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Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers
Full text linkMild cognitive impairment (MCI) is a clinical condition characterized by a decline in cognitive ability and progression of cognitive impairment. It is often considered a transitional stage between normal aging and Alzheimer’s disease (AD). This study aimed to compare deep learning (DL) and traditional machine learning (ML) methods in predicting MCI using plasma proteomic biomarkers. A total of 239 adults were selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort along with a pool of 146 plasma proteomic biomarkers. We evaluated seven traditional ML models (support vector machines (SVMs), logistic regression (LR), naïve Bayes (NB), random forest (RF), k-nearest neighbor (KNN), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost)) and six variations of a deep neural network (DNN) model—the DL model in the H2O package. Least Absolute Shrinkage and Selection Operator (LASSO) selected 35 proteomic biomarkers from the pool. Based on grid search, the DNN model with an activation function of “Rectifier With Dropout” with 2 layers and 32 of 35 selected proteomic biomarkers revealed the best model with the highest accuracy of 0.995 and an F1 Score of 0.996, while among seven traditional ML methods, XGBoost was the best with an accuracy of 0.986 and an F1 Score of 0.985. Several biomarkers were correlated with the APOE-ε4 genotype, polygenic hazard score (PHS), and three clinical cerebrospinal fluid biomarkers (Aβ42, tTau, and pTau). Bioinformatics analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed several molecular functions and pathways associated with the selected biomarkers, including cytokine-cytokine receptor interaction, cholesterol metabolism, and regulation of lipid localization. The results showed that the DL model may represent a promising tool in the prediction of MCI. These plasma proteomic biomarkers may help with early diagnosis, prognostic risk stratification, and early treatment interventions for individuals at risk for MCI
Ethical AI in Teacher Preparation: Exploring Mindsets to Transform Practice
Full text linkThis paper describes a survey study on the AI perspectives of pre-service teachers. The study included 115 participants who completed a survey to gather quantitative and qualitative data on their use and perceptions AI, as well as on ethical concerns surrounding its use. The conceptual framework guiding this work is the “Human – AI – Human” developed by the Washington Office of Superintendent of Public Instruction (2024). This framework offered a lens that grounded ethical behavior through a human-guided perspective. This study found that pre-service teachers did not generally prefer to use AI and were concerned with its ethical implications. They voiced the need for greater guidance and clearer guidelines for its use. This suggests a need for phasing in a human-guided framework that allows for AI practices to involve human inquiry into the processes, responsible implementation, and a great deal of human reflection
Anticancer Activity of Allium cepa through the Inactivation of NF-κB Pathway: A Literature-based Study
Full text linkAllium cepa (onion) is a bulbous herb renowned for its diverse health benefits. This study aims to summarize the anticancer potential of A. cepa through the inactivation of the Nuclear Factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway by reviewing experimental findings across various cancer models. A thorough examination of the literature was carried out using databases like Google Scholar, PubMed, Web of Science, and ScienceDirect. The findings revealed that phytochemicals such as stigmasterol, fisetin, quercetin, isorhamnetin, morin, kaempferol, luteolin, β-carotene, and β-sitosterol exhibit strong anticancer properties against various cancers, including breast, bladder, colon, colorectal, cervical, lung, liver, oral, pancreatic, and skin cancer, as supported by both in vivo and in vitro studies. These phytochemicals primarily exert their anticancer effects by blocking the NF-κB signaling pathway. However, further clinical research is needed to investigate its in vivo and in vitro study to determine its safety and efficacy in cancer treatment, with a focus on optimizing its therapeutic nature
lncRNA UCA1: A novel regulator of glucose metabolism in colorectal cancer
