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Epidemiology of Symptomatic Respiratory Viral Codetections Among Children and Adults in the Cascadia Community-Based Cohort, 2022-2024
No full textThesis (Master's)--University of Washington, 2025Background: Respiratory virus codetection, or the concurrent presence of multiple viruses in a single host, can complicate diagnoses and may influence clinical severity. This study examined the burden and clinical impact of viral codetections in a community-based U.S. cohort, focusing on eight common respiratory pathogens, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza virus (Flu), respiratory syncytial virus (RSV), and others. Methods: We conducted a secondary analysis of data from the CASCADIA study, a prospective cohort of 3,500 participants aged 6 months to 50 years, followed from June 2022 to March 2024. Illness episodes with single vs. multiple virus detections at illness onset were compared in terms of demographic and clinical characteristics. Multivariable logistic regression models were used to assess the association between codetection and illness severity outcomes, including extended symptom duration, medically attended illness, and absenteeism. Analyses were conducted both overall and stratified by involved viral pathogens and age groups. Results: Among 8,908 symptomatic illness episodes, 9.6% involved codetection. Codetections were most frequent in children aged 5-11 years, while single detections were more common in adults. Overall, codetection was not significantly associated with illness severity. However, in children, codetection was linked to higher odds of extended symptoms (aOR: 1.27; 95% CI: 1.06–1.53) and medical visits (aOR: 1.53; 95% CI: 1.17–1.99). A positive, though non-significant, association was observed with absenteeism in adults. Conclusion: Although most associations between codetection and illness severity were not statistically significant, some patterns suggest potential links to more severe outcomes, particularly in pediatric populations. Further research is needed to better understand viral interactions and their influence on clinical trajectories over time
Epitoky, brooding, and precocial larvae in Epigamia magna (Polychaeta: Syllidae)
No full textAbstract: Syllidae (Polychaeta) show a remarkable variety of reproductive strategies related to their transformation into the pelagic spawning epitoke form. Syllids can create epitokes through schizogamy, where stolons bud off, or epigamy, where the entire adult transforms in order to swim, and after fertilization they often brood their young with a variety of mechanisms. Epigamia magna (Berkeley, 1923) is a large syllid in the subfamily Autolytinae, and although its reproduction and larval development are poorly known, the subfamily is known for ventral brooding. I collected two fertilized female epitokes, one unfertilized female, and one male by night-lighting in late June and recorded their development. The fertilized females exhibited a long brooding period, at least 13 days, and the larvae which emerged from the brood sac were ciliated and highly developed, already at the 4-setiger stage. They quickly developed the fifth setiger within 2 hours, and grew to be 1100 μm by hour 44. My observations of these larvae somewhat conflict with previous reports, as I found they were 800 μm long at hatching, with six red eyes. E. magna's large body size and long brooding period appear to allow heavy investment before larvae are released into the water column. I discuss a few of the many unresolved questions about this species' reproduction, including seasonal limits, the cues for swarming, and a potentially annual life cycle
The relationship between nitrate and phosphate nutrients in substrates with seagrass density: Implications for marine ecosystem sustainability
No full textIntroduction: Water conditions that greatly affect the density of seagrass species are the substrate fraction and nutrient content of the base substrate where the seagrass grows. This is important because seagrass utilizes dissolved nutrients in the waters and nutrients on the substrate for the production process. This study aims to determine the relationship between nitrate and phosphate nutrients on the substrate with seagrass density in Pajenekang Island, Liukang Tuppabiring District, Pangkep Regency, South Sulawesi. Methods: Data sampling of seagrass was carried out at north and west station using the line transect method where the data taken included seagrass frequency, cover, and density. Meanwhile, data collection of nitrate and phosphate nutrients on the substrate used a random sampling method and analysis with macro nutrien determination Morgan-Wolf extract. Findings: The results of study indicated that the seagrass density values in Pajenekang Island have various values, with the average seagrass density at the west station are 275 stands/m² and 356 stands/m² at the north station. The nutrient content on substrate in Pajenekang Island is low range when compared the result of other studies. Nitrate (0.36 ppm) and phosphate (0.49 ppm) at west station was significantly lower (P<0.05) compared to nitrate (0.49 ppm) and phosphate (0.64 ppm) at north station. The results of the Pearson correlation analysis among nitrate content and seagrass density on Pajenekang Island showed a negative relation with moderate correlation (-0.496*). Meanwhile, Pearson correlation analysis of phosphate content showed a negative relation by showing no relation (-0.166). This can be explained that there are other factors that effect on seagrass density in research location. Conclusion: It can be concluded that the seagrass density at Pajenekang Island varies. The average seagrass density at the western station is 275 individuals/m², while at the northern station, it is 356 individuals/m². Novelty/Originality of this article: The novelty of this research lies in analyzing the relationship between nitrate and phosphate nutrient content in the substrate and seagrass density on Pajenekang Island, which has not been widely studied in this region
