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Changes in Ambient [Ca2+] Impact Reproduction and Development in Lymnaea Stagnalis
No full textThe freshwater pond snail, Lymnaea stagnalis, is extremely well studied and a model organism in many scientific disciplines including physiology and ecotoxicology. This species produces egg masses that hatch within an average of 10-14 days of development with a fully developed CaCO3 shell. A lot is known regarding calcium (Ca2+) acquisition in adult mollusks but less is known about the development and Ca2+ acquisition of mollusk embryos. Ca2+ is a vital element for reproduction, development, and overall biological function. Previous studies have shown that the development of L. stagnalis is impaired by a reduction in ambient Ca2+ concentrations, and that adults prioritize Ca2+ uptake over other biological functions. In this study I delineate the impacts of Ca2+ on the reproduction and development of Lymnaea stagnalis. Impacts were determined through monitoring of the Ca2+ acquisition by laid egg masses as well as the provisioning and production of eggs by adults in varying Ca2+ environments. Adult L. stagnalis fully provision egg masses with [Ca2+] despite low [Ca2+] environments while significantly reducing reproductive output. Interestingly, there were no measured impacts on adult Ca2+ homeostasis, and adults did not consume more food to supplement their Ca2+ needs in low [Ca2+] conditions. Lymnaea stagnalis appears to be able to control the calcium content of their egg masses and control their reproductive output to adapt to changes in their environment. Results show that adult snails prioritize the quality of egg masses over quantity in low [Ca2+] environments. Limited reproduction in low [Ca2+] can have detrimental impacts on L. stagnalis populations as water acidification and freshwater [Ca2+] continues to impact aquatic systems.</p
An Ecological Momentary Assessment Study of Intolerance of Uncertainty: Linking Computational Measures with Clinical Factors
No full textIntolerance of uncertainty (IU) is the tendency to react negatively to uncertainty across emotional, cognitive, and behavioral domains. Research on IU is limited from a conceptual and measurement perspective. The current study compares a self-report measure of IU to task-based computational indices. We relied on ecological momentary assessment (EMA) to assess whether clinical and computational measures of IU predicted daily negative affect and avoidance, and examined whether IU contributed to anxiety one month later. A diverse sample of community adults (N = 236) completed a self-report measure of IU and the Risk and Ambiguity Task, a computerized monetary decision-making task that captures gain and loss decisions under uncertainty. Computational modeling was used to estimate risk and ambiguity aversion for both gains and losses. Participants then reported momentary affect and avoidance via EMA over 21 days, followed by a semi-structured diagnostic interview one month later. Greater self-reported IU was significantly associated with greater risk aversion for both gains and losses, as well as lower ambiguity aversion for gains. Greater self-reported IU predicted higher levels of daily negative affect and avoidance, whereas greater ambiguity aversion for gains predicted lower daily avoidance. Self-reported IU predicted both self-reported and interview-based anxiety, but only greater risk aversion for losses was associated with greater interview-based anxiety. Negative affect, but not avoidance, mediated the link between self-reported IU and later anxiety. Findings suggest people with higher self-reported IU were more likely to make fewer risky decisions in a monetary gambling task. This relationship was reversed for ambiguity aversion. Our findings also suggest that a person’s perception of their uncertainty tolerance rather than their behavioral task performance is more predictive of anxiety. Daily negative affect may be a key mechanism linking IU to anxiety. This study advances our understanding of IU by integrating clinical and computational assessments.</p
Water - An Underappreciated Medium for Photoreactions
No full textWater, despite being Earth's most abundant and sustainable solvent, has been historically underutilized in photochemistry due to conventional assumptions that hydrophobic organic molecules require organic solvents for efficient reactions. This dissertation establishes water as a superior medium for intermolecular photoreactions by demonstrating that its unique properties drive molecular pre-organization that dramatically enhances photodimerization rates and enables control over product selectivity.Through systematic investigation of model olefins and cyclic enones, this research reveals that photodimerization quantum yields in water exceed those in organic solvents by up to two orders of magnitude, achieving efficient reactions at concentrations as low as 0.02 M, an order of magnitude below the theoretical diffusion-controlled threshold. Computational studies employing quantum chemical calculations and molecular dynamics simulations establish a predictive framework integrating experimental characterization with computational modeling, enabling rational design of aqueous photochemical processes. By eliminating organic solvents while enhancing reaction efficiency and selectivity, this research advances green chemistry principles and provides methodologies for sustainable photochemical technologies
