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Comprehensive Immunogenomic Landscape of Uveal Melanoma
No full textUveal melanoma (UM) is the most common intraocular cancer in adults, with a high metastatic mortality rate due to early micrometastasis and an immunosuppressive microenvironment. Its genetic landscape includes two main mutation groups: activating mutations in the Gq signaling pathway, also found in benign nevi, which are insufficient alone for malignancy; and BSE mutations (BAP1, SF3B1, EIF1AX) that drive malignant progression and correlate with poor, intermediate, and favorable prognoses, respectively. UM can be classified into four prognostic groups based on Class status and PRAME, a cancer-testis antigen. To investigate early tumor evolution for these genomic subtypes, we utilized a targeted sequencing panel for more than 1000 cases, revealing biomarkers for the transition from low- to high-grade UM, aiding early diagnosis and treatment. The composition of the tumor immune microenvironment (TIM) influences prognosis, particularly a poor prognosis associated with increased infiltration of M2 macrophages linked to BAP1 inactivation. Loss of BAP1 derepresses PROS1 expression, subsequently activating MERTK to promote immunosuppressive macrophages, which supports metastasis and immune evasion. Using single-cell sequencing, we characterized how BAP1 inactivation and PRAME expression jointly develop an immunosuppressive TIM, aiding metastasis via pathways like cGAS-STING. Furthermore, we characterized T cell response during LAG3 inhibition therapy for metastatic disease. The research employs genetic analyses, scRNA-seq, and cell culture models to better understand the immunogenomic landscape of UM and develop targeted therapies. These findings offer new insights into early detection, prognosis, and potential treatments for UM.</p
On Extending Predictable Forward Performance Processes to Cumulative Prospect Theory
No full textThis dissertation advances the theory of Predictable Forward Performance Processes (PFPPs) by developing new models inspired by behavioral finance, focusing on rank-dependent and loss-averse preferences.Chapter 1 introduces PFPPs in continuous and discrete time, reviews the limitations of expected utility in dynamic settings, and provides background on behavioral models—particularly Cumulative Prospect Theory and its three components: probability distortion, loss aversion, and reference dependence.Chapter 2 develops Rank-Dependent PFPPs (RDPFPPs) by incorporating probability distortions into the PFPP framework. In conditionally complete markets, their existence reduces to solving a sequence of integral equations. Using Volterra techniques, we construct explicit solutions, including closed-form expressions when inverse marginal functions are completely monotonic. A conditionally complete Black-Scholes market model illustrates the approach.Chapter 3 addresses PFPPs for loss-averse agents. Instead of S-shaped utilities, we apply the concavification principle to use concave envelopes that preserve loss aversion while avoiding non-concavities. The resulting forward problem leads to a free-boundary Fredholm integral equation of the first kind. We study its ill-posed structure via a characteristic system and Tikhonov regularization, using resolvent analysis and finite-rank kernel approximations to ensure existence and uniqueness. The resulting methods offer constructive, tractable solutions for PFPPs with behavioral features.Overall, this work bridges forward investment theory with behavioral decision making models, contributing to the broader effort of incorporating empirically relevant preferences into stochastic control frameworks.</p
Spectroscopic Investigation of Protein Dynamics and Small Molecule Binding in PGRMC1 and Fe-S Clusters using Biomimetic Models
No full textIron-containing proteins are essential for processes such as respiration, metabolism, and signaling, relying on cofactors like heme and iron–sulfur (Fe–S) clusters to enable redox reactions and catalysis. Heme proteins (e.g., hemoglobin, cytochrome P450s) mediate oxygen transport and drug metabolism, while Fe–S proteins support electron transfer and carbon fixation.My dissertation focuses on Progesterone Receptor Membrane Component 1 (PGRMC1), a single-pass transmembrane protein involved in steroid signaling, membrane trafficking, and drug metabolism. Overexpressed in cancers, PGRMC1 contributes to chemoresistance, yet its heme binding and protein interactions remain unclear. Using site-directed mutagenesis and spectroscopy, we probed the roles of Tyr113 and Cys129 in heme-induced dimerization and oligomerization. Mutants Y113F and C129S revealed how specific