LLNL’s new machine learning platform generates novel COVID-19 antibody sequences for experimental testing

Lawrence Livermore National Laboratory researchers have identified an initial set of therapeutic antibody sequences, designed in a few weeks using machine learning and supercomputing, aimed at binding and neutralizing SARS-CoV-2, the virus that causes COVID-19. The research team is performing experimental testing on the chosen antibody designs.

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Upgrades for LLNL supercomputer from AMD, Penguin Computing aid COVID-19 research

To assist in the COVID-19 research effort, Lawrence Livermore National Laboratory, Penguin Computing and AMD have reached an agreement to upgrade the Lab’s unclassified, Penguin Computing-built Corona high performance computing (HPC) cluster with an in-kind contribution of cutting-edge AMD Instinct™ accelerators, expected to nearly double the peak performance of the machine.

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Scientists developing portable viral tests for future pandemics

Iowa State University researchers are developing a portable, inexpensive technology that could allow people to test for the presence of a virus or antibodies without having to go to a medical facility. The technology is still about a year away, but it could come in handy in the event of a resurgence of the coronavirus or for future pandemics.

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New Method Detects Toxin Exposure from Harmful Algal Blooms in Human Urine

A newly developed method can detect even low-dose human exposure to microcystins and nodularin in human urine. During harmful algal blooms (HABs), species of cyanobacteria release toxic peptides, including microcystins and nodularin into waterways, impacting wildlife and humans living in these marine environments. These findings are the first to report microcystin concentrations directly from exposed residents impacted by cyanobacteria in Florida, and is a critical step in developing and interpreting clinical diagnostic tests for HABs exposure worldwide.

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