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Cis-2-Decenoic Acid Modulation of \u3cem\u3eVibrio vulnificus\u3c/em\u3e Biofilms
No full textVibrio vulnificus is a marine bacterial pathogen perpetrating severe wound and gastrointestinal infections in humans. The growing incidence of V. vulnificus infections and the widening ecological range of the species, closely linked to climate change trends, draw attention to an increasing need for methods to combat V. vulnificus infections without further augmenting the threat of antimicrobial resistance. The results presented herein highlight the ability of an unsaturated fatty acid diffusible signal factor, cis-2-decenoic acid (CDA), to influence the behavior of biofilms formed by a virulent human isolate strain of V. vulnificus (ATCC 27562). Results indicated that joint treatment of biofilms with 150 μM CDA and ciprofloxacin allowed for three-fold reduction in cell growth and ten-fold reduction in biofilm biomass (p \u3c 0.05) at inhibitory ciprofloxacin concentrations of 1 mg/L and 2 mg/L, respectively. These concentrations were lower concentrations than those sufficient for inhibitory effects under treatment with ciprofloxacin alone (\u3e4 mg/L). Additionally, it was observed that CDA treatment resulted in reduced cell densities in the stationary and early death phases of planktonic growth. Finally, another interesting facet of the data collected was that CDA treatment alone did not induce dispersal in V. vulnificus biofilms. This contrasts with findings previously reported for similar treatments of other bacterial species. Taken together, the results suggest that the planktonic and biofilm forms of V. vulnificus are responsive to CDA treatment, which increases antibiotic susceptibility and mediates multifaceted responses and mechanisms that merit further investigation and develop of applications that improve human and environmental health outcomes
Unveiling the Nexus of Glyphosate, Diet, and Gut Microbiota: Implications for Human Health and Intervention Strategies
No full textGlyphosate-based herbicides (GBHs), such as Roundup®, are widely used in agriculture and increasingly scrutinized for their health impacts. Although glyphosate targets the shikimate pathway—absent in humans but present in many gut microbes—research shows it may disrupt gut microbiota, contributing to dysbiosis, inflammation, and chronic disease. This thesis investigates the relationship between glyphosate exposure, diet, and the gut microbiome, with a specific focus on co-formulants like polyethoxylated tallow amine (POEA) that enhance toxicity.
A systematic literature review was conducted using peer-reviewed studies from 2010 to 2025. Results show glyphosate suppresses beneficial bacteria such as Lactobacillus, Bifidobacterium, and Faecalibacterium prausnitzii, while enabling the overgrowth of harmful strains like Clostridium difficile and Salmonella spp. These microbial imbalances have been linked to gastrointestinal, neurological, and metabolic disorders, particularly in vulnerable populations.
The findings suggest a need for revised regulatory policies, greater public awareness, and intervention strategies—such as dietary changes and probiotic use—to mitigate glyphosate’s effects. By exploring the complex interactions between chemicals and gut health, this thesis underscores the importance of a microbiome-conscious approach to environmental health and agriculture
Neural Efficiency Hypothesis in Motor Actions; Spectral Power of Balancing Ballet Dancers
Full text linkReduced activity observed during functional imaging of motor experts led to the Neural Efficiency Hypothesis (NEH) in motor skills, which posits that expert performers use fewer neural resources during motor execution than novices, reflecting greater neural efficiency.