residues govern coordination geometry and dimer stability. UV–vis, EPR, fluorescence, and computational modeling identified distinct coordination states and stoichiometry, showing that heme binding drives dimerization via π-stacking and axial ligation, potentially modulating PGRMC1’s interaction with cytochrome P450s. I further reconstituted full-length PGRMC1 into MSP-based nanodiscs composed of POPC and POPS, mimicking the endoplasmic reticulum membrane. Nanodiscs, validated by SDS-PAGE, SEC, AFM, and DLS, provide a detergent-free platform to study membrane protein structure and interactions.Additionally, I investigated [NiFe3S4]-containing ferredoxins using variable-field Mössbauer spectroscopy, revealing temperature-dependent spin-state equilibria and magnetic interactions. Together, these studies elucidate how heme modulates PGRMC1 structure and function while highlighting the broader significance of metalloproteins in cellular processes, drug resistance and catalysis.</p
Biomarkers of the Inflammatory Response Following Brain Injury
No full textBrain injury remains a devastating condition with long-lasting consequences. Brain injury triggers a cascade of pathophysiological processes that contribute to both immediate and long-term damage. Excessive inflammation disrupts the repair and regeneration of damaged neurons, accelerates neuronal degeneration, and predisposes patients to developing neurodegenerative conditions. A significant consequence of brain injury is cognitive, emotional, and physical impairments, leaving patients unable to make their own healthcare decisions. In such cases, designated proxies are tasked with making difficult decisions on behalf of the patient. The accurate prediction of functional recovery and long-term outcomes remains challenging due to the heterogeneity of the condition and individual patient factors, such as age, pre-existing health conditions, and genetic predispositions to comorbidities. Biomarkers have shown promise in identifying injury severity and providing insights into secondary injury mechanisms. There is a pressing need to identify a set of biomarkers that can be integrated into multi-modal assessments to reliably predict patient outcomes. Inflammasome proteins have gained attention as essential regulators of the neuroinflammatory response following injury, thus extensive research has been conducted to understand their roles and therapeutic potential, including biomarker potential in TBI. Inflammasomes are multiprotein complexes of the innate immune system. Upon activation, inflammasome complexes lead to cleavage of pro-inflammatory cytokines. It has been previously established that inflammasome overactivation contributes to poor outcomes following brain injury. This work in this thesis explores novel biomarkers associated with the inflammatory response in aneurysmal subarachnoid hemorrhage (aSAH) and acute brain injuries, including traumatic brain injury (TBI), subarachnoid hemorrhage, and intracerebral hemorrhage.</p
The Development of an International Computational Framework to Optimize and Size Premise Plumbing Systems
No full textAccurately estimating design flow rates is essential for sizing premise plumbing components, yet prediction remains difficult because demand depends on occupancy, fixture characteristics, regional behavior, and event timing. Current design methods still rely on approaches developed in the 1940s, which studies show consistently overestimate simultaneous flow rates and lead to oversized systems. Oversizing increases capital costs, raises water age and disinfectant decay, promotes microbial growth, and reduces pump efficiency. To address these limitations, empirical, probabilistic, and stochastic methods have been explored. Stochastic models offer the greatest potential because they represent behavioral variability, but existing versions often rely on outdated assumptions, exclude key behavioral factors, or lack practical implementation pathways. This dissertation introduces a novel demand estimation framework using a stochastic event-generation model that applies events to a user-defined digital twin of a plumbing system. The work includes four components: (1) evaluation of assumptions and distributions used in existing stochastic models against regional empirical data; (2) identification of socioeconomic, demographic, and housing factors influencing water use using MGWR and OLS; (3) analysis of high-resolution datasets to determine whether consumption patterns reveal latent user populations; and (4) development of a Monte Carlo simulation that generates water-use events and routes them through the digital twin to estimate simultaneous hot- and cold-water flow rates without relying on unrealistic assumptions.</p
