. Collectively, this work establishes LHSPs as a scalable, and integrable platform for diverse nonlinear and excitonic photonic technologies.Materials scienceEngineeringOpticsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/9t19r1sghttps://escholarship.org/content/qt9t19r1sg/qt9t19r1sg.pdfetdoai:escholarship.org:ark:/13030/qt80t6154d2026-03-04T06:33:01Zqt80t6154dSeeds of Hope, Fruits of Failure: Scaling Climate-Smart Agriculture in IndiaAgrawal, Nikhit KumarGupta, AkhilKelty, Christopher M2026-02-27This dissertation is an ethnography of climate-smart agriculture (CSA) in India. It examines how CSA programs, promoted by organizations like the World Bank as responses to agriculture’s ecological challenges, are reshaping agrarian futures. Despite opposition from global peasant movements such as La Via Campesina, CSA continues to expand globally. India has become a key site for investments in climate-smart technologies. The central questions this research engages are: How is sustainability conceptualized and operationalized through technological infrastructures in Indian agriculture? What frictions, failures, and resistances emerge in efforts to scale these initiatives, and how are such tensions managed, absorbed, or rendered invisible?
Based on eighteen months of ethnographic fieldwork across agriculture technology start-ups, rural farms, industry events, and bureaucratic workshops, this dissertation examines two contrasting cases––a start-up that attempted to rapidly scale (“blitzscale”) sustainability interventions in rice cultivation and generate carbon credits, and a digital organic marketplace that aimed to connect farmers to urban consumers and grow “patiently.” Through these cases, this dissertation offers critiques from within that remain attentive to the lived contradictions of climate-smart interventions. At the same time, it seeks to trace the possibilities of desirable futures emerging from within the fissures of these ongoing interventions.
This dissertation conceptualizes “violent sustainability” to highlight the unintended harm inflicted on farmers and rural marketing agents due to structural violence underlying tech-entrepreneurialism and design flaws of blitzscaled programs. It shows how rural agents, facing immense pressure, turned to the affordances of nonhuman actors to push back against the startup and secure their livelihoods. In doing so, it extends James Scott’s notion of “infrapolitics” to conceptualize “more-than-human infrapolitics” as the multispecies entanglements through which everyday forms of resistance take shape. Here, resistance is not understood as the sole action of human agents but as emerging relationally, co-constituted by interactions among humans, technologies, and ecological processes. In doing so, it proposes that the cumulative effect of more-than-human infrapolitics may also serve the interests of the powerful. Finally, this research underscores the challenges of translating ethical intentions into sustainable practices.Cultural anthropologySustainabilityEntrepreneurshipAgricultureClimate changeAgricultureClimate changeIndiaScaleSustainabilityTech-entrepreneurshippubliceScholarship, University of Californiahttps://escholarship.org/uc/item/80t6154detdoai:escholarship.org:ark:/13030/qt4w28h8m42026-03-03T06:30:30Zqt4w28h8m4Analyzing Semantic Understanding in Large Language Models through Embedding and Clustering TechniquesChen, YutongWu, Ying Nian2026-02-27We study whether large language model (LLM) embeddings exhibit human-interpretable cluster structure. Using the Yelp Open Dataset for topic and sentiment labels, we extract sentence embeddings (primarily all-MiniLM-L6-v2) and evaluate unsupervised clustering via intrinsic indices (Silhouette, DBI) and extrinsic alignment (NMI, ARI, Purity). On a stratified sample of 60000 reviews across ten coarse topics (Restaurants, Food, Nightlife, Hotels, Shopping, Beauty, Automotive, Home, Health), fixed-k clustering with cosine geometry yields NMI=0.239, ARI=0.108, Purity=0.580 for topics, and NMI=0.060, ARI=0.040, Purity=0.740 for sentiment (positive=41755, negative=11362, neutral=6883). Encoder comparison indicates small but consistent gains from MPNet on NMI with a lower DBI, while MiniLM attains slightly higher Silhouette. Sensitivity analyses show modest degradation under class imbalance and little change with light Gaussian noise. We conclude that modern sentence embeddings support meaningful clustering at coarse topical granularity while sentiment separation remains limited without supervision.StatisticsApplied mathematicsComputer scienceapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/4w28h8m4https://escholarship.org/content/qt4w28h8m4/qt4w28h8m4.pdfetdoai:escholarship.org:ark:/13030/qt5wz6s0hp2026-03-02T06:30:23Zqt5wz6s0hpDevelopment of a Single Cell Gene Regulatory Network Modeling EcosystemCheng, MichaelYang, Xia2026-02-27Advances in single cell transcriptomics enable biomedical researchers to understand the molecular underpinnings of physiology and disease at unprecedented throughput and resolution. Gene regulatory networks (GRNs) have been powerful computational tools for single cell RNA sequencing (scRNAseq) to infer these molecular mechanisms in disease. While GRN methods and databases have been expanding in response to the expansion of scRNAseq data, they restrict their GRN modeling to known transcription factors and their targets, thereby overlooking many novel forms of gene regulation and interaction. Therefore, we created an open-access unbiased cell type GRN database and analysis platform to model global cell type interactions, hosting over 1300 human and mouse cell types across 8 scRNAseq data atlases to facilitate network biology research. We applied the database to study the genetic mechanisms driving learning and memory deficits in pediatric mild traumatic brain injury (mTBI) patients, as well as the molecular networks underlying immunotherapy resistance in late-stage cancers. These projects provide innovative frameworks for GRN analysis, accessible resources for biomedical research, and insights into cell type specific disease mechanisms.BioinformaticsPhysiologyCellular biologyGeneticsHealth care managementGene Regulatory NetworksSingle Cell AtlasSingle Cell RNA-sequencingapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/5wz6s0hphttps://escholarship.org/content/qt5wz6s0hp/qt5wz6s0hp.pdfetdoai:escholarship.org:ark:/13030/qt2bk071mt2026-03-02T06:30:18Zqt2bk071mtNot Just Noise: Modeling Annotation Ambiguity in Text AnalysisLiang, SiyuHazlett, Chad J2026-02-27Annotated data are central to text-as-data methods in political science. To resolve annotator disagreement, researchers typically rely on the majority vote, collapsing multiple judgments into a single label. While effective for objective