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THE OPPORTUNITY GAP IN READING BETWEEN MULTILINGUAL LEARNERS AND THEIR NATIVE ENGLISH-SPEAKING COUNTERPARTS
Full text linkThe following empirical study examined the opportunity gap in reading between Multilingual Learners (MLLs) and their native-speaking counterparts. Through the use of the Networked Ecological Systems Theory (EST) model, factors associated with the problem were explored at all levels. From the comprehensive literature review, teachers’ preparedness, beliefs, and pedagogy were identified as three factors that influence MLL success in reading and require more context-based research. Following this research, semi-structured interviews were conducted among seven educators at SouthSide Elementary Charter School in Providence, Rhode Island. After an intensive coding process, seven themes emerged from the data. Based on the data, recommendations were provided to continue providing an excellent and empowering reading education for all students at SouthSide Elementary Charter School. This research is critical to ensure that solutions meet the needs determined by a crucial component of MLL learning- their teachers
AN EMPIRICAL CASE STUDY ON DATA-INFORMED STUDENT AND TEACHER WELL-BEING
No full textThis dissertation examined how data can be used to improve teacher well-being in K–12 schools, with a particular focus on a high-achieving independent school. Guided by the research question, “How can data be used to improve teacher well-being?”, the study framed data-informed decision-making (DIDM) as a human-centered practice that promotes operational change, along with relational trust, dialogue, and continuous learning. Drawing on Networked Ecological Systems Theory (Neal & Neal, 2013), this study situates teacher well-being within a broader framework of organizational, cultural, and leadership dynamics, addressing concerns about teacher burnout, post-pandemic recovery, and the need to align institutional practices with both faculty and student well-being more effectively.
Using a multi-method case study approach, the research combined quantitative data from the Authentic Connections Faculty Resilience Survey with qualitative interviews and document analysis. Participants included administrators from a single all-boys independent school. The study examined how leaders interpreted and responded to disaggregated well-being data and how their actions fostered institutional learning and collaborative change.
Key findings identified five themes: (1) prioritizing teacher well-being; (2) the importance of transformative, emotionally intelligent leadership; (3) using data and feedback for decision-making and shared responsibility; (4) the role of communication and collaboration in building trust; and (5) the emergence of innovation through systems thinking and improvement science. Faculty reported feeling more seen, supported, and involved when leadership engaged in transparent dialogue and actively used feedback in planning and wellness efforts.
The case study emphasizes the importance of aligning operational strategies with faculty lived experiences and utilizing data to foster dialogue, establish trust, and sustain meaningful change in schools. This research contributes to the growing body of literature advocating for holistic school improvement, with a focus on placing educator mental health at the core of student success
Unifying Neural and Symbolic Computation for Compositional Generalization: Representation and Processing
Full text linkContemporary neural networks, despite their remarkable achievements, often fall short of the robust compositional generalization that characterizes human cognition, particularly in tasks demanding symbolic manipulation and algorithmic reasoning. This dissertation investigates the mechanisms underlying compositional generalization in neural networks and proposes novel neurosymbolic architectures that bridge the gap between connectionist and symbolic computation, primarily by leveraging Tensor Product Representations (TPRs) to embed symbolic structures within vector spaces.
First, I introduce the Role Learning Network (ROLE), a diagnostic model that automatically discovers latent structure in neural representations. This analysis reveals how networks can solve compositional tasks by converging on solutions that approximate compositional vector embeddings of symbolic structures. The causal importance of these discovered structures is demonstrated through activation patching, enabling targeted control over model behavior.
Next, I present the Differentiable Tree Machine (DTM), a unified neurosymbolic architecture that implements symbolic tree operations via a differentiable interpreter. An agent learns to produce a neurosymbolic program, while this interpreter executes the programs. To scale this approach, I develop Sparse Coordinate Trees, a TPR-equivalent encoding scheme that reduces parameters by 70x, memory by 100x, and latency by 34x. Across a range of distributional shifts from training to testing, DTM with Sparse Coordinate Trees achieves the best out-of-distribution performance compared to both neural and neurosymbolic baselines.
Finally, I focus on enhancing Transformers for modeling formal languages. I analyze the trade-off between parallelism and generalization in Recurrent Transformers for modeling Regular Languages, identifying token-layer recurrence as a key factor and examining how chunk size affects both parallelizability and length generalization. Additionally, I explore augmenting Transformers with stack-like structures for context-free languages, demonstrating that the choice of stack encoding mechanism can significantly impact performance, especially on nondeterministic languages.
