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Use of a Genetically Modified Cell Culture Model to Investigate Human Breast Cancer Resistance Protein-mediated Milk Secretion of Drugs
Get PDFThesis (Master's)--University of Washington, 2025Over 90% of breastfeeding women use at least one medication, which can expose their infants to drugs and potential toxicity. The Breast Cancer Resistance Protein (BCRP) transporter, encoded by the ABCG2 gene, is extensively studied in lactation. BCRP protein expression is elevated in lactating mammary epithelial cells (MECs). It is localized on the apical membrane of the MEC, where it plays a pivotal role in the secretion of endogenous and exogenous compounds into breast milk. BCRP actively transports nutrients such as riboflavin into milk, supporting infant development. However, BCRP can actively transport drugs and other xenobiotics into breast milk, increasing the risk of infant drug exposure and toxicity. Currently, there is no validated in vitro human mammary epithelial cell (hMEC) model to assess BCRP-mediated drug secretion during lactation. The goal of this study is to validate the MDCK-hBCRPcMDR1KO cell line as an in vitro model for evaluating BCRP-mediated drug transport into breast milk under physiologically relevant pH conditions. The MDCK-hBCRPcMDR1KO cell line that was engineered to overexpress human BCRP while lacking endogenous canine P-glycoprotein (P-gp), allows us to isolate BCRP-mediated transport without confounding effects from canine P-gp, which shares overlapping substrate specificity with BCRP. that eliminated substrate overlaps, We conducted bidirectional transport assays with cimetidine at apical pH 7.0 (human breast milk), pH 6.5 (intestine), and pH 7.4 (plasma). There was an approximately 2-fold increase in the efflux ratio compared to the MDCKcMDR1KO (control) cells, and the B-to-A transport of cimetidine was inhibited by the BCRP inhibitor KO143. Cimetidine transport was comparable across the three pHs. Our data suggest that, in contrast to membrane vesicles that showed increased BCRP activity under acidic conditions, pH had little impact on cimetidine transport in the monolayer model. These findings highlight that the MDCK-hBCRPcMDR1KO in vitro system is a useful system to study BCRP-mediated drug transport and should be further investigated as a versatile platform to evaluate BCRP-mediated drug secretion and drug-nutrient interaction at the blood-milk barrier
Improved XOR Lemmas for Communication Complexity
Get PDFThesis (Ph.D.)--University of Washington, 2025We give communication lower bounds for computing the -fold XOR of a given Boolean function , denoted , in both the deterministic and the randomized setting. In addition, we also give deterministic communication lower bounds on computing the composition of 2 functions, . Below for some absolute constant and all we show the following:\begin{enumerate}
\item \textbf{Randomized XOR Lemma.} If requires bits to be computed with some constant success probability then, computing with probability at least requires bits.
\item \textbf{Deterministic XOR Lemma.} If requires bits to be computed deterministically then, computing deterministically requires bits.
\item \textbf{Lifting Theorem.} For any function , having sensitivity and degree , and any requiring bits to be computed deterministically, computing deterministically requires bits.
\end{enumerate} We prove the above results using information theory.In particular, the randomized XOR lemma is proved using a new notion of information that we call marginal information
Resilience and Adaptation to Climate Change Among Organic Farmers in the United States: A Mixed Methods Investigation
Get PDFThesis (Master's)--University of Washington, 2025Farmers around the world are adapting to less predictable seasons and more frequent extreme weather events. This challenge is no less for organic farmers, who contribute to social well-being and ecological regeneration through their food production practices. This thesis employs a mixed-methods approach to explore resilience and perceived adaptive capacity to climate change among organic farmers in western Washington and the United States, more broadly. Chapter 2 uses qualitative methods to explore the relationship between climate resilience and crop diversity among organic vegetable farmers in western Washington. Findings contribute to academic debates around diversity and climate resilience by offering (i) a grounded perspective of how diversity confers socio-ecological resilience by those who enact it; (ii) an explanation for how climate interacts with social contexts to shape diversity; and (iii) an analysis of the limits of diversification and benefits of specialization to confer resilience. Chapter 3 uses Bayesian structural equation modeling to assess the relationship between land access and perceived adaptive capacity to climate change among certified organic farmers in the United States. Beyond the specific associations reported, the Chapter signals how Bayesian approaches can integrate qualitative and quantitative analyses, while accounting for uncertainty inherent in complex socio-ecological systems
Design of Thermoformable Composites and Advanced Manufacturing: Linking Processing, Microstructure, and Service Life Performance