tasks, this approach is less well suited to subjective classification tasks, where disagreement often reflects meaningful variation in human interpretation rather than measurement error. This thesis introduces a soft-label approach that treats annotator disagreement as an informative signal rather than noise. Instead of assigning a single label to each text, soft labels represent each document’s annotations as probability distributions over categories, preserving heterogeneity in human judgment. Using simulations that vary the number of annotators, categories, and training sample sizes, I show that soft-label models outperform majority-vote models under conditions of subjectivity and class complexity. A case study of stance detection toward China in U.S. news further shows improved performance across both conventional classification metrics and distribution-sensitive evaluation measures.StatisticsLinguisticsComputer scienceArtificial intelligencepubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2bk071mtetdoai:escholarship.org:ark:/13030/qt8ts242zw2026-03-01T05:03:01Zqt8ts242zwOrganic Semiconductor Aggregates: from Molecular Designs to Device ApplicationsZheng, RanYang, Yang2024-01-01Organic semiconductors built on π-conjugated structures have attracted significant attention due to their distinctive optical, electronic, and mechanical properties. These characteristics position them as ideal materials for various electronic devices. Notably, their strong light absorption and efficient charge transport capabilities mark organic semiconductors as promising solutions for addressing the global energy crisis through solar energy conversion. However, precise control of “soft” nanostructures formed by the noncovalently aggregated organic semiconductors for achieving desired optoelectronic properties is challenging, compared to covalently or ionically inorganic semiconductors with rigid architectures. In Chapter 1, I will introduce basic structures and properties of organic semiconductors, especially on their molecular packing behavior. The structure-property relationship of organic semiconductors will be discussed in terms of device applications, such as the development of near-infrared donor and acceptor materials for organic photovoltaics. From molecular designs to device applications, in the following chapters, I will introduce the chemistry of several organic semiconductor aggregates constructed from twisted and nonplanar π-systems and their performances in various solar energy fields, such as photocatalytic hydrogen reaction, organic photovoltaics and perovskite solar cells. In Chapter 2, I will show the self-assembly of noncovalent π-stacked organic frameworks that shows a higher activity for photocatalytic hydrogen evolution. I will first introduce the background of noncovalent π-stacked organic frameworks, which are a subclass of porous materials that consist of crystalline networks formed by self-assembly of organic building blocks through π-π interactions. π-stacked organic frameworks based on spirofluorene as central units and 3-(dicyanomethylidene)indan-1-one as end groups demonstrate strong visible light absorption from 500 nm to 700 nm and high surface area (248 m2 g–1) with 1.8 nm hydrophilic micropores, rendering them well-suited for applications in photocatalysis. The fabricated π-stacked organic frameworks nanoparticles exhibit hydrogen evolution rate up to 152 mmol h-1g-1 at room temperature and 618 mmol h-1g-1 at 70 °C. Cryo-transmission electron microscopy further reveal the native morphology of these nanoparticles and the cocatalyst Pt loading status on them.
In Chapter 3, I will discuss the singlet fission property of pentacene polymer and its application in organic photovoltaics. Singlet fission is a process that converts one singlet exciton into two triplet excitons while conserving spin. This exciton multiplication process has the potential to overcome the Shockley-Queisser limit of solar power conversion efficiency. Pentacene with high mobility has proved to be ideal organic semiconductors for singlet fission organic photovoltaics. Nevertheless, the cost-intensive vacuum deposition process and the propensity of molecular aggregation in the solid state to prematurely quench triplet excitons pose challenges for their application in photovoltaics. To address these issues, a pentacene polymer is engineered with pentacene units arranged orthogonally to the polymer backbone. This design facilitates the use of pentacene-based materials in organic photovoltaics as donor materials through solution processing. Rapid conversion of photoexcited singlets into triplet pairs, occurring on a picosecond time scale (495 ps) and further dissociate into two “free” triplet excitons in 9.8 µs are observed in the pentacene polymer via transient absorption spectroscopy. The resulting photovoltaic devices based on pentacene polymer and nonfullerene acceptors demonstrate 1.92% power conversion efficiency.
In Chapter 4, I will focus on a helicene-based organic semiconductor and its application as electron transport layer in inverted perovskite solar cells. Electron transport layer materials based on fullerene tend to form large clusters and undergo dimerization when exposed to light, leading to a deterioration in electron transport capability and device degradation. The nonplanar geometry of helicenes proves effective in preventing such aggregation issues, thereby enhancing device stability. We have successfully synthesized a small-molecule n-type organic semiconductor utilizing [6]helicene. This compound was employed as the electron transport layer, n-doped by organic amines, in an inverted perovskite solar cell, achieving an impressive power conversion efficiency of over 16%.
Materials ScienceOrganic photovoltaicOrganic semiconductorPerovskite photovoltaicPhotocatalytic hydrogen evolutionSinglet fissionenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8ts242zwhttps://escholarship.org/content/qt8ts242zw/qt8ts242zw.pdfetdoai:escholarship.org:ark:/13030/qt7rw2q3q12026-02-25T06:30:28Zqt7rw2q3q1On the Reliability of Learning Systems across Supervision ParadigmsXue, YihaoMirzasoleiman, Baharan2026-02-20Modern machine learning systems achieve strong performance by leveraging large models, diverse data sources, and flexible supervision paradigms. However, when deployed in real-world settings, these systems are often exposed to imperfect supervision, distribution shift, and safety-critical constraints, raising fundamental concerns about their reliability. This thesis studies the reliability of learning systems across three major supervision paradigms—labeled supervision, self-supervision, and language supervision—and develops a unifying, representation-centric perspective on when and why reliable behavior emerges.
