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Essays in Youth Mental Health: Exploring Emergency Care, Policy Interventions, and Pandemic Challenges
No full textIn this dissertation, I explore youth mental health and youth mental health services through three interrelated studies.
In the first study, I examine the impact of inpatient psychiatric and residential care on children's and adolescents' healthcare service utilization following emergency department boarding. Analyses utilized matching techniques and augmented inverse probability weighting to estimate causal effects. Data included Massachusetts Medicaid and Children’s Health Insurance Program claims data from 2016 to 2019, obtained from the Transformed Medicaid Statistical Information System Analytic Files. I found that in the short term, inpatient admission after boarding significantly reduced return visits to emergency departments and subsequent psychiatric admissions. These findings highlight the importance of robust follow-up care after boarding for improving youth mental health outcomes and reducing the need for additional high-acuity care.
In the second study, I utilize a stacked difference-in-difference approach to assess the impact of state bans on Sexual Orientation and Gender Identity Change Efforts (SOGICE), also known as conversion “therapies,” on youth mental health. Using data from the Youth Risk Behavior Survey (2011-2019), I found that bans led to significant and meaningful decreases in rates of seriously considering suicide among high school students. This evidence underscores the positive influence legislative bans can have on youth populations that are at increased risk for suicide.
In the third study, my coauthors and I analyze trends in youth mental health care utilization during the COVID-19 pandemic, specifically emergency department visits, boarding durations, and inpatient admissions. Our analysis of national health insurance claims reveals increased ED visits, particularly among adolescent females, and a rise in prolonged boarding. This study highlights the urgent need to increase psychiatric resources across the care continuum to meet growing demands and reduce the strain on emergency departments.
Together, these essays contribute to our understanding of how policies can impact mental health and consequences of having insufficient mental health resources to meet need, which can provide insight into how we can improve mental health and optimize care for children and adolescents.Health Polic
Fantastic B-Cells and Where to Find Them
No full textSince the SARS-CoV-2 pandemic began in 2020, viral sequencing has documented the emergence of hundreds of individual mutations in the viral spike protein across numerous variants. With the drastic viral evolution over the past four years resulting in variants with greater than 60 mutations relative to the Wuhan strain the vaccine has been updated three times. Studies have demonstrated that repeated mRNA vaccination enhances the breadth of neutralization against diverse SARS-CoV-2 variants. However, the development of antibodies and humoral immune responses capable of neutralizing across the coronavirus family is poorly understood. The ongoing spread of COVID-19 underscores the importance of identifying and characterizing neutralizing monoclonal antibodies capable of neutralizing all variants. The fusion peptide (FP) region of the viral spike protein is a crucial target for neutralization given its broad cross reactivity and conservation among all the coronaviruses.
Here, we determine the ability of vaccine-mediated humoral immunity to keep pace with continued SARS-CoV-2 evolution. Updated vaccines increase neutralization breadth, but viral evolution continues to outpace them. Using newly developed depletion protocols we show that broadly neutralizing responses are often a result of receptor binding domain (RBD) specific antibodies, but some donors produce FP-specific responses that contribute to neutralization breadth. In a comprehensive ELISA study, we find that FP-specific antibodies arise only in response to natural infection and not vaccination. Next, we developed a novel protocol to isolate antigen-specific memory B-cells and monoclonal antibodies (mAbs) in less than 1 week. Using this highly efficient and effective protocol, we isolated 11 FP-specific antibodies with varying neutralization breadths. Finally, we designed a set of pseudoviral spike proteins to assess epitope and escape mutations for FP-specific antibodies. We found that mutations at S816 and F823 escape all FP-specific antibodies. Collectively, this work helps elucidate the co-evolution of the humoral immune response and the SARS-CoV-2 virus, suggesting that novel vaccine strategies are needed to end the spread of the virus and prevent further viral evolution. In this thesis we also present a roadmap for the selection of ideal donors for mAb isolation and offer a highly efficacious protocol for generation of mAbs from donor PBMCs.Medical Science
Neural Network Belief Propagation and Ordered Statistics Decoding for Quantum Error Correction Codes
No full textIn the quest for fault-tolerant quantum computation, the delicate nature of qubits demands the need for robust quantum error correction. Quantum error correcting codes, the quantum counterpart to classical error correcting codes in information theory, have been developed to address the unique challenges quantum bits face against the noise of their environment in storing information. These codes include but are not limited to the Kitaev surface code, toric code, rotated surface code,
generalized bicycle code, and bivariate bicycle code.
