Next-gen bioinformatics tool enables big data analysis without programming expertise

A new data analysis tool developed by researchers at The University of Texas MD Anderson Cancer Center incorporates a user-friendly, natural-language interface to allow biomedical researchers without specialized expertise in bioinformatics or programming languages to conduct intuitive analysis of large datasets.

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With Digital Phenotyping, Smartphones May Play a Role in Assessing Severe Mental Illness

Digital phenotyping approaches that collect and analyze Smartphone-user data on locations, activities, and even feelings – combined with machine learning to recognize patterns and make predictions from the data – have emerged as promising tools for monitoring patients with psychosis spectrum illnesses, according to a report in the September/October issue of Harvard Review of Psychiatry. The journal is published in the Lippincott portfolio by Wolters Kluwer.

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BGSU’s Novak Family Professor of Data Science helps journalists understand polls

Being able to vet surveys and election polls is important for journalists and other media experts, making Dr. Trent Buskirk a very popular person this time of year. Buskirk is the Novak Family Professor of Data Science and the chair of the Applied Statistics and Operations Research Department at BGSU.

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The University of Chicago is awarded a major federal contract to host a new COVID-19 medical imaging resource center

A new center hosted at the University of Chicago — co-led by the largest medical imaging professional organizations in the country — will help tackle the ongoing COVID-19 pandemic by curating a massive database of medical images to help better understand and treat the disease. The work is supported by a $20 million, two-year federal contract that could be renewable to $50 million over five years.

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Countries Group into Clusters as COVID-19 Outbreak Spreads

Mathematicians based in Australia and China have developed a method to analyze the large amount of data accumulated during the COVID-19 pandemic. The technique, described in the journal Chaos, can identify anomalous countries — those that are more successful than expected at responding to the pandemic and those that are particularly unsuccessful. The investigators analyzed the data with a variation of a statistical technique known as a cluster analysis.

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Modeling COVID-19 Data Must Be Done With Extreme Care

As the virus causing COVID-19 began its devastating spread, an international team of scientists was alarmed by the lack of uniform approaches by various countries’ epidemiologists. Data modeling to predict the numbers of likely infections varied widely and revealed a high degree of uncertainty. In the journal Chaos, the group describes why modeling and extrapolating the evolution of COVID-19 outbreaks in near real time is an enormous scientific challenge that requires a deep understanding of the nonlinearities underlying the dynamics of epidemics.

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Researchers work on early warning system for COVID-19

To better understand early signs of coronavirus and the virus’ spread, physicians around the country and data scientists at UC San Diego are working together to use a wearable device to monitor more than 12,000 people, including thousands of healthcare workers. The effort has started at hospitals in the San Francisco Bay Area and at the University of West Virginia.

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New Software Tests Asphalt Performance More Efficiently

New Brunswick, N.J. (Feb. 26, 2020) – Rutgers University–New Brunswick researchers have created a software tool that more efficiently analyzes how

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New ORNL software improves neutron spectroscopy data resolution

Neutron spectroscopy is an important tool for studying magnetic and thermoelectric properties in materials. But often the resolution, or the ability of the instrument to see fine details, is too coarse to clearly observe features identifying novel phenomena in new advanced materials. To solve this problem, researchers at Oak Ridge National Laboratory, developed a new super-resolution software, called SRINS, that makes it easier for scientists to better understand materials’ dynamical properties using neutron spectroscopy.

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Department of Energy Announces $21.4 Million for Quantum Information Science Research

The following news release was issued on Aug. 26, 2019 by the U.S. Department of Energy (DOE). It announces funding that DOE has awarded for research in quantum information science related to particle physics and fusion energy sciences. Scientists at DOE’s Brookhaven National Laboratory are principal investigators on two of the 21 funded projects.

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