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Molecular and Cellular Mediators of Renal Fibrosis in Lupus Nephritis
No full textLupus nephritis (LN), a significant complication of systemic lupus erythematosus (SLE), represents a challenging manifestation of the disease. One of the prominent pathophysiologic mechanisms targeting the renal parenchyma is fibrosis, a terminal process resulting in irreversible tissue damage that eventually leads to a decline in renal function and/or end-stage kidney disease (ESKD). Both glomerulosclerosis and interstitial fibrosis emerge as reliable prognostic indicators of renal outcomes. This article reviews the hallmarks of renal fibrosis in lupus nephritis, including the known and putative drivers of fibrogenesis. A better understanding of the cellular and molecular processes driving fibrosis in LN may help inform the development of therapeutic strategies for this disease, as well as the identification of individuals at higher risk of developing ESKD
How Gender-Language Arts Stereotypes Predict Achievement for Adolescents
No full textA persistent gender gap in language arts (LA) achievement exists, with girls consistently outperforming boys. Gender-language arts ability stereotypes frame language arts as a subject where girls are perceived to excel while boys are viewed as weaker. These may contribute to shaping students' motivation, self-concept, and academic performance. This study examines how ability-related gender stereotypes impact language arts achievement for boys and girls during adolescence. A univariate general linear model was used, including gender, ability stereotypes, and school level as independent variables and students' cumulative English Language Arts (ELA) report card grades as the dependent variable. Survey data were collected from students in Grades 7-10, assessing their perceptions of gendered ability in language arts through Likert-scale measures of talent, performance, and competence beliefs. Expectancy-Value Theory provided the theoretical framework, stating that beliefs about one's ability and the value placed on a task influence motivation and performance. The results showed that gender, ability stereotypes, and school level significantly predicted language arts achievement. However, interactions between school level and ability stereotypes and between gender and ability stereotypes were not significant. The model accounted for 5.3% of the differences in achievement, showing that while stereotypes play a role, other factors also shape students' success in language arts. Addressing these stereotypes through thoughtful teaching strategies and supportive classroom environments could help close achievement gaps, promote motivation, and create a more equal learning environment for both boys and girls.Psychological, Health, and Learning Sciences, Department ofHonors CollegePsychology, Department o
Treatments of Urinary Leaks: Spectrum of Interventions for Diverse Pathologies and IR Treatment Experience
No full textUrinary leaks can result from trauma, surgery, or inflammatory diseases, requiring diverse treatment approaches. This study highlights interventional radiology techniques used to manage urinary leaks, including stent placement, recanalization, embolization, and percutaneous nephrostomy. Through case examples, the effectiveness of these treatments in preserving kidney function, preventing infection, and improving patient outcomes is demonstrated. The findings emphasize the vital role of IR in managing urinary leaks and guiding treatment decisions. [This project was completed with contributions from Moawiz Saeed, Cameron Lucitt, Jorge Lopera, Rajeev Suri, Samy Al-Bayati, and Momin Hussain from UTHealth San Antonio.]Honors Colleg
An Experimental Investigation of the Effect of Pressure and Salinity on IFT in Live Oil/Brine Systems
No full textResidual oil saturation in reservoirs is primarily influenced by viscous and capillary forces, with interfacial tension (IFT) being a critical factor in fluid distribution due to capillary pressure. Adjusting IFT is essential for enhancing oil recovery, particularly in waterflooding, which is the most common secondary recovery technique after primary production. The salinity of injected water directly affects the IFT between crude oil and brine, making it a crucial factor in optimizing recovery. However, limited studies have examined IFT using live oil samples under actual reservoir conditions. In this study, a high-pressure, high-temperature (HPHT) drop shape analyzer was used to measure the IFT between live oil and brine under reservoir conditions. Five live oil samples and two sodium chloride (NaCl) brine concentrations (30,000 and 100,000 ppm) were tested at a reservoir temperature of 180 °F. Measurements were conducted above the bubble points of the oils, replicating undersaturated reservoir conditions. The results revealed that the impact of pressure on IFT was more complex than that of salinity. IFT generally decreased with increasing pressure but showed mixed behavior across different samples. Conversely, IFT consistently increased with higher salinity. These findings enhance the understanding of IFT behavior under reservoir conditions, supporting improved reservoir simulations and oil recovery strategies
Intertidal Oyster Reef Mapping and Population Analysis in West Galveston Bay, Texas