No full textThesis (Ph.D.)--University of Washington, 2025Engineered materials continue to evolve at incredible rates, serving an expansive range of consumer, industrial, and research needs. Composites, specifically polymer composites, offer lightweight alternatives with nearly unlimited combinations of constituents for both niche and broad applications. In parallel, advanced manufacturing techniques expand to accommodate precise control over these novel materials. For instance, additive manufacturing (AM) has revolutionized the rapid production of component parts since the commercialization of stereolithography in the 1980s and is now hailed for design freedom in complex structures and low material waste. Originally conceived for polymers, all classes of materials are now being exploited and produced in tandem with additive processes to push the possibilities of engineered micro- and macro-structures. In the case of AM, as well as other manufacturing techniques, there is an opportunity to thoughtfully design multifunctional materials, especially composites, in which each element can provide specific functions for targeted applications. Bulk material properties are induced through careful selection of the matrix reinforcement elements and further property tuning can stem from refinement of component manufacturing parameters, such as production temperature or post-process treatment. However, despite being crucial towards the improvement of composite design, developing the interconnected knowledge for each composite system and/or manufacturing technique can be exhaustive. Through two unique composite systems, this research investigates the relationship between manufacturing parameters, microstructural elements, and physical performance to conclude generalizable correlations for future iterations. In the first system, we analyze a continuous carbon fiber reinforced polyphenylene sulfide filament for fused filament fabrication. Here, we characterize two generations of filament designs and modify print tooling in attempt to maximize composite mechanical performance and potential service life/reliability. While still stronger (in tension) than all commercially available polymer composite filaments, we find that the stiffness of carbon fibers combined with the severe deposition angle of current commercial 3D printers leads to inherent process induced defects, which reduce strength and reliability. We can begin to alleviate these defects with improvements to initial composite morphology and the extruder nozzle design. In the second system, we utilize a sustainable feedstock, algae, as a potential matrix platform for biodegradable biocomposites. A deep investigation into algal biomatter transformation informs our correlation between its thermomechanical manufacturing (hot-pressing) and mechanical/chemical properties. We explore the complete life cycle of our biocomposites, from feedstock composition to changes during thermomechanical processing and ultimately end-of-life degradation. That knowledge informs intentional manipulation of the service life in a targeted product application.
While vastly different material systems, the findings from these efforts will provide both researchers and manufacturers critical and diverse knowledge as to how production processes influence the manufacturability and resulting mechanical behavior of both synthetic and natural composite structures. Armed with informed cause and effect, we can then lead production of a new generation of advanced composite materials
Can an App Close the Pleasure Gap?: Changes in Gendered Patterns of Sexual Pleasure, Closeness, and Emotional Labor After a Digital Intervention
Get PDFThesis (Master's)--University of Washington, 2025Persistent gender disparities in sexual satisfaction and orgasm frequency remain a hallmark of inequality in heterosexual relationships, rooted less in biology than in entrenched cultural scripts and inequitable distributions of emotional and sexual labor. This study examines whether a digitally guided intimacy intervention can begin to recalibrate these patterns. The first "scene" of a mobile intimacy app (Arya) was evaluated using a mixed-methods, pre–post design with 180 participants in relationships. Quantitative measures captured changes in sexual satisfaction, relational closeness, and outlook; qualitative open-ended responses were thematically coded with attention to constructs from Self-Expansion Theory, Social Learning Theory, and feminist scholarship on emotional labor. Findings indicate that women experienced larger gains in sexual satisfaction than men, narrowing the "pleasure gap" modestly. Increases in satisfaction were often—but not universally—paired with greater closeness, particularly among couples who began with lower baseline intimacy. Many women described relief from the cognitive burden of planning intimacy, suggesting that digital guidance might redistribute relational labor. While exploratory and not generalizable, these results highlight the potential for technology-based interventions to disrupt entrenched sexual scripts and promote more equitable intimacy at scale
Mixing in a Novel Rocket Engine
Get PDFThesis (Ph.D.)--University of Washington, 2025A novel rocket engine concept, based on a transverse combustor design, is explored and modeled using Computational Fluid Dynamics (CFD). The design utilizes both Direct Numerical Simulation (DNS) and Large Eddy Simulation (LES) to simulate the flows of fuel and oxidizer pairs over a simple cylindrical combustion chamber geometry. Two nozzles, positioned radially along the upstream end of the cylinder, serve as inlets for the fuel and oxidizer reactants. Mixing is achieved by a pair of large, counter-rotating vortices. The mixing behavior is analyzed, both spatially and temporally, across transverse cross sections along the characteristic length of the chamber. Cylinder lengths are varied to optimize the cavity, aiming for near-perfect mixing. The determination of regions where near perfect mixing occurs informs the design configurations of the transverse rocket engine chamber by optimizing the cavity length. This configuration is anticipated to be more cost effective to manufacture, lighter, and more reliable than existing designs
Streptococcus pneumoniae carriage and antimicrobial resistance among Kenyan children discharged from hospital
