Electronic Thesis or Dissertation

Cardiovascular disease is a broad term that encompasses a variety of disorders in which the heart and its associated blood vessels lose the capacity to deliver blood efficiently and effectively throughout the body. Cardiac endothelial cells play a vital role in maintaining the homeostatic balance of cardiac physiology. Research into c-Myc, a master regulator involved in the transcription of a large set of genes that regulate inflammation, has been the focus of new therapeutics aimed at treating or lessening the deleterious effects of cardiovascular disease. This project serves to explore how endothelial loss of c-Myc impacts cardiac function under normal and stress conditions, using ultrasound echocardiography image analysis to determine the key differences between all models.

This study aims to address the unique challenges of transportation in rural and disconnected communities through innovative data-driven methodologies. The primary methods employed in this research involve Geographic Information Systems (GIS) tools and simulation techniques to model and assess the impact of flood zones on rural traffic dynamics. The study recognizes the distinct mobility patterns and limited infrastructure prevalent in rural areas, emphasizing the need for tailored solutions to manage flood-induced disruptions. By leveraging GIS tools, the study intends to spatially analyze existing transportation networks, population distribution, flood-prone areas, and key points of interest to formulate a comprehensive understanding of the local context. Simulation-based approaches using the PTV VISSIM platform will be employed to model and assess various flood scenarios and their effects on traffic flow and accessibility. This study’s outcomes aim to contribute valuable insights into improving accessibility, efficiency, and safety in transportation for these underserved areas during flood events. By combining GIS tools and simulation techniques, this research seeks to provide a robust framework for data-driven decision-making and policy formulation in the realm of rural and disconnected community mobility, particularly in the context of flood risks.

Impoundments in the Indian River Lagoon, originally built to control saltmarsh mosquitoes, led to the isolation of fish nursery habitats. Rotational Impoundment Management (RIM) aims to mitigate this by hydrologically reconnecting impoundments during mosquito non-breeding seasons. However, current practices may not effectively facilitate juvenile fish emigration. This study incorporates summer openings (drawdowns) of culverts into RIM to improve the emigration of juvenile tarpon and snook. Tagged fish were monitored with RFID technology for 18 months in four impoundments. The abundance and size distribution of tarpon and snook populations differed among impoundments, reflecting variations in habitat structure and water quality. Summer drawdowns did not significantly increase emigration rates. However, tarpon detections increased during closed-culvert periods in all impoundments, and snook detections increased in one impoundment. Despite low emigrations, the study offers insight into the behavior of juvenile fish in these impoundments and suggests ways to enhance their nursery functionality.

Cyanobacteria are ancient prokaryotes that use photosynthesis and an accumulation of other adaptations to dominate aquatic ecosystems around the world. They are thus major contributors to biogeochemical cycling, a threat to human and environmental health, and an intriguing source for novel chemistry. We begin by providing an overview of bloom-forming cyanobacteria and their many toxic metabolites. We then discuss the characterization of some abundant extracellular pili of Microcystis aeruginosa, reporting a 2.4 Å cryoelectron microscopy pilus structure, revealing a novel class of pili that we have termed cyanobacterial tubular (CT) pili. The CT pili in M. aeruginosa were determined to be multi-functional, with a primary role in networking cells and enhancing colony formation, but also in controlling colony buoyancy, enriching iron, and accumulating toxins in the extracellular mucilage. Lastly, we explore the potential of heavy-labeling cyanobacterial cultures for the sake of isolating natural products that can be studied by vibrational spectroscopic imaging. The vibrational spectra of three classes of cyanopeptides along with their heavy-labeled counterparts are reported, and Density Functional Theory calculations are used to describe mode character, clarifying some unexpected changes in vibrational spectra upon heavy-labeling. As a whole, this work offers new insight into cyanobacterial physiology as well as a means to study cyanopeptides with imaging techniques and stable-isotope labeling.

