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Scalable Biomarkers for Psychiatry via Digital Phenotyping, Sensorimotor Modeling, and Neuroimaging
No full textPsychiatric illnesses present a formidable challenge due to their heterogeneity, subjective diagnosis, and limited objective biomarkers. As the field moves toward precision psychiatry, there is a pressing need to identify scalable, mechanistically grounded, and behaviorally relevant biomarkers that can be deployed across diverse populations. This dissertation addresses that need by integrating methods from digital phenotyping, sensorimotor modeling, and neuroimaging to uncover new avenues for scalable psychiatric biomarker development.
In Chapter 1, we address clinical heterogeneity in schizophrenia through digital phenotyping using smartphone-derived ecological momentary assessments (EMA) and passive sensor data. By employing a rigorous data imputation and clustering pipeline across an international cohort, we identify three data-driven subtypes that show distinct symptomatology and functional profiles. These clusters correspond to different symptom domains (e.g., affective vs. non-affective presentations) and reveal meaningful clinical correlations that may inform personalized interventions.
Chapter 2 focuses on scalable sleep estimation using smartphone sensors, tackling the limitations of traditional actigraphy and self-report methods. We introduce a Bayesian hidden Markov model that estimates sleep states using screen-state and accelerometer data alone, enabling continuous sleep tracking without the need for wearables. We validate the model using simulated datasets and self-reported EMA surveys from multiple cohorts, highlighting its robustness and potential utility in psychiatric contexts, particularly where sleep variability is a key feature.
In Chapter 3, we explore the cognitive architectures underlying motor learning through the lens of explicit and implicit adaptation. Using carefully designed behavioral experiments, we disentangle the feedback-dependent dynamics of reward-based and error-based learning. Our findings show that reward feedback engages explicit strategies and indirectly drives implicit learning through a serial pathway, whereas error feedback drives both systems in parallel. These architectures provide a novel mechanistic framework for understanding motor learning deficits in psychiatric populations. A meta-analysis of schizophrenia studies reveals impairments in both implicit recalibration and explicit strategy use, suggesting dysfunction in cerebellar and prefrontal systems and highlighting motor adaptation as a candidate biomarker.
Finally, Chapter 4 investigates the impact of chronic adolescent THC exposure on large-scale brain network connectivity in nonhuman primates. Using resting-state functional MRI and dual regression analysis, we uncover non-linear, dose-dependent effects on the default mode, salience, and central executive networks. These alterations, especially within the central executive network, persist even after THC discontinuation and implicate adolescent exposure in long-lasting network reconfiguration. These findings underscore the utility of translational neuroimaging models in probing the neurodevelopmental origins of psychiatric vulnerability.
Together, this dissertation offers a multifaceted yet integrative approach to psychiatric biomarker discovery. By leveraging scalable technologies and biologically grounded frameworks, we advance the potential for objective, continuous, and individualized assessment tools in psychiatry. The combined insights from digital phenotyping, sleep modeling, motor learning, and functional connectivity provide a robust foundation for future biomarker-based interventions and personalized mental health care.Engineering and Applied Sciences - Engineering Science
That Knowledge May Flow: Coptic Intellectuals and the Making of Public Knowledge in Late Ottoman Egypt, ca. 1850-1900
No full textScholars have long debated the social and institutional conditions that shaped inter-communal relations in the late Ottoman Empire. While earlier scholarship emphasized the segmentation of religious communities into separate spheres or “millets,” more recent studies have complicated this model by examining the quotidian interactions of Christians, Jews, Muslims, and others in contact zones such as bathhouses, marketplaces, and courts. This dissertation shifts the focus to knowledge as a site of inter-communal exchange and boundary-making, focusing on the emergence of Coptic intellectuals in late Ottoman Egypt between 1850-1900. By exploring the production, circulation, and reception of knowledge within and across communal lines, the study argues that knowledge flows offer unexplored insight into everyday negotiations of religious difference and communal belonging. It addresses two related gaps in the literature. First, the changing status of Copts from non-Muslim subjects (so-called dhimmīs) to co-citizens remains understudied, even though it was central to the making of the Egyptian body politic. Second, the growing literature on the Arab Renaissance or "Nahḍa" has yet to engage with the contributions of minority intellectuals such as the Copts, whose writings are essential to understanding how reform materialized across the religiously heterogeneous empire.
