Leading genome sequencing groups are launching the first meta-analysis in the hunt for genetic factors that explain why some people have worse COVID-19 symptoms than others, after agreeing to share patient sequence data from around the world.
The COVID-19 Host Genetics Initiative (CHGI), set up by scientists at the Institute for Molecular Medicine Finland (FIMM), now includes 151 registered studies that are searching for genetic variation associated with severity and outcomes. The findings will be a potential source of drug targets, both for de novo discovery and repurposing, and also could form the basis of prognostics for identifying people at unusually high risk. The aim of the initiative is to make it possible to work cooperatively, for example, by agreeing to standard protocols; to increase the statistical power of the various studies by organizing analyses across datasets; and to provide a platform to share research.
With a recent grant from the National Science Foundation (NSF), planning is underway for a new center that will bring researchers from business, computer science, engineering, and law together with public and private sector representatives for interdisciplinary collaboration around cyber and financial technologies.
Aparna Gupta, an associate professor of quantitative finance at Rensselaer Polytechnic Institute, is leading the process of developing what will become the Center for Risks and Advances in Financial Technologies (CRAFT). Hosted jointly by Rensselaer, Stevens Institute of Technology, and the Cyber SMART Center at Georgetown University, it will support the development of innovations, policies, and regulations critical for the new cyber technologies.
Researchers hope that, ultimately, they will be able to accurately predict how adding and removing control measures affects transmission rates and infection numbers. This information will be essential to governments as they design strategies to return life to normal, while keeping transmission low to prevent second waves of infection. “This is not about the next epidemic. It’s about ‘what do we do now’?” says Rosalind Eggo, a mathematical modeller at the London School of Hygiene and Tropical Medicine (LSHTM).
Researchers are already working on models that use data from individual countries to understand the effect of control measures. Models based on real data should be more nuanced than those that, at the start of the outbreak, necessarily predicted the effect of interventions mainly using assumptions. Combining data from around the world will allow researchers to compare countries’ responses. And compared with studies of individual countries, it should also allow them to design models that can make more accurate predictions about new phases of the pandemic and across many nations.
But untangling cause and effect is extremely challenging, in part because circumstances differ in each country and because there is uncertainty over how much people adhere to measures, cautions Eggo. “It’s really hard but it doesn’t mean we shouldn’t try,” she adds.
BAE Systems was awarded a contract by the US Defense Advanced Research Projects Agency (DARPA) to develop machine learning analytics as a service – a first-of-its-kind, cloud-based model for the government.
BAE Systems aims to develop machine learning analytics as a service that can leverage commercial and open source data to deliver constant worldwide situational awareness for a diverse range of challenges.
A March paper by researchers at Imperial College London that, in the words of the Washington Post, “helped upend U.S. and U.K. coronavirus strategies,” cited a preprint that had been withdrawn.
Retraction Watch became aware of the issue after being contacted by a PubPeer commenter who had noted the withdrawal earlier this month. Following questions from Retraction Watch this weekend, the authors said they plan to submit a correction.
Businesses and government entities are using smart city technologies to improve targeted operational areas such as traffic management, waste management, parking, smart lighting, and more. While many of these efforts will likely deliver short-term success, the organizations deploying them may be limited in future efforts due to the use of outdated architectures.
New York Magazine, Intelligencer, David Wallace-Wells
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Over the last few weeks, the country has managed to stabilize the spread of the coronavirus sufficiently enough to begin debating when and in what ways to “reopen,” and to normalize, against all moral logic, the horrifying and ongoing death toll — thousands of Americans dying each day, in multiples of 9/11 every week now with the virus seemingly “under control.” The death rate is no longer accelerating, but holding steady, which is apparently the point at which an onrushing terror can begin fading into background noise. Meanwhile, the disease itself appears to be shape-shifting before our eyes.
While governments and tech titans are rushing to create contact-tracing apps to curb the spread of COVID-19, a new review published by the Ada Lovelace Institute in the United Kingdom says not so fast.
“Based on the current evidence in this review, the significant technical limitations, and deep social risks, of digital contact tracing outweigh the value offered to the crisis response,” authors of the report write. “Overcoming these limitations and risks is not impossible but will require, at a minimum, that Government establishes a multidisciplinary Group of Advisors on Technology in Emergencies (GATE) to stand alongside the Scientific Advisory Group on Emergencies (SAGE) and act as gatekeepers of the deployment of technologies in support of a transition strategy.”
At a time when much of the retail sector is collapsing, Amazon is strengthening its competitive position in ways that could outlast the pandemic — and raise antitrust concerns.