No full textColorectal cancer (CRC) is the primary cause of cancer-related deaths worldwide. According to the American Cancer Society, it is the third most common cancer diagnosed in adults in the United States. It will have an estimated 149,500 new cases of colon cancer in 2021. In addition, studies show CRC has increased morbidity with obesity and obesity-related diseases such as diabetes. In a geographical area of The Rio Grande Valley which has a high incidence of diabetes, understanding the connection between CRC and diabetes is essential. In our lab, we have identified LncRNA UCA1, a poor prognosis marker in CRC, responsible for increased proliferation and upregulating glucose metabolic pathway. First, we investigated the possible role of UCA1 in glucose metabolism, finding that UCA1 overexpressing cells (SW480+UCA1) showed a higher glucose consumption than their vector. The inverse of this trend was established when we knockdown UCA1 (SW620+shUCA1). Further, we found that overexpressing UCA1 increases the migration, invasion, and proliferation of SW480 cells. A vital aspect of the metastatic progression is when the cell survives detachment from the extracellular matrix. A route in which they overcome apoptosis is through modulation of the metabolic pathways, upregulating such markers as GLUT1, FDFT1, SGK1, and HIF1α. At 36hrs of anchorage-independent stimulation, we found an uptick in glucose consumption and lactate production and an increase in the expression of GLUT1, FDFT1, SGK1, and HIF1α compared to 0hrs. The Warburg effect is when glycolysis is no longer connected to the tricarboxylic acid cycle or oxidative phosphorylation. The increase in expression of the markers GLUT1, FDFT1, SGK1, and HIF1α indicates aerobic metabolic activity during anchorage-independent growth with UCA1 expression. This relationship needs to be further examined and could lead to understanding the increased morbidity associated with diabetes and CRC
Library Talk and Everything Else - 2025-04-03
No full texthttps://scholarworks.utrgv.edu/libtalk/1015/thumbnail.jp
Women Writers in the Romantic Age
Full text linkThis groundbreaking book offers a comprehensive review of six hundred and fifty women writers from over fifty national traditions, spanning Europe and the Americas during the transformative years of 1776 to 1848. Framed by revolutionary upheavals, the book explores how women writers shaped and reflected Romanticism’s global currents. It fills a critical scholarly gap, connecting disparate traditions and uncovering voices often overlooked in male-dominated literary histories. Through concise entries, the book names every woman writer identified in its vast research, from celebrated figures like Phillis Wheatley to lesser-known authors whose manuscripts lay buried in archives. Each entry provides essential biographical details, while select excerpts in seventeen languages bring these voices to life, revealing how women navigated the era’s revolutionary ideals and patriarchal barriers. Structured democratically, the volume treats all writers equally—whether anonymous, pseudonymous, or celebrated in their time. It highlights their diverse experiences: poets and novelists, abolitionists and suffragists, mothers and mill workers. From memoirs to political tracts, their works testify to the rich tapestry of women’s contributions to Romanticism. By illuminating these stories, this book challenges national silos, offering a panoramic view of Romanticism as a truly transnational, female-inclusive phenomenon. It represents a go-to resource for students and interested readers, while setting the ground for future scholars to expand this vital field of study
Conditional Constrained and Unconstrained Quantization for Uniform Distributions on Regular Polygons
No full textWe consider a uniform distribution on a regular polygon with k sides for some k⩾3 and the set of all its k vertices as a conditional set. For the uniform distribution under a given conditional set, we first obtain the conditional optimal sets of nn points and the corresponding nnth conditional quantization errors for all positive integers n⩾k. Then, we calculate the conditional quantization dimension and the conditional quantization coefficient in the unconstrained scenario. Next, for the uniform distribution on a polygon with the same conditional set, we investigate the conditionally constrained optimal sets of n points and the conditional constrained quantization errors for all n⩾6, under constraints such as the circumcircle, the incircle, and various diagonals of the polygon
[San Juan] Photograph of New Bank
No full textNew bank located in San Juan, Texas.https://scholarworks.utrgv.edu/hidalgohist_aa/1369/thumbnail.jp
[Pharr] Photograph of PSJA High School
No full textExterior of PSJA High School.https://scholarworks.utrgv.edu/hidalgohist_aa/1344/thumbnail.jp
[Mercedes] Photograph of American Rio Grande Land & Irrigation Company
No full textMain Canal of American Rio Grande Land & Irrigation Co.https://scholarworks.utrgv.edu/hidalgohist_aa/1421/thumbnail.jp