UCLA

Gold-standard clinical dementia adjudication is resource intensive and infeasible in large, population-representative studies which are critical for public health research. Algorithmic dementia classification uses models to predict cognitive impairment and was developed to circumvent challenges of the gold-standard adjudication process. Several algorithms have been developed to classify dementia in the Health and Retirement Study (HRS) and rely on information in the Aging, Demographics, and Memory Study (ADAMS), a substudy of HRS initiated in 2001. Existing algorithms cannot incorporate neuropsychological measures as they are unavailable in HRS, and models cannot be adapted to include more comprehensive measures available in newer studies.I propose a novel Bayesian latent class mixture modeling framework for algorithmic dementia classification that incorporates information from neuropsychological measures and can be adapted to include more comprehensive measures available in updated studies. The model uses latent class mixture models to create synthetic versions of datasets, incorporating information on relationships between sociodemographic, health, and cognitive measures and cognitive impairment classes through prior distributions based on studies with gold-standard adjudicated cases. This work involves three studies on aging: The Health and Retirement Study (HRS), The Harmonized Cognitive Assessment Protocol (HCAP, HRS substudy), and the Aging and Demographics Study (ADAMS, HRS substudy). Simulation studies were conducted to evaluate the role of study sample size and priors specified based on different data sources and sampling frames and their impact on algorithmic dementia classification results and inferences on racial/ethnic differences in dementia. Analyses using priors from ADAMS accurately captured cognitive impairment classes preserved racial/ethnic differences in dementia for Black vs. White participants. Priors better calibrated to the analytic sample however improved estimates for Black and Hispanic participants and preserved racial/ethnic differences in dementia for Black vs. White and Hispanic vs. White participants. Applying the model to HCAP 2016 yielded reasonable estimates of cognitive impairment classes with proportions of impaired participants in line with findings published by HCAP investigators. This dissertation lays important groundwork for strengthening algorithmic dementia classification in population-representative studies. Outcomes from this work are directly applicable to existing studies on AD/ADRD that are harmonizable with HRS/HCAP.

Cardiac pacemakers and spaceborne instruments require integrated circuits that prioritize low power consumption and high reliability to ensure their longevity over several years. This dissertation aims to enhance power efficiency and functionality of integrated circuits used in these applications.Modern leadless pacemakers have a limited battery lifetime of 1 to 2 years due to their small form factor, necessitating device replacement. To address this issue, a subcutaneous module with a rechargeable battery has been proposed to wirelessly power the pacemaker. However, the battery life of the subcutaneous module is also limited to a few hours due to the high power demand of its power transmitter (TX), making it impractical for an implant. This thesis presents a multi-pulse modulated power link with a wirelessly powered closed-loop cardiac pacemaker to significantly reduce the subcutaneous module power and extend the battery life to a month. Because the power provided by the subcutaneous module is largely reduced, a low-power closed-loop pacemaker is designed to accommodate the power budget. The proposed closed-loop cardiac pacemaker is able to receive bursts of power from the TX, store energy, demodulate pulses, stimulate the tissue, record cardiac signals and transmit the data back. The pacemaker only stimulates when the TX is “on” and uses the stored energy for sensing and data uplink. The pacemaker can apply monophasic, cathodic, current/voltage stimulation with a programmable pulse period and a programmable current/voltage amplitude. The proposed pacemaker advances state-of-the-art reducing the subcutaneous module power consumption making the wirelessly powered closed-loop pacemaker system feasible. For the spaceborne instrument application, the thesis addresses the effects of total ionized dose (TID) from gamma and cosmic ray exposure during a mission lifetime. This exposure can distort baseline readings in instrumentation by changing DC conditions of electronics, limiting the fidelity of collected measurements. To address this issue, a gamma dosimeter is integrated within a high-voltage (HV) CMOS process for system monitoring and calibrating high-voltage control circuitry for microwave-based spaceborne science instrumentation. The proposed CMOS dosimeter exploits differences in radiation sensitivity between the threshold voltage of the available I/O and core devices in HV CMOS. With a built-in dual-slope-based digitizer, the effects of clock variation and amplifier offsets are alleviated in the dosimeter, providing an accurate and temperature-independent measure of the total received dose.

