UTSA Runner Research Press (Univ. of Texas at San Antonio)
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Multimodal Identification of Molecular Factors Linked to Severe Diabetic Foot Ulcers Using Artificial Intelligence
No full textDiabetic foot ulcers (DFUs) are a severe complication of diabetes mellitus (DM), which often lead to hospitalization and non-traumatic amputations in the United States. Diabetes prevalence estimates in South Texas exceed the national estimate and the number of diagnosed cases is higher among Hispanic adults compared to their non-Hispanic white counterparts. San Antonio, a predominantly Hispanic city, reports significantly higher annual rates of diabetic amputations compared to Texas. The late identification of severe foot ulcers minimizes the likelihood of reducing amputation risk. The aim of this study was to identify molecular factors related to the severity of DFUs by leveraging a multimodal approach. We first utilized electronic health records (EHRs) from two large demographic groups, encompassing thousands of patients, to identify blood tests such as cholesterol, blood sugar, and specific protein tests that are significantly associated with severe DFUs. Next, we translated the protein components from these blood tests into their ribonucleic acid (RNA) counterparts and analyzed them using public bulk and single-cell RNA sequencing datasets. Using these data, we applied a machine learning pipeline to uncover cell-type-specific and molecular factors associated with varying degrees of DFU severity. Our results showed that several blood test results, such as the Albumin/Creatinine Ratio (ACR) and cholesterol and coagulation tissue factor levels, correlated with DFU severity across key demographic groups. These tests exhibited varying degrees of significance based on demographic differences. Using bulk RNA-Sequenced (RNA-Seq) data, we found that apolipoprotein E (<i>APOE</i>) protein, a component of lipoproteins that are responsible for cholesterol transport and metabolism, is linked to DFU severity. Furthermore, the single-cell RNA-Seq (scRNA-seq) analysis revealed a cluster of cells identified as keratinocytes that showed overexpression of <i>APOE</i> in severe DFU cases. Overall, this study demonstrates how integrating extensive EHRs data with single-cell transcriptomics can refine the search for molecular markers and identify cell-type-specific and molecular factors associated with DFU severity while considering key demographic differences.Electrical and Computer Engineerin
Effect of Streamwise/Streamline Pressure Gradients on Turbulent Flow Separation at High Reynolds Numbers With Passive Scalar Transport
No full textIn this study, Spatially-Developing Turbulent Boundary Layer (SDTBL) detachment is numerically analyzed via the two-dimensional (2D) Reynolds-averaged Navier-Stokes (RANS) equations, and its implication on passive scalar transport. In Adverse Pressure Gradient (APG), or flow decelerating, conditions, the transport of momentum and scalars in wall-bounded flows are impeded. In strong APG conditions, this can result in flow separation which interrupts this transport behavior. Due to the reality of engineering hydrodynamic, aerodynamic, or aerothermal designs, APG conditions are all but avoidable, and thus must be studied. To achieve this, the proposed objectives are:ÿ
1. Analyze the influence of strong streamwise/streamline APG's by decelerating flow and wall curvature on massively separated flow and SDTBL at the verge of surface detachment. The geometries must be relevant and corollary to engineering applications.
2. Evaluate the performance of popular RANS and passive scalar turbulence models on strong APG conditions over flat and curved walls. The first turbulence model used is the Spalart-Allmaras model, which is common for high Reynolds numbers and is a robust RANS model. The second model is the k ?? model with Shear Stress Transport (SST) treatment or formulation due to its specialty in APG conditions. These models will be tested in high Reynolds number flows.
3. Understand the mechanisms of passive scalar transport in highly separated or separating flows.
The objectives are achieved by numerically replicating two experimental studies from the literature. In Patrick (1987), an experimental investigation of SDTBL separation with reattachment over a flat plate was carried out by prescribing a strong APG via flow potential and subsequent favorable pressure gradient (FPG) by manipulating the opposite surface. The mean flow and Reynolds stresses were measured via laser PIV, hot-wire anemometry, and pneumatic probing techniques. The significant flow deceleration, infringed on the incoming turbulent flow, produced a separated SDTBL over a streamwise distance of approximately 55cm, representing a potential challenge for standard turbulence models. Additionally, the high momentum-thickness Reynolds number range considered in (Re? ? 11.1e3) adds difficulties to numerical modeling and computational resources. The second reproduced experimental setup was conducted by So and Mellor (1972). They investigated the effect of uniform (zero-pressure gradient) and moderately adverse pressure distributions on incoming SDTBL's along convex and concave walls. The hot-wire measurements confirmed the enhancement of turbulent mixing over the concave surface, while the opposite occurred in the convex surface.