Purpose: This study examined inter-individual response variation in muscle size and strength following training with different resistance training (RT) volumes. We hypothesized that despite clear gross variability, we would not detect clear evidence of inter-individual response variation for the primary outcomes. Additionally, we hypothesized that higher weekly set volumes would benefit muscle hypertrophy but not strength outcomes at the group-level. Methods: Sixteen recreationally trained individuals had their lower limbs randomized into either a low (LV = 8 sets per week) or high volume (HV = 16 sets per week) training condition for an initial 11-week intervention (phase 1). After a washout period, a second identical 11-week intervention (phase 2) was conducted with limbs re-randomized to the training conditions. Primary outcomes measured were vastus lateralis (VL) cross-sectional area (CSA), muscle thickness (MT), leg press one-repetition maximum (1RM), and isometric force (MVIC) at baseline, midpoint, and post-intervention for each phase. Results: Higher RT volumes benefited muscle hypertrophy (CSA = 2.04 cm2 [95% HDI: 0.11, 3.81], MT = 0.55 mm [95% HDI: -0.06, 1.19]) to a larger degree than strength outcomes (1RM = 4.05 kg [95% HDI: -1.67, 10.14], MVIC = 0.66 kg [95% HDI: -3.83, 5.07]) at the group-level. Clear gross variability was observed for all primary outcomes, but we did not detect strong evidence in support of true inter-individual response variation (CSA = 0.17 cm2 [95% HDI: 0, 3.54], MT = 0 mm [95% HDI: 0, 1.1], 1RM = 0.59 kg [95% HDI: 0, 7.92], MVIC = 4.49 kg [95% HDI: 0, 9.43]).Conclusion: Higher volumes appear to benefit muscle hypertrophy but not strength at the group-level. Additionally, we failed to detect strong evidence of interindividual response variation to different RT weekly set volumes, despite clear gross
variability.

Walton County, Florida, a low-lying coastal region, is highly susceptible to flooding, sea level rise, and storm surges. These hazards disproportionately impact communities, with socially vulnerable populations being less likely to recover from disaster events. This study presents an integrated assessment of vulnerability to flooding, considering natural hazards such as a 1-day 100-year storm event, a 3-foot sea level rise scenario, and storm surge risk, combined with a social vulnerability analysis, aiming to identify the most socially vulnerable communities within Walton County's flood-prone areas. Additionally, the integrated analysis takes into consideration a priority of land use approach, identifying facilities that are critical or essential for an emergency response and recovery. The study also recommends a series of projects, including green, gray, and hybrid solutions, as well as policy changes to mitigate flood risks and enhance resilience within these communities.

This dissertation explores one-class classification (OCC) in the context of big data and fraud detection, addressing challenges posed by imbalanced datasets. A detailed survey of OCC-related literature forms a core part of the study, categorizing works into outlier detection, novelty detection, and deep learning applications. This survey reveals a gap in the application of OCC to the inherent problems of big data, such as class rarity and noisy data. Building upon the foundational insights gained from the comprehensive literature review on OCC, the dissertation progresses to a detailed comparative analysis between OCC and binary classification methods. This comparison is pivotal in understanding their respective strengths and limitations across various applications, emphasizing their roles in addressing imbalanced datasets. The research then specifically evaluates binary and OCC using credit card fraud data. This practical application highlights the nuances and effectiveness of these classification methods in real-world scenarios, offering insights into their performance in detecting fraudulent activities. After the evaluation of binary and OCC using credit card fraud data, the dissertation extends this inquiry with a detailed investigation into the effectiveness of both methodologies in fraud detection. This extended analysis involves utilizing not only the Credit Card Fraud Detection Dataset but also the Medicare Part D dataset. The findings show the comparative performance and suitability of these classification methods in practical fraud detection scenarios. Finally, the dissertation examines the impact of training OCC algorithms on majority versus minority classes, using the two previously mentioned datasets in addition to Medicare Part B and Durable Medical Equipment, Prosthetics, Orthotics and Supplies (DMEPOS) datasets. This exploration offers critical insights into model training strategies and their implications, suggesting that training on the majority class can often lead to more robust classification results. In summary, this dissertation provides a deep understanding of OCC, effectively bridging theoretical concepts with novel applications in big data and fraud detection. It contributes to the field by offering a comprehensive analysis of OCC methodologies, their practical implications, and their effectiveness in addressing class imbalance in big data.