No full textIntertidal reefs comprised of the eastern oyster (Crassostrea virginica) are an important habitat type within the estuarine landscape and provide many unique ecosystem services. Within West Galveston Bay (WGB), Texas, this type of reef plays an important ecological role; however, the system’s intertidal reef abundance, structure, and habitat provisions are relatively understudied, and the current spatial extent of these reefs has not been recently quantified. The primary objectives of the study were to identify intertidal oyster reefs utilizing GIS models and sample representative reefs for topographical characteristics, oyster demographics, and the associated benthic macrofauna (ABM) community composition in WGB from August 2019 to February 2020. Secondarily, GIS models and oyster population abundance were utilized to estimate the intertidal oyster abundance in WBG. The total area of intertidal oyster reefs in WGB was estimated to be 818,128 m2, with 59,931 m2 of reefs confirmed through GIS analysis and ground truthing, and the GIS model estimating an additional 758,197 m2 of reef. Through ground truthing, reefs were found to be either shell rakes, consisting of piled shell with minimal three-dimensional structure and oysters, or true intertidal reefs with high reef structure and oyster abundance. High oyster abundance was spatially distributed within the northeastern and southwestern areas of WGB and the total intertidal oyster population, coupling the GIS models and reef sampling, was estimated to be 500 million individual oysters. The ABM community was sparse in terms of richness and diversity, further indicating a lack of structural complexity in most of the reefs within this system. This study demonstrates the importance of coupling field results with GIS modeling to estimate system level population sizes and furthers the understanding of the spatial distributions of intertidal oyster reef to promote management, conservation, and restoration efforts
An Evolutionary Deep Reinforcement Learning-Based Framework for Efficient Anomaly Detection in Smart Power Distribution Grids
No full textThe increasing complexity of modern smart power distribution systems (SPDSs) has made anomaly detection a significant challenge, as these systems generate vast amounts of heterogeneous and time-dependent data. Conventional detection methods often struggle with adaptability, generalization, and real-time decision-making, leading to high false alarm rates and inefficient fault detection. To address these challenges, this study proposes a novel deep reinforcement learning (DRL)-based framework, integrating a convolutional neural network (CNN) for hierarchical feature extraction and a recurrent neural network (RNN) for sequential pattern recognition and time-series modeling. To enhance model performance, we introduce a novel non-dominated sorting artificial bee colony (NSABC) algorithm, which fine-tunes the hyper-parameters of the CNN-RNN structure, including weights, biases, the number of layers, and neuron configurations. This optimization ensures improved accuracy, faster convergence, and better generalization to unseen data. The proposed DRL-NSABC model is evaluated using four benchmark datasets: smart grid, advanced metering infrastructure (AMI), smart meter, and Pecan Street, widely recognized in anomaly detection research. A comparative analysis against state-of-the-art deep learning (DL) models, including RL, CNN, RNN, the generative adversarial network (GAN), the time-series transformer (TST), and bidirectional encoder representations from transformers (BERT), demonstrates the superiority of the proposed DRL-NSABC. The proposed DRL-NSABC model achieved high accuracy across all benchmark datasets, including 95.83% on the smart grid dataset, 96.19% on AMI, 96.61% on the smart meter, and 96.45% on Pecan Street. Statistical t-tests confirm the superiority of DRL-NSABC over other algorithms, while achieving a variance of 0.00014. Moreover, DRL-NSABC demonstrates the fastest convergence, reaching near-optimal accuracy within the first 100 epochs. By significantly reducing false positives and ensuring rapid anomaly detection with low computational overhead, the proposed DRL-NSABC framework enables efficient real-world deployment in smart power distribution systems without major infrastructure upgrades and promotes cost-effective, resilient power grid operations
A Grounded Theory of Pathologists’ Assistants Professional Identity Formation
No full textBackground: Pathologists' Assistants (PathAs) are vital members of surgical and autopsy pathology teams. Professional identity formation is critical in healthcare, as misalignment between self-perception and external recognition can lead to dissatisfaction, burnout, and limited career progression. Prior research suggests that professional identity strongly influences career sustainability. With over 60% of PathAs certified for less than ten years, sustainability is increasingly important in this high-demand field amid workforce shortages. Purpose: This study explores how PathAs form their professional identity by examining tensions between their self-perception and external recognition. These dynamics may be shared by other allied health or emerging professions. Findings offer implications for PathA education, workplace culture, and workforce policy. Methods: A qualitative grounded theory approach was used, incorporating semi-structured interviews with 11 practicing PathAs. Interview questions addressed educational experiences, clinical training, mentorship, workplace culture, and perceived roles within healthcare settings. Data were analyzed using constant comparative methods to generate themes and theoretical insights on PathA identity formation. Results: Key influences on professional identity development included education, mentorship, workplace culture, and institutional