Under labeled supervision, we show that increasing model capacity does not universally improve reliability in the presence of label noise. Instead, overparameterization can amplify variance and induce a degradation in generalization, even beyond the classical double descent regime. We provide theoretical explanations for this phenomenon and demonstrate that commonly used remedies such as stronger regularization or robust objectives can exacerbate it. We further study reliability under distribution shift in low-data regimes and propose a simple, data-efficient adaptation method that leverages pretrained representation geometry to enable effective few-shot transfer.
Under self-supervision, we investigate why contrastive learning often yields representations that are robust to label noise and transferable across tasks. We show that contrastive objectives induce favorable spectral structure in learned representations, enabling downstream models to recover clean semantic signals under severe noise. At the same time, we identify fundamental limitations of contrastive learning, including class collapse and feature suppression, and trace them to the implicit simplicity bias of gradient-based optimization. This analysis unifies several empirical phenomena and motivates principled remedies, including architectural and objective-level interventions. We further provide a theoretical explanation for the effectiveness of projection heads in representation learning through layer-wise implicit bias.
Under language supervision, we study reliability in multimodal learning and large language models. We identify concrete mechanisms through which language supervision improves robustness under distribution shift in multimodal contrastive learning. We then analyze hallucination detection in black-box language models, showing fundamental limitations of self-consistency-based methods and proposing a budget-aware verification framework grounded in a geometric interpretation. Finally, we develop a representation-based theory of weak-to-strong generalization and study safety alignment in reasoning-capable language models, demonstrating that low-rank adaptation can largely mitigate the trade-off between safety and reasoning performance.
Taken together, this thesis shows that reliability is fundamentally a representation-level phenomenon. Across supervision paradigms, reliable behavior emerges when the supervision signal, optimization dynamics, and architectural constraints align with the intrinsic structure and dimensionality of the task, while failures arise from mismatches that induce variance amplification, shortcut exploitation, or destructive interference. By combining theoretical analysis with extensive empirical validation, this thesis contributes a unified framework for understanding and improving the reliability of modern learning systems.Artificial intelligenceComputer engineeringComputer sciencepubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7rw2q3q1etdoai:escholarship.org:ark:/13030/qt6db9m0mr2026-02-25T06:30:19Zqt6db9m0mrSleep and Prostate Cancer Outcomes in Black Men: A Prospective Cohort StudyAnukam, DanicaZhang, Zuo-FengHashibe, Mia2026-02-18Background Sleep is essential for overall health, and growing evidence suggests that poor sleep may be linked to the development and progression of non-communicable diseases, including cancer. However, the specific associations between sleep duration, sleep quality (e.g., restless sleep), and prostate cancer outcomes remain poorly understood, particularly in racially diverse populations.Objectives and Specific Aims The overall objective of this doctoral dissertation is to explore the role of sleep in prostate cancer risk and outcomes, with a focus on a predominantly Non-Hispanic Black cohort—a population that bears a disproportionate burden of prostate cancer. The specific aims were: (1) to estimate the magnitude of the association between sleep duration and restless sleep and overall prostate cancer incidence and aggressiveness in the overall cohort and when stratified by Non-Hispanic Black men and Non-Hispanic White men; (2) to estimate the magnitude of the association between sleep duration and restless sleep and prostate cancer-specific mortality in the overall cohort and when stratified by Non-Hispanic Black men and Non-Hispanic White men; and (3) to assess the association between baseline sleep characteristics and subsequent prostate cancer-specific death and all-cause death among prostate cancer patients.Study Design and Population For all three specific aims, we analyzed a prospective cohort from the Southern Community Cohort Study (SCCS). Study participants were recruited between March 2002 and September 2009 from 12 Southeastern states, and participants were predominantly African American. Participants were eligible for inclusion in the SCCS if they were aged 40-79 years, not previously diagnosed with cancer or not undergoing treatment for cancer within the past year (with the exception of non-melanoma skin cancer), English-speaking, and able to provide informed consent. Participants completed either in-person interviews or self-administered questionnaire at baseline, and the questionnaire included information on sleep, demographics, medical history, health-related and lifestyle factors, and socioeconomic status. Statistical Methods For specific aim 1, Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between sleep characteristics and prostate cancer incidence. Multinomial logistic regression models were used to obtain odds ratios (ORs) with 95% CIs for the association between sleep characteristics and prostate cancer risk and aggressiveness. For specific aim 2, Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between sleep characteristics and prostate cancer-specific mortality. For specific aim 3, Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association of sleep on prostate cancer-specific death and all-cause death among prostate cancer patients. For all aims, analyses were stratified by Non-Hispanic Black and White men to assess potential effect modification. Potential confounders that were adjusted for in the analyses included: age at enrollment, age at diagnosis (Specific Aim 3 only), race/ethnicity, enrollment source, education, income, employment status, body mass index (BMI), depression, alcohol consumption, smoking status, diabetes, hypertension, chronic obstructive pulmonary disease (COPD), asthma, stroke, total physical activity, and benign prostatic hyperplasia (BPH). In Specific Aim 3, additional clinical covariates included Gleason score, cancer stage, prostate cancer surgery, radiotherapy, and chemotherapy.Results For specific aim 1, 1345 men developed prostate cancer during follow-up (median 10.9 years) of 33,568 men in the overall cohort. Shorter sleep duration (<6 hours), compared with optimal duration (7-8 hours), was suggestively associated with a decreased risk of prostate cancer in the overall cohort (adjusted hazard ratio (HR)=0.83, (95% confidence interval (CI): 0.68-1.01) for average sleep; HR=0.79, 95% CI: 0.65-0.95 for weekday sleep; and HR=0.81, 95% CI: 0.66-0.99 for weekend sleep), and among Black men (HR=0.77, 95% CI: 0.62-0.97 for average sleep; HR=0.76, 95% CI: 0.61-0.94 for weekday sleep; and HR=0.74, 95% CI: 0.59-0.94 for weekend sleep) when stratified by race. Sleep duration and restless sleep were not associated with prostate cancer aggressiveness in the overall cohort. For specific aim 2, 225 men died from prostate cancer during follow-up (median 15.9 years). Longer sleep duration (≥9 hours) on weekends compared with 7-8 hours, was associated with an increased risk of prostate cancer-specific mortality (HR= 1.44, 95% CI: 1.02-2.03). When stratified by race, longer sleep duration (≥9 hours) on weekends was suggestively associated with an increased risk of prostate cancer mortality for both Black and White men. Overall sleep duration and restless sleep were not associated with prostate cancer-specific mortality. For specific aim 3, there were 579 deaths overall and 116 deaths due to prostate cancer during follow up (through 2022). Prostate cancer patients had a mean age at diagnosis of 63.1 years. In the multivariable models adjusted for both baseline and clinical factors, baseline self-reported sleep traits were not associated with prostate cancer-specific death or all-cause death. However, shorter sleep duration (< 6 hours) and longer sleep duration (≥9 hours), compared with optimal sleep duration of 7-8 hours, were associated with an increased risk of prostate cancer-specific and all-cause deaths. Dose-response relations were not clearly demonstrated based on p-values for trends. No significant heterogeneity was observed in the association of sleep on all-cause death by race/ethnicity.Conclusions: Shorter sleep duration was associated with a decreased risk of prostate cancer, but sleep duration was not associated with aggressive prostate cancer. In stratified analysis, a reduction of prostate cancer risk was observed among Non-Hispanic Black men who reported less than 6 hours of sleep, compared to those sleeping 7-8 hours. Additionally, men reporting ≥ 9 hours of weekend sleep had an elevated risk of prostate cancer-specific mortality compared with those sleeping 7-8 hours. An increased risk of prostate cancer-specific mortality was observed among both Black men and White men with ≥ 9 hours of sleep versus 7-8 hours, though estimates were imprecise and did not reach statistical significance due to small event numbers. Lastly, no significant associations were found between short or long sleep duration prior to cancer diagnosis and prostate cancer-specific death and all-cause death. Our study is one of the first to examine self-reported sleep characteristics and prostate cancer outcomes among Black men.EpidemiologyPublic healthOncologyHealth sciencespubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6db9m0mretdoai:escholarship.org:ark:/13030/qt2hv655zj2026-02-24T06:30:31Zqt2hv655zjSensor Data Processing with Foundation Models: From Time-Series Analytics to Spatiotemporal ReasoningQuan, PengruiSrivastava, Mani B2026-02-19Foundation Models (FMs) have demonstrated strong generalization across a wide range of tasks. This success has motivated increasing interest in applying these models to sensor data processing, which underlies many Cyber-Physical Systems (CPS) such as autonomous transportation, smart infrastructure, and health monitoring. However, deploying FMs in sensor-driven applications introduces a complex design space. How the components in the system, such as data representation and model selection, interact and how they collectively shape model performance remains insufficiently understood.
This dissertation investigates how FMs process sensor data from an end-to-end system. Rather than assuming FMs as a universal solution, the dissertation investigates their capabilities and limitations across different sensor processing problems, data characteristics, model families, inference interfaces, and adaptation strategies. To support this analysis, the dissertation introduces benchmark datasets that span a broad range of sensor processing tasks, including traditional time-series analytics, general sensor data processing, and spatiotemporal reasoning. These benchmarks enable controlled evaluation of representative FM families under multiple inference paradigms, including direct inference, standalone coding, and tool-augmented coding, as well as different adaptation strategies such as prompt engineering and fine-tuning.
Across evaluations, the dissertation identifies consistent limitations of current FMs in timeseries analysis and spatiotemporal reasoning tasks that arise naturally in sensor data processing pipelines. The results also reveal that model effectiveness is sensitive to pipeline choices: identical models can exhibit substantially different behavior depending on task structure, interaction settings, and adaptation strategies. These findings suggest that performance bottlenecks often stem not from model capacity alone, but from how to apply sensor processing knowledge robustly to diverse sensor processing domains and to incorporate feedback mechanisms that detect and mitigate errors.
Motivated by these observations, the dissertation synthesizes its findings into an Evolutionary-Algorithm-based Search (ES) framework for sensor-FM systems. This framework highlights key pipeline components and design considerations necessary for building efficient and reliable sensor processing systems with FMs. Overall, this work provides a structured evaluation of FMs within the sensor processing pipeline and clarifies how pipeline-level choices could benefit future intelligent sensing systems.Electrical engineeringComputer engineeringComputer scienceInformation technologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/2hv655zjhttps://escholarship.org/content/qt2hv655zj/qt2hv655zj.pdfetdoai:escholarship.org:ark:/13030/qt3p89g75q2026-02-24T06:30:26Zqt3p89g75qStochastic Discount Factors Implied by Machine Learning Forecasts of Equity ReturnsBerzins, NilsMichailidis, George2026-02-17This thesis examines whether different machine learning models, when used to forecast firm-level equity returns, imply materially different stochastic discount factors, and whether those differences translate into economically meaningful variation in firm-level risk exposures and pricing errors. The analysis compares linear and nonlinear models including ordinary least squares regression, least absolute shrinkage and selection operator (LASSO) regression, principal components regression, gradient boosted decision trees, feedforward neural networks, and long short-term memory (LSTM) networks under a unified empirical design.Using monthly excess returns for S&P 500 constituent stocks from the Center of Research in Security Prices over the period 2000 to 2024, with predictors drawn from standard factor models and return-based anomalies, expected returns are first estimated at the firm level. These forecasts are then mapped into traded, time-varying stochastic discount factors viamean–variance efficient portfolio constructions derived from unconditional moment conditions. Model performance is evaluated using out-of-sample cross-sectional R2 and average absolute pricing errors.Across all specifications, the