Journal Usage Level Changes at Morehouse School of Medicine Library 2011-2020

OBJECTIVE: To determine faculty and researcher journal usage levels and their implications on library’s journal collection and access models for the past five years and compare to previous usage and implications. 1. The librarians would like to investigate changes made in the journal collection as we moved to fewer print based resources through a comparison of usage levels prior to the changes to usage levels after the changes. We had sought to seek optimum pathways for supporting the school’s curricula, research agendas, and health care enterprise. Our ultimate question is ‘How have changes correlated and compared to previous usage and needs of our users?’ 2. METHODS: Using counter statistics (resources such as ScienceDirect, Ovid, Wiley, Nature, and others), ILL records, in-house usage statistics, and interviews, librarians will determine for the past five years (2016-2020) and compare to the previous five years (2011-2015) the: 1. 20 most and 20 least used journals by department and major subject division; 2. 20 most needed, but not owned, journals by department and major subject area; 3. Emerging areas of interest among faculty and researchers; 4. A short survey is to be released to all patrons to determine their preferred resources; 5. Two focus group sessions will be held with faculty and researchers and two with students to solicit additional feedback; 6. Descriptive statistics will be used to show patterns of usage; 7. Comparisons of levels of usage and costs will be made using analyses of variance in mean levels of usage and mean costs; 8. Correlations (Pearson’s r) will be determined between relative costs and usage; 9. Level of MSM related published articles. RESULTS: FY2011-15: 20 journals with the highest numbers of successfully retrieved articles over past 5 years included 2 that were on the list for all 5 years (Journal of Virology and Nature), accounting for 10 of the 20 slots. FY2016-20: All journals in the top 20 used in FY2016 remained for each of the 5 years. FY2011-15 successful use trend was up and down, while for FY2016-20, the trend generally increased until 2020. The sum of the usage of the top 20 journals for each year trended in different ways for 2011-2015 and 2016 and 2020: FY2011-15 successful use trend was up and down, FY2016-20, the trend generally increased until 2020. FY2011-2015 averaged 52,044 successful retrievals per year, while FY2016-2020 averaged 68,549 successful retrievals per year. The sums of the usage of the top 20 journals for 2015 and 2020 totaled 11,776 and 18,908, respectively, representing a 60.51% increase. For FY2019-2020 there were 8 publishers for the 20 most used journals. From FY2010-2011 to FY2019-2020 the fluctuating pattern of faculty publications appear to loosely follow the pattern of journal usage as reflected in the JR1reports. Correlation of publication to use was moderate at .666 and significant at .036. Data shows successful retrievals declined over time for top 20 for FY2011-15, but tended to increase for FY 2016-20 until FY2020. FY2011-2015 averaged 52,044 successful retrievals per year, while FY2016-2020 averaged 68,549 successful retrievals per year. When looking for a relationship between journal cost and usage, only moderate correlations of .406, .638, and .407 were found for FY2016, FY2018, and FY2020, respectively. However, none of the correlations were statistically significant (p = .177, .065, and .133, respectively). CONCLUSIONS: Data shows successful retrievals declined over time for top 20 for FY2011-15, but tended to increase for FY2016-20 until AY2020. FY2011-2015 averaged 52,044 successful retrievals per year, while FY2016-2020 averaged 68,549 successful retrievals per year. When looking for a relationship between journal cost and usage, only moderate correlations of .406, .638, and .407 were found for FY2016, FY2018, and FY2020, respectively. However, none of the correlations were statistically significant (p = .177, .065, and .133, respectively). A significant (p= .036) and devilishly moderate correlation (r=.666) between faculty publication levels and journal use levels was found

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

City of Hitchcock Comprehensive Plan 2020-2040

Hitchcock is a small town located in Galveston County (Figure 1.1), nestled up on the Texas Gulf Coast. It lies about 40 miles south-east of Houston. The boundaries of the city encloses an area of land of 60.46 sq. miles, an area of water of 31.64 sq. miles at an elevation just 16 feet above sea level. Hitchcock has more undeveloped land (~90% of total area) than the county combined. Its strategic location gives it a driving force of opportunities in the Houston-Galveston Region.The guiding principles for this planning process were Hitchcock’s vision statement and its corresponding goals, which were crafted by the task force. The goals focus on factors of growth and development including public participation, development considerations, transportation, community facilities, economic development, parks, and housing and social vulnerabilityTexas Target Communitie

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups

Prevalence and architecture of de novo mutations in developmental disorders.

The genomes of individuals with severe, undiagnosed developmental disorders are enriched in damaging de novo mutations (DNMs) in developmentally important genes. Here we have sequenced the exomes of 4,293 families containing individuals with developmental disorders, and meta-analysed these data with data from another 3,287 individuals with similar disorders. We show that the most important factors influencing the diagnostic yield of DNMs are the sex of the affected individual, the relatedness of their parents, whether close relatives are affected and the parental ages. We identified 94 genes enriched in damaging DNMs, including 14 that previously lacked compelling evidence of involvement in developmental disorders. We have also characterized the phenotypic diversity among these disorders. We estimate that 42% of our cohort carry pathogenic DNMs in coding sequences; approximately half of these DNMs disrupt gene function and the remainder result in altered protein function. We estimate that developmental disorders caused by DNMs have an average prevalence of 1 in 213 to 1 in 448 births, depending on parental age. Given current global demographics, this equates to almost 400,000 children born per year

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms