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UC Davis Electronic Theses and Dissertations

Cover page of Language Ideologies and Belonging: Educational Experiences of Chicanx and Latinx University Students

Language Ideologies and Belonging: Educational Experiences of Chicanx and Latinx University Students


This dissertation presents findings from an ethnographic exploration of the role of language in the academic experiences of Chicanx and Latinx university students at an emerging Hispanic Serving Institution. Specifically, this dissertation presents an extensive analysis of the language ideologies encountered, enacted, and described by Chicanx-Latinx identifying students at Patwin University. The findings of this dissertation highlight the role of a space called el centro, a campus center designed to provide academic support to retain Chicanx and Latinx students, and in particular the importance of this space for nurturing students’ sense of belonging in academia by modeling and enabling inclusive language ideologies and practices.

This dissertation relies on data collected during the 2018-2019 academic year through detailed interviews with fifteen students, participant-observation of students at el centro, and documented examples of the linguistic landscape of the focus site. Based on this data, I examine and elaborate on the role of language in contributing to students’ sense of belonging in academic spaces and exchanges. The findings contribute to closing the gap in current sociolinguistic and education literature in regards to our understandings of the role that language ideologies have in the academic experiences, opportunities, and outcomes of linguistically racialized and marginalized students. In particular, this dissertation steps away from deficit views of language by not focusing on language proficiency. Instead, this dissertation examines both exclusionary language ideologies and inclusionary language ideologies and specifically their impact on students’ sense of belonging as well as their use of English and Spanish in academic settings. Within these general categories of language ideologies are underlying belief systems and structures which impact how students feel about their own language identities and capacities and how such beliefs influence the exchanges and spaces that students engage.

Ecological Genetics of Life History Variation in Oncorhynchus Mykiss


Intraspecific diversity is a crucial part of conservation genetics because it buffers populations from decline after catastrophic events. There is incredible intraspecific life history diversity within the species Oncorhynchus mykiss, commonly known and rainbow trout and steelhead. The life history of a freshwater residing rainbow trout differs greatly from that of a steelhead, who’s life history mirrors a salmon. There are many gaps in understanding of the life history variation in O. mykiss that can aid in its’ conservation and management. Here we apply genetics to better understand the ecology of steelhead throughout their range. We used a genetic marker in the North Umpqua River in Oregon to identify where early migrating steelhead are spawning. We also ran genetic associations with the half-pounder life history and early migration steelhead in northern latitudes. We found that some streams have variable use by early migrating steelhead depending on the spawning year. Additionally, the half-pounder life history is not likely to be genetically controlled, even though this variant occurs in two locations at the extreme ends of the species’ range: Kamchatka, Russia and California, USA. We also found the genetic region that controls migration time in the Situk River, Alaska, which is different, but close in position to the GREB1L/ROCK1 genes that control migration time in souther latitude populations. These results show unique differences in life-history diversity O. mykiss that are population specific. Ultimately, discoveries in conservation genetics in salmonids should not be extrapolated, but should be tested due to the incredible intraspecific diversity.

Automated Experimentation and Machine Learning for Perovskite Photovoltaics


As anthropogenic global warming and climate change continues to intensify, it is more important than ever to curb our use of fossil fuels for heat and electricity. The clean energy transition requires investment in photovoltaic (PV) technology to replace unsustainable generation sources and to meet increasing electrification demand. Most currently available PV modules use Si as the light absorber material. Advancements in the manufacturing process of Si solar cells has led to a reduction in cost over the past decade, yet there are still inherent difficulties in the refinement process. Next-generation PV must improve upon Si by maintaining or increasing device efficiency while simplifying manufacturing. Metal halide perovskite solar cells (PSC) have proven to meet these criteria, with >25% efficiency and cost-effective fabrication options such as blade coating and ink jet printing. However, degradation in perovskite materials under several environmental stressors (light, humidity, temperature, bias, and oxygen) precludes commercial adoption. Stability testing of PSC is time-consuming, particularly due to the large compositional space available. As a result, methods to quickly vet the stability of various perovskite compositions are critically needed. My thesis addresses this open problem by applying automated, in situ characterization and machine learning (ML) forecasting to PSC degradation studies.