Characterizing the genomic basis of climate adaptation is essential both for understanding the process of evolution and for conserving populations under climate change. Here, I use several methods to investigate the genomic basis of adaptation across oak species and populations. In Chapter 1, we compared the gene expression response to a simulated drought stress across six oak species, two species from each of the three taxonomic sections of Quercus in California, which varied in their drought tolerance. We found that drought tolerant species had a less plastic response to leaf drying, suggesting that phenotypic traits and non-plastic patterns of gene expression contribute to their drought tolerance more than plasticity. We also found that the two deciduous trees, which were the most drought sensitive species, responded to drying with 22% of the same genes, indicating these responses had evolved in parallel across distantly related clades. In Chapter 2, we used whole-genome sequencing to characterize the rangewide genetic structure of a rare island endemic oak species, Quercus tomentella, from the California Channel Islands and Guadalupe Island in Mexico. We found evidence for widespread hybridization with a related oak species, isolation of the Guadalupe Island trees, and some gene flow among the California islands that is likely mediated by wind pollination. We also identified putatively adaptive SNPs that were associated with climate variables, compared the spatial patterns of neutral SNPs and candidate SNPs, and used this information to make recommendations for choosing seed sources for restoration projects in the light of climate change. In Chapter 3, we characterized the heat stress response across species and populations. We performed a heat wave experiment and compared the gene expression responses among three oak species and two populations within each species, one from a southern, warmer site and one from a northern, cooler site. We found shared responses to heat stress among all species and populations, including the responses of individual genes as well as genes with related functions. We found limited evidence of differences in stress response among populations, suggesting a lack of local adaptation in the plastic heat stress response.

Uterine disorders are common and can be debilitating, but they are poorly understood. Endometriosis is the most common cause of secondary dysmenorrhea, yet existing treatments have substantial side effects and are not able to fully resolve pain for most patients. Many studies have shown that endometrial tissue from individuals with endometriosis has differential gene expression and DNA methylation patterns compared to endometrial tissue from controls. Data sharing efforts have made large amounts of genetic and epigenetic data available to researchers. However, it is technically challenging to compare datasets from different platforms and study designs, and these resources are underutilized. In this dissertation, I explore methods of combining DNA methylation and gene expression data to gain additional insights into endometrial disorders. First, I demonstrate that DNA methylation age can be used to understand ectopic endometriosis lesions. Then, I correlate DNA methylation age changes caused by hormonal treatment with differential gene expression in endometriosis. Given a candidate pathway, I then use datasets of infertility to determine whether these expression changes could provide insight into clinical phenotypes. Finally, I explore whether clinical phenotypes of menstrual pain resistant to non-steroidal anti-inflammatory drugs (NSAIDs) can predict altered signaling of related pathways in menstrual-derived tissues. This work explores methods of utilizing the vast amounts of genetic and epigenetic data that have already been generated, while taking into consideration both the unique dynamic properties of uterine tissue and the aspects of uterine dysfunction that are most disruptive to quality of life.

Recent research in political science documents the “nationalization” of U.S. state and local politics; the down-ballot results of partisan elections tend to reflect the outcomes of presidential contests. The three papers of this dissertation examine this phenomenon in greater detail. The first paper critically assesses nationalization as a top-down force. I decompose a large set of election results into candidate specific, partisan, and idiosyncratic components. While it is true election results are tied increasingly to partisanship, I find it is not true that presidential elections are most strongly tied to partisanship. In the second paper, I utilize a supervised machine learning technique to determine the extent to which gubernatorial rhetoric mirrors that of presidential candidates, finding candidates largely speak on topics germane to their own jurisdictions. Finally, the third paper uses a survey experiment to find that voters use national policy signals when choosing between candidates for state and local office. I also find voters use state and local policy signals when evaluating national candidates. The stronger the partisan signal, the greater the effect on behavior.

Heuristic search and planning model real-world problems as graphs, where each node represents a unique state or configuration of the problem, and each edge represents an operator that changes one state to another state. The task is to find a shortest or lowest-cost path between the initial state and a goal state in such a graph, which corresponds to a sequence of operators to solve the given problem instance with minimum cost.

A* is the standard algorithm for a single-agent heuristic search or planning problem. However, A* stores all nodes generated during the search and can run out of the available memory on common heuristic search and planning domains in a few minutes. Therefore, A* usually cannot solve hard problems. On the other hand, the memory requirement of iterative-deepening-A* (IDA*) is only linear in the search depth. However, on a domain where many distinct paths exist between the same pair of nodes, IDA* may generate too many duplicate nodes and hence run for a prohibitively long time.