Fast and reliable decoders for these quantum error correcting codes are needed to correct errors and achieve fault tolerance on these qubits. The belief propagation algorithm, which is a efficient and reliable heuristic for decoding classical low-density parity check codes, has been generalized to different types of quantum error correction codes. Paired with the ordered statistics decoding algorithm as a post-processing step from the soft outputs of the belief propagation decoder, belief propagation and ordered statistics decoding has been one of the most promising avenues of efficient
quantum error correction.
This thesis combines the state-of-the-art belief propagation and ordered statistics decoding algorithms with the exploration of the neural belief propagation algorithm to leverage classical deep learning machines to learn message passing patterns in decoding the newest quantum codes, the rotated surface code and bivariate bicycle code. The neural network exploits the local lattice structure of these codes to adjust the trainable weights and biases to more learn message update patterns more efficiently than the vanilla belief propagation algorithm.
In this thesis, we prove a new threshold for the physical error rate on the rotated surface code using a neural belief propagation algorithm and ordered statistics decoding that consistently outperforms the threshold established by the vanilla belief propagation algorithm with ordered statistics decoding. We additionally show the results for similar efforts on the bivariate bicycle codes, and discuss the difficulties in achieving a threshold using the current neural belief propagation algorithms.Computer Scienc
Do All Roads Lead to Rome? Exploring Representational Similarities Between Latent Spaces of Generative Image Models
No full textGenerative image models learn to produce images by transforming vectors drawn from a marginally simple latent space. Extensive research has shown that latent spaces develop meaningful structure; images with different values of an attribute (e.g. bright versus dark lighting) correspond to vectors from different parts of the latent space. Less work has gone into comparing the latent spaces of different models. In this thesis, we investigate this question, building on a preliminary work that showed that it is possible to take a vector in the latent space of one model corresponding to some image and lin- early map it to a vector in the latent space of another model corresponding to a very similar image. Using this methodology in tandem with other metrics, we compare 4 model architectures trained on 2 datasets and find that much of the semantic structure between their latent spaces is the same up to a linear transformation. The level of similarity is highest between models with similar architectures as well as between expressive models. The set of similarly represented features is not always intuitive; while gender is represented similarly in all face-generating models, class (e.g. airplane, horse, etc...) is represented differently among multi-class image generating models. We also use linear maps as tools to investigate how the structure of a face-generating model’s latent space changes during training, concluding that the representation of gender-related concepts becomes more disjoint while that of orthogonal concepts like skin tone remain stable. We also find that intermediate latent spaces in models with hierarchical latent space structures are similar.Computer Scienc
The function and development of the left-right asymmetric duck syrinx
No full textDissertation Advisor: Dr. Cliff Tabin Darcy Mishkind
The function and development of the left-right asymmetric duck syrinx
Abstract
Avian diversity, ranging from the stunning tailfeathers and advanced mimicry of the Lyrebird, to the bright red and varied calls of the Northern Cardinal, to the well camouflaged feathers and booming call of the American Bittern, has always captured the interest of biologists. This work explores another example of these varied and striking vocalizations, that of Anatidae (ducks and their relatives), where males are known to produce courtship whistle vocalizations. Unique to ducks, the avian vocal organ, the syrinx, demonstrates left-right asymmetry, and this left-right asymmetry is only in males. In males, there is a left-sided bulla that has long been hypothesized to be necessary for courtship vocalizations, though this has not been tested previously. Further, our understanding of left-right asymmetric development of organ primordia is limited, and the syrinx is a tissue yet to be examined with modern molecular approaches. Finally, the sexual dimorphism of this trait allows us to ask how pathways that are ordinarily separate, the left-right cascade and estrogen signaling, achieve crosstalk.
