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Organic Chemistry II Drill (CHEM2220D). Practice Final Exam Answer Key
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Organic Chemistry II Drill (CHEM 2220D). Syntheis. Sample Problems
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McNair Program - Projects with a Purpose - Dr. Melvinia M. Martin\u27s Students - Group Photo # 7
No full textGroup photograph of McNair Program students engaged in a service project. This photograph includes a person using a jack hammer.https://digitalcommons.xula.edu/mcnair_service/1008/thumbnail.jp
An Overview of Handheld Sun Photometer Measurements of Atmospheric Aerosols in New Orleans, Louisiana: A Case Study of the Xavier University Study Site.
Get PDFAerosol optical depth (AOT) was measured at Xavier University of Louisiana (XULA, 29.96ᵒ N, 90.11° W and 3m above sea level) using a GLOBE handheld sun photometer. The measurements were done at two different wavelengths, 505nm and 625nm. The measured values were used to extrapolate the AOT values for wavelengths 667nm, 551nm, 532nm and 490nm at the XULA site. The measured and calculated AOT values were then compared with values from the nearest AERONET station at Wave CIS site 6 (AERONET, 28.87ᵒ N, 90.48° W and 33m above sea level), which is 60 miles south of XULA. In this study we tracked the annual and daily variations of AOT for a 12-month period from September 2017 to August 2018. These data show good qualitative agreement between the two stations in the 12-month period. Both sets of data show distinct peaks in February and May. Both sets of data show low AOT values in the winter months and high AOT values in the summer months. The hourly AOT variations averaged over the 12-month period was also investigated for the XULA site. The data show two peaks, one at 9:00 am and another at 3:00pm. We also compared AOT data from two independently calibrated GLOBE sun photometers at the XULA site. The data show that the two instruments are in excellent agreement. The R-squared value for the 505nm channel is 0.92 and the R-squared value for the 625nm channel is 0.95
A Computational Framework For Predicting Direct Contacts and Substructures Within Protein Complexes.
No full textUnderstanding the physical arrangement of subunits within protein complexes potentially provides valuable clues about how the subunits work together and how the complexes function. The majority of recent research focuses on identifying protein complexes as a whole and seldom studies the inner structures within complexes. In this study, we propose a computational framework to predict direct contacts and substructures within protein complexes. In this framework, we first train a supervised learning model of l2-regularized logistic regression to learn the patterns of direct and indirect interactions within complexes, from where physical subunit interaction networks are predicted. Then, to infer substructures within complexes, we apply a graph clustering method (i.e., maximum modularity clustering (MMC)) and a gene ontology (GO) semantic similarity based functional clustering on partially-and fully-connected networks, respectively. Computational results show that the proposed framework achieves fairly good performance of cross validation and independent test in terms of detecting direct contacts between subunits. Functional analyses further demonstrate the rationality of partitioning the subunits into substructures via the MMC algorithm and functional clustering
A Single-Nucleus RNA-Sequencing Pipeline to Decipher the Molecular Anatomy and Pathophysiology of Human Kidneys.
No full textDefining cellular and molecular identities within the kidney is necessary to understand its organization and function in health and disease. Here we demonstrate a reproducible method with minimal artifacts for single-nucleus Droplet-based RNA sequencing (snDrop-Seq) that we use to resolve thirty distinct cell populations in human adult kidney. We define molecular transition states along more than ten nephron segments spanning two major kidney regions. We further delineate cell type-specific expression of genes associated with chronic kidney disease, diabetes and hypertension, providing insight into possible targeted therapies. This includes expression of a hypertension-associated mechano-sensory ion channel in mesangial cells, and identification of proximal tubule cell populations defined by pathogenic expression signatures. Our fully optimized, quality-controlled transcriptomic profiling pipeline constitutes a tool for the generation of healthy and diseased molecular atlases applicable to clinical samples