First, I present a generalized ML roadmap for perovskite PV. I delineate three levels of PSC design and provide examples of ML projects which could accelerate the development process. Next, I design and build a high-throughput setup for in situ, environmental photoluminescence (PL) spectroscopy, a technique which requires <2 seconds to acquire data. The system uses a custom chamber containing up to 14 samples and an x-y translation stage to automatically move from one sample to the next. We select ten CsyFA1-yPb(BrxI1-x)3 perovskite thin films of varied composition and track changes in radiative recombination under repeated 6-hour temperature and rH cycles. Using the high-throughput setup, I obtain 240 PL spectra every hour and 14,000 spectra over the course of a single experiment. The temperature cycling results show increased non-radiative carrier recombination as samples are heated above 23°C. We show that FA-rich perovskites with 10-30% Cs+ have minimal lattice strain which promotes high structural and thermal stability. During rH cycling, all compositions displayed a PL enhancement with increasing rH as H2O passivates band gap trap states and suppresses non-radiative recombination. FA-rich films show the greatest PL increases over the course of the rH cycling while Cs-rich films reach a plateau in maximum PL value after 5-10 cycles. Finally, I apply ML models to the datasets and generate forecasts of environment-dependent PL responses. I use linear regression, Echo State Network (ESN), and Auto-Regressive Integrated Moving Average with eXogenous regressors (ARIMAX) algorithms. For the temperature cycling, I attain an average normalized root mean square error (NRMSE) over all compositions of 24.4% (linear regression), 16.6% (ESN), and 7.3% (ARIMAX) for prediction windows extending 70 hours into the future. For the rH cycling, NRMSE values of 72.5% (linear regression) and 44.0% (ESN) indicate difficulty in tracking long-term changes over a 50-hour window. Using ARIMAX with seasonality components, I achieve an error of only 10.3%, demonstrating the algorithm’s capability to model complex, non-linear data from varied perovskite compositions. My high-throughput characterization results and accurate time series forecasts illustrate the potential of data-centric approaches for perovskite stability investigations and showcase the promise of automation, data science, and ML as tools to drive PSC commercialization.

Cover page of Numerical Modeling of Floodplains: Evaluating Ecological Outcomes

Numerical Modeling of Floodplains: Evaluating Ecological Outcomes


Rivers and their floodplains are a complex system that presents numerous challenges when coupled with human adaptation. The many adaptations humans have made to the landscape have come at the cost of aquatic ecology. Efforts have gone underway to mitigate those costs and begin to repair our relationship with the aquatic landscape via restoration and re-engineering systems to benefit habitat. Of the myriad of tools at our disposal to investigate different restoration strategies, this thesis explores the application of hydrodynamic models to ecosystems and how the linkages between numerical models can evaluate ecological outcomes.

The application of this thesis has centered around the Sacramento-San Joaquin Delta, a focal point of California’s Central Valley and a critical ecosystem in the state. The Sacramento-San Joaquin Delta has many “islands” which are essentially parcels of land that have levees surrounding them to protect against flooding from the Detla’s channels. One island in the Delta is the McCormack-Williamson Tract, which lies just below the confluence of the Cosumnes and Mokelumne Rivers. The McCormack-Williamson Tract is the study area for Chapters 2 and 3, where a hydrodynamic model was develope and calibrated to investigate natural phenomena. The models were calibrated against observed flood events and then used to compare against field data collected in the study systems within the model calibration period. By pairing observed field data with replicated modeling data, linkages between source water distribution, age, and general hydrodynamic features were drawn. These linkages provide useful tools in the field of floodplain restoration engineering.

Chapter 2 demonstrates a hydrodynamic model’s ability to resolve the spatio-temporal distribution of the water from the convergence of the Mokelumne and Cosumnes Rivers as they advance through the McCormack-Williamson Tract and its surrounding channels and sloughs. This modeling effort was coupled with an isotopic mixing model using water sampling in the study area, and shows that the two methods agree on source water distribution.

The subsequent chapter (Chapter 3) that focuses on the McCormack-Williamson Tract evaluates a modeled water age against field-collected zooplankton abundance. Although this study did not find a robust relationship between the two variables, the chapter lays out the methodology used to investigate the relationship.

The second study area centered around a key feature in the Delta’s hydrography - the Yolo Bypass. The Yolo Bypass is a major floodplain to convey flood waters from the Feather, American, and Sacramento Rivers around major cities and dwellings and into the Delta. Chapter 4 outlines the development and calibration of a model of the Yolo Bypass and discusses the applications the model can have to answer ongoing questions about the study system.

Cover page of Epidemiological inference from pathogen genome data

Epidemiological inference from pathogen genome data


The use of whole genome sequencing in infectious disease diagnostics generated an unprecedented amount and resolution of information. Large-scale sequencing of pathogens requires scalable methods in species identification, outbreak clustering, virulence phenotyping, antimicrobial resistance profiling, and epidemic modeling.