Collectively, this dissertation contributes novel analysis techniques (ROLE) and unified neurosymbolic architectures (DTM, sDTM) that integrate differentiable symbolic operations and structured representations within neural networks. By exploring latent structures, explicit tree manipulation, efficient sparse representations, and recurrence, this work offers insights and methodologies for developing next-generation neural models capable of more human-like compositional generalization
Alzheimer's Disease- and Aging-Mediated Blood-Brain Barrier Dysfunction in a Tissue-Engineered Microvascular Model
Full text linkAlzheimer’s disease is a disease of neurodegeneration and aging that affects millions of Americans, and is expected to impact millions more without further significant breakthroughs. Though years of preclinical and pharmaceutical investment have yielded some advances in treatment strategies, current approaches only modestly alter disease progression. A critical underlying difficulty in Alzheimer’s treatment is a lack of understanding of the etiology and progression of Alzheimer’s, especially given the complex interactions of many different molecular, cellular, and environmental cues that are correlated with phenotypic outcomes. An emerging focus in Alzheimer’s study is the role of the cerebrovasculature in the initiation, progression, and exacerbation of symptomatic disease. Disruption of the blood-brain barrier, which tightly controls any exchange between systemic circulation and brain tissue, has manifested in post-mortem and in vivo studies of late-stage Alzheimer’s disease as microbleeds, dysfunctional glucose transport, and impaired efflux of toxins; additional animal studies have indicated that some vascular dysfunction precedes neuronal degeneration in the progression of the disease. Thus, to understand the drivers and progression of Alzheimer’s disease in hopes of identifying therapeutic breakpoints, the role of blood-brain barrier dysfunction must be investigated. To do so, here we utilize a tissue-engineered model of the blood-brain barrier with high spatiotemporal resolution to assess its dysfunction under key categories of perturbation associated with Alzheimer’s disease. These perturbations will span extrinsic cues of oxidative stress (hydrogen peroxide exposure), the systemic influence of aged blood components (exposure to aged vs. young human serum), cell-intrinsic mutations associated with Alzheimer’s (APP(Swe) and PSEN1(M146V)), and select combinations thereof. This combinatorial approach allows for modular study of each contributor and their impact on transcriptome, proteome, and blood-brain barrier function; meaningful functional changes to highlight include the disruption of cellular patency under acute and chronic oxidative stress, increased transcellular transport with exposure to aged human serum in a tissue-agnostic microvessel precursor, and additive exacerbation of paracellular permeability, endothelial activation, and angiogenesis with the combination of intrinsic Alzheimer’s-related mutations and circulatory cues of aging
Solving a Stochastic Dynamical System with the Lattice Boltzmann Method
Full text linkThis thesis explores several numerical approaches to determining the state probability densities of a stochastic dynamical system, including an approach called the Lattice Boltzmann Method that is seldom used in this application. The stochastic differential equations describing a two-state stochastic dynamical system are rewritten into the Fokker-Planck equation that describes the time evolution of the state probability densities as a partial differential equation. The Fokker-Planck equation takes the same form as the advection-diffusion equation, which describes the evolution of densities of physical concentration, and can be solved using the Lattice Boltzmann Method. We have identified an extant Multiple Relaxation Time variant of the Lattice Boltzmann Method that shows promise to be used in the instances where a stochastic system contains states that do not have stochastic state derivatives.
Four stochastic systems are posed and integrated from initial conditions using four different approaches that include two Lattice Boltzmann Method variants, a Monte Carlo approach, and an analytical solution. The resulting probability density solutions from the possible methods are compared and the Lattice Boltzmann Method is shown to have practically low and continuous errors
Appraising the Value of New and Emerging Technologies and Approaches for Serosurveillance in Low- and Middle-Income Countries
No full textLow- and middle-income countries (LMICs) bear a disproportionate burden of the world’s cases and deaths due to vaccine-preventable diseases like measles. Serological surveillance or serosurveillance could help prevent infectious disease outbreaks and close immunity gaps, but more evidence is needed to support widespread use. This dissertation examines three new and emerging approaches used in serosurveillance and their potential to help identify and prevent outbreaks.
The first paper assesses the cost-effectiveness of measles IgM rapid diagnostic tests (RDTs) compared to enzyme-linked immunosorbent assays (ELISAs) in detecting measles outbreaks. The cost to conduct an ELISA was 3.64x the cost to conduct an RDT, primarily due to high specimen transportation and labor costs. Combining cost estimates with the results of an infectious disease dynamics model developed by collaborators, we found that RDTs were more cost-effective than ELISAs in 63% of the scenarios we considered while modifying several variables.
