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Effects of disulfiram on the metabolome of MRSA
Get PDFDisulfiram, known as Antabuse®, is an oral drug for the treatment of alcohol dependence. Previous studies have indicated that disulfiram (DSF) exhibits antibacterial effects, particularly against Gram-positive bacteria, such as methicillin-resistant Staphylococcus aureus (MRSA). Our study delves into the antibacterial mechanism of DSF in MRSA through High-Pressure Liquid Chromatography (HPLC) metabolomics, investigating the underlying mechanism of DSF effects on thiamine and amino acid metabolism. Thiamine pyrophosphate (TPP) plays a crucial role as a cofactor for critical enzymes such as transketolase, pyruvate dehydrogenase, and 2-oxoglutarate dehydrogenase. These enzymes are integral to the carbohydrate metabolism process within bacterial cells. TPP also contributes to coenzyme A (CoA) biosynthesis, identified as a prospective drug target for DSF in MRSA. Recent research highlighted DSF\u27s role in lowering intracellular CoA levels in MRSA, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways helped to uncover enriched genes related to the biosynthesis of TPP. Our transcriptome data further illuminated different amino acid metabolism shifts within DSF-treated MRSA. In-depth HPLC investigations utilized various methods to gauge TPP and amino acid levels in DSF-treated MRSA. These HPLC results effectively validated our hypothesis, confirming DSF\u27s influence on increasing cellular levels of TPP and amino acids like glutamate, glutamine, arginine, glycine, β-alanine, and lysine. Notably, our study also revealed diminished cellular levels of aspartate, valine, phenylalanine, and threonine. Our comprehensive study offers further insight on why DSF treatment alters TPP and select amino acid levels. These findings add to our understanding of DSF\u27s antibacterial mechanism in MRSA
20230601: Libraries, Drinko Blueprints, 1997-1999
Get PDFThese items include materials from the University Libraries at Marshall University from 1997-1999. Items were received in 2023 from Jody Perry in IT, but these materials existed when the Libraries and IT were under one unit. Items were transferred to University Libraries upon receipt. This collection includes notable materials about blueprints for Drinko Library. This is not an exhaustive list. Please download the finding aid for a full list of contents
Identifying hazardous patterns in MSHA data using random forests
Get PDFMining safety and health in the US can be better understood through the application of machine learning techniques to data collected by the Mine Safety and Health Administration (MSHA). By identifying hazardous conditions that could lead to accidents before they occur, valuable insights can be gained by MSHA, mining operators, and miners. In this study, we propose using a Random Forest machine learning model to predict whether a given mining violation will lead to an accident, and if so, whether it will be fatal or non-fatal. To achieve this, the model is trained on MSHA violation data and the sum of scheduled accident charges within 35 days of the violation. We experiment with different predictive models using varying data columns, training set sizes, prediction classes, and hyperparameters to achieve a reliable prediction. One of the challenges in generating these models is accurately predicting the sparse class of accidents, as opposed to the abundant class of no accidents. To address this, we propose utilizing sample minimizing to balance the false negative and false positive rate and create a more accurate predictive model. Our results demonstrate, with a high degree of confidence, the potential for machine learning to improve mine safety and health by identifying hazardous conditions and mitigating the risk of accidents
Leveraging Explainable Artificial Intelligence (XAI) to Understand Performance Deviations in Load Tests of Large Software Systems
Get PDFPerformance testing generates vast amounts of data, making it challenging for human analysts to process within a reasonable timeframe. Therefore, black-box machine learning models are often used to determine pass/fail status, but these models lack transparency and cannot explain why a test has failed, leading to a time-consuming manual analysis process. To address this issue, this thesis proposes using Explainable Artificial Intelligence (XAI) to improve the trustworthiness of black-box and interpretable models in performance testing. The proposed approach leverages the Shapley Additive Explanation (SHAP) algorithm as a surrogate model to help performance analysts understand the decision-making process of black-box machine learning models. By wrapping SHAP around black-box models, analysts can gain explainability on why a model predicted a test\u27s pass or fail status and identify the relative importance of performance data to machine learning models. The proposed approach was evaluated through several load text experiments on a real testbed, using industry-standard performance benchmarks, manually injecting performance bugs into the system to synthesize ground truth and building machine learning models using a black box learner (Artificial Neural Network) and an interpretable learner (Random Forest) to predict the test\u27s pass or fail status. The results demonstrate that classical performance measures such as precision, recall, F-measure, and accuracy are not sufficient to gauge the reliability and trustworthiness of machine learning models. Instead, the proposed approach stands out by providing the explanations behind the decisions made by learning algorithms and enhancing their trustworthiness. The proposed approach, though evaluated through load testing can be generalized to other domains and require little or no effort to operate
Third Circuit Deals HHS Another 340B Blow: Congressional Action Needed to Fill Regulatory Gaps
No full textThe purpose of this commentary is twofold: (1) to identify deficiencies in the 340B statute related to the use of multiple contract pharmacies and delivery to them—deficiencies that have resulted in the need for courts to resolve issues through judicial interpretation, and (2) to encourage policymaker action to address these deficiencies
Girl Scouts of Black Diamond Council
No full textGirl Scouts working on a badge during Girl Scout Badge College, December 13, 2023 on the Marshall University, Huntington campus.https://mds.marshall.edu/bd_girl_scout_badge_college_2023/1014/thumbnail.jp