No full textThesis (Ph.D.)--University of Washington, 2025Streptococcus pneumoniae is a leading cause of morbidity and mortality among children under five in sub-Saharan Africa, particularly in the vulnerable period following hospital discharge. This dissertation investigates the dynamics of pneumococcal carriage and antimicrobial resistance (AMR) in this high-risk population through a series of analyses nested within the Toto Bora trial, a randomized, placebo-controlled study of azithromycin in 1,398 Kenyan children discharged from hospital and followed for six months. This collection of nested studies aims to inform strategies for preventing AMR and improving child health by evaluating the impact of azithromycin treatment, identifying risk factors for carriage and resistance, and assessing the clinical consequences of pneumococcal carriage.The first study evaluates the effect of a 5-day course of azithromycin versus placebo administered at hospital discharge on S. pneumoniae carriage and AMR. No significant differences were observed in carriage prevalence or azithromycin resistance at 3- or 6-months post-discharge between treatment arms. This may be due in part to high inpatient antibiotic use in this population, reducing any further impact of azithromycin. Notably, resistance to multiple antimicrobial classes was already prevalent at discharge, with nearly 35% of isolates classified as multidrug-resistant. This study underscores the importance of considering inpatient antibiotic use when evaluating the downstream effects of post-discharge antimicrobial interventions.
The second study explores clinical and environmental risk factors for pneumococcal carriage and AMR using longitudinal data from nasopharyngeal swabs collected at discharge and follow-up. Pneumococcal carriage at hospital discharge was inversely associated with inpatient antibiotic use and longer hospital stays whereas carriage at 3 and 6 months was linked to household-level exposures such as unimproved water sources, untreated drinking water, and shared sanitation facilities. Resistance to doxycycline was more common among children with multiple young siblings, while receipt of the recommended doses of pneumococcal conjugate vaccine (PCV) was associated with reduced resistance to doxycycline and clindamycin. These findings highlight the role of water, sanitation, and hygiene (WASH) interventions in shaping post-discharge colonization and support the role of PCVs in AMR mitigation strategies.
The third study investigates whether S. pneumoniae carriage at discharge is associated with increased risk of rehospitalization or death. Notably, carriage was not linked to adverse outcomes and was associated with a significantly lower risk of all-cause mortality, even after adjusting for key confounders. This inverse association may reflect protective mucosal immunity conferred by colonization with less virulent strains. Given the persistently high post-discharge mortality observed among children without S. pneumoniae carriage, this group may warrant particular attention in future interventions targeting vulnerable populations. Azithromycin-resistant pneumococcal carriage was associated with poorer clinical outcomes among children treated with azithromycin, underscoring the importance of appropriate antimicrobial therapy.
Together, this dissertation provides a comprehensive view of pneumococcal dynamics in the period following discharge from hospital. The findings suggest that short-course azithromycin does not meaningfully alter carriage or antimicrobial resistance patterns in settings with high inpatient antibiotic use, and that household environmental conditions play a critical role in shaping post-discharge colonization. Importantly, pneumococcal carriage at discharge was not associated with increased morbidity or mortality. These insights have implications for antimicrobial policy, vaccine strategy, and post-discharge care in low-resource settings, emphasizing the need for integrated approaches that address both clinical and environmental drivers of AMR and child health
Control Methodologies for Systems with Set-Valued Uncertainties
Get PDFThesis (Ph.D.)--University of Washington, 2025This dissertation develops control methodologies for systems with set-valued uncertainties in modeling and estimation, with applications spanning spacecraft navigation and neuromodulation. The work is organized into two major parts. The first part addresses estimation-related uncertainties in vision-guided navigation and their integration into planning and control. The second part focuses on modeling uncertainty in neuronal systems, presenting a controller design and model inference framework for neuromodulation.In the context of spacecraft navigation, we design a pose-estimation pipeline supported by a photorealistic simulation environment for satellite rendezvous operations. A Machine Learning (ML)-based platform is developed to detect the pose of a target spacecraft, and the simulation environment is used to generate test and validation data with a minimal simulation-to-reality (sim2real) gap. The platform also serves as a tool for modeling ML-based uncertainties, thereby enabling robust controller design. Building on this foundation, two approaches are proposed for incorporating ML-based estimation into navigation systems. The first introduces a controller design methodology that constructs invariant funnels for slope-bounded uncertainty models around nominal trajectories. The second employs a passivity-based framework to characterize uncertainties that define a family of feasible controllers. Furthermore, we demonstrate that multi-agent consensus, viewed as an interconnection of passive agents, can enhance estimation performance in distributed settings.