estimated stochastic discount factors are well behaved and economically sensible. Nonlinear models generate more state-contingent and heavy-tailed pricing kernels than their linear counterparts, reflecting richer dynamics in state pricing. However, these differences do not translate into substantial improvements in firm-level pricing accuracy, with average pricing errors remaining similar across model classes. This evidence suggests that model choice primarily affects the dynamics and distributional properties of the pricing kernel rather than overall cross-sectional explanatory power.Overall, the findings indicate that while machine learning methods meaningfully alter the implied structure of the stochastic discount factor, gains from nonlinear modeling are limited in terms of average firm-level pricing performance. A key limitation is the reliance on mean–variance efficiency in stochastic discount factor construction, as a nontrivial subset of firms exhibit idiosyncratic behavior that is difficult to price accurately within this framework. These results highlight the distinction between modeling state-dependent pricing dynamics and improving cross-sectional pricing accuracy, and point to directions for future work that relax portfolio efficiency assumptions or incorporate alternative pricing restrictions.FinanceStatisticsEconomicsDeep LearningEquity ReturnsForecastingMachine LearningRiskStochastic Discount Factorsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/3p89g75qhttps://escholarship.org/content/qt3p89g75q/qt3p89g75q.pdfetdoai:escholarship.org:ark:/13030/qt30k007sb2026-02-15T05:02:06Zqt30k007sbLanguage and Student Political Participation in ChileSlobe, TyannaMendoza-Denton, Norma2023-01-01This dissertation considers how secondary school students in Santiago, Chile come into forms of political participation through social movements. It deploys linguistic anthropological methods to consider relationships between youth semiotic practices and institutional structures. I conducted ethnographic fieldwork in two socioeconomically segregated secondary schools—referred to as The Private School and The Public School—between 2016 and 2020. This research occurred against the backdrop of waves of student movements for education reform that began a decade prior, which helped created the conditions for an uprising in 2019 known as ‘el estallido social’ [the social explosion]. The estallido social resulted in democratic efforts to replace Chile’s constitution, which was implemented in 1980 under a military dictatorship. The first two chapters describe sociopolitical context. Chapter One provides a historical account of Chile’s dictatorship (1973-1990), its neoliberal economic reforms, and student political movements against dictatorship-era policies since the return to democracy. I also introduce The Private School and The Public School research sites and outline my methods. Chapter Two describes the 2019 estallido social as a phenomenon of ‘collective effervescence’ (Durkheim 1912 [1995]), a feeling of emotional intensification and unity that occurs when a group of people participates in shared action. I provide an account of what happened in the first weeks of the estallido, and document collective effervescence by bringing together forms of art that were inspired by and helped to sustain participation in the social movement. Chapter Three takes place during the first week of the estallido social in The Private School, an upper-class institution in a wealthy suburban neighborhood of Santiago. It examines conversations from a 2° medio [10th grade] Social Studies class about events and experiences related to the uprising. Data analysis highlights teacher corrective practices that consistently orient students’ attention to the language that they use to talk about the estallido. I argue that these discourse practices reflect students’ socialization into forms of professional expertise, and dispassionately objective stances, associated with the careers of technocratic elites. Chapter Four takes place in The Public School, a working-class institution in the heart of Santiago. I introduce ‘protest contours’ to highlight a relationship between material features of language used by students to construct oppositional stances—pitch contours—and a collective memory formed around historical contours of state violence. The first part of the chapter examines 1° medio [9th grade] students’ use of pitch to construct affective stances in narrative accounts of police brutality experienced at a 2016 protest for education reform. The second part is based in ethnographic research that reveals The Public School students’ socialization into a historical memory about repressive tactics deployed during the dictatorship, which contributes to their understanding of contemporary state violence. LinguisticsCultural anthropologyenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/30k007sbhttps://escholarship.org/content/qt30k007sb/qt30k007sb.pdfetdoai:escholarship.org:ark:/13030/qt7hn5f9nd2026-02-13T06:30:29Zqt7hn5f9ndThree Essays in Political Economy: Institutions, Information, and Civic EngagementGeyn, IgorTausanovitch, Chris N2026-02-11This dissertation presents three empirical studies examining the intersection of political institutions, information, and civic engagement in the United States. Together, these essays contribute to our understanding of how institutional design shapes political behavior and democratic participation, while also demonstrating some advantages and some nuances of using contemporary causal inference approaches in observational settings.The first essay examines whether federal disaster relief favors congressional districts represented by the president's copartisans. Using a regression discontinuity design that exploits close congressional elections from 2001-2020, I compare FEMA funding in districts that narrowly elect a member of the president's party to those that narrowly elect an opposition member. I find that copartisan districts receive approximately 45% less federal disaster relief than opposition districts (p=0.062), with this negative effect consistent across the Bush, Obama, and Trump administrations (estimates ranging from -45% to -57%). Extensive mechanism tests point toward a compositional explanation: disaster-prone areas with particular demographic and infrastructure characteristics systematically differ in their political representation. The effect concentrates in low-visibility infrastructure spending, exhibits a striking gradient across district racial composition, and shows no response to electoral incentives—patterns inconsistent with intentional political targeting but consistent with underlying geographic and compositional differences in disaster vulnerability and preparedness.The second essay investigates whether ballot measures enhance civic engagement by increasing voters' knowledge and political interest. Using comprehensive data on 1,547 ballot measures across all U.S. states from 2006-2020 merged with 480,383 individual survey responses, I demonstrate how methodological choices fundamentally determine conclusions about this question. While traditional two-way fixed