First, we explore the possibility that the hollow bulla may be responsible for the whistling vocalizations common to male duck courtship displays. It has been hypothesized that the bulla functions as a Helmholtz resonator, and we began to test this by predicting bulla resonance frequencies in duck species based on measurements of the structure. When predictions are compared to frequencies emphasized in various call types in these species, we see evidence for bulla-influenced vocalizations, in particular in species with clear whistling vocalizations. We also see overlap with our predictions in non-courtship vocalizations as well as in female calls, an observation that should be explored in the context of other vocal tract features that alter vocalization frequencies. We find that bulla size is generally positively correlated with body mass. We conclude that there is support for the hypothesis that the bulla functions as a resonance chamber, in particular emphasizing courtship whistle vocalizations.
Following this work, we investigate the development of the laterally asymmetric duck syrinx. We begin by examining literature that describes a possible mechanism of intra-syrinx inhibition by which the left side inhibits right side growth. Previous work observed that, when sliced in half, the growth of the right half is limited when grown in culture with the left half but grows as large as the left when grown in isolation1. Using ex vivo cultures, we did not observe any changes to right side growth as a result of either the culturing of right halves with left syrinx halves at various ratios or the presence of conditioned media taken from left-side cultures. This indicates an intrinsic developmental mechanism that is independent of any secreted factors produced by the opposing syrinx half.
The next chapter explores how hormonal and left-right signaling interface to direct development of the duck syrinx. We characterize the cellular mechanisms at play and find cell division to be the main driver of early left-right asymmetry. Additionally, PITX2, a member of the canonical left-right asymmetry cascade, is present in the duck syrinx in males and females and we see evidence that left-sided expression in the syrinx is derived in ducks as it is not observed in other bird species examined. Results indicate asymmetric PITX2 expression occurs in two waves, the second of which is in the primordial syrinx. We see evidence that bilaterally-expressed BMP activates the second wave of left-sided PITX2 in the tissue primordium through laterally differentially accessible chromatin. Estrogen signaling is then responsible for establishing a sexually dimorphic developmental plan by activating left-sided ESR1 and reducing cell proliferation in the female syrinx and thus promoting bilaterally symmetric growth. Here, we characterize a novel system of left-right asymmetry at the organ level and how pathway integration can occur.
Overall, this work describes the function and development of the duck syrinx. It adds to the discourse on sound production in birds and the question of how novel instances of left-right asymmetry are patterned.Biology, Molecular and Cellula
Study of Genetic Determinants of Antibiotic Resistance in Endodontic Pathogen; Enterococcus faecalis
No full textEnterococcus faecalis is a resilient Gram-positive bacterium commonly associated with persistent infections in endodontics, including root canal treatment failures. This dissertation explores the genetic determinants of antibiotic resistance and persister formation in E. faecalis, as well as its survival strategies under antibiotic stress. Understanding these mechanisms is essential for developing more effective therapeutic approaches in endodontic and clinical settings.
Chapter 1 provides an overview of E. faecalis as a significant pathogen in endodontics, detailing its ability to survive harsh environments and form biofilms, which contribute to its persistence in root canals. The chapter further discusses the bacterium’s intrinsic resistance to common antibiotics and its ability to acquire resistance genes through horizontal gene transfer, with a focus on vancomycin-resistant strains. Additionally, the phenomenon of persister cell formation, which allows E. faecalis to survive in a dormant state under antibiotic pressure, is introduced as a key factor in treatment failure.