Conceptualizing and Exploring ESG (Environmental, Social, Governance) in Internal Public Relations: An Employee-Centered Perspective
No full textThis dissertation explores ESG (Environmental, Social, and Governance) from the perspective of internal public relations, aiming to understand employee perceptions of ESG and its communication through two consecutive studies. Both studies employed quantitative surveys conducted with U.S. employees.Study 1 examined how employee perceptions of ESG can be structured by conceptualizing and operationalizing it through a reliable, measurable quantitative scale. Grounded in stakeholder theory, the study introduced a scale of 24 items that address the elements underlying the ESG construct. This scale specifically focused on employee perceptions of corporate ESG efforts from both external (i.e., initiatives targeting the public or broader management strategies) and internal (i.e., initiatives directly affecting employees’ experiences within the company) perspectives of environmental (E), social (S), and governance (G).Study 2 built upon Study 1, examining the role of communication in shaping employee perceptions of ESG and, in turn, fostering positive relationships with their companies. Adopting media richness theory and relationship management theory, Study 2 highlighted the importance of rich-feature mediums in enhancing the communication quality of ESG content and format, as compared to lean-feature mediums, which had a negative impact on both aspects. The results showed that high-quality ESG content significantly affects employee perceptions of ESG, thereby strengthening the employee-organization relationship (EOR) in terms of trust, control mutuality, satisfaction, and commitment. In contrast, communication quality in terms of format did not show a significant relationship with employee perceptions of ESG, nor did it contribute to a positive EOR.This dissertation makes significant theoretical contributions by expanding the scope of ESG studies to the public relations framework. It also provides practical guidelines for companies to enhance ESG initiatives and strengthen internal relationships through strategic ESG communication and management.</p
Proton Exchange Membrane Fuel Cell's Degradation Under Voltage Reversal and Innovation for Electrochemical Lithium Extraction
No full textProton exchange membrane fuel cells are likely to encounter voltage reversal under hydrogen starvation conditions during real-life operations.However, it is still not fully resolved which side experiences faster or more severe degradation. Different operating conditions can further complicate this issue. In this study, we investigate the degradations on the anode and cathode sides separately after voltage reversal under different humidity conditions. The experimental results show that the anode side experiences faster and more severe degradations, but the cathode side has more pronounced effects on cell performances due to the sluggish nature of oxygen reduction reaction (ORR). Our experimental results also show measuring oxygen transport resistance can be a diagnostic tool to monitor the degree of fuel cell degradation before observable significant cell performance declines.Fabrication of thick (900-1500 µm), crack-free lithium manganese oxide (LMO) electrodes was reproducible by using an innovated slurry casting method. The selectivity and intercalation capacity of the thick electrodes were evaluated in chloride solutions with main cations in brines and in synthetic Salar de Atacama brine using cyclic voltammetry (CV) measurements. Analyses of the CV data indicated an excellent Li+ selectivity of Li+/Na+=152.7 was achievable under voltage-controlled conditions. The mass specific intercalation capacity of the thick electrodes was 6.2-11.3 mg per gram of LMO whereas an area specific capacity of 0.282 mg/cm2 was achieved with the thickest electrode, which was 3-11 folds of that for the thin electrodes reported in literature. In addition, 82% of capacity was retained over 30 intercalation/deintercalation cycles. XRD measurements revealed that both Faradaic diffusion-controlled or battery-like intercalation and pseudocapacitive reaction contributed to the selectivity. This work establishes practical technology for thick electrode fabrication that promises reduction of the manufacturing and operational cost. </p
A Novel Blockchain Paradigm for Creating, Maintaining, and Sharing Personal Blockchain Ledgers