Tailoring Perovskite Quantum Dot Surface Chemistry for Single Photon Emission
No full textThesis (Ph.D.)--University of Washington, 2025Perovskite quantum dots are one of the most promising colloidal single photon sources with high single photon purity, narrow linewidths and fast radiative recombination at all temperatures. However, these materials still suffer from non-ideal behavior in the form of blinking and spectral diffusion. Both blinking and spectral diffusion are correlated to quantum dot surface quality and as such improving the surface of perovskite quantum dots is key to their long-term success as a single photon source. Here we explore the surface chemistry of perovskite quantum dots through tailoring ligand passivation and morphology. We find that CsPbBr3 quantum dots passivated with zwitterionic lecithin exhibit significantly less blinking than their oleylammonium/oleate passivated counterparts because lecithin binds to the surface ten times more strongly resulting in less ligand desorption during sample preparation. We also find that, in contrast to cubic CsPbBr3 quantum dots, spheroidal CsPbBr3 quantum dots have an asymmetric photoluminescence with a red tail due to emissive traps. Finally, we benchmark silane-based passivation for formamidinium lead bromide (FAPbBr3) quantum dots against phosphoethylammonium-based passivation for FAPbBr3 quantum dots. We find that, while silane-based passivation works extremely well at room temperature, the performance of these materials at 4K is hampered by increased trion formation which results in irreversible photodegradation
Impacts of the Religious Right Agendas on LGBTQ+ Students
No full textMaster of Education (MEd)This paper examines the impact of the religious right's policy activism on the health and well-being of LGBTQ+ students. It also explores how educators can collaborate with advocacy groups and how state policies can ensure that each district implements LGBTQ+ inclusive curricula uniformly. Gender identities are particularly vulnerable in 2025, and culture wars are intensifying. I aim to identify modifiable factors that can promote positive school environments throughout our state and ensure that inclusive curricula are equitably distributed across districts. Three key themes were identified: LGBTQ+ underrepresentation in curricula, policies related to LGBTQ+ student protections, parental rights, and backlash bills, and disparities in student health outcomes among LGBTQ+ students compared to their cisgender peers. The paper then explores how state-level practices can be aligned with research and discusses the implications for future research and transformed practice
Probing Neurovascular Unit Dysfunction in Alzheimer’s Disease
No full textThesis (Ph.D.)--University of Washington, 2025Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common form of dementia. Nearly 80% of individuals with AD present with cerebrovascular pathologies such as microinfarcts, atherosclerosis, and cerebral microbleeds. The brain vasculature, which delivers oxygen and nutrients to surrounding neurons, is regulated by the neurovascular unit (NVU). The NVU is a multicellular system composed of neurons, astrocytes, microglia, pericytes, and brain endothelial cells (ECs). While NVU dysfunction is a hallmark of AD, the interactions between brain and vascular cell types remain incompletely understood. Current in vitro NVU models often lack perfusable 3D vasculature and omit critical cell types such as microglia, highlighting the need for more physiologically relevant systems. To address this gap, the body of work presented here reports a series of NVU models of increasing cellular complexity and applies them to investigate NVU dysfunction in AD. First, we examined the effects of AD neuronal secretomes on ECs using an engineered microvessel system. We perfused microvessels with conditioned medium (CM) generated from induced pluripotent stem cell (iPSC)-derived neurons containing an AD mutation that increased amyloid-beta (Aβ) production. We observed that increased Aβ production by AD neurons strongly correlated with features of EC activation. Further, when we depleted Aβ from our CM through several methods, we saw that the EC activation we observed was attenuated. This study provided a direct link between the EC activation observed in AD and Aβ. It also established a model of indirect EC-neuron interactions in the NVU. We next investigated EC-microglia interactions by developing a suite of models, including a 2D co-culture system, a 3D microvessel model containing iPSC-derived microglia-like cells (iMGL), and a multicellular 3D NVU model comprising neurons, astrocytes, iMGL, and ECs. Using these systems, we found that iMGL supported EC structure and barrier integrity under basal conditions. We then applied an AD-mimicking neuroinflammatory stimulus, TNFα, to our microglia-vessel model and determined that iMGL could help ameliorate the EC inflammatory response and prevent EC barrier breakdown. Incorporating neurons and astrocytes into the model revealed that iMGL also enhanced neuronal morphology, highlighting their broader supportive roles within the NVU. These findings deepen our understanding of how microglia interact with the brain vasculature and provide models to uncover the mechanisms driving these interactions. Together, these studies provide novel, physiologically relevant models for probing NVU dysfunction in AD. Beyond advancing mechanistic understanding of neurovascular interactions, these platforms offer a foundation for therapeutic discovery and for examining the impact of disease genetics on NVU cell types
Listening Between the Lines: Dialogic Listening in Transracial Adoptive Families