We further investigate estimation-aware trajectory design for improving the performance of state-dependent sensors such as perception maps. A class of state-dependent, set-valued output uncertainty models is formalized as state-to-output uncertainty set maps. An observability-based metric is introduced to quantify the estimator’s sensitivity to output perturbations, and this metric is optimized to generate trajectories that improve estimation performance. Extensions of this framework to multi-agent trajectory planning are also presented.
The final part of the dissertation develops a feedback control framework for neuromodulation. By analyzing neuronal system trajectories during experimental sessions, we show that average neuronal dynamics in closed-loop scenarios can be approximated as a linear parameter-dependent system, with parameter-dependent internal processes. For a fixed parameter, the trial-averaged dynamics exhibit closed-loop linear behavior. A proportional–integral (PI) feedback controller is demonstrated to effectively track reference signals over a finite horizon, outperforming feedforward control in both tracking accuracy and disturbance rejection, while also reducing trial-to-trial variability. Moreover, in a ``reward-induced'' brain state with more consistent parameters, a sample-based approach is shown to enable controller optimization.
Together, these contributions advance the integration of machine learning, robust control, and trajectory optimization in the presence of set-valued uncertainty, providing new methodologies for controlling uncertain dynamical systems in both engineering and biological domains
Learning Representations from Neural Population Dynamics: Addressing Neural Variability Across Scales
Get PDFThesis (Ph.D.)--University of Washington, 2025Interactions between individual neurons, each characterized by distinct intrinsic physiological properties, collectively give rise to population responses underlying complex animal behaviors. These responses exhibit variable dynamics across trials, recording sessions, and behavioral contexts—arising from stochastic spiking at the trial level, electrode drift and neural plasticity across sessions, and task- or state-dependent modulation across behavioral contexts. This multiscale variability complicates the reliable extraction of scientific insights from population activity. Consequently, modeling and decoding from population activity necessitate methods capable of learning stable representations that capture the underlying structure of neuronal activity in the presence of neural variability caused by noise, partial observability, and domain shifts inherent in population recordings. In this dissertation, I present my studies that aim to extract useful information from population dynamics while addressing neural variability across different scales: single trials, recording sessions, and behavioral contexts. In the first study, I developed a spatiotemporal transformer to learn stable neural representations underlying stochastic firing activity of neural population on the single-trial basis. In the second study, I introduced a self-supervised framework for extracting time-invariant representations of individual neurons by modeling their dynamics across partially overlapping populations over multiple recording sessions. In the third study, I developed a lightweight adaptive framework for online neural decoding, enabling rapid and robust generalization in unseen sessions with minimal unlabeled calibration trials and no model fine-tuning. In the fourth study, I exploited the dependence of population dynamics on behavioral contexts and presented a decoding framework leveraging context-aware representations for effective decoding of speech from population activity. Together, these studies advance a representation-centric paradigm for neural population analysis—delivering generalizable abstractions that are robust across contexts, scale to large recordings, and leverage inductive biases embedded in the population—thereby enabling effective extraction of scientific insights from population analysis and paving a way towards high-performing and robust brain–computer interfaces
Space as Strategy: The Implementation Architecture of the Federal Indian Boarding School Policy
No full textThesis (Master's)--University of Washington, 2025This study explores the history of the United States' Federal Indian Boarding School program from a policy perspective, the architectural design of the early school sites and buildings, and issues in contemporary historic preservation planning regarding management of the system's sites. A review of the history of the Federal Indian Boarding School system documents its origins in federal legislation authorizing partnerships between the US government and private religious organizations to operate schools for the purpose of the civilization of Native children through assimilation. This inquiry traces one line of the history from New England to Hawaiʻi to Virginia to Pennsylvania, documenting the implementation architecture including the administrative logic that informed the organizational structure and the strategic use of the built environment in the evolution of the early schools from experiment to prototype to pilot, enabling the replicability and scalability of the system over time. Analysis of the policies and the use of architectural design as dual implementation strategies yield findings that are used to generate recommendations for contemporary historic preservation approaches contributing to the acknowledgement, documentation, and reconciliation of this history toward the self-determination of Native communities