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The University of Houston Institutional Repository (UHIR) collects, preserves and distributes scholarly output and creative works produced by the University of Houston community. UHIR provides free and open online access to the university’s research and scholarship, including electronic theses and dissertations.
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Recent Submissions
Hospital Aesthetics: Rescripting Medical Images of Disability
(2024-09-25) Cachia, Amanda
This talk is based on an overview of my second book project, Hospital Aesthetics: Rescripting Medical Images of Disability. In the book, I argue that contemporary disabled artists are offering a new hospital aesthetics by taking health and care into their own hands and bodyminds. These artists are considering their lives on their own terms, outside of clinical and therapeutic settings. Hospital aesthetics shows a different side to disabled bodies that attempts to undo the social and cultural impacts the hospital has had on its disabled patients, both historically and in the contemporary moment. The book will contribute to a radical activism that reveals the inadequacies of the medical industrial complex. The talk will provide a discussion of numerous artworks to feature in the different chapters of the book that focus on charting immunocompromised bodies, disabling medical assistive devices, sensual hospital aesthetics, crip networks of care, and alt medicine.
Role of Gut Microbial Metabolites in Cardiovascular Diseases—Current Insights and the Road Ahead
(2024-09-23) Datta, Sayantap; Pasham, Sindhura; Inavolu, Sriram; Boini, Krishna M.; Koka, Saisudha
Cardiovascular diseases (CVDs) are the leading cause of premature morbidity and mortality globally. The identification of novel risk factors contributing to CVD onset and progression has enabled an improved understanding of CVD pathophysiology. In addition to the conventional risk factors like high blood pressure, diabetes, obesity and smoking, the role of gut microbiome and intestinal microbe-derived metabolites in maintaining cardiovascular health has gained recent attention in the field of CVD pathophysiology. The human gastrointestinal tract caters to a highly diverse spectrum of microbes recognized as the gut microbiota, which are central to several physiologically significant cascades such as metabolism, nutrient absorption, and energy balance. The manipulation of the gut microbial subtleties potentially contributes to CVD, inflammation, neurodegeneration, obesity, and diabetic onset. The existing paradigm of studies suggests that the disruption of the gut microbial dynamics contributes towards CVD incidence. However, the exact mechanistic understanding of such a correlation from a signaling perspective remains elusive. This review has focused upon an in-depth characterization of gut microbial metabolites and their role in varied pathophysiological conditions, and highlights the potential molecular and signaling mechanisms governing the gut microbial metabolites in CVDs. In addition, it summarizes the existing courses of therapy in modulating the gut microbiome and its metabolites, limitations and scientific gaps in our current understanding, as well as future directions of studies involving the modulation of the gut microbiome and its metabolites, which can be undertaken to develop CVD-associated treatment options. Clarity in the understanding of the molecular interaction(s) and associations governing the gut microbiome and CVD shall potentially enable the development of novel druggable targets to ameliorate CVD in the years to come.
The Evolution and Impact of Distilled Spirits Regulation in the United States: Considerations for Policymakers and Academia
(2024-09-23) Norris, Cortney L.; Taylor, Scott; Taylor, D. Christopher
This manuscript analyzes the issues presented in Executive Order 14036, which aimed to promote competition in the American economy, including specific directives for the alcohol industry. Specifically, this manuscript focuses on the issues regarding distilled spirits regulation and delves into the implications for the distilled spirits sector. The order addresses anti-competitive practices, encouraging regulatory bodies to review and revise existing policies that may hinder fair market practices. This paper explores these issues and provides a historical context of distilled spirits regulation in the United States, examining how past policies have shaped the current landscape. It analyzes the key provisions of Executive Order 14036, highlighting its potential to foster increased competition, innovation, and consumer choice within the distilled spirits market. Lastly, the article provides a call to action for policymakers, academia, and consumers which will aid distilled spirits producers in gaining parity with beer and wine producers.
Is Seeing Believing? A Practitioner’s Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies
(2024-09-16) Fan, Kun; Subedi, Srijana; Yang, Gongshun; Lu, Xi; Ren, Jie; Wu, Cen
Variable selection methods have been extensively developed for and applied to cancer genomics data to identify important omics features associated with complex disease traits, including cancer outcomes. However, the reliability and reproducibility of the findings are in question if valid inferential procedures are not available to quantify the uncertainty of the findings. In this article, we provide a gentle but systematic review of high-dimensional frequentist and Bayesian inferential tools under sparse models which can yield uncertainty quantification measures, including confidence (or Bayesian credible) intervals, p values and false discovery rates (FDR). Connections in high-dimensional inferences between the two realms have been fully exploited under the “unpenalized loss function + penalty term” formulation for regularization methods and the “likelihood function × shrinkage prior” framework for regularized Bayesian analysis. In particular, we advocate for robust Bayesian variable selection in cancer genomics studies due to its ability to accommodate disease heterogeneity in the form of heavy-tailed errors and structured sparsity while providing valid statistical inference. The numerical results show that robust Bayesian analysis incorporating exact sparsity has yielded not only superior estimation and identification results but also valid Bayesian credible intervals under nominal coverage probabilities compared with alternative methods, especially in the presence of heavy-tailed model errors and outliers.
A Comparative Analysis of the Prediction of Gas Condensate Dew Point Pressure Using Advanced Machine Learning Algorithms
(2024-09-16) Lertliangchai, Thitaree; Dindoruk, Birol; Lu, Ligang; Yang, Xi; Sinha, Utkarsh
Dew point pressure (DPP) emerges as a pivotal factor crucial for forecasting reservoir dynamics regarding condensate-to-gas ratio and addressing production/completion hurdles, alongside calibrating EOS models for integrated simulation. However, DPP presents challenges in terms of predictability. Acknowledging these complexities, we introduce a state-of-the-art approach for DPP estimation utilizing advanced machine learning (ML) techniques. Our methodology is juxtaposed against published empirical correlation-based methods on two datasets with limited sizes and diverse inputs. With superior performance over correlation-based estimators, our ML approach demonstrates adaptability and resilience even with restricted training datasets, spanning various fluid classifications. We acquired condensate PVT data from publicly available sources and GeoMark RFDBASE, encompassing dew point pressure (the target variable), as well as compositional data (mole percentages of each component), temperature, molecular weight (MW), and specific gravity (SG) of heptane plus, which served as input variables. Before initiating the study, thorough assessments of measurement quality and results using statistical methods were conducted leveraging domain expertise. Subsequently, advanced ML techniques were employed to train predictive models with cross-validation to mitigate overfitting to the limited datasets. Our models were juxtaposed against the foremost published DDP estimators utilizing empirical correlation-based methods, with correlation-based estimators also trained on the underlying datasets for equitable comparison. To improve outcomes, pseudo-critical properties and artificial proxy features were utilized, leveraging generalized input data.