This dissertation presents a new approach in defining species membership using a pangenome framework explicitly applied to the whole genome sequences of the genus Hungatella which effectively identified a misclassified reference strain. Genomic epidynamics is a phylogenetic free approach in epidemiological inference, particularly the disease transmission parameter reproductive number (R). This approach offers a scalable process in elucidating heterogeneous transmission of genomic variants of SARS-CoV-2. Genomic epidynamics bridges pathogen population genomics and epidemic modeling. A genome-first approach to antimicrobial resistance definition combines automated machine learning rank resistance genes and phenotypic data thru genomic MICs. This approach was applied to a multidrug-resistant serotype of Salmonella enterica subsp. enterica serovar Dublin (S. Dublin). Machine learning-based approach to genome-wide association study revealed allelic variants of porA in Campylobacter jejuni leading to an abortive phenotype when the organism is invasive from the gut and resides in the reproductive system.

Cover page of Re-markable Print: Historiography and A Seconding Instinct in the Work of Sutton E. Griggs

Re-markable Print: Historiography and A Seconding Instinct in the Work of Sutton E. Griggs


Sutton Griggs’s 1899 novel, Imperium in Imperio, is widely recognized as an early African American militant novel. It depicts an underground all-Black government dating back to the American Revolution whose documented history is private until the novel’s fictional narrator submits it for publication. Twenty years later, Sutton Griggs would publish his nonfiction Guide to Racial Greatness; or, The Science of Collective Efficiency wherein he argues that members of all great races must possess the spirit of seconding through “the ready support of another’s deeds.” (132) By imagining the ways Griggs’s theory of seconding theorizes a print practice rooted in collaboratively recording history, we can read his earliest novel as a reflection on the crisis of record-keeping and -making during the age of New Journalism. This involves tracing a rich tradition of African American print practices during the long nineteenth century, which reveals that newspapers were initially heralded as the ideal medium by which to achieve Black self-determination. They curated historical records meant to instruct readers on how to live and encompassed multivocal historiographical projects, cultivating historical consciousness by regularly intervening on white supremacist myths about Blackness. Imperium in Imperio both emulates a historical record by presenting itself as a found manuscript and undermines its own authenticity by employing multiple narrative frameworks that present contradictory accounts of its characters. These formal elements work to make the processes of historical writing, circulation, and interpretation transparent for readers to warn of the dangers of single-authored histories and destabilize our notion of historical knowledge as fixed.

The divergent Filamin FLN-2 maintains nuclear integrity during P-cell nuclear migration through constrictions in Caenorhabditis elegans


Cellular migration is essential for an assortment of developmental processes ranging from embryogenesis to neurodevelopment. Oftentimes, cellular migration through narrow spaces is limited by nuclear migration,such as is the case for cancer metastasis and in immune response (i.e. white blood cells rushing to the site of injury). Nuclear migration through small constrictions has been studied in vitro but not in vivo. Here, we look at nuclear migration in the context of the developing vulval and neural precursors of Caenorhabditis elegans. During the mid-L1 larval stage, six pairs of P-cell nuclei migrate from the lateral half of the worm to the ventral cord, where they will divide and progress into the vulval and neural precursor cells. These nuclei must migrate through a constriction between the cuticle and the muscle of the developing animal that is about 5% of the diameter of nucleus in the early L1 stage. We explore the mechanisms which allow them to stably migrate in this normal developmental process. Typically, this process has been facilitated by the microtubule-based SUN-KASH pathway, which serve as the tracks for the migrating nuclei. Previous studies have shown that in the absence of the SUN-KASH pathway, these nuclei still migrate at 15°C. We found that there is an actin-based pathway which aids the migration process at 15°C in parallel and in absence of the SUN-KASH pathway. We hypothesized that FLN-2, a filamin, crosslinks branched actin networks and organized actin bundles to support stable nuclear migration through narrow spaces. We found that in the absence of unc-84 (a SUN protein) and/or fln-2, that nuclear rupture events were more prevalent in the migrating P cells, while the absence of unc-84 resulted in the formation of micronuclei in the early and mid-migration stages. We propose future work to determine how FLN-2 may be facilitating P-cell nuclear migration, along with its relationship to UNC-84, LMN-1 (lamin), and CGEF-1 (Cdc42 guanine exchange factor).

Cover page of Protected Region Radio Map Estimation

Protected Region Radio Map Estimation


Passive radio frequency (RF) sensors and receivers are highly vulnerable to unintended radio interference from deployment of active RF transmitters in nearby areas of service. Often, these RF receivers may also be susceptible to overloading damages. High likelihood scenarios of overloading damages include ultra-sensitive receivers that cannot afford front-end protection, or receivers deployed while powered down without the ability to measure the environment before powering on. It is often costly to measure RF signal strength and assess potential interference over wide urban/suburban areas among various building structures and complex terrains. Moreover, these passive RF sensors and receivers are sometimes deployed in locations that are difficult to access and to measure radio signal strength from new RF transmissions or those under planning. Consequently, it is important in the service planning stage to estimate a wide area radio map from only limited RF measurement at locations of convenience. We propose that a network of cheap and robust RF receivers may be sparsely deployed in a geographical region to estimate a completed radio map. After receiving power measurements from the sparse network of RXs, several different estimation methods may be applied to reconstruct the region’s radio map. These estimation methods may be in the form of kernels, random processes, basis functions, and Machine Learning (ML) algorithms. We aim to provide a certain level of confidence in multiple estimation methods that may be used for estimating a completed radio map. Many of the interpolation methods produced favorable results when estimating a radio map. The Inverse Distance Weighting (IDW) algorithm performed the best overall due to being one of the most accurate estimators, having the fastest processing time, and robust performance with system parameter selection. Overall, the Machine Learning (ML) algorithms processed much faster than the average interpolation method, but performed worse on average. Iterative Shrinkage and Thresholding Algorithm (ISTA) Net performed the best due to estimating the most accurate radio maps.