No full textThesis (Master's)--University of Washington, 2025This article asserts that dialogic listening is an effective skill for the co-creation of understanding and connection within transracial adoptive family units. I analyze and explore this listening style within the parameters of a case study. This case study dissects a conversation observed between a twenty-six-year-old black man and his eighty-one-year-old adoptive white father regarding topics surrounding identity and racial negotiation. The dialogue from this interaction creates the perfect opportunity to explore how the concept of Dialogic listening is essential in listening through difference. Dialogic listening calls for open-mindedness and mutual respect. It also emphasizes deep engagement where all parties not only work to understand the context, but the emotions and experiences behind those contexts. Many adoptive families face unique communication challenges such as cultural, racial, and intergenerational differences can be amplified as the children begin to question what it means to discover their identities outside of their adoptive family unit. In many of these situations the adoptive parents and children, especially around social issues and identity, encounter exchanges that are complex and emotionally charged. Dialogic listening allows the family members to move from basic level understanding to engaging with the deeper narratives that shape their views
Statistical Learning from Shifting, Indirect, or Unseen Data: Efficient Algorithms and Theoretical Guarantees
No full textThesis (Ph.D.)--University of Washington, 2025A fascinating phenomenon underlying statistical machine learning and artificial intelligence is "out-of-distribution" (or OOD) generalization. Data can (and in some settings, must) be used to draw inferences regarding probability distributions other than the one from which they were sampled. Understanding this mystery gives promise to statistical analyses that exhibit a degree of universality, such as clinical trials whose conclusions reflect many subpopulations or pre-defined image/text encodings that can be used to solve many classification tasks simultaneously. This dissertation tackles the theoretical and algorithmic challenges of designing methods that exhibit these modern notions of generalization. Chapter 2 studies a learning framework called distributionally robust optimization (DRO), which promotes OOD by training models to optimize the worst-case expected loss achievable within a collection of possible training distributions. These maximum-type objectives present challenges for designing stochastic learning algorithms, as unbiased estimates of the gradient are not easily computed. We design an estimator equipped with a progressive bias (and variance) reduction scheme, for which the resulting algorithm is shown to have a linear convergence guarantee. Although our optimization results apply more generally to DRO problems, we focus attention on a subclass of objectives called spectral risk measures, which have appealing statistical and computational properties previously unexplored in machine learning. We provide theoretical and practical guidance on selecting the various problem parameters, such as the collection of distributions over which to maximize. Finally, we present (among others) extensions to group DRO, a popular extension of the framework amenable to training neural network models. Chapter 3 takes insights from the DRO application and pursues stochastic algorithms for a more general class of optimization problems, dubbed semilinear min-max problems. These objectives interpolate between the well-understood class of bilinear and relatively less-understood nonbilinear min-max problems, and have applications to problem classes such as convex minimization with functional constraints as special cases. We present the first complexity guarantees for this problem class, using a randomized algorithm with components inspired by the simulation literature (such as adaptive sampling of new data and adaptive averaging of historical data). We prove convergence guarantees in both convex and strongly convex settings with a fine-grained dependence on individual problem constants. The results yield complexity improvements in even specific cases, such as bilinearly coupled problems. We also provide a lower complexity bound on the performance of deterministic algorithms applied to the semilinear problem class. Chapter 4 shifts focus from the implementation of large-scale learning algorithms to their output. We investigate predictive models that learn via a pre-training procedure with unlabeled data and can then make predictions for downstream classification tasks (without having seen any directly labeled training data from that task). This capability, known as zero-shot prediction, is made possible by three ingredients: 1) massive, carefully curated pre-training datasets, 2) "self-supervised" labels that allow models to learn universal features of structured data (e.g., images/text), and 3) the translation of downstream data into the format seen during pre-training using a technique called prompting. We analyze all three ingredients theoretically by establishing both the sample complexity and the limits of prompting in terms of simple distributional conditions. Inspired by this theory, we explore variants on the pre-training objective and prompting strategies that show practical benefits such as improved zero-shot classification accuracy
Health perspectives: Exploring differential reporting across sex and generations