effects models suggest positive engagement effects, modern difference-in-differences estimators designed for staggered treatment adoption reveal essentially null effects. The analysis provides both a substantive contribution—challenging optimistic accounts of direct democracy's civic benefits—and a methodological cautionary tale about the implications of estimator selection in applied, observational data settings.The third essay—co-authored with Daniel Firoozi and published in the journal American Economic Journal: Economic Policy—estimates the impact of California's Cal Grant program, America's largest tuition-free 4-year college program, on political participation. Using a regression discontinuity design based on strict income eligibility thresholds and 16.4 million financial aid applications, my co-author and I find that Cal Grant receipt increases voter turnout by approximately 10 percentage points. The effect operates almost entirely through increased registration and turnout among left-leaning voters, with the 2.6 million grants awarded over the 2010s inducing an additional 259,000 votes in the 2020 general election. These findings highlight how education subsidies generate important civic externalities beyond their effects on human capital and earnings.Political scienceStatisticsEconomicsAmerican politicsCalifornia politicsdirect democracyeconometricslocal political economyvotingapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/7hn5f9ndhttps://escholarship.org/content/qt7hn5f9nd/qt7hn5f9nd.pdfetdoai:escholarship.org:ark:/13030/qt76w0d9k12026-02-11T06:32:11Zqt76w0d9k1Engineering Vascular Endothelial Growth Factor-Neutralizing Chimeric Antigen Receptor-T cells for the Treatment of Solid TumorsGao, Torahito AdachiChen, Yvonne Y2026-02-06Adoptive cell therapy using T cells engineered to express chimeric antigen receptors (CARs) has shown remarkable clinical efficacy in treating B-cell malignancies, yet its efficacy against solid tumors has been poor in part due to the highly immunosuppressive nature of the solid tumor microenvironment (TME). Specifically, vascular endothelial growth factor A (VEGF-A) presents as a physical barrier through its pro-angiogenic properties and contribution to excessive tumor vascularization, giving rise to abnormal and dysfunctional vessels with impaired perfusion thus leading to intratumoral hypoxia. Furthermore, VEGF-A serves as a chemical barrier by suppressing T-cell activation and cytotoxicity, upregulating immune checkpoints on T cells, inhibiting dendritic cell maturation, and recruiting immunosuppressive cells. In this dissertation, we present a strategy to enhance CAR-T cell efficacy and reprogram the TME by engineering CAR-T cells to secrete a newly developed anti-VEGF single-chain variable fragment (CAR-αVEGF T cells). We demonstrate that CAR-αVEGF T cells achieve superior anti-tumor efficacy against syngeneic models of both ovarian cancer and glioma, outperforming conventional CAR-T cells with and without combination anti-VEGF antibody therapy. Mechanistically, treatment with CAR-αVEGF T cells reduces or reverses multiple adverse effects associated with on-target CAR-T cell therapy, including intratumoral infiltration of myeloid cells, exaggerated vasculature abnormalities, and hypoxia. Treatment with CAR-αVEGF T cells also results in an enrichment of immune-stimulatory and anti-tumor signatures among intratumoral immune cells, indicating that blocking of intratumoral VEGF by CAR-αVEGF T cells enable favorable remodeling of the immunosuppressive TME. Together, our results provide rationale for the clinical translation of CAR-αVEGF T cells as a safe and potent therapy for solid tumors characterized by elevated VEGF.ImmunologyBiomedical engineeringCellular biologyCAR-T cellCell therapyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/76w0d9k1https://escholarship.org/content/qt76w0d9k1/qt76w0d9k1.pdfetdoai:escholarship.org:ark:/13030/qt8fp651bd2026-02-11T06:32:07Zqt8fp651bdDynamic Functional Connectivity Analysis of Forelimb Stimulated Traumatic Brain Injury Rodents Using Gaussian Mixture Variational AutoencodersAzargushasb, AzadDeeds, EricJoshi, Shantanu2026-02-05Traumatic brain injury (TBI) is a major public health concern affecting millions worldwide, yet the underlying alterations in brain dynamics remain poorly understood. This thesis presents a novel approach to analyzing dynamic functional connectivity in TBI using Gaussian Mixture Variational Autoencoders (GMVAE). We apply this deep learning framework to blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) data from a controlled cortical impact (CCI) rodent model to identify distinct brain states and characterize their temporal dynamics.Our GMVAE approach demonstrates superior clustering performance compared to traditional methods including Leading Eigenvector Dynamics Analysis (LEiDA) with k-means clustering. The model reveals interpretable latent spaces and meaningful brain state configurations. Statistical analysis of fractional occupancy, dwell times, and transition probabilities reveals significant alterations in brain dynamics following TBI compared to sham controls. Graph-theoretic analysis of identified brain states provides insights into the biological significance of these findings, demonstrating altered modularity and network organization. These results suggest that GMVAE-based dynamic functional connectivity analysis offers a powerful tool for understanding TBI pathophysiology and may inform future therapeutic interventions.BioinformaticsNeurosciencesMedical imagingapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8fp651bdhttps://escholarship.org/content/qt8fp651bd/qt8fp651bd.pdfetdoai:escholarship.org:ark:/13030/qt73x2g7qr2026-01-31T06:31:15Zqt73x2g7qrMechanical Regulation of Vascular Dysfunction in Inflammatory Retinal DiseasesSantiago Tierno, IreneGhosh, Kaustabh2025-12-12Diabetic retinopathy (DR) and age-related macular degeneration (AMD) are major retinal diseases and the leading causes of vision loss in the working-age and elderly populations, respectively. Although current clinical therapies aim to resolve the advanced stages of both DR and AMD, there is a recognition that more effective therapy management can be achieved by intervening at earlier stages.Vascular inflammation is considered a key contributor to retinal and choroidal vascular degeneration, a clinical hallmark of both early DR and AMD. Yet, the underlying mechanisms leading to inflammation-mediated endothelial cell (EC) death, which ultimately leads to vascular degeneration, remain poorly understood.Past studies investigating these vascular abnormalities have focused primarily on the role of molecular or biochemical cues. However, scientific discoveries now highlight the critical role of mechanical cues in maintaining vascular stability in healthy tissue. Additionally, vascular stiffening has been implicated in the development of vascular complications by altering endothelial mechanotransduction. Since both diabetes and aging are associated with stiffening of non-ocular vessels like the aorta and arteries, the current work investigated whether diabetes and aging simultaneously lead to