The Computational Fluid Dynamics (CFD) analysis is performed with the open-source flow solver OpenFOAM using the IncompressibleFluid solver that uses the SIMPLE methodology, on UT Austin's TACC Lonestar6 system.
The RANS models in 2D steady CFD are able to simulate the conditions prior to and just into separation, but struggle to reach full separation or reattachment. Additionally, the normal stresses that a curvature imposes on the RANS models appears to cause issues with the CFD analysis coming to a resolved solution. This may be due to the 2D steady analysis that the RANS models are used in, and potential solutions are highlighted in this study.Mechanical Engineerin
Urban climate action plans in the United States: A textual content analysis and evaluation
No full textCities within the United States (US) have sought to address climate change by creating local Climate Action Plans (CAPs). This paper explores the state of urban climate planning by performing a textual content analysis of CAPs for 203 cities in the US. Specifically, the analysis quantified the degree to which CAPs emphasized each component of the following three climate planning triads: 1) greenhouse gas emissions, urban heat, and urban flooding; 2) economic, environmental, and health aspects; and 3) mitigation, adaptation, and equity-based strategies. The study also evaluated how the balance across these three climate planning triads varied spatially, temporally, and based on city population. The findings indicated that CAPs overall placed a notable emphasis on reducing emissions while adapting to localized urban climate hazards featured less frequently. More recent plans were less emissions-centric and exhibited a greater focus on equity and health. Differences across city size were also observed, as larger cities produced CAPs that more frequently mentioned health and equity as well. Overall, the analysis highlighted that a broader holistic approach focused on addressing the full spectrum of local to global climate hazards is necessary to ensure CAPs effectively protect the future livability and economic vitality of urban centers.Political Science and Geograph
Investigating MS with MS: Mass Spectrometry profiling of neuroinflammation and demyelination in Multiple Sclerosis
No full textThe full text of this item is not available at this time because the author has placed this item under an embargo until June 12, 2025.Multiple sclerosis (MS) is a neuroinflammatory autoimmune disease that affects millions of people worldwide. Unfortunately, successful management of MS and its progression continues to elude the MS medical community. Considering that current MS treatments are focused on modulating inflammatory cells and molecules such as cytokines and other inflammatory mediators thought to contribute to the neuroinflammatory mechanisms of this disease, our research aims to explore an understudied yet critical element of the brain, its lipid components. Along these lines, a hallmark of the disease is demyelination, the degradation of the myelin sheath wrapped around axons. Myelin is a fatty, protective coating that supports axons in many ways and has a molecular profile that is predictively rich with lipids. Myelin?s lipid profile has been under-explored for its disease-modifying therapeutic potential.
The molecular profiling of lipids presents technical and conceptual challenges that have constrained the investigation of lipid physiochemistry. However, advancements in the field of mass spectrometry now provide the means to overcome lipid?s profiling complexities.
This body of research utilizes matrix-assisted laser desorption/ionization (MALDI) and time-of-flight (TOF) mass spectrometry imaging (MSI) to profile lipids involved in neuroinflammation and the demyelination mechanism at multiple EAE disease course time points. Identifying neuronal lipids and their alterations in expression during disease will provide alternate molecular targets for potential disease-modifying therapies.Chemistr
A Numerical Study of Quantum Entropy and Information in the Wigner–Fokker–Planck Equation for Open Quantum Systems
No full textKinetic theory provides modeling of open quantum systems subject to Markovian noise via the Wigner–Fokker–Planck equation, which is an alternate of the Lindblad master equation setting, having the advantage of great physical intuition as it is the quantum equivalent of the classical phase space description. We perform a numerical inspection of the Wehrl entropy for the benchmark problem of a harmonic potential, since the existence of a steady state and its analytical formula have been proven theoretically in this case. When there is friction in the noise terms, no theoretical results on the monotonicity of absolute entropy are available. We provide numerical results of the time evolution of the entropy in the case with friction using a stochastic (Euler–Maruyama-based Monte Carlo) numerical solver. For all the chosen initial conditions studied (all of them Gaussian states), up to the inherent numerical error of the method, one cannot disregard the possibility of monotonic behavior even in the case under study, where the noise includes friction terms.Physics and AstronomyMathematic
Overlap and Interrelations Between (Im)mobility Motivations