Chapter 2 investigates the genetic basis of persister formation in E. faecalis using the Dunny transposon mutant library. The library, containing mutants covering approximately 70% of the E. faecalis genome, was screened for genes associated with persister formation under antibiotic stress. Genes such as PhoU, uvrD/REP, and Fis were identified as potential candidates for regulating persister formation. However, experimental results showed no significant difference in persister formation between knockout mutants and the wild-type strain, suggesting that other genetic factors may play a critical role in this process.
Chapter 3 focuses on the identification of antibiotic resistance genes in E. faecalis using the same transposon mutant library. A screening for resistance to ampicillin led to the identification of several mutants with resistance-associated genes, including sensor histidine kinases and phosphate-binding proteins. These findings highlight the complexity of resistance mechanisms in E. faecalis, with metabolic regulation, stress responses, and biofilm formation playing integral roles in survival under antibiotic pressure.
Chapter 4 presents experiments assessing the microbial contamination and disinfection efficacy of gutta-percha (GP) cones in endodontics. While GP cones are generally clean, they can harbor bacteria, which may lead to infections if not properly disinfected. The effectiveness of sodium hypochlorite (NaOCl) as a disinfecting agent was evaluated in clinical settings. 16S rRNA sequencing revealed that NaOCl treatment significantly reduced bacterial contamination on GP cones, underscoring the importance of disinfection protocols in preventing infection during root canal therapy.
This dissertation provides insights into the genetic determinants of antibiotic resistance and persister formation in E. faecalis, contributing to the understanding of its role in endodontic infections. It also emphasizes the need for improved disinfection strategies to ensure the safety and success of root canal treatments. Further exploration of the genetic factors involved in E. faecalis persistence and resistance is necessary to develop novel therapeutic strategies to combat this challenging pathogen.Endodontic
Learning to Renew: Optimizing Organizational Learning at Purpose Built Communities for Strategic Renewal
No full textPurpose Built Communities ®, a national nonprofit dedicated to holistic neighborhood revitalization, recognized that its strategic growth and operational effectiveness depended on its ability to optimize and institutionalize learning across the organization. Although Purpose Built Communities had developed a suite of learning approaches over time, the absence of a cohesive learning strategy hindered the organization’s capacity for continuous improvement and strategic renewal. In response, Purpose Built Communities launched a multi-phase Learning Strategy Development Project to assess, refine, and integrate its organizational learning approaches into a unified system capable of supporting adaptation and growth.
This capstone recounts and analyzes the Discovery and Evaluation Phase of that project, which I led during my 10-month residency. By conducting an internal landscape analysis, developing a learning evaluation framework, and providing research-based recommendations, I worked to equip Purpose Built Communities with the structures and evidence necessary to advance toward becoming an effective learning organization. My strategic project was grounded in organizational learning scholarship and drew upon Crossan et al.’s (1999) 4I Organizational Learning Framework and Heifetz et al.’s (2009) Adaptive Leadership Framework to guide project execution and analysis.
