March 27, 2020
The Laboratory has moved to Minimum Safe Operations in cooperation with Bay Area public health departments’ shelter-in-place orders, which went into effect on March 17, as well as state and federal government recommendations.
The shelter-in-place orders were issued in response to an increasing number of confirmed cases in the area and the CDC’s determination that COVID-19 is now in a community transmission mode. Individuals can be exposed through a variety of untraceable vectors at a variety of locations. Tracking potential exposure has become increasingly difficult because a significant proportion of transmission occurs by infected individuals showing no symptoms.
By practicing the measures outlined in the shelter-in-place orders, the most important of which is remaining at home to increase social distancing (with the exception of accessing essential services), individuals help to slow transmission. This reduces the rate of new COVID-19 cases, allowing the health care system to manage the current caseload and provide more time for development of vaccines and treatments for COVID-19.
Lawrence Livermore National Laboratory (LLNL) 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.
The White House Office of Science and Technology Policy this week announced the Covid-19 High Performance Computing Consortium, a partnership that includes Lawrence Livermore and other national labs, IBM, Alphabet Inc.