In this dissertation, we provide three new algorithms, A*+IDA*, A*+BFHS, and IDUCHS, for solving problems that cannot be solved by A* due to memory limitations, nor IDA* due to the existence of many distinct paths between the same pair of nodes. We show that A*+IDA* is the state-of-the-art algorithm on the classic benchmark 24-Puzzle, while A*+BFHS and IDUCHS can generate significantly fewer nodes than A*, hence solving more problems than A* given the same memory limitations.

Conventional type 1 dendritic cells (cDC1s) act as sentinels of the immune system, surveying the surrounding environment to integrate, process, and present signals in order to initiate inflammatory responses. In concert with additional tissue-resident innate lymphocyte populations including type 1 innate lymphoid cells (ILC1s), these subsets initiate and potentiate inflammation early during infection to restrict pathogen expansion. However, the role that cDC1s and ILC1s play during “sterile” pathogen-less inflammation is less well understood. As sterile inflammation is activated during wound healing, trauma, drug-induced injury, obesity, and anti-tumor responses, understanding the mechanisms that drive sterile inflammation is critical to the development of therapeutic strategies aimed at improving multiple disease treatments. In Chapter 2 we therefore investigated the role of ILC1s in the pathogenesis of drug-induced acute liver injury. We found that activated liver ILC1s robustly produced interferon (IFN)-g, protecting mice from acute liver injury through the promotion of pro-survival signals in hepatocytes. However, the mechanisms and cell types that led to ILC1 activation were unclear. Therefore, in Chapter 3, we developed a novel method to rapidly study gene function in multiple innate immune cell linages using a CRISPR-Cas9 Ribonucleoprotein (cRNP)-based approach. In Chapter 4, we used this system to dissect the liver-resident immune cell types and interactions that regulate early sterile inflammation during acute liver injury. We identified tissue-resident cDC1s and cDC1-derived Interleukin (IL)-12 as required for protective ILC1 IFN-g signaling. Additionally, using a targeted in vivo cRNP screen, we found that activation cGAS-STING signaling was required for IL-12 production and initiation of protective responses. To determine whether cDC1s initiate sterile inflammation through a conserved mechanism that can have differential effects in the acute versus chronic contexts, we developed a model of chronic sterile inflammation using diet-induced obesity. In Chapter 5, we generated a large scale, high-dimensional atlas of sorted human immune cells derived from healthy lean and obese patient white adipose tissue (WAT) to define the changes in immune composition and signaling networks that are associated with human obesity. Our analysis discovered 8 previously uncharacterized cell types and revealed distinct obesity-associated inflammatory interactions enriched in WAT-resident immune cells. Finally, in Chapter 6, we performed a comparative analysis of mouse and human obese single cell RNA-sequencing datasets, demonstrating that activated cDC1s act as conserved central regulators of obesity-associated sterile inflammation. We found that cDC1s contribute to WAT inflammation and systemic metabolic dysfunction through cGAS-STING-mediated production of IL-12 and subsequent activation of ILC1 IFN-g signaling. Together, these results suggest a novel role for cDC1s in the initiation of both acute and chronic sterile inflammation and highlight the importance of context-dependent analysis of cell function.

The misalignment error during assembly and/or manufacturing error of robot linkages maycompromise the accuracy of a serial manipulator, causing the actual forward kinematics model parameter to differ from the designed value. This demands the need for forward kinematics calibration.

To identify the actual model parameter, the calibration process involves recording datafrom a set of robot poses as input to a parameter identification algorithm. In order to save time on the data acquisition, optimal design of experiment is employed to use the least amount of data points. Also, to avoid unnecessary calculations and numerical issues during parameter identification, identifiability analysis is performed to determine and eliminate unidentifiable parameters. Lastly, this work proposes novel nested algorithm to improve computation efficiency and robustness for parameter identification. This approach exploits the fact that part of the model parameter can be obtained explicitly given the rest of the parameters. This effectively reduces the parameters to find during the iterative identification process and is shown to be more efficient and robust than the commonly used generic method in the literature.

The calibration method is applied to a common 6-DOF industrial robot and a customarilydesigned and fabricated 4-DOF surgical robot. For the industrial robot, the nested algorithm demonstrates better robustness, faster error convergence over iteration, and less calculation time than the commonly used generic algorithm. For the surgical robot, the results for optimally designed data points demonstrate a faster convergence of residual error over the number of data points compared to randomly designed data points.