Modeling Opioid Overdose Related Indicators for Data Driven Decision Making
No full textOpioid use disorder (OUD) is a chronic condition affecting more than 2.1 million individuals in the United States and over 16 million worldwide. The HEALing Communities Study® aimed to reduce opioid overdose deaths through data-driven interventions. In this study, we evaluate alternative statistical frameworks for interpreting community-level data on three interrelated outcomes—opioid overdose deaths, substance use treatment, and naloxone (Narcan) administration. Given their dynamic interdependence over time, these outcomes were modeled using panel vector autoregression model estimated using both the Maximum Likelihood Estimator (MLE) and the Generalized Method of Moments (GMM). The models incorporate geospatial effects and social determinants of health to capture spatial dependencies and societal factors influencing opioid-related outcomes. Simulation studies were conducted to assess model selection and compare estimator performance under varying conditions. Additionally, out-of-sample forecasting analyses were performed to evaluate predictive accuracy across modeling approaches. Findings indicate that the MLE provides more stable parameter estimates and superior forecasting performance relative to GMM methods in this application.</p
Investigating the Combined Detrimental Effects of Noise Exposure and Electrode Insertion Trauma for Hearing Preservation
No full textIn our increasingly noisy world, the general population experiences damaging environmental noise exposures routinely that may contribute to noise-induced hearing (NIHL). It is known that acoustic trauma leads to auditory dysfunction known as NIHL and eventually manifests into permanent sensorineural hearing loss (SNHL). The negative effects of NIHL are not limited to extreme cases, such as concert attendance, or in chronically noise-exposed groups, like firefighters. Unfortunately, the average person experiences damaging ambient noise during everyday life. The gold standard treatment option for those with severe-to-profound SNHL is cochlear implantation (CI). CIs are one of the most successful neuroprostheses to date and have benefited a vast number of patients. Due to their success, CI have expanded to include those with normal low frequency hearing function allowing for acoustic and electric stimulation. However, hearing preservation CI outcomes can vary significantly, often leading to loss of residual hearing. Prior limited research has shown that CI candidates with NIHL may be an at increased risk to lose residual hearing. However, while some studies have investigated this relationship between prior NIHL damage and loss of residual hearing after implantation, most results are contradictory or inconclusive. Independent examinations of NIHL and electrode insertion trauma (EIT) damage have been established, but there is a lack of agreement within the field regarding their negative synergistic effects on hearing preservation outcomes.Therefore, this proposal will ascertain how combined noise and EIT contributes to poor residual hearing outcomes in a controlled preclinical environment. Here, we propose to first characterize noise sensitivity profiles in rats to mimic CI candidacy and in turn, establish a novel double-insult rodent model with added EIT. Through timepoint-based experiments, we will distinguish the underlying electrophysiological mechanisms at play following noise-induced SNHL with CI-driven postoperative residual hearing loss.</p
A Deep Neural Network Based Variational Bayesian Approach to Sensor-Driven System Monitoring and Control
No full textModern industrial systems (e.g., aerospace, manufacturing, energy) increasingly rely on condition-monitoring sensor data to detect early signs of failure and optimize maintenance. Traditional models struggle with the high-dimensional, non-linear, and time-dependent nature of such data, limiting predictive accuracy. Prognostic Health Management (PHM) leverages sensor data to assess system health and predict failures, but modeling degradation in run-to-failure scenarios remains challenging due to evolving conditions and complex dependencies.This dissertation introduces a deep state-space modeling (DSSM) framework that integrates deep learning, variational inference, and recurrent neural networks (RNNs) to model latent system dynamics. Combining RNNs with variational autoencoders (VAEs), the framework captures long-term temporal dependencies and performs robust probabilistic inference. A hybrid latent state structure—continuous and discrete—allows it to represent diverse degradation behaviors, from gradual wear to sudden faults, enhancing interpretability and robustness.Extensive evaluation on simulated and real-world datasets, including a wind turbine case study, demonstrates the model’s ability to track latent degradation and accurately estimate remaining life (RL) or time of event (TE). To adapt to dynamic environments, the framework also integrates active learning for selective model fine-tuning, reducing the need for retraining with new data. This approach advances predictive maintenance and reliability assessment in complex, sensor-driven systems.</p
A Computational Approach in Comparing Emotional vs. Cognitive Aspect on Working Memory: Examining Anxiety-Related Differences