increased stiffness of retinal and choroidal vessels and, if so, whether vascular stiffening mechanically regulates vascular dysfunction in both DR and AMD, respectively.Findings from the studies showed retinal inflammation and vascular degeneration in diabetes are driven by increased retinal vascular stiffness due to upregulated lysyl oxidase (LOX), a collagen-crosslinking enzyme that increases retinal subendothelial matrix stiffness. In diabetic mice, LOX inhibition with β-Aminopropionitrile (BAPN) prevented retinal subendothelial matrix/vascular stiffening, which led to reduced vascular inflammation (ICAM-1 upregulation), leukocyte adhesion, and caspase-3-induced apoptosis. This, in turn, resulted in reduced capillary degeneration, increased pericyte coverage, and rescue of the diabetes-induced loss of contrast sensitivity. In vitro, subendothelial matrix stiffening alone was found to increase endothelial adhesiveness and susceptibility to neutrophil elastase-induced death, thereby implicating LOX-dependent retinal vascular stiffening as a potentially important therapeutic target for DR. Follow up mechanistic studies revealed that this diabetes-induced LOX upregulation is caused by AGE/RAGE-mediated NF-kB activation in retinal ECs that leads to simultaneous ICAM-1 upregulation and leukocyte-EC adhesion.Studies of early AMD revealed a similar causal role for vascular stiffening and abnormal endothelial mechanotransduction. Specifically, choroidal ECs from the monkey model of early AMD were found to exhibit increased cell and subendothelial matrix stiffness that was associated with increased deposition of matrix proteins fibronectin and collagen IV, and crosslinking enzyme inflammation-mediated cell death, impaired neovascularization, reduced matrix adhesion, slower motility, and lower proliferation compared to non-AMD age-matched controls. These changes correlated with dysregulated matrix metalloproteinases and their inhibitors TIMPs while transcriptomic data highlighted endothelial mechanobiology pathways involving cytoskeleton and extracellular matrix as the top differentially regulated biological processes between AMD ECs and their age-matched controls. Finally, choroidal ECs grown on synthetic matrices of tunable stiffness showed that increasing substrate stiffness is sufficient to sensitize non‑AMD choroidal ECs to stress‑induced apoptosis and necrosis, whereas matrix softening partially rescues early AMD ECs, improving survival under identical stress conditions.Importantly, this dissertation demonstrated that reducing the stiffness of ECs as well as their secreted subendothelial matrix inhibits vascular/endothelial atrophy characteristic of both DR and AMD. These findings establish stiffening as a previously unrecognized but significant driver of early disease progression and identify endothelial biomechanics as a promising therapeutic target for early intervention in both DR and AMD.Cellular biologyBiomechanicsBiophysicsImmunologyBiophysicsImmunologyMechanobiologyVascular Biologyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/73x2g7qrhttps://escholarship.org/content/qt73x2g7qr/qt73x2g7qr.pdfetdoai:escholarship.org:ark:/13030/qt8m02x3832026-01-31T05:04:03Zqt8m02x383Participation and Conversational Involvement in Brokered Medical Interviews: A case of Iraqi Patients in Southern CaliforniaNash, AfafThompson, Katrina DalyGoodwin, Charles2014-01-01Employing the theoretical foundations of conversation analysis, this dissertation examines the actions of patients with limited English ability who depend on family members to mediate their medical interaction. Analysis shows how patients seek immediate contact with their interlocutors despite limited English competencies. Using a variety of semiotic resources, non-English speaking patients are found to override the brokering process and construct their patienthood independently. The study discusses communicative competence, conversational commitment, asymmetries of participation, and decision-making in brokered medical encounters. It demonstrates the complexity of participation and decision-making in triadic interactions in the medial setting, in which roles of participation intersect and overlap in non-conventional ways. The dissertation's findings lay significant claims on novice communicative competence, brokered medical discourse, and doctor-non-English-speaking patient relationship. It also suggests future research direction and provides recommendation to improve interaction in cross-linguistic medical encounters.SociolinguisticsCommunicationMedical ethicscommunicative competenceIraqi-Arab patientslanguage brokeringmedical interactionnovice communicationSocial interactionenapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8m02x383https://escholarship.org/content/qt8m02x383/qt8m02x383.pdfetdoai:escholarship.org:ark:/13030/qt28d8b7wd2026-01-30T06:30:23Zqt28d8b7wdBeyond Taking Turns in Human-AI InteractionLiu, BruceChen, Xiang2026-01-23Most human–AI systems today are built around a default paradigm of turn-by-turn interaction: users act, systems respond, and collaboration unfolds through discrete exchanges. While effective for many tasks such as conversational agents, this model shapes interaction to be linear rather than multidimensional, reactive rather than proactive, and transient rather than persistent.Drawing on theories from communication and linguistics, this thesis argues that turn-taking has become an implicit and largely unexamined design constraint in human–AI interaction. I propose moving beyond turn-taking by introducing interaction structures that support parallel communication, proactive participation, and persistent shared spaces.This thesis presents three interactive systems that instantiate and evaluate these structures in real-world contexts: Visual Captions demonstrates parallel communication by augmenting verbal conversations with real-time, AI-generated visuals. Inner Thoughts demonstrates proactive participation in multi-party settings. Doki demonstrates persistent shared space with a text-native interface for generative video authoring. Through system design, implementation, and empirical evaluation, this thesis shows how moving beyond turn-taking can enable richer and more flexible forms of human–AI interaction, and outlines a set of design principles for building the next generation of interactive AI systems.Computer engineeringComputer scienceArtificial intelligenceHCIHuman-AI interactionapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/28d8b7wdhttps://escholarship.org/content/qt28d8b7wd/qt28d8b7wd.pdfetdoai:escholarship.org:ark:/13030/qt8xj4c26h2026-01-29T06:35:36Zqt8xj4c26hEssays in Financial EconomicsCheah, May LynGarmaise, Mark J.2026-01-27This dissertation examines how regulatory shocks, information transmission, and policy uncertainty affect financial markets across three distinct settings. In Chapter 1, I study the financial market response to the SEC’s proposed climate disclosure rule announced on March 21, 2022. Using an event study, I document that firms with high environmental scores experienced negative abnormal returns while low-scoring firms earned positive returns. ESG mutual funds