This pilot study compared EEG recordings from five novices and four expert ballet dancers during standing and one-leg balance. Whole-brain theta (4–8 Hz) and alpha (8–12 Hz) power were quantified, and topographic maps were generated. In addition to testing the primary hypotheses, the experimental design was evaluated as a practical and scalable approach for investigating the NEH in motor skills. Novices showed significantly larger increases in both theta and alpha power when switching from standing to balancing. Increased theta power is theorized to reflect greater cortical engagement and lower neural efficiency. Although the rise in alpha power is unusual, greater change in power (despite direction) may still suggest less efficient cortical functioning. Topographic maps revealed similar spatial distributions across groups. These preliminary findings support the NEH, indicating that extensive practice yields more efficient neural processing during balance. The success of this pilot study suggests this design is a promising strategy for large-scale investigations the NEH, provided that limitations are properly addressed
The Effectiveness of Sabermetrics in Baseball: Moneyball Revisited
No full textThis thesis revisits the principles of sabermetrics as outlined in Michael Lewis\u27s \u27Moneyball\u27, investigating whether traditional statistics hold up to their analytical counterpart. By employing univariate linear regression, the study evaluates correlations between advanced metrics such as OPS+, wRC+, and ERA+ against traditional measures like batting average, slugging percentage, and ERA. Data spanning over a century of Major League Baseball (1908–2019) were analyzed to assess the evolving relationship between these metrics. Results indicate that advanced statistics often provide a more nuanced and reliable assessment of player performance, with strong correlations observed in metrics designed to refine traditional measures. However, the analysis also highlights limitations and areas of divergence, such as weaker correlations between Wins Above Replacement (WAR) and traditional statistics like wins and saves. These findings underscore the transformative role of sabermetrics in shaping modern baseball strategy while emphasizing the need for a balanced approach that integrates both historical and innovative perspectives. Ultimately, this study affirms the enduring relevance of sabermetrics in enhancing player evaluation and team decision-making processes, offering insights into the ongoing evolution of baseball analytics
SWVL: A Custom AI-Powered Face Tracking Camera Gimbal
No full textIn response to the growing demand for smarter, more responsive face tracking cameras in the post-pandemic world, our team designed SWVL, a custom AI-powered face tracking gimbal meant to address the limitations commonly encountered by the commercial models currently on the market. These commercially available gimbals come with several issues, such as frequently losing track of the person in the frame and requiring manual resets, which we sought to fix with our implementation. We designed a system with fully custom hardware and software including a 3D printed dual-axis camera gimbal driven by stepper motors, a control PCB based around an ESP32 microcontroller, and a Python-based face tracking pipeline with a custom model and MobileNetV3-Large backbone. The model communicates with an Electron UI and Python backend that work together to keep the user centered in the frame. Although it has some shortcomings, we largely achieved all our objectives and were able to produce a gimbal that is quite e!ective in tracking the user as they move throughout the environment. All of the code and project files for SWVL can be found on GitHub at https://github.com/alexthecat123/SWVL
Longitudinal Assessments of Viral Rebound Among People with HIV in South Carolina: A Population-Based Cohort Study
No full textRoutinely monitoring viral rebound (VR) is important in the life course of people with HIV (PWH). This study examined risk factors for time to the first VR, the number of VRs and their association with VR history in men who have sex with men (MSM). It includes 8176 adult PWH diagnosed from January 2005 to December 2018, followed until July 2021. We used the Cox model for time to the first VR, the Poisson model for a number of VRs, and logistic regression for VR history in MSM. Younger individuals (50–59 years vs 18–29 years, aHR: 0.43, 95% CI: [0.34, 0.55]) were more likely to experience VR. Black individuals (Black vs White, IRR: 1.61, 95% CI [1.38, 1.88]) had more VR, while MSM (MSM vs Heterosexual, IRR: 0.68, 95% CI: [0.57, 0.81]) was negatively associated with number of VsR. Furthermore, individuals engaging illicit drug use (IDU) (aOR: 1.50, 95% CI: [1.03, 2.17]) were more likely to experience VR in the MSM subgroup. This study highlighted the alarming risk factors related to VR among PWH. Tailored intervention should also be deployed for young, Black MSM patients with substance use for more effective and targeted public health strategies concerning VR
Narratives in Motion: the New Era of Dam Building in East Africa