The second paper compares the additional costs of identifying and preparing residual blood specimens taken for routine testing purposes at hospitals to traditional dried blood spot (DBS) specimens from a community-based household serosurvey in Choma and Ndola Districts, Zambia. The cost to prepare a residual specimen for testing was nearly one-eighth of the cost per DBS specimen. Using residual specimens could lead to substantial cost savings but could also introduce selection bias.
The third paper presents a thematic analysis of data from 22 key informant interviews on multiplex bead immunoassays (MBIAs). MBIAs can help to predict a community’s level of exposure or immunity to several pathogens at once. However, MBIAs may not be suitable for all LMICs due to competing investment priorities and the need for more evidence. Partnerships with other countries could support wider use, but collaborations between high-income countries and LMICs can be unequal. Similarly, the industry could support efforts to standardize technologies, but this could present long-term sustainability issues.
Overall, this dissertation employs economic and qualitative research techniques to examine the potential value of new and emerging technologies and approaches to serosurveillance in LMICs. This information could inform policymakers considering different approaches to adopt or strengthen serosurveillance programs in support of immunization and other public health goals
RADical Shifts: A Futurist's Guide to Ecological Transformation and Biodiversity Stewardship
No full textRADical Shifts: A Futurist’s Guide to Ecological Transformation and Biodiversity Stewardship provides natural resource managers and conservationists with practical tools to navigate the unprecedented challenges posed by climate change. The guide introduces the Resist—Accept—Direct (RAD) framework, offering a flexible, adaptive approach to managing ecological transformation. By using RAD, conservation leaders can identify strategies to resist, accept, or direct ecological changes in ways that enhance biodiversity stewardship.
The guide emphasizes that climate change can be meaningfully addressed through informed planning and on-the-ground action. It introduces the concepts of RAD menus, RAD portfolios, and RAD decision context, which help managers brainstorm adaptation strategies, track decisions over time and space, and adapt decision-making processes.
Successful RAD actions are often the culmination of many years of collective work, not easily visible from an outsider’s perspective. Radical Shifts helps to demystify these processes. It includes case studies that examine the behind-the-scenes realities of RAD decisions in different regions of the United States.
With RAD menus, managers can explore a full spectrum of adaptation actions. RAD portfolios assist in planning and tracking these decisions, accounting for both spatial and temporal factors. The guide also stresses the importance of collaborative, deliberative engagement to adjust social and institutional contexts in response to changing ecological conditions. It notes that failure to adapt decision-making processes can obstruct progress, as past values, rules, and knowledge may hinder the ability to respond to change.
Whether managing a small-scale project or leading larger efforts, this guidebook equips natural resource managers with adaptable approaches to manage biodiversity and ecosystem services in an era of ecological uncertainty. It empowers conservation leaders to act now while embracing the ongoing journey of learning and adaptation, making it an essential resource for navigating the complexities of climate change and ecological transformation
DECIPHERING TRANSCRIPTIONAL PROGRAMS OF CD8+ T CELLS ACROSS TREATMENT MODALITIES IN RESECTABLE LUNG CANCER
No full textNon-small cell lung cancer (NSCLC) remains the leading cause of cancer-related mortality, with a 5-year overall survival rate of only 21% for metastatic cases. Perioperative immune checkpoint blockade (ICB) combined with chemotherapy is the standard of care for patients with resectable lung cancer. ICB leverages endogenous tumor-reactive T cells within the microenvironment, making it crucial to understand their baseline functional characteristics and how these treatments activate these T cells for effective tumor eradication. Our research focuses on the functional programming of tumor-infiltrating lymphocytes (TILs) and the differential effects of neoadjuvant PD-1 blockade (αPD-1) alone versus in combination with chemotherapy (αPD-1+Chemo) on CD8+ TILs. We performed single-cell immunogenomic profiling on 21 untreated tumor resections and 16 resections from patients treated with neoadjuvant αPD-1+Chemo, integrating these data with profiles from 19 additional neoadjuvant αPD-1-treated tumors. This analysis aimed to elucidate how chemotherapy enhances the efficacy of neoadjuvant nivolumab in NSCLC. Our findings provide a comprehensive baseline characterization of TILs in treatment-naïve NSCLC, using single-cell TCRseq/RNAseq to identify diverse TIL subsets and their implications for disease recurrence. We identify 13 CD8+ TIL subsets with distinct tissue-resident memory (TRM) patterns, reflecting stages of memory to effector differentiation. We show that neoadjuvant treatment enhances the expression of CXCL13+ TILs, shifting them from a canonical TRM state to more effector subsets. Interestingly, within treatment-naïve CD8+ TILs, these TRM changes may coincide with disease recurrence at two years. We also examine the effects of neoadjuvant therapies on CD8+ TILs, revealing distinct transcriptional changes induced by αPD-1 monotherapy versus αPD-1 combined with chemotherapy. Specifically, chemotherapy enhances the formation of GZMK+CD8+ TILs. Notably, we show that αPD-1+Chemo treatment improves TIL activation and memory potential, with both single-cell and spatial transcriptomics highlighting differential TIL localization in areas of tumor regression. These insights underscore the importance of personalized treatment strategies tailored to genetic and treatment-specific factors