No full textThesis (Ph.D.)--University of Washington, 2025In health metrics, health surveys are an important source of information, particularly for the estimation of health risk exposures and outcomes. This dissertation studies how the same survey questions are answered differently depending on whether the respondent is a mother of a child in the 90s, the child as a young adult, or whether the respondent is female or male. Topics explored were chosen as those in which sex and social norms around sex and across generations can influence how we experience health, the risk factors we are exposed to, and our relationship with morbidity. In the first chapter, Differences in reporting of child abuse by mothers and young adults, we used a longitudinal study to compare mothers’ prospective accounts of their child experiencing different forms of violence against children (VAC), and young adults’ retrospective self-reports of experiencing VAC. We then studied the socioeconomic factors associated with mothers reporting abuse of their child among children that were classified as having experienced abuse. For this end, we used the Avon Longitudinal Study of Parents and Children (ALSPAC), a 30-year prospective birth cohort study in England. This chapter addresses a well discussed issue in the field, the underreporting of VAC depending on the survey respondent. We used longitudinal data, unlike previous work where mostly cross-sectional data was used. We found that when questions are asked in the same way, there was no evidence of mothers underreporting physical or psychological abuse in comparison to children, even though there was little reliability across respondents. Among the pairs of mothers and children in which at least one of them reported abuse, we found that the sex of the child and other mother characteristics are associated with mothers’ reporting of physical or psychological abuse. Finally, the first chapter reflects on the social norms around discipline, as both mothers and the young adults described physical cruelty to be related to acts of severe physical violence, in which case acts such as pushing, smacking or kicking, would not be classified as abuse if mothers were the only respondents.
Chapters two and three focus on a fundamental topic in the measurement of the burden of disease, the measurement of morbidity through disability weights. In chapter two, Differential health loss valuation by sex of the respondent in the Global Burden of Disease (GBD) study we analyzed differences by sex of the respondents in the disability weights used for the GBD study. Similar to literature focused on paired comparison questions from Martens de Noordout et al. in 2018, Liu et al. in 2020, and Haagsma et al. in 2024, we found high correlation of health preferences by sex. This translated into anorexia nervosa being the only health state for which there a was a significant difference between disability weights estimated with female only and male only paired comparison data. In contrast, the sex stratification of the population health equivalence questions resulted in significantly different disability weights for females and males in almost all health states measured. In other words, we found that in the disability weights used in the GBD study, preferences for health states do not differ by sex, but females are less willing to accept disability as health program evaluations in comparison to males.
Finally, in chapter three, Differential health loss valuation by sex on population health equivalence questions (PHE), we further explored the sex differences found in chapter two and analyzed willingness to accept disability using the disability weights data from the GBD study. Through a marginal logistic model using generalized estimating equation, we found that even when we take age and education into account, females are more likely to choose programs that avoid deteriorating health over preventing death for the relative few. Before this study there was no empirical evidence on the differences in PHE valuation by sex of the respondent, mainly due to the use of PHE data as a methodological step. These questions are not used to rank health states but to anchor the preferences revealed through paired comparison questions in values that are useful for the estimation of Years Lived with Disability (0 to 1 ranges). It is in these questions, that we found females are more likely to choose the program that averts lifelong consequences of disease over programs that avert death as creating the greater population health benefit. Consequently, we estimated that if all disability weights input data were stratified by sex, female disability weights would be larger for every heath state. Notably, because 70 percent of the respondents of population health equivalence questions are female, the current set of disability weights in the GBD study reflect more the preferences of disability weights of females than males
Towards Robust and Effective Human Pose Estimation and Generation
No full textThesis (Ph.D.)--University of Washington, 2025Human pose estimation (HPE) in both 2D and 3D remains a fundamental yet challenging problem in computer vision, with broad applications in action recognition, human-computer interaction, motion analysis, and object tracking. Despite recent advances, achieving robustness and efficiency in real-world and edge-device scenarios remains difficult. This dissertation presents a series of contributions toward making HPE more effective and robust. Specifically, we propose (1) a temporal-based 2D HPE method for golf swing analysis, (2) an optimization-driven pipeline for 3D HPE, and (3) a unified contrastive learning-based framework for 2D-3D pose representation. Furthermore, building upon HPE, we explore its potential in human motion generation. In particular, we introduce PackDiT, a novel diffusion-based framework for joint motion and text generation via mutual prompting. PackDiT effectively integrates text and motion generation by leveraging a unique training strategy with two DiT models (Text-DiT and Motion-DiT) with shared latent spaces, enabling text-to-motion, motion-to-text, and joint motion-text synthesis. Evaluated on the HumanML3D dataset, PackDiT outperforms state-of-the-art generative models across multiple tasks, demonstrating its capability as a unified framework for motion understanding and generation. The dissertation discusses challenges, limitations, and potential directions for advancing HPE and human motion generation in future research