By positioning Copts as active participants in the Nahḍa, this dissertation contends that the transformation of the Egyptian public sphere into a space of collective debate and political imagination was contingent, on the one hand, on the increased circulation and production of knowledge across religious boundaries; and on the other, on the integration of minority intellectuals and their traditions into a shared knowledge economy. An emergent culture of “public knowledge”—neither fully secular nor wholly religious—fostered new forms of civic participation that brought Copts and Muslims together within the framework of a shared homeland (waṭan). Coptic scholars, publishers, teachers, and clergy were central to reshaping both the terms of public discourse and the boundaries of intellectual authority. In five chapters, this dissertation explores the contributions of Coptic intelligentsia in official institutions, local associations, print media, and the built environment. It draws on a wide array of previously unexamined sources, including family archives, manuscripts, early-printed books, petitions, and missionary records. The grassroots perspectives afforded by these sources call for a fuller integration of minority archives into the study of Middle Eastern history and, by extension, a reconceptualization of the role of minorities and their knowledge networks in the canon of Arabic intellectual history.Near Eastern Languages and Civilization
Laboratory evolution of botulinum neurotoxins for therapeutic applications
No full textThe goal of this thesis is to expand the therapeutic potential of botulinum neurotoxins (BoNTs) by using directed evolution to reprogram both major functional domains. Specifically, we aimed to engineer the protease domain to cleave novel, disease-relevant substrates and to reprogram the receptor-binding domain towards targeting of non-neuronal cell types, such as cancer cells. Through this work, BoNTs are transformed from neurotoxins into programmable delivery systems for targeted therapies, with applications ranging from cancer treatment to broader modulation of cell fate.
Cancers evolve numerous mechanisms to evade both cell death and immune surveillance. Pyroptosis, an inflammatory form of programmed cell death, offers a promising therapeutic strategy by eliminating cancer cells while simultaneously activating immune responses. In this work, phage-assisted evolution was used to reprogram BoNT proteases to cleave two key activators of pyroptosis: procaspase-1 and gasdermin D. A substrate profiling platform was developed to assess the specificity of both wild-type and engineered proteases. While the wild-type protease failed to induce cell death, evolved variants triggered robust cytotoxicity in multiple cancer cell lines. The gasdermin D-cleaving variant induced strictly pyroptotic death, while the procaspase-1-cleaving variant triggered both pyroptosis and apoptosis, suggesting broader caspase-like activity. To enable targeted delivery, evolved proteases were reconstituted into BoNT toxins containing the native translocation domain, resulting in selective death of cancer cells but not non-cancerous cells. These findings demonstrate that BoNT proteases can be reprogrammed to modulate inflammatory cell death and serve as programmable tools for targeted cancer therapy.
In addition to protease engineering, we reprogrammed the BoNT receptor-binding domain as a potential strategy for cell-specific targeting. Using two target nomination strategies—manual selection and a weighted alignment tool—we successfully evolved binders against all nominated targets. A bacterial two-hybrid circuit paired with phage-assisted evolution supported the evolution of binders to CD44v6, CD30, ACHA3, CD1b, RXFP1, and TSN8. Notably, evolved variants targeting ACHA3 and RXFP1 bound to full-length ectodomains with single-digit nanomolar affinity, exceeding the affinity of the wild-type BoNT HC for its native receptor. While in-cell and in vivo validations are ongoing, this work lays the foundation for BoNT HC evolution, analogous to antibody engineering strategies.
Collectively, these studies demonstrate that BoNTs are highly reprogrammable and can be evolved for diverse therapeutic applications. By combining protease and receptor-binding domain engineering, this platform offers a blueprint for developing programmable protein-based therapeutics that modulate cell fate with precision.Chemical Biolog
Withering on the Vine: Political and Policy Lessons from Extractive Industry Decline in the United States
No full textOver the last several decades, macroeconomic forces have upended industries across advanced capitalist societies, displacing the workers and communities that once relied upon them. In the United States, places with ties to manufacturing and extractive industries have been hit particularly hard by these changes. As a result, many regions have experienced steep economic declines and major social disruptions. To weather these negative shocks, individuals in impacted communities have turned to the social safety net for help. Today, government transfers are a key source of income in many postindustrial areas.