Thermodynamic consequences of the coupling between structural reorganization and the ionization of buried residues in proteins
Full text linkIonizable amino acids buried in hydrophobic environments in proteins are rare but play essential functional roles in energy transduction processes.1 Buried ionizable groups have unusual physical properties, and the impact of dehydration of charged moieties in hydrophobic environments is poorly understood.2–5 Previously in this laboratory, we demonstrated that the pKa values of Asp, Glu, and Lys residues buried in the interior of staphylococcal nuclease (SNase),6,7 can be shifted by as many as 5 pH units relative to their normal pKa values in water, always in the direction that favors the neutral state. Most of the buried Lys, Glu, and Asp residues are neutral at physiological pH range. The correlation of the anomalous pKa values of these buried ionizable residues with the properties of their microenvironments revealed by crystal structures suggests that the pKa values cannot be determined from the electrostatic and chemical properties of their microenvironments in the folded structure. Previous NMR spectroscopy studies in this laboratory have shown that the ionization of these buried groups is coupled to local, sub-global, or global reorganization of the protein backbone. The ionization of the buried group appears to involve the transfer of the neutral group buried inside the hydrophobic protein interior to an environment where the charged form of the group is in contact with water.8–12
The data from this laboratory and others collectively suggest that the most important determinant of the pKa values of buried ionizable groups in proteins is the propensity of the protein backbone to reorganize in response to changes in pH. In this dissertation, I test the hypothesis that the pKa values of ionizable residues are determined by the thermodynamic stability of the protein because the ionization of buried groups is coupled to structural reorganization of the protein backbone. This hypothesis is based on the fact that the propensity for the protein to reorganize to alternative conformations (i.e. local, sub-global, or global unfolding) that is governed by the thermodynamic stability of the protein (∆Gconf) in the folded and denatured states.13,1
On the Frontlines - Investigating Heavy Alcohol Use Among Virginia Emergency Medical Services Clinicians
Full text linkBackground: Emergency Medical Services (EMS) clinicians experience high levels of emotional injury in the course of their work due to repeated exposure to trauma, extended work shifts, sleep interruptions, and more. Substances such as alcohol may be used by clinicians to cope with mental health challenges, as a part of social norms, or for relaxation. However, little is known about the burden and impacts of alcohol misuse among EMS clinicians.
Methods: Crude rates of alcohol-related death, and corresponding rate ratios, were compared among Virginia EMS clinicians, nurses, and the general population using Virginia death certificate data from 2018—2022. Alcohol-attributable deaths were identified using the Centers for Disease Control and Prevention’s Alcohol-Related Disease Impact Methodology. Leading causes of alcohol-related death were summarized for each group. Numbers and proportions of deaths among EMS clinicians by sociodemographic characteristic were calculated. Using a 2022 survey of Virginia EMS clinicians, associations were evaluated between self-reported heavy alcohol consumption and clinicians’ ages, sexes, marital statuses, geographical areas of employment, certification levels, employment/volunteer statuses, EMS service years, and reports of anxiety, burnout, depression, post-traumatic stress disorder (PTSD), insufficient sleep, suicide contemplation, pay satisfaction, and intentions to quit EMS.
Results: Alcohol-related death rates were higher among EMS clinicians than nurses but were equivalent between clinicians and the general population. Most clinician alcohol-attributable deaths were due to suicide. Disproportionate burdens of death were seen among unmarried clinicians and those ≤44 years old. Survey analyses showed 35.9% of respondents reported heavy alcohol use at least monthly. Odds of heavy drinking were higher in males, 18—24-year-olds, unmarried clinicians, paramedics and intermediate/advanced emergency medical technicians, clinicians with 16—25 service years, and those reporting depression, PTSD, anxiety for ≥26 days, or insufficient sleep for ≥6 days.
Conclusion: Heavier drinking was noted among EMS clinicians experiencing mental health disorders, with an overlap seen between alcohol use and suicidality. This research demonstrates the importance of promoting positive mental health and reducing alcohol consumption among U.S. EMS clinicians. Future studies should examine substance misuse among U.S. EMS clinicians, with the goal of safeguarding the health of those who serve and protect so many