Cover page of Efficient Mapping-Free Methods for Discovery and Genotyping of Structural Variations

Efficient Mapping-Free Methods for Discovery and Genotyping of Structural Variations


Structural variants (SVs) account for a large amount of sequence variability across genomes and play an important role in human evolution and diseases. Despite massive efforts over the years, the discovery of SVs in individuals remains challenging due to the highly repetitive nature of the human genome and the existence of complex SVs. The dominant mapping-based framework for SV discovery has several drawbacks including dependence on resource intensive mapping algorithms and an increased possibility of error in repetitive regions of the genome due to ambiguous read mappings. As a result, new computational methods are needed that can genotype different types of SVs in both short and long read data with high accuracy.

In this thesis, we first propose an ultra-efficient mapping-free approach for genotyping common structural variations on short Illumina reads in Chapter 2. Our method Nebula generates databases of k-mers for catalogs of common SVs and counts these k-mers in unmapped samples to predict SV genotypes using a likelihood model of the k-mer counts. Nebula is the first method known to us that's capable of directly mapping-free SV genotyping from raw FASTQ files. We show that Nebula is not only an order of magnitude faster than mapping-based approaches for genotyping SVs, but it also has comparable accuracy to state-of-the-art approaches. Furthermore, Nebula is a generic framework that is not limited to specific types of SVs.

Next we introduce the concept of substring-free sample-specific strings (SFS) as an effective tool for comparative variant discovery between pairs of accurate long-read sequencing samples (e.g., PacBio HiFi) in Chapter 3. The SFS are sequences specific to a genome (or equivalently its sequencing reads) with regards to another genome that also do not occur as substrings of one another. We then introduce the Ping-Pong algorithm for theoretically and practically efficient extraction of SFS between a pair of target and reference samples by building an FMD index of the reference sample and querying the reads of the target sample against this index. Ping-Pong is a mapping-free method and is therefore not hindered by the shortcomings of the reference genome and mapping algorithms. We show that Ping-Pong is capable of accurately finding SFS representing nearly all variation (>98%) reported across pairs or trios of WGS samples using PacBio HiFi data.

Finally in Chapter 4 we introduce SVDSS, a novel hybrid method for discovery of SVs from PacBio HiFi reads that combines the SFS concept with partial-order alignment (POA) and local assembly to yield highly accurate SV predictions. With experiments on three human samples, we show that SVDSS outperforms state-of-the-art methods for SV discovery on long-read data and achieves significant improvements in recall and precision particularly when discovering SVs in repetitive and traditionally difficult regions of the genome.

Decarbonizing Transportation using Market-Based Low-Carbon Fuel Incentives


Decarbonizing the transportation sector, which is the largest contributor to U.S. greenhouse gas (GHG) emissions, requires a transition away from fossil fuels to renewables like biofuels and electricity. An increasing number of policies have been enacted to incentivize the use of renewable fuels. The U.S. Renewable Fuel Standard, California Low Carbon Fuel Standard, and Oregon Clean Fuels Program are in the vanguard of such policies in the United States. California and Oregon’s policies are examples of a carbon intensity standard, a tool that has become increasingly popular among U.S. policymakers. This dissertation explores the past, present, and future of these policies. The first chapter begins with the present, addressing an important challenge of achieving efficient outcomes currently faced by policy stakeholders: pass-through of policy costs and incentives to fuel prices. Findings suggest that compliance costs are fully passed through, and biofuel incentives are fully passed through in some regions and less than fully passed through in others. The second chapter looks ahead to the end of the decade, forecasting a range of compliance outcomes under California’s LCFS through 2030. Annual compliance requires that by 2030, the state’s transportation sector must achieve a 20 percent carbon intensity reduction below 2010 levels. Achieving the target will require the majority of diesel demand to be supplied with biomass-based diesel unless electric vehicle adoption grows substantially. Finally, lessons from the last decade are drawn in the third and final chapter, exploring trends in the three standing carbon intensity standards in California, Oregon, and British Columbia utilizing publicly available historical data.