The insights gained through this project offer practical implications for organizational learning, nonprofit leadership, and strategic renewal. They provide a model for assessing and strengthening learning practices in mission-driven organizations seeking to sustain strategic relevance and long-term impact.Educatio
Quantum Simulation of Dipolar Itinerant Lattice Models with Magnetic Erbium Atoms
No full textLong-range interactions are important to complex physical systems. Specifically in quantum mechanical many-body systems, long-range interactions promote spatial structures, give rise to quantum frustration, and generate quantum entanglement. Nevertheless, quantum simulations of lattice systems have largely not been able to realize long-range interactions. Many efforts are underway to explore long-range interacting lattice systems using polar molecules, Rydberg atoms, and magnetic atoms. In this thesis, I present the realization of long-range interacting itinerant systems using magnetic erbium atoms. I start by discussing the construction of the Erbium quantum gas microscope, including the installation of an in-vacuum high-numerical-aperture objective, various optical lattices, and potential projection through the objective. Then, I present emerging quantum phases with half-filling and directly probe the spatial structures of dipolar quantum solids using site-resolved imaging. A kaleidoscope of phases emerges as we tune the dipolar interaction, demonstrating the great tunability of this experimental platform. The dipolar interactions can be tuned slowly to study first-order quantum phase transitions between different solids. Next, I discuss topological phases that emerge with unity filling. With a zoo of Fano-Feshbach resonances, erbium atoms are gifted with the flexibility of widely tunable on-site interaction, allowing continuous probing of the quantum phase transitions between topologically trivial and non-trivial phases. We explore average symmetry protected topological phases as an instance of the mixed state quantum order. Finally, I briefly discuss the observation of spin squeezing with itinerant systems and super- and sub-radiance in a sub-wavelength optical lattice.Physic
Real Bubbles, Synthetic Traders: AI-Agent-Based Simulations of the Speculative Market
No full textWith the rise of agentic AI comes a powerful opportunity to rethink how we model
economic complexity and nonlinear systems. I create an extensible, AI-agent-based
market simulation sandbox for exploring behavioral finance questions and speculative
dynamics. This paper presents the first AI-agent-based simulation of a speculative
stock market built on a general-purpose agentic framework—a high-quality, consumerfacing software development kit (SDK) rather than a bespoke, special-purpose scaffold
around a LLM using chat completions API features. The framework is fully customizable, enabling researchers to design and run experiments by defining trader archetypes,
injecting news shocks, testing different market microstructures, and observing interactions—including those via a simulated social platform, enabling rich experimentation
with speculation, herding, pricing dynamics, and financial stability. Empirical simulation results echo findings from prior literature while introducing new nuance. Across
markets with varying share of trader archetypes, results show that trading volume
negatively predicts future returns when irrational agents dominate, suggesting that
high-volume events can signal upcoming price reversals. In contrast, even though results show that rationalists reduce momentum overall, when they do trade in high
volume, it’s often with conviction, which can support trend continuation (i.e., real
momentum). These findings point to a possibly more nuanced relationship between
volume and returns — one distinctly modulated by the rationality of market participants. Notably, this does not imply that rational markets exhibit more volume
overall (simply because prices may appear more supported); rather, it reflects a shift
in the informational character of volume—from noise to signal—as market composition
increases in rational agents. Lastly, volume and rationality in my simulations are uncorrelated, suggesting their separate influences on price dynamics are not confounded
— but rather, arise endogenously from the system’s own emergent complexity. This
study demonstrates the viability of AI-agent-based simulations and their potential to
generate nuanced insight into enduring financial economics questions.Applied Mathematic
Assisting and Evaluating Upper Extremity Movements with Wearables Systems
No full textUpper extremity impairments from injuries and illnesses can result in physical disabilities, medical expenses, and productivity loss in labor intensive occupations. In recent years, wearable sensing and assistive robots have risen as promising tools for general kinematics tracking, injury prevention, and rehabilitation. These wearable options offer a new realm of solutions avoiding issues such as limited clinician availability and expensive infrastructural changes in the workplace. For wearable robotics specifically, soft robots also have the potential for extra comfort, minimal restrictions and harm from joint misalignments as compared to their rigid counterparts. This thesis seeks to bridge the gaps in current wearable assistive and rehabilitative technologies through introducing control and evaluation strategies for a soft inflatable shoulder robot to assist with industrial overhead work and presenting a motor function estimation algorithm with wearable inertial sensors.
The thesis begins with the development of a kinematics-based state machine controller for a soft inflatable shoulder wearable robot for industrial work. The controller is aimed to provide assistance to the shoulder quickly and accurately when needed during overhead industrial work. The state machine was designed to classify user intent using shoulder and trunk kinematics estimated with body-worn inertial measurement units. Through human subject experiments, we evaluated the controller’s intent classification accuracy and response times, by using the users’ reactions to cues as their ground truth intentions. On average, we found that the kinematics controller had 99% classification accuracy, and responded 0.8 seconds after the users reacted to the cue to begin work and 0.5 seconds after the users reacted to a cue to stop the task. In addition, we implemented an EMG-based controller for comparison, with state transitions determined by EMG-based thresholds instead of kinematics. Compared to the EMG controller, the kinematics controller required similar time to detect the users’ intentions to stop overhead work but an additional 0.17 seconds on average for detecting users’ intentions to begin. We also implemented an online adaptive tuning algorithm for the kinematics controller to speed up
response time while ensuring accuracy during offset transitions.