No full textScholarship in Migration Studies and Forced Migration and Refugee Studies recognizes that migration and immobility can be the result of various, mixed motivations. Empirical work and conceptualizations of forced and “lifestyle” migration consider some of this complexity. Scholarship on immobility has also examined various, mixed motives. Finally, migration theory development has recently begun to incorporate various “non-economic” motivations, mainly into frameworks originally aimed at tackling economic/labor migrations, mainly integrating force and/or environmental factors. However, efforts to conceptualize and theorize on how and why motivations overlap or are interrelated (positively or negatively) are more scant, less explicit, and less systematic. In this paper, I provide a broad systematic taxonomy of migration and immobility motivation overlap and interrelation. First, I describe the six main (im)mobility motivations discussed in the literature—namely economic, labor-related, safety-related, environmental, family-related, and related to self-fulfillment—organizing them around the degree to which they are driven by extrinsic and/or intrinsic rewards and costs. Second, I provide a general typology of possible ways in (im)mobility motivations become “alternative” to and/or concurrent with each other, and how these instances operate at individual and/or population levels. Third, I examine how the different motivations fit within three important theories of micro-level decision-making in the literature, exploring different points of overlap and interrelation between mechanisms within and across analytical perspectives. I conclude discussing the potential implications of this motivation integration.The University of Colorado at Boulder; The University of Texas – San Antonio; National Science FoundationSociology and DemographyInstitute for Health Disparities Researc
Towards Automatic Oracle Prediction for AR Testing: Assessing Virtual Object Placement Quality under Real-World Scenes
No full textAugmented Reality (AR) technology opens up exciting possibilities in various fields, such as education, work guidance, shopping, communication, and gaming. However, users often encounter usability and user experience issues in current AR apps, often due to the imprecise placement of virtual objects. Detecting these inaccuracies is crucial for AR app testing, but automating the process is challenging due to its reliance on human perception and validation. This paper introduces VOPA (Virtual Object Placement Assessment), a novel approach that automatically identifies imprecise virtual object placements in real-world AR apps. VOPA involves instrumenting real-world AR apps to collect screenshots representing various object placement scenarios and their corresponding metadata under real-world scenes. The collected data are then labeled through crowdsourcing and used to train a hybrid neural network that identifies object placement errors. VOPA aims to enhance AR app testing by automating the assessment of virtual object placement quality and detecting imprecise instances. In our evaluation of a test set of 304 screenshots, VOPA achieved an accuracy of 99.34%, precision of 96.92% and recall of 100%. Furthermore, VOPA successfully identified 38 real-world object placement errors, including instances where objects were hovering between two surfaces or appearing embedded in the wall.Computer Scienc
Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm
No full textInspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.Mechanical EngineeringCivil and Environmental EngineeringComputer Scienc
S2 E1: The National Science Foundation Scholarship for Service Program: A Pathway to a Cyber Security Career
No full textJoin our host Zavier Rahman with guest Dr. Philip Menard as he discusses the National Science Foundation Scholarship for Service program for cyber security students. Learn how to apply, advice on how to become a successful applicant, and how recipients have the potential for a rewarding career in cyber security with government agencies. Hear how the program offers an internship, a generous stipend, and guaranteed employment in one of the many federal executive branch agencies.Information Systems and Cyber Securit
Local and Landscape Effects on Wild Bees Along an Urbanization Gradient in Central Texas
No full textPublic awareness and concern for widespread declines in pollinator populations have sparked interest in investing in urban green spaces as habitats for these populations. This study sampled a gradient of urban green spaces to determine what factors may influence bee community attributes. Bee communities from nine major urban green spaces and the local site characteristics were surveyed for seven months, and the landscape composition surrounding each site was quantified to determine the influence of land use on bee genera diversity and richness at both the local and landscape scales. Multiple linear regressions were constructed to identify what variables may be influencing the differences in bee communities between sites. The models broadly suggest that at the local level, urban bee genera diversity and richness were negatively impacted by an increase in floral species richness. Additionally, bee genera richness increased as litter cover increased, and manure decreased. At the landscape level, an increase in water cover and decrease in fine vegetation were associated with an increase in bee genera diversity. This study will provide information on the impact of urban development on wild Central Texas bee populations and contribute to knowledge on the use of urban green spaces as sites for urban bee conservation.Integrative Biolog