visibility. Participants expressed a strong internal identity rooted in diagnostic expertise and commitment to patient care, yet also described feeling undervalued or misunderstood within broader healthcare systems. Challenges included limited career advancement, lack of billing autonomy, and minimal representation in interdisciplinary forums. Mentorship and inclusion in institutional structures—such as tumor boards or interdepartmental initiatives—were identified as critical to fostering confidence, professional growth, and long-term retention. Conclusion: Findings highlight the need for structural and cultural changes to support PathA professional identity. Strengthening mentorship, expanding leadership opportunities, increasing institutional visibility, and advocating for billing and licensure reform are essential to advancing the profession. This study contributes to a broader understanding of professional identity construction in mid-level healthcare roles and provides practical guidance to improve workforce development, satisfaction, and recognition within pathology
Examining Pre-service Teachers' Perceptions of Narrative Language Quality in School-Age African American English Speakers
No full textThis study examines pre-teachers' perceptions of narrative language quality of school-age AAE speakers. Specifically, we first collect teachers' ratings of Black students' narrative language quality, examine the impact language variation and narrative structure have on their ratings of students' narrative quality, and identify language ideologies that underpin pre-service teachers' ratings of Black students' narrative language quality. Taking our design cue from Mills et al. (2021), we interviewed and collected ratings from 4 pre-service teachers (PSTs). As part of our mixed methods design (Creswell, 2003), our quantitative measures included: a) a listener rating task and a 20-item post-rating survey on narration features. The qualitative measure was a semi-structured interview containing five lead-off prompts designed to elicit participants' beliefs about English and its variation. Our working hypothesis is that PSTs' ratings of Black students' narrative quality will vary based on language variation of student narrators, narrative structure, and demographic background of raters. This study is intended to raise teachers' awareness of potential linguistic bias, so that they can critically reflect on and alter how they view AAE and its speakers.Communication Sciences and Disorders, Department ofHonors Colleg
Adaptive, Federated, and Resilient Health Monitoring via Novel Online Collaborative Learning Algorithms
No full textAdaptive monitoring of a large population of dynamic processes is critical for the timely detection of abnormal events under limited resources. This issue is pervasive in various sectors, including healthcare and engineering systems, due to the disparity between available monitoring resources and the large population of units, and the uncertain and heterogeneous dynamics of unit progression. To effectively address this problem, in this dissertation, we introduce advanced methodologies for designing adaptive monitoring strategies. We first develop an online collaborative learning framework that efficiently models and monitors a population of dependent units under resource constraints. We then develop a decentralized online collaborative framework that enables online modeling and monitoring of units with latent dynamics while preserving data privacy. Finally, we develop a novel robust online multi-task learning algorithm designed to capture latent structures inherent in the population from sequentially observed data under corruption. We have demonstrated the effectiveness of the proposed methods through rigorously proven theoretical analysis and experiments, including simulation studies and real-world world applications including cognitive degradation monitoring in Alzheimer’s Disease (AD) and battery degradation monitoring
Alterations in Tear Proteomes of Adults with Pre-Diabetes and Type 2 Diabetes Mellitus but Without Diabetic Retinopathy
No full textBackground: Type 2 diabetes mellitus (T2DM) is an epidemic chronic disease that affects millions of people worldwide. This study aims to explore the impact of T2DM on the tear proteome, specifically investigating whether alterations occur before the development of diabetic retinopathy. Methods: Flush tear samples were collected from healthy subjects and subjects with preDM and T2DM. Tear proteins were processed and analyzed by mass spectrometry-based shotgun proteomics using a data-independent acquisition parallel acquisition serial fragmentation (diaPASEF) approach. Machine learning algorithms, including random forest, lasso regression, and support vector machine, and statistical tools were used to identify potential biomarkers. Results: Machine learning models identified 17 proteins with high importance in classification. Among these, five proteins (cystatin-S, S100-A11, submaxillary gland androgen-regulated protein 3B, immunoglobulin lambda variable 3–25, and lambda constant 3) exhibited differential abundance across these three groups. No correlations were identified between proteins and clinical assessments of the ocular surface. Notably, the 17 important proteins showed superior prediction accuracy in distinguishing all three groups (healthy, preDM, and T2DM) compared to the five proteins that were statistically significant. Conclusions: Alterations in the tear proteome profile were observed in adults with preDM and T2DM before the clinical diagnosis of ocular abnormality, including retinopathy