Post assessment of the controller, we evaluated the robot’s performance with this controller with
a portable system. The updated portable robot is worn like a shirt with integrated textile pneumatic actuators, inertial measurement units (IMUs), and a portable actuation unit. It can provide up to 6.6 newton-meters of torque to support the shoulder and cycle assistance on and off at six times per minute. From human participant evaluations during simulated industrial tasks, the robot reduced agonist muscle activities (anterior, middle, and posterior deltoids and biceps brachii) by up to 40% with slight changes in joint angles of less than 7% range of motion while not increasing antagonistic muscle activity (latissimus dorsi) in the current sample size. Comparison of controller parameters further highlighted that higher assistance magnitude and earlier assistance timing resulted in statistically significant muscle activity reductions. During a task circuit with dynamic transitions among the tasks, the kinematics-based controller of the robot showed more robustness to misinflations (96% true negative rate and 91% true positive rate), indicating minimal disturbances to the user when assistance was not required. A preliminary evaluation of a pressure modulation profile also highlighted a trade-off between user perception and hardware demands. Finally, five automotive factory workers used the robot in a pilot manufacturing area and provided feedback.
Building upon the robot controller and biomechanics evaluation, we aimed to improve the adaptability of the robot through developing a dynamic controller with the purpose of providing assistance during quasi-static motion and allowing for minimal restrictions during fast movements. Furthermore, we designed an on-body measurement tool to enable direct measurements of elevation torque during unconstrained tasks for the first time. With the development of these tools, we evaluated the effect of various support types on user biomechanics and perception. We found that the improved robot can provide 7.3 newton-meters of elevation torque with controller gains being effective in modulating the absolute torque delivered. The robot reduced muscle activity in the agonist muscles without affecting the antagonist ones during controlled and functional activities. Comparing the amount of support
delivered to the biomechanical outcomes, we observed a pseudo-linear relationship between the percentage of support to the user and the percentage of muscle activation reduction. Finally, the users were able to perceive the differences among the assistance types, in terms of perceived robot support, transparency and precision, with a main preference for sufficient support during loaded conditions.
In addition to control and evaluation methods for assistive robots, this thesis aims to contribute to wearable sensing algorithms. In a final demonstration of an application of wearable sensing, we present an estimation algorithm for estimating upper extremity Fugl-Meyer Assessment (FMA-UE) scores, a well-accepted post-stroke recovery metric for motor function assessments. To address the need for simplified automated FMA-UE assessments, we present an estimator which can make score predictions for a subset of the full assessment using data from inertial measurement units placed on the hands, arms and the trunk from a minimal number of volitional reaching motions representative of functional daily activities. To develop the estimator, we collected a dataset of eleven stroke participants performing a key subset of FMA-UE motions, and three reaching motions. The FMA-UE of each participant was assessed by an occupational therapist providing the labeled score for the training data. The estimator was trained on windowed data during FMA-UE motions and was able to make score estimates from reaching motions. Through leave-one-subject-out cross validation, the estimator achieved a normalized RMSE of 7%, which is comparable to or below the established minimal clinically important difference and minimal detectable change of FMA-UE for individuals with chronic stroke. Comparison experiments of various model designs also revealed the importance of trunk-based features inspired by compensation strategies common post stroke and features extracted from the hand sensor.
Together, this thesis contributes to control and evaluation methods for wearable systems for both healthy and clinical populations targeting the use cases of injury reduction and rehabilitation progress tracking respectively.Engineering and Applied Sciences - Computer Scienc