Lab researchers aid COVID-19 response in antibody, anti-viral research

Lawrence Livermore National Laboratory scientists are contributing to the global fight against COVID-19 by combining artificial intelligence/machine learning, bioinformatics and supercomputing to help discover candidates for new antibodies and pharmaceutical drugs to combat the disease.

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When coronavirus is not alone

Interacting contagious diseases like influenza and pneumonia—and perhaps coronavirus too—follow the same complex spreading patterns as social trends, like the adoption of new slang or technologies. This new finding, published in Nature Physics, could lead to better tracking and intervention when multiple diseases spread through a population at the same time.

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Duchenne Muscular Dystrophy Diagnosis Improved by Simple Accelerometers

Testing for Duchenne muscular dystrophy can require specialized equipment, invasive procedures and high expense, but measuring changes in muscle function and identifying compensatory walking gait could lead to earlier detection. This week in Chaos, researchers present a relative coupling coefficient, which can be used to quantify the factors involved in the human gait and more accurately screen for the disorder. They measured movements of different parts of the body in test subjects, viewing the body as a kinematic chain.

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DOE to Provide $10 Million for New Research into Ecosystem Processes

The U.S. Department of Energy (DOE) announced a plan to provide $10 million for new observational and experimental studies aimed at improving the accuracy of today’s Earth system models. Research will focus on three separate types of environments—terrestrial, watershed, and subsurface—where current models fall short of providing fully accurate representation.

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LLNL leads multi-institutional team in modeling protein interactions tied to cancer

Computational scientists, biophysicists and statisticians from Lawrence Livermore National Laboratory (LLNL) and Los Alamos National Laboratory (LANL) are leading a massive multi-institutional collaboration that has developed a machine learning-based simulation for next-generation supercomputers capable of modeling protein interactions and mutations that play a role in many forms of cancers.

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Studying Ice to Understand Astrophysical Bodies

Understanding the formation and evolution of ice in astrophysical environments can provide information about the physical conditions encountered in space and the chemical similarities and differences between planetary and stellar systems. At the AVS 66th International Symposium and Exhibition, Edith Fayolle, an astrochemist at NASA’s Jet Propulsion Laboratory, will talk about how scientists are trying to understand properties of ice on astrophysical bodies, such as its formation, composition and sublimation — the process by which ice transitions directly into gas, without being in its liquid phase in between.

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