No full textBlockchain technology was first created to enable a peer-to-peer payment system known as Bitcoin. Since its inception, blockchain technology has evolved to satisfy more diverse use cases, with blockchain development platforms like Ethereum, allowing for complex applications to be built using blockchain. However, despite its utility, blockchain has failed to gain widespread utilization outside of a few niche use cases. In this work, we analyze the most popular blockchain development platforms to identify the limitations of current blockchain technology; finding that the failure to adopt blockchain is due to scalability, data privacy protections, and complexity. To overcome the identified limitations we propose a new blockchain paradigm, Personal Blockchain Ledgers. In our this paradigm, each user stores data on a separate blockchain, in contrast to the traditional blockchain, where all users store data on a single large blockchain. To support our paradigm, we propose the PBL System, which creates and maintains Personal Blockchain Ledgers. We provide a theoretical soundness for our system. We then provide an extension to our system for sharing blockchain data. We show how users can utilize our extension to selectively share data with third parties who may not participate in the PBL System. Finally, we present a prototype implementation of our system. To show the effectiveness of our system, we conduct empirical evaluations to compare it to existing popular blockchain platforms. Likewise, to show the scalability of our system, we simulate high system load using a High Performance Computer. Through this work we have proposed a novel paradigm, a system for utilizing the paradigm, and an empirical implementation of the system.</p
Assessing the Movement Ecology and Habitat Use of the Atlantic Guitarfish (Pseudobatos lentiginosus) in South Florida Using Acoustic Telemetry
No full textGuitarfish are a vulnerable and understudied group of ray species, and Florida is home to one species of guitarfish, the Atlantic Guitarfish (Pseudobatos lentiginosus). To date, fewer than ten studies have been published on Atlantic guitarfish, focused on their reproduction, feeding, mycobacteriosis, and vision. Many of these studies are >25 years old and offer limited information regarding the Atlantic guitarfish ecology. This project aims to fill knowledge gaps about the movement ecology and habitat use of Atlantic guitarfish. Throughout their range, Atlantic guitarfish encounter numerous threats, including commercial trawling, handline fishing, and gillnet fisheries, which target them directly or capture them incidentally as bycatch. Other threats include habitat loss and degradation from coastal development and oil exploration. As fishing effort in the Western Central Atlantic increases and populations of guitarfish face anthropogenic threats, it is important to address our lack of knowledge and expand research on this species. As part of this project, 28 individuals were located and captured via net using a roving diver survey off Palm Beach County and Broward County, FL, and internally implanted with acoustic tags to provide data on their movement patterns. Data were analyzed in R Studio to assess diel patterns, residency, and movement using both a coastal telemetry array and a fine-scale VPS array. Since there is no existing published information about the movement and habitat use of the Atlantic guitarfish, data from this study can inform fisheries management strategies and conservation efforts. </p
Analyzing the Effects of COVID-19 on the Catering Industry in Florida Using Machine Learning
No full textBy combining consumer spending records and points of interest (POI) data from SafeGraph and aggregating them to the block group level, this thesis investigates the impact of the Coronavirus Disease 2019 (COVID-19) pandemic on customer traffic at restaurants in Florida. It compares changes in customer traffic patterns and recovery — including their origins (local, within Florida, and out-of-state) — across three phases: before, during, and after the pandemic. Using advanced spatiotemporal analyses, the study finds that the pandemic made restaurant customer traffic more localized and that suburban areas experienced less fluctuation and recovered more quickly than urban cores. Results from machine learning (ML) models further confirm that local neighborhood characteristics (e.g., ethnicity) remained the most important factors contributing to a restaurant’s resilience during the pandemic. Additionally, traditional economic geography determinants, such as agglomeration effects, regained importance in explaining customer traffic patterns in the post-pandemic phase. These findings suggest that although restaurant customer behavior shifted during the pandemic, locational theory continues to provide a robust framework for understanding the spatial dynamics of restaurant customer traffic.</p