Full text linkNarratives of energy insecurity, underdevelopment, and resource conflict influence how African hydropower projects are justified within the broader context of climate change and demands for sustainable development. The challenge of meeting energy demands provides the overarching motivation for ambitious large-scale hydropower projects in East Africa, especially in Tanzania and Ethiopia. As development projects, the Julius Nyerere Hydropower Project (JNHPP) and the Grand Ethiopian Renaissance Dam (GERD) can be understood as vehicles of political discourse, framed as the technopolitical solution to questions of national identity that both countries face in the midst of sociopolitical and economic transformation periods. This research shows the discursive struggles between proponents and opponents of these large-scale dam projects, as well as the varying justifications for large-scale infrastructure projects. In the Tanzanian context, the JNHPP is legitimized by a discourse centered around industrial transformation, while in the case of Ethiopia, the GERD is legitimized by a discourse of ‘unity in diversity’. Oppositional claims to each project mobilize their own discursive representations to undermine state justifications for the infrastructure projects
Understanding the Instructional Decisions of Middle-School Mathematics Teachers: Three Case Studies
Full text linkThis action research study aimed to explore how middle-school mathematics teachers make instructional decisions in the absence of a required curriculum. While the school site has an adopted textbook, many of the mathematics teachers view this resource as invaluable. Better understanding the decision-making process of mathematics teachers at the school site can provide critical information to better support the teachers. The research question that guided this action research was: How does it seem that middle-school mathematics teachers make instructional decisions? This sequential two-phase qualitative study examined decision-making from a 6th, 7th, and 8th grade mathematics teacher. Data collected within the first phase of research was used to inform and develop a more intentional second phase. Data was deductively analyzed based on Schoenfeld’s (2011) ROG framework, as well as inductively analyzed. The results of this study were aligned with Schoenfeld’s (2011) ROG framework. However, the findings of this study also determined ways in which participants relied on each facet of the ROG framework when making instructional decisions (Schoenfeld, 2011). There were both similarities and differences between participant cases when examining their decision-making processes
Bayesian Joint Modeling of Longitudinal Data and Interval-Censored Failure Time Data
No full textLongitudinal data are a collection of repeated observations of the same subjects at different points in time. Interval-censored data arise when the time to the event of interest for each subject is never exactly observed but known to fall between two consecutive points in time. Joint analysis of longitudinal data and interval-censored failure time data can lead to more accurate estimates compared to separate modeling when the correlation among events of interest or intracluster correlation is present. The aim of this dissertation is to develop efficient and reliable joint analyses of longitudinal and interval-censored failure time data using Bayesian methods. Three research projects are conducted in this dissertation.
The first project of this dissertation focuses on joint modeling of longitudinal data and interval-censored survival data. The joint modeling of longitudinal and survival data is a popular topic in statistics, but most of the existing studies on joint modeling focus on longitudinal data and right-censored data, and there are only a few works on longitudinal data and interval-censored data. A new joint model is proposed and has several appealing properties. Based on the proposed model, a novel Bayesian approach is developed for the parameter estimation, and the proposed approach is computationally efficient and has good performance as shown in numerical studies.
The second project extends the proposed joint analysis of longitudinal data and interval-censored survival data to incorporate variable selection. Variable selection in the context of joint modeling of longitudinal and right-censored survival data has received attention in the recent literature. However, variable selection via Bayesian approaches in the context of joint modeling of longitudinal and interval-censored survival data has not yet been proposed. Bayesian Lasso, Bayesian adaptive Lasso, and spike-and-slab priors are used for simultaneous variable selection and parameter estimation in such context. The proposed approaches are shown to perform better in variable selection as compared to the Bayesian approach under a normal prior via simulation studies.
The third project is motivated by tumorigenicity studies conducted at National Toxicology Program. Such studies test the toxicity of chemical agents by exposing rats to some test agent at different dose levels and checking the tumor status at different organs of each rat for typically 2 years. When the onset times of tumor at different organs are of interest, multivariate current status data arise. In the current literature, a limited number of studies have focused on joint analysis of multivariate current status data while there are even fewer works on joint modeling of longitudinal data and multivariate current status data. A joint model considering semiparametric multivariate survival submodels, and a semiparametric longitudinal submodel is proposed. The Bayesian approach developed for the parameter estimation has good performance as shown in simulation studies. The proposed method is further illustrated by application to the data from a 2-year study conducted by NTP that tested isoprene for carcinogenicity in male rats