Applying machine learning capabilities developed at Berkeley Lab to an extensive dataset created by the TRACK-TBI collaboration led to a six-fold improvement in the precision with which TBI patient outcomes can be predicted.
A Common Data Format for Neurophysiology
A Berkeley Lab team developed a novel software architecture called Neurodata Without Borders (NWB) to serve as a standardized language for neurophysiology data and data-descriptors. The resulting data is findable, accessible, interoperable, and reusable (FAIR), and enables neuroscientists to effectively describe and communicate about their experiments and share data.
Simulations at NERSC Reveal Origins of Brain Electrical Signals
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.
Neurodata Without Borders Project Wins 2019 R&D 100 Award
The Neurodata Without Borders: Neurophysiology (NWB:N) project, led by Berkeley Lab in collaboration with the Allen Institute for Brain Science and multiple neuroscience labs, has been selected for a 2019 R&D 100 Award by R&D World magazine. The annual R&D 100 Awards are given in recognition of exceptional new products or processes that were developed and introduced into the marketplace during the previous year. The awards will be presented on December 5 in San Mateo, California.
Biosciences Area FY19 LDRD Projects
The projects of 13 Biosciences Area scientists and engineers received funding through the FY19 Laboratory Directed Research and Development (LDRD) program. The funded projects span a diverse array of topics and approaches including the harnessing of microbiome data to uncover patterns of mutualism, evaluating radiobiological effects of laser-accelerated ion beams, improving bioenergy yield under drought stress, and the application of machine learning in tomogram segmentation. Lab-wide, 89 projects were selected from a field of 158 proposals. Biosciences Area efforts account for 15.07 percent of the $22.2 million allocated.
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