exhibited a similar pattern, reallocating capital toward environmental laggards in the post-announcement quarter. These results suggest investors interpreted the Scope 3 mandate as a regulatory equalizer that erodes the advantage of voluntary disclosure leaders.In Chapter 2, I examine whether sentiment expressed by social media influencers propagates through follower networks to affect housing prices. Using 1.2 million real estate-related tweets from the 50 most populous U.S. cities, I find that influencer sentiment predicts city-level housing price changes and transmits to engaged connections who have directly replied to the influencer. This transmission is unidirectional: influencer sentiment predicts follower sentiment, but not the reverse. An instrumental variables strategy exploiting geographic variation in influencer-follower relationships confirms that influencer sentiment causally propagates to their followers in other markets.In Chapter 3, I investigate whether policy uncertainty affects cleantech venture funding by exploiting the unexpected 2016 U.S. presidential election outcome as an exogenous shock. Using deal-level data covering 83,496 U.S. venture capital deals from 2010 to 2021, I employ a difference-in-differences framework and find that cleantech deals received approximately 23% less funding during the Trump administration relative to non-cleantech deals. The effect appeared immediately following the election and largely persisted throughout the administration. Overall, these chapters contribute to the literatures on ESG investing, disclosure regulation, investor sentiment, social information networks, and the role of policy uncertainty in shaping capital allocation.FinanceManagementEconomic theoryEconomicsapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/8xj4c26hhttps://escholarship.org/content/qt8xj4c26h/qt8xj4c26h.pdfetdoai:escholarship.org:ark:/13030/qt6qp7469s2026-01-29T06:35:32Zqt6qp7469sDesigning Safer Batteries: From Electrode Materials to Electrolyte InnovationsYang, ZhiyinKaner, Richard B2026-01-27Energy storage devices, particularly batteries, are indispensable in modern society. They provide power for electronic devices such as smartphones and electric vehicles and play a critical role in storing renewable energy for the electrical grid. Despite their widespread use, batteries still face major challenges, including limited energy density, slow charging rates, high cost, and short cycle life. Among these issues, safety remains one of the most critical concerns. Lithium-ion batteries, the most widely used battery system, are favored for their high energy density, high operating voltage, low self-discharge rate, and minimal maintenance requirements. However, the number of fire incidents caused by lithium-ion batteries in electric vehicles has steadily increased in recent years, posing severe threats to human safety.To address these challenges, this dissertation focuses on the development of safer battery materials and electrolytes prepared through facile and scalable methods. Sodium-ion batteries, in particular, have emerged as a promising research frontier due to their low cost, intrinsic safety, environmental friendliness, and other unique advantages. Compared with lithium-ion batteries, sodium-ion batteries exhibit higher tolerance to abuse and better thermal stability, though typically at the expense of energy density. Chapters 2 and 3 present two advanced sodium-based electrode materials that demonstrate exceptional energy and power densities, superior rate capability, and ultralong cycling life. The electrolyte is another key component that critically influences the capacity, safety, and cost of both lithium-ion and sodium-ion batteries. One of the major challenges arises from the flammability and environmental hazards of conventional organic electrolytes. To overcome these limitations, aqueous rechargeable lithium-ion and sodium-ion batteries have attracted increasing attention as promising technologies for large-scale energy storage applications, owing to their intrinsic safety, non-toxicity, and cost-effectiveness. Chapters 4 and 5 explore the design and electrochemical behavior of water-in-salt aqueous electrolytes for lithium-ion and sodium-ion batteries. These innovative strategies provide a pathway to integrate advanced electrode materials with safe and sustainable electrolytes, enabling low-cost and environmentally friendly operation of large-scale energy storage systems with excellent electrochemical performance.ChemistryEnergyElectrical engineeringAqueous batteryLaser-scribeLithium-ion batterySodium-ion batteryVanadium oxideWater-in-salt electrolytepubliceScholarship, University of Californiahttps://escholarship.org/uc/item/6qp7469setdoai:escholarship.org:ark:/13030/qt61f2t5g42026-01-29T06:31:47Zqt61f2t5g4More Autonomy, Less Safety: Practicing Birth Control in Ming and Qing ChinaZhao, WanGoldman, Andrea2025-12-12The thesis examines how birth control was practiced, represented, and morally judged in Ming-Qing China through legal, literary, and medical sources. Here, I define birth control as practices engaged in by women, couples, or families encompassing everything from contraceptive methods to abortion and infanticide. Because the state discouraged birth control, the archival record is marked by silence: medical handbooks did not record many effective and safe contraceptive or abortifacient formulae; legal cases frame abortion as evidence of illicit sex; and literary works picture birth control as dangerous moral transgression. However, by reading against the grain, it reviews that people across social classes employed a wide range of methods to manage fertility under economic or emotional pressures.The thesis argues that women’s autonomy in reproductive decision-making did not always align with better outcomes. When birth control was a collective family decision, women had access to safer options, such as bringing in concubines or seeking professional physicians. In contrast, when birth control was a woman’s individual decision, especially in contexts of adultery, her limited access to reliable medical help often produced severe consequences. The study demonstrates that birth control was an open secret in Late Imperial China, and the history of reproductive decision-making thus offers a window into women’s constrained bodily autonomy. The thesis concludes by tracing how these dynamics persisted into the twentieth century, as the criminalization of abortion (1910-1935) and later PRC birth planning policies continued to situate women’s bodies as the center of reproductive governance.East Asian studiesHistoryAsian studiesChinese HistoryGender HistoryLate ImperialLegal HistoryMedical Historyapplication/pdfpubliceScholarship, University of Californiahttps://escholarship.org/uc/item/61f2t5g4https://escholarship.org/content/qt61f2t5g4/qt61f2t5g4.pdfetdoai_dc:ucla_etd:500:14128:eyJmaXJzdCI6NTAwLCJiZWZvcmUiOiIyMDI2LTA2LTA5VDIxOjU0OjQwKzAwOjAwIiwiYWZ0ZXIiOiIyMDExLTAzLTE4VDE0OjMyOjQyKzAwOjAwIiwiaW5jbHVkZSI6WyJQVUJMSVNIRUQiLCJFTUJBUkdPRUQiXSwib3JkZXIiOiJVUERBVEVEX0RFU0MiLCJsYXN0SUQiOiJxdDYxZjJ0NWc0IiwibGFzdERhdGUiOiIyMDI2LTAxLTI5VDA2OjMxOjQ3KzAwOjAwIn0