Endometriosis is a chronic disease that causes endometrial tissues to migrate and grow outside the uterus and is often associated with pain and infertility. The growths are estrogen-dependent but progesterone-resistant due to the lack of progesterone receptors. In the U.S., estrogen deprivation is the primary approach to treating the disease, which often leads to severe consequences such as osteoporosis and menopausal symptoms. KBU2046 is a chemical analog of genistein that has been shown to effectively inhibit the motility of prostate cancer cells with no toxicity to normal cells or estrogenic activity (Li Xu et al., 2010). This in vitro study showed that KBU2046 at 10μM significantly decreased the viability of 12Z cells to 27% and 34% at 24 hours and 48 hours posttreatment, respectively. At 48 hours post-treatment, micromolar concentrations of the combinations of KBU2046 with dienogest or calcitriol effectively decreased the viability of 12Z cells to 16% and 58.9%, respectively. KBU2046 with sodium butyrate decreased viability to 7.7%, but millimolar concentrations of the latter were required. KBU2046, in combination with calcitriol, synergistically decreased the migration and colony formation of the 12Z cells to 19.3% and 45.7%, respectively. KBU2046 and Calcitriol-treated 12Z cells are slower to the recovery of growth following treatment. KBU2046 and calcitriol decreased the secretion of PGE2 to 6.5% and 16.7%, respectively, while ethanol and the combinations of ethanol and DMSO increased the secretion of PGE2 to 83.8%, and 63.2%, respectively. In conclusion, a combination of KBU2046 and calcitriol at micromolar concentrations markedly inhibited the migration and growth of endometrial cells while decreasing the secretion of a key inflammatory molecule. In vivo studies with mouse models are needed to evaluate using a combination of KBU2046 and calcitriol for endometriosis therapy and whether millimolar plasma concentrations can be safely achieved by dietary means.

The relentless expansion of space exploration necessitates the development of robust and dependable anomaly detection systems (ADS) to safeguard the safety and efficacy of space missions. Conventional anomaly detection methods often falter in the face of the intricate and nuanced dynamics of space systems, resulting in a proliferation of false positives and/or false negatives. In this study, we explore into cutting-edge techniques in deep learning (DL) to tackle the challenges inherent in ADS. This research offers an in-depth examination of recent breakthroughs and hurdles in deep learning-driven anomaly detection tailored specifically for space systems and operations. A key advantage of deep learning-based anomaly detection lies in its adaptability to the diverse data encountered in space missions. For instance, Convolutional Neural Networks (CNNs) excel at capturing spatial dependencies in high-dimensional data, rendering them well-suited for tasks such as satellite imagery analysis. Conversely, Recurrent Neural Networks (RNNs), with their temporal modeling prowess, excel in identifying anomalies in time-series data generated by spacecraft sensors. Despite the potential of deep learning, several challenges persist in its application to anomaly detection in space systems. The scarcity of labeled data presents a formidable hurdle, as acquiring labeled anomalies during space operations is often prohibitively expensive and impractical. Additionally, the interpretability of deep learning models remains a concern, particularly in mission-critical scenarios where human operators need to comprehend the rationale behind anomaly predictions.

The capability to navigate in the proximity of solid surfaces while avoiding collision and maintaining high efficiency is essential for the effective design and operation of underwater vehicles. The underlying capability involves a variety of challenges, and a potential approach to overcome such obstacles is to rely on biomimetic or bio-inspired design. Through evolution, organisms have developed methods of locomotion optimized for their specific environment. One of the common forms of locomotion found in underwater organisms is undulatory swimming. These undulatory swimmers display different swimming behaviors based on the flow conditions in their environment. These behaviors take advantage of changes in the flow field caused by the presence of obstructions and obstacles upstream or adjacent to the swimmer. For example, a free swimmer in near-proximity to a flat plane can experience changes in lift and drag during locomotion. The reduced drag can benefit the swimmer, however, changes in lift may lead to a collision with obstacles. Despite the abundance of qualitative data from observing these undulatory swimmers, there is a lack of quantitative data, creating a disconnect in understanding how these organisms have evolved to exploit the presence of walls and obstacles. By employing a combination of traditional computational fluid dynamics and novel neural network-based techniques it is possible to emulate the evolution of learned behavior in biological organisms. The current work uses deep reinforcement learning coupled with two-dimensional numerical simulations of self-propelled swimmers to better understand behavior observed in nature.