No full textThe effect of anxiety on working memory deficits has been extensively studied, with impairments particularly emerging when processing negative emotional information. These findings suggest that anxiety may alter the decision-making process by disrupting working memory. Additionally, traditional performance measures, such as reaction time (RT) and accuracy, provide limited insight into the cognitive processes underlying decision-making. To address this limitation, we applied Drift Diffusion Modeling (DDM), a method that estimates decision-making parameters reflecting the evidence accumulation process, allowing us to probe anxiety-related cognitive mechanisms of deficits in working memory. </p
Unveiled Voices: Women, Politics, and the Changing Sound of Iranian Protest Music
No full textThis thesis explores the transformative evolution of Iranian protest music, tracing its trajectory from the politically charged anthems of the 1970s to the innovative, digitally mediated expressions of dissent witnessed during the 2009 Green Movement and the 2022 “Women, Life, Freedom” protests. Grounded in both historical analysis and contemporary ethnographic research, the study investigates how musicians have continuously adapted protest music in response to shifting political landscapes. Drawing on a diverse range of sources, including archival recordings, interviews with musicians and activists, and a critical review of existing literature, my research highlights the ways in which protest music serves as a cultural artifact and a potent instrument of social mobilization.</p
Particulate Matter Sensing and Characterization in Occupational Environments
No full textIncreasing knowledge about the health impacts of particulate matter (PM) air pollutants resulted in the widespread development and use of compact low-cost particulate matter sensors that have enabled spatially dense and high temporal resolution measurements. These PM sensors are advantageous due to their small size, lightweight design, affordability, and ability to connect to the internet for real-time data analysis. Most of these sensors operate on the principle of light scattering and require calibration based on the environment in which they are deployed. While there is a proliferation of such sensors, the performance over a wide range of particle properties needs to be firmly established. To the best of my knowledge, the current market (2025) has no commercial portable particulate matter sensor that can provide particle composition properties. The chemical, biological composition of a particle directly influences its refractive index. The refractive index of the particles plays an important role in establishing the inversion algorithms for accurate determination of PM concentration levels. In addition, not being able to determine the composition limits our understanding of how particulate matter affects the health and safety of humans in different occupational environments on a real-time scale. This dissertation focuses on the real-time measurement of particulate matter exposure and characterization of particles in occupational environments such as healthcare, orchestra, firefighting environments by utilizing a network of calibrated low-cost PM sensors complemented by research grade reference aerosol instruments for particle characterization. Furthermore, a novel multi-wavelength multi-angle optical sensor prototype using light scattering theory was developed to determine the refractive index of particles in addition to size and number concentration.</p
Harnessing the Immunomodulatory Effect of Mesenchymal Stem Cells for Applications in Transplantation and Type 1 Diabetes
No full textType 1 diabetes (T1D) is an autoimmune disorder characterized by immune-mediated destruction of pancreatic islet β-cells. While islet transplantation can restore insulin production, long-term success remains limited by immune rejection and the toxicity of systemic immunosuppression. Mesenchymal stem cells (MSCs) and their extracellular vesicles (EVs) possess immunomodulatory properties and can protect β-cells from immune-mediated damage.This dissertation investigates strategies to potentiate and mechanistically define the immunomodulatory properties of human umbilical cord–derived MSCs (UC-MSCs) and their secretome to develop cell-based and cell-free therapies capable of protecting islets against immune attack. We utilized scalable methods to enhance UC-MSC potency, including calcitriol, all-trans retinoic acid, eicosapentaenoic acid, and thermal conditioning, which increased regulatory T cell expansion and activation. UC-MSCs were further engineered with rapamycin-loaded nanocarriers, enabling intracellular retention, controlled release, and suppression of activated T cell proliferation. When aggregated with human islets, engineered UC-MSCs formed uniform perislet coatings without impairing glucose-stimulated insulin secretion and, in diabetic mice, prevented graft rejection and sustained normoglycemia.Clinical-grade UC-MSC secretome was processed to enrich for EVs. UC-MSC EVs expanded and activated regulatory T cells, inhibited CD8+ T cells, induced CD4+ and CD8+ T cell exhaustion, polarized macrophages toward an M2-like phenotype, and preserved β-cell function in human in vitro systems of inflammation and immune attack. EVs transferred mitochondria to islets and T cells, protecting against mitochondrial damage and supporting T cell immunoregulation. Additionally, EVs delivered coordinated microRNAs targeting key immunoregulatory pathways.This work establishes a mechanistic and translational framework for advancing UC-MSC– and EV-based immunomodulatory therapies for T1D and transplantation