During the American Progressive Era, discourses of progress were co-constructed with racialized ideas about habitation. Communal, matriarchal, semi-nomadic, and self-built dwellings and their racialized inhabitants were positioned as antagonists to a single-family, heteropatriarchal, Anglo-American ideal. As associated with the Arts and Crafts Movement, the Craftsman, Spanish Colonial and Mission Revival style bungalows that defined Los Angeles’ suburbs presented an illusion of self-made, simple living in connection with nature and frontier ideologies. Though purportedly democratic, the development of the suburbs involved the conversion of Indigenous lands into private property. Meanwhile, Indigenous peoples, Black migrants and ethnic Mexicans were funneled into worker housing while employed in the construction and maintenance of a domestic sphere that secured social and financial capital for beneficiaries of Whiteness. The dissertation focuses on three sites where this occurred that have since been erased in the physical landscape, as much as in the public imaginary: 1) The Pacific Electric Railway Company’s labor camps, home to Mexican workers who built and maintained Henry Huntington’s exclusive Pasadena suburbs and resorts; 2) The homes built and maintained by students of the Sherman Institute, an Indian Boarding School in Riverside, California for the vocational training of Indigenous youth; 3) The bungalows of the industrial suburbs marketed to Black and unskilled employees of the Los Angeles Investment Company, a home-building enterprise that went on to build racially restricted, residential subdivisions in southwestern Los Angeles. In each case, laborers were racially targeted and housed in overcrowded, unsanitary, and flimsily built structures that materially foretold their demise and future redevelopment. This research challenges conceptions of the “slums” familiarized by neighborhood surveys, by exposing how their production was instrumental to the construction and maintenance of the suburbs. The chapters of this dissertation devote themselves to the designed details of these hidden histories, as emerging from three distinct labor camp, domestic service, and industrial suburbs. Though historically unique in their racial, material, geographic, and social composition, when considered together, the three sites demonstrate a commitment to settling labor and race through the uneven development of the domestic sphere.

Epigenetic regulation, changes in gene expression without alterations to the genetic material, has brought to light another layer of regulatory mechanisms that control immune effector processes in response to immune threats such as viral infection, cancer, and autoimmunity. Elucidating epigenetic regulators driving immune cell differentiation and modulation of effector processes are critical to our understanding of endogenous immune responses. Our studies aim to delineate mechanisms driven by an X-linked epigenetic regulator, UTX, in regulation of natural killer (NK) and T cells, two cell types important in the innate and adaptive arms of our immune system.Viral infection outcomes are sex-biased, with males generally more susceptible than females. Paradoxically, the numbers of anti-viral NK cells are increased in males. We demonstrate that while numbers of NK cells are increased in male mice, they display decreased effector function compared to females in mice and humans. These differences were not solely dependent on gonadal hormones, since they persisted in gonadectomized mice. Kdm6a (UTX), an epigenetic regulator which escapes X inactivation, was lower in male NK cells, while NK cell-intrinsic UTX deficiency in female mice increased NK cell numbers and reduced effector responses. Furthermore, mice with NK cell-intrinsic UTX deficiency showed increased lethality to mouse cytomegalovirus (MCMV). Integrative multi-omics analysis revealed a critical role for UTX in regulating chromatin accessibility and gene expression critical for NK cell homeostasis and effector function. Collectively, these data implicate UTX as a critical molecular determinant of sex differences in NK cells. Low oxygen levels, or hypoxia, has been associated with immune defects in multiple contexts. Hypoxia is associated with higher levels of H3K27me3 in CD4+ T cells. T cell-specific deletion of the histone demethylase is sufficient to recapitulate multiple features of hypoxia, including increased H3K27me3 accumulation and decreased production of IFN-γ+ CD4+ T cells in response to IL-12 cytokine stimulation. T cell specific UTX deletion has functional consequences, as mice are more susceptible to colon cancer and is not responsive to IFN-γ-dependent checkpoint therapy with anti-PD-1 treatment. However, mice with loss of UTX in T cells protected from colitis in which IFN-γ production has been tied with pathogenesis. Concomitant RNA and H3K27me3 CUT&Tag sequencing demonstrate an important role for UTX in removing repressive H3K27me3 marks to promote upregulation of IL12/STAT4 pathway genes including Il12rb, Tbx21, and Ifng. Together, these data demonstrate that UTX functions through its demethylase activity to promote Th1 cell differentiation and suggest that hypoxia’s HIF-independent effects on Th1 effector function may be mediated through UTX.