How do these trends shape the political behaviors of the people who call these places home? A growing body of evidence from the social sciences indicates that industrial decline is associated with shifts to the political right and rising support for right-wing populism, fueled by place-based grievances and feelings of resentment. However, existing accounts tend to overlook the role that the state has played in mediating the political consequences of industrial decline. How does widespread reliance on federal social policies affect individuals and communities in postindustrial contexts? And what are the consequences of these policy interventions for political participation?
In this dissertation, I draw on theories of policy feedback to better understand how community-wide economic disruptions intersect with individual-level policy interventions to influence patterns of political participation. In Chapter 1, I advance a new model that reexamines the channels through which social programs generate feedback effects. I propose that social programs, even when they are targeted at the individual-level, can generate community-wide feedback effects that ultimately inform the political participation of beneficiaries and non-beneficiaries. I argue we are most likely to observe the effects of community-level mechanisms in contexts of high policy concentration, which we now typically find in postindustrial communities. I examine the theory through one social program in particular: Social Security Disability Insurance (SSDI). In Chapter 2, I introduce SSDI and draw on a longitudinal dataset of county-level administrative records to illustrate how the program has become highly concentrated in certain areas of the U.S., especially in the coal-producing counties of Appalachia. The data also reveal a puzzle: unlike other social insurance programs, high concentrations of SSDI beneficiaries fail to produce high levels of political participation.
In the remaining chapters, I leverage my theoretical framework to explore why we observe this puzzling outcome. Chapter 3 reviews my approach to case selection and qualitative data collection, including archival research, interviews, and participant observation. Ultimately, I center my analysis on McDowell County, West Virginia. Chapter 4 relies on archival records and interviews in McDowell to document the local contextual factors that explain how SSDI became so highly concentrated in the county. In Chapters 5 and 6, I draw on interviews with SSDI beneficiaries, lawyers, and community members to show that community-level policy feedback effects emerge as a stronger explanation for the low political participation in McDowell than individual-level effects. Mechanisms at the community level, therefore, are crucial for understanding patterns of political behavior in contexts of high policy concentration. The study’s findings contribute new insights into the complex political and policy dynamics at play in postindustrial communities and underscore the importance of local-level factors in driving political outcomes.Social Polic
Computational Methods of Advancing Therapeutic Genome Editing
No full textThe cells of all living organisms across every domain of life contain a heritable DNA genome that encodes all of the requisite information needed to recapitulate their structure, function, and behavior. The development of programmable tools capable of editing DNA in living cells has enabled a revolution in the biological and biomedical sciences. Despite their enormous potential, the application of these tools to personalized medicine has been challenging due to wide variability in editing efficiency across different sequences. In this thesis, I describe the development of computational tools for predicting and analyzing the outcomes of therapeutic genome editing experiments. I show that these tools enable the rapid development of editing strategies for correcting both common and rare pathogenic mutations.
In Chapter 2, I describe the development and exploratory data analysis of pooled lentiviral screens for rapidly assessing the outcomes of prime editing experiments. First, I detail the design and construction of paired gRNA–target site libraries for high-throughput evaluation of editing efficiencies for both pegRNAs and nsgRNAs. Then, I use the results from paired pegRNA–target site screens to characterize the sequence determinants of mammalian mismatch repair. I show that mismatch repair efficiency depends on both the specific mismatched bases as well as the length of uninterrupted mismatches. Using data from paired nsgRNA–target site screens, I show that prime editing efficiency with PE3 systems is not correlated with predicted Cas9-nuclease efficiency scores, motivating the development of predictive machine learning models specific for complementary-strand nicking.
In Chapter 3, I formulate mechanistic machine learning, a paradigm for performing machine learning on chemical systems wherein domain knowledge about reaction mechanisms can be directly incorporated into the underlying structure of data-driven models. Using mechanistic machine learning, I describe the development of OptiPrime, a model of prime editing efficiency and show that its exquisite predictive performance is dependent on its mechanistic formulation. Additionally, I show that the intermediate values computed by OptiPrime are physically interpretable and can be used for accurate predictions of outcomes of prime editing experiments with complementary strand nicking guides (i.e., PE3) and with paired prime editing guide RNAs (i.e., twinPE).
