Zhao Bendtsen (purplewire02)

By providing a light-weight plugin for interactive visualization to existing NGS CNV pipelines, reconCNV can facilitate efficient NGS CNV visualization and interpretation in both research and clinical settings. The source code and documentation including a tutorial can be accessed at https//github.com/rghu/reconCNV as well as a Docker image at https//hub.docker.com/repository/docker/raghuc1990/reconcnv. Supplementary data are available at Bioinformatics online. Supplementary data are available at Bioinformatics online. During the current COVID-19 health crisis virtual geriatric clinics have become increasingly utilised to complete outpatient consultations, although concerns exist about feasibility of such virtual consultations for older people. The aim of this rapid review is to describe the satisfaction, clinic productivity, clinical benefit, and costs associated with the virtual geriatric clinic model of care. A rapid review of PubMed, MEDLINE and CINAHL databases was conducted up to April 2020. Two independent reviewers extracted the information. Four subdomains were focused on satisfaction with the virtual geriatric clinic, clinic productivity, clinical benefit to patients, costs and any challenges associated with the virtual clinic process. Nine studies with 975 patients met our inclusion criteria. N6-methyladenosine All were observational studies. Seven studies reported patients were satisfied with the virtual geriatric clinic model of care. Productivity outcomes included reports of cost-effectiveness, savings on transport, and imtricians to continue to provide an outpatient service, despite the encountered inherent challenges. Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours. We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual's SARS-CoV-2 infection status. Laboratory testing results obtained within 2 days before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital. The model achieved an area under the receiver operating characteristic curv in areas where RT-PCR testing is not accessible due to financial or supply constraints. Voice assistants have become increasingly embedded in consumer electronics, as the quality of their interaction improves and the cost of hardware continues to drop. Despite their ubiquity, these assistants remain underutilized as a means of accessing biological research data. Gene Teller is a voice assistant service based on the Alexa Skills Kit and Amazon Lambda functions that enables scientists to query for gene-centric information in an intuitive manner. It includes several features, such as synonym disambiguation and short-term memory, that enable a natural conversational interaction, and is extensible to include new resources. The underlying architecture, based on S3 and AWS Lambda, is cost-efficient and scalable. A publicly accessible version of Gene Teller is available as an Alexa Skill from the Amazon Marketplace at https//. The source code is freely available on GitHub at https//github.com/solinvicta/geneTeller. A publicly accessible version of Gene Teller is available as an Alexa Skill from the Amazon Marketplace at https//. The sour