A newly discovered connection between control theory and network dynamical systems could help estimate the size of a network even when a small portion is accessible.
Understanding the spread of coronavirus may be the most alarming and recent example of a problem that could benefit from a fuller knowledge of network dynamical systems, but scientists and mathematicians have been grappling for years with ways to draw accurate inferences about these complex systems by working with partial data from available measurements.
In a new Physical Review Letters paper, New York University Tandon School of Engineering Institute Professor Maurizio Porfiri demonstrates a profound connection between mathematical control theory and the problem of determining the size of a network dynamical system from the time series of some accessible units. For homogeneous networks — in which every unit plays the same — accessing a mere 10% of the units could be sufficient to exactly infer the size of the entire network, Porfiri concludes.
With kids home from school around the world, online learning and edtech have suddenly captured the spotlight. Many investors, scrambling to chase hot deals, are already preemptively offering term sheets to companies that are enjoying unprecedented levels of growth, Reach Capital’s Shauntel Garvey said in an interview. … Protocol spoke with Garvey about what’s changed (and what hasn’t) since school went virtual, her bet that there’s a big opportunity in reskilling a new unemployed workforce, and why she doesn’t think the online graduation business will stick.
Less than halfway through 2020, Stanford and the world have undergone rapid change. Still, in this time of great uncertainty, we at The Stanford Daily Data Team want to take a step back and look at the more gradual changes that took place over the previous decade.
Stanford in the 2010s is a new series featuring data visualizations on how things changed (or did not change) in the University community. We will explore academics, athletics, social life and more.
Harvard University, Kennedy School of Government, Misinformation Review, Kathleen Hall Jamieson and Dolores Albarracín
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A US national probability-based survey during the early days of the SARS-CoV-2 spread in the US showed that, above and beyond respondents’ political party, mainstream broadcast media use (e.g., NBC News) correlated with accurate information about the disease’s lethality, and mainstream print media use (e.g., the New York Times) correlated with accurate beliefs about protection from infection. In addition, conservative media use (e.g., Fox News) correlated with conspiracy theories including believing that some in the CDC were exaggerating the seriousness of the virus to undermine the presidency of Donald Trump. Five recommendations are made to improve public understanding of SARS-CoV-2.
Online May 6. Natural Graph Networks. Speaker: Taco Cohen, Qualcomm AI Research. “For link and password to the talks, please sign up for the Physics ∩ ML mailing list.”
Organizers: Susan Athey, Guillaume Basse, Peter Bühlmann, Peng Ding, Andrew Gelman, Guido Imbens, Fabrizia Mealli, Nicolai Meinshausen, Maya Petersen, Thomas Richardson, Dominik Rothenhäusler, Jas Sekhon, Stefan Wager
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Online May 5, starting at 8:30 a.m. PDT. Speaker: Eric Tchetgen (Wharton). “To stay up-to-date about upcoming presentations and receive Zoom invitations please join our mailing list.”
“The Challenge will focus on projects that generate growth and diversification of revenue for local media who elevate underrepresented audiences and promote diversity, equity and inclusion (DEI) within their journalism. Google will fund selected projects up to USD $300,000 and will finance up to 70% of the total project cost. Special discretion on the total project cap may be considered by The Jury depending on the scale and impact of a very large collaborative application.” Deadline to apply is August 12.
University of Maryland, College of Information Studies, Hayleigh Moore
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Researchers and faculty at the University of Maryland Human-Computer Interaction Lab (HCIL) began compiling a central collection of data visualization charts, videos, and other resources covering COVID-19. Below are four of the most impactful data visualizations gathered by the HCIL team that have aided in illustrating the emerging COVID-19 cases and where the cases are located, the reasons behind why the virus spread its way across the world in a matter of days, and projections based on the current situation.
arXiv, Computer Science > Digital Libraries; Lucy Lu Wang et al.
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The Covid-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on Covid-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been downloaded over 75K times and has served as the basis of many Covid-19 text mining and discovery systems. In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and preview tools and upcoming shared tasks built around the dataset. We hope this resource will continue to bring together the computing community, biomedical experts, and policy makers in the search for effective treatments and management policies for Covid-19.
“This site provides users demographic risk factor variables along with economic data on 20 key industries impacted by Coronavirus. Each data set can be displayed in different visualizations, maps, can be shared, and available for download.”
We are thrilled to announce support for Zoom in the Obsidian CDR platform. Obsidian now enables organizations to safely embrace the leading video communications service as the business critical application it has become. Security teams can get visibility into how Zoom is being used and discover security risks resulting from weak configuration and inappropriate use of video conferencing.