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A Machine Learning Pipeline for Interpreting Large Tomography Datasets

January 27, 2023

3D rendering of six regions obtained via the machine learning pipeline developed by Berkeley Lab scientists to improve cryo-ET image data analysis. Each region is labeled by a unique color.

A team of Berkeley Lab scientists has developed a machine-learning pipeline to facilitate segmentation of tomograms of cell membrane structures. The project was an LDRD-funded collaboration among Chao Yang from the Applied Mathematics and Computational Research Division, and Nick Sauter and Karen Davies from the Molecular Biophysics and Integrated Bioimaging (MBIB) Division.

Simulations at NERSC Reveal Origins of Brain Electrical Signals

July 13, 2022

The origin of the electrical signals recorded at the brain’s surface by electrocorticography (ECoG) has long remained a mystery. Kris Bouchard, a staff scientist in the Biological Systems and Engineering (BSE) Division, led a seven-year research effort to understand precisely which neurons are generating the recorded signals.

New Refinement Technique Promises Greater Protein Structure Accuracy

December 21, 2020

Collage of partial Bragg spots recorded during experiments at the XFEL. For each pixel in the `shoe-box' of a given Bragg spot, diffBragg applies precise modelling of the various parameters affecting their shape and intensity.

X-ray free-electron lasers (XFELs) came into use in 2010 for protein crystallography, allowing scientists to study fully hydrated specimens at room temperature without radiation damage. Researchers have developed many new experimental and computational techniques to optimize the technology and draw the most accurate picture of proteins from crystals. Now scientists in the Molecular Biophysics and Integrated Bioimaging (MBIB) Division have developed a new program, diffBragg, which can process every pixel collected from an XFEL for a protein structure independently. In a recent IUCrJ paper, the team led by MBIB Senior Scientist Nicholas Sauter proposed a new processing framework for more accurate determination of protein structures.

Using Machine Learning to Estimate COVID-19’s Seasonal Cycle

May 20, 2020

A cross-disciplinary team of Berkeley Lab scientists with expertise in climate modeling, data analytics, machine learning, and geospatial analytics is launching a project to determine if the novel coronavirus might be seasonal. The team will apply machine-learning methods to a plethora of health and environmental datasets, combined with high-resolution climate models and seasonal forecasts.

NERSC’s Scientific Computing Power Steps into Coronavirus Battle

April 2, 2020

SARS-CoV-2-illo

Biological Systems and Engineering (BSE) Division Director Blake Simmons is involved in facilitating a City of Hope led project that is using supercomputing resources to scrutinize the viral SARS-CoV-2 genome for insights into the COVID-19 disease in the hope of developing antiviral treatments.

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