Next, in Chapter 4, I demonstrate several prospective use-cases of OptiPrime towards the development of therapeutic approaches for correcting pathogenic mutations in human and mouse models of disease. Using cystic fibrosis as a test case, I show that OptiPrime can be used to generate pegRNA sequences that achieve high editing efficiencies at three common pathogenic mutations in CFTR, including one that resulted in double the editing efficiency of a pegRNA that required 3 years to hand-optimize. I then show that OptiPrime-generated sequences can be used directly in primary cells for correction of pathogenic mutations in mouse models of Alport syndrome and KIF1A-associated neurological disorder. Moreover, I show several "nonconventional" use cases for OptiPrime, including for T cell engineering in primary human cells, generating a pair of pegRNAs capable of installing a recombinase landing site that enabled over 10\% integration efficiency into CFTR intron 1, and combining OptiPrime with SpliceAI to correct a cause of HLA class II immunodeficiency.
In Chapter 5, I describe the development and application of powTNRka, a dynamic programming algorithm for assessing the outcomes of base and prime editing experiments at highly repetitive genomic loci. PowTNRka enabled the development of base editing and prime editing strategies in the trinucleotide repeat tracts of HTT and FXN, the genes associated with Huntington’s disease and Friedreich’s ataxia, respectively. Base editing was able to abate somatic repeat expansion in HTT and FXN in both in vitro and in vivo models, providing a potential strategy for preventing repeats from reaching pathogenic length. Moreover, prime editing was able to precisely excise repeats at HTT and FXN in models that contained pathogenic numbers of trinucleotide repeats. In an in vivo model of Friedreich’s ataxia, prime editing-mediated repeat excision resulted in successful restoration of FXN transcript levels.
Lastly, in Chapter 6, I provide a brief outlook on the state of current research at the intersection of computation and genome editing technologies, along with future research directions that will further pave the path for the field’s continued development.Chemistry and Chemical Biolog
Dead Right
No full textIt’s so easy to judge people and their choices from the outside, but what do you do when there are no good choices left—when the right decision may not be the popular one? How do you decide what it is that you’re willing to live with? Do you throw away your marriage because of one bad year or do you focus on the 20 good ones? “Dead Right” is a story told by three perspectives—Alex, her husband, Matthew, and his long-time best friend, Paul—about what Alex stands to gain—or lose—by being right.Extension Studie
The IL-4Rα Q576R Polymorphism Promotes systemic Th2 Skewing In Atopic Dermatitis Patients
No full textAtopic Dermatitis (AD) is an inflammatory skin disease characterized by barrier disruption and T helper 2 (Th2)-driven inflammation. The Th2 cytokine receptors IL-4 and IL-13 share the common IL-4 receptor alpha (IL-4Rα) chain. The Q576->R576 polymorphism in the IL-4Rα chain, which is common in African Americans and Hispanics (~35%), has been linked with atopy and asthma. Meta-analysis of clinical scoring of 1116 patients reveals the R576 polymorphism is associated with increased AD severity. Epicutaneous sensitization of mice carrying the R576 mutation with ovalbumin reveals that the polymorphism behaves in a dominant fashion causing increased type 2 allergic skin inflammation and an increased systemic Th2 response. Patients with active AD were recruited to determine the effect of the R576 polymorphism on the Th2 response in patients with AD. The results revealed that the R576 polymorphism promotes the generation of skin homing T cells and systemic Th2 skewing in AD patients as evidenced by increased percentages of circulating CD4+CLA+ skin homing T cells, CD4+IL-13+ Th2 cells, and elevated serum levels of the Th2 driven chemokine TARC. Unsupervised transcriptome analysis of of lesional skin and circulating peripheral blood mononuclear cells segregated patients that carry the R576 polymorphism from those who do not. Additionally, the R576 polymorphism is associated with increased S. aureus colonization on non-lesional skin. Our findings suggest that the IL-4R R576 polymorphism exaggerates the systemic Th2 response in AD and has important implications for therapy of AD patients who carry the IL-4R R576 variant.Medical Scienc
Investigation of small molecule modulators of O-GlcNAc
No full textIn the central dogma of molecular biology, DNA is transcribed to RNA, which is spliced and then translated into proteins, which execute various biological functions and themselves are regulated by post-translational modifications. O-GlcNAc is an essential and ubiquitous monosaccharide post-translational modification involved in many major cellular processes. O-GlcNAc is installed and removed by a pair of enzymes, the writer O-GlcNAc transferase (OGT) and the eraser O-GlcNAcase (OGA). The cell tightly regulates these enzymes and thus O-GlcNAc homeostasis through a highly conserved post-transcriptional mechanism. Through this detained intron feedback mechanism, OGT and OGA levels are inversely adjusted to maintain O-GlcNAc levels. For example, if O-GlcNAc is elevated, the cell responds by downregulating OGT and upregulating OGA to diminish O-GlcNAc, and vice versa in the case that O-GlcNAc is lowered. Here, I present work detailing the development of assays to study O-GlcNAc and its cycling enzymes in vitro and in cells and the application of these assays to high-throughput screening, leading to the discovery of small molecule splicing modulators that can disrupt O-GlcNAc homeostasis.
In Chapter 1, I review the O-GlcNAc field, covering the multitude of protein biochemistry, medicinal chemistry, and chemical biology studies performed since the initial discovery of O-GlcNAc over four decades ago. This chapter also provides a timely update to clinical efforts to therapeutically modulate O-GlcNAc, primarily focused on OGA inhibition for the treatment of neurodegenerative diseases such as Alzheimer’s disease. In addition to highlighting the major fundamental discoveries within the O-GlcNAc field, I also emphasize opportunities for further research into the basic biology of O-GlcNAc and its cycling enzymes and the potential therapeutic application of these findings.
In Chapter 2, I discuss our assay development efforts towards discovery of OGT ligands. We adapted and developed protocols to purify OGT constructs and subsequent assays to characterize protein—ligand binding interactions including fluorescence polarization (FP), time-resolved Förster resonance energy transfer (TR-FRET), and differential scanning fluorimetry (DSF). We additionally discuss efforts to use DSF as a high-throughput screening assay to discover OGT binders from a bioactive compound library and a screening informer set and subsequent validation of these hits. While ultimately no hits were discovered to be selective OGT binders from these screens, the protocols developed here will be valuable for future efforts to discover OGT ligands through similar assays.
In Chapter 3, I describe three parallel screening campaigns employing the Broad Institute Drug Repurposing Hub. Specifically, we screened this library using in vitro enzymatic assays against OGT and OGA and using a cellular reporter of OGT splicing in a high-content microscopy format to understand how clinically relevant compounds and pathways may intersect with O-GlcNAc homeostasis. We describe the optimization and miniaturization of these assays and the subsequent validation of hits from these screening efforts. I report that the dopamine receptor agonist piribedil and the AKT inhibitor GSK690693 have previously unannotated OGA inhibitor activity, and that GSK690693 and the ROCK inhibitor Y-33075 are able to disrupt O-GlcNAc homeostasis in cells, downregulating both OGT and OGA.
In Chapter 4, I detail mechanistic investigations of how GSK690693 and Y-33075 disrupt O-GlcNAc homeostasis. I find that these compounds have unique effects on OGT and OGA transcripts compared to previously characterized O-GlcNAc perturbogens. We further demonstrate that these compounds act orthogonally to previously characterized cis-regulatory elements within OGT. After identifying an alternative 5’ splice site induced by Y-33075 within OGA, we conducted RNA sequencing and related alternative splicing analyses, finding that GSK690693 is a broad inhibitor of intron processing and that Y-33075 is a specific modulator of exon inclusion across the transcriptome. I conclude with a discussion of the implications of these findings for therapeutic targeting of O-GlcNAc and other targets by splicing modulators and understanding cross-talk between O-GlcNAc and splicing.Chemistry and Chemical Biolog
Delta Opioid Receptor Agonist Potential Use in Mitigating Opioid Withdrawal Symptoms
Get PDFThe opioid epidemic continues to pose one of the most urgent public health crises worldwide, with relapse frequently fueled by affective symptoms of withdrawal, anxiety, and depression. Current treatments such as methadone and buprenorphine, target mu- opioid receptors (MORs), address cravings and somatic symptoms but provide limited relief for affective symptoms. Delta-opioid receptors (DORs), may offer therapeutic advantages, showing anxiolytic and antidepressant-like effects in preclinical models without the abuse liability of MOR agonists or the dysphoria linked to kappa-opioid receptor (KOR) agonists. This meta-analysis synthesized evidence from 42 controlled preclinical comparisons. Random-effects models indicated that DOR agonists produced large reductions in anxiety-like behaviors (Hedges’ g= 1.40, 95% CI [0.98, 1.82]) and depression-like behaviors (g= 1.72, 95% CI [0.85, 2.59]). In withdrawal models, DOR agonists significantly reduced withdrawal-induced anxiety (g= 1.85, 95% CI [1.08, 2.61]) and moderately improved withdrawal-induced depression (g= 0.76, 95% CI [0.14, 1.37]). Comparative analyses indicated that DOR agonists have substantial advantages over MOR agonists (g = 2.68, 95% CI [1.45, 3.90]) and exhibit favorable profiles relative to KOR agonists, particularly in terms of analgesia and adverse effect outcomes. These findings highlight DOR agonists as promising candidates for addressing both the emotional and physiological dimensions of opioid withdrawal. Although heterogeneity and limited chronic models remain challenges, this analysis establishes a strong foundation for translational research into DOR-targeted therapies for opioid use disorder.Extension Studie
Advancing Multi-Agent Systems with Scalable and Robust Learning and Control
Get PDFMany modern infrastructures—such as smart cities, power grids, and transportation networks—are inherently multi-agent systems. Designing effective coordination mechanisms in these settings is challenging due to model uncertainty, scalability constraints, and unaligned agent incentives. This dissertation addresses these challenges by developing scalable and efficient learning-based control algorithms for multi-agent systems with provable and verifiable performance guarantees. The
work is organized into three major parts.
Part I focuses on designing scalable control and reinforcement learning (RL) algorithms for networked systems. In large-scale cyber-physical systems—such as smart grids, intelligent buildings, and traffic networks—agents are often embedded in graph structures where coordination relies on local interactions and communication. Distributed control and RL become essential due to communication constraints and the need for scalability. This part delves into the fundamental capabilities and sample-based design of distributed control and RL algorithms for networked systems. By leveraging the underlying network topology, we demonstrate that distributed controllers can achieve near-optimal global performance (Chapter 2). Furthermore, we develop distributed RL algorithms that are both communication- and sample-efficient, providing theoretical guarantees alongside strong empirical results (Chapter 3).
Part II investigates strategic behavior in multi-agent systems. In applications such as traffic, trading and energy market, systems are generally comprised of agents that may act non-cooperatively due to unaligned incentives. In such settings, the goal shifts from achieving global optimality to finding a Nash equilibrium. In Chapter 4, we develop efficient, data-driven algorithms for Nash equilibrium seeking using multi-agent reinforcement learning (MARL). Building on the insight that all first-order stationary points correspond to Nash equilibria in Markov potential games, we derive sample-based algorithms to compute them effectively using gradient-based methods. In Chapter 5, we take a step further by exploring equilibrium selection methods aiming at promoting socially optimal outcomes. We propose a unified framework that systematically integrates the sequential structure of multi-agent reinforcement learning (MARL) with equilibrium selection, enabling agents
to converge to equilibria that are both stable and socially desirable.
Part III addresses robustness and risk sensitivity in uncertain environments. Real-world systems often operate under imperfect models, noisy data, and external disturbances. To ensure reliable performance under such conditions, we develop robust and risk-sensitive RL algorithms. These include formulations of soft robust Markov Decision Processes (MDPs) and risk-aware policy optimization techniques with theoretical convergence guarantees.
Together, these contributions advance the theoretical and practical frontiers of learning and control in multi-agent systems. The algorithms developed in this work are validated across a range of real-world-inspired applications, including robotics, smart buildings, and energy management. This thesis lays the groundwork for resilient, efficient, and cooperative autonomous systems in increasingly complex and uncertain environments.Engineering and Applied Sciences - Applied Mat