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BACKGROUND On-field visible signs (VS) are used to help identify sport-related concussion (SRC) in the National Football League (NFL). STO-609 However, the predictive utility of a VS checklist for SRC is unknown. OBJECTIVE To report the frequency, sensitivity, specificity, and predictive value of VS in a cohort of NFL athletes. METHODS On-field VS ratings from 2 experts who independently reviewed video footage of a cohort of 251 injury plays that resulted in an SRC diagnosis (n = 211) and no diagnosis (n = 40) from the 2017 NFL season were examined. The frequency, sensitivity, specificity, and a receiver operating characteristic (ROC) curve with area under the curve (AUC) were calculated for each VS. RESULTS Slow to get up (65.9%) and motor incoordination (28.4%) were the most frequent VS in concussed athletes, and slow to get up (60.0%) was the most common VS among nonconcussed athletes. The most sensitive VS was slow to get up (66%); the most specific signs in concussed NFL athletes were blank/vacant look and impact seizure (both 100%). Approximately 26% of concussed NFL players did not exhibit a VS, and the overall sensitivity and specificity for the VS checklist to detect SRC were 73% and 65%, respectively. The VS checklist demonstrated "poor" ability to discriminate between SRC and non-SRC groups (AUC = 0.66). CONCLUSION In the NFL, the diagnosis of concussion cannot be made from on-field VS alone. The VS checklist is one part of the comprehensive sideline/acute evaluation of concussion, and the diagnosis remains a multimodal clinical decision. Copyright © 2020 by the Congress of Neurological Surgeons.The recent outbreaks of African swine fever (ASF) in China and Europe have threatened the swine industry globally. To control the transmission of ASF virus (ASFV), we developed the African swine fever virus database (ASFVdb), an online data visualization and analysis platform for comparative genomics and proteomics. On the basis of known ASFV genes, ASFVdb reannotates the genomes of every strain and newly annotates 5352 possible open reading frames (ORFs) of 45 strains. Moreover, ASFVdb performs a thorough analysis of the population genetics of all the published genomes of ASFV strains and performs functional and structural predictions for all genes. Users can obtain not only basic information for each gene but also its distribution in strains and conserved or high mutation regions, possible subcellular location and topology. In the genome browser, ASFVdb provides a sliding window for results of population genetic analysis, which facilitates genetic and evolutionary analyses at the genomic level. The web interface was constructed based on SWAV 1.0. ASFVdb is freely accessible at http//asfvdb.popgenetics.net. © The Author(s) 2020. Published by Oxford University Press.MOTIVATION Biomolecular data stored in public databases is increasingly specialized to organisms, context/pathology and tissue type, potentially resulting in significant overhead for analyses. These networks are often specializations of generic interaction sets, presenting opportunities for reducing storage and computational cost. Therefore, it is desirable to develop effective compression and storage techniques, along with efficient algorithms and a flexible query interface capable of operating on compressed data structures. Current graph databases offer varying levels of support for network integration. However, these solutions do not provide efficient methods for the storage and querying of versioned networks. RESULTS We present VerTIoN, a framework consisting of novel data structures and associated query mechanisms for integrated querying of versioned context-specific biological networks. As a use case for our framework, we study network proximity queries in which the user can select and compose a combination of tissue-specific and generic networks. Using our compressed version tree data structure, in conjunction with state-of-the-art numerical techniques, we demonstrate real-time querying of large network databases. CONCLUSION Our results show that it is possible to support flexible queries defined on heterogeneous networks composed at query time while drastically reducing response time for multiple simultaneous queries. The flexibility offered by VerTIoN in composing integrated network versions opens significant new avenues for the utilization of ever increasing volume of context-specific network data in a broad range of biomedical applications. AVAILABILITY AND IMPLEMENTATION VerTIoN is implemented as a C++ library and is available at http//compbio.case.edu/omics/software/vertion and https//github.com/tjcowman/vertion. CONTACT [email protected]. © The Author(s) 2020. Published by Oxford University Press.Circulating tumor cells/microemboli (CTCs/CTMs) are malignant cells that depart from cancerous lesions and shed into the bloodstream. Analysis of CTCs can allow the investigation of tumor cell biomarker expression from a non-invasive liquid biopsy. To date, high-throughput technologies have become a powerful tool to provide a genome-wide view of transcriptomic changes associated with CTCs/CTMs. These data provided us much information to understand the tumor heterogeneity, and the underlying molecular mechanism of tumor metastases. Unfortunately, these data have been deposited into various repositories, and a uniform resource for the cancer metastasis is still unavailable. To this end, we integrated previously published transcriptome datasets of CTCs/CTMs and constructed a web-accessible database. The first release of ctcRbase contains 526 CTCs/CTM samples across seven cancer types. The expression of 14 631 mRNAs and 3642 long non-coding RNAs of CTCs/CTMs were included. Experimental validations from the published literature are also included. Since CTCs/CTMs are considered to be precursors of metastases, ctcRbase also collected the expression data of primary tumors and metastases, which allows user to discover a unique 'circulating tumor cell gene signature' that is distinct from primary tumor and metastases. An easy-to-use database was constructed to query and browse CTCs/CTMs genes. ctcRbase can be freely accessible at http//www.origin-gene.cn/database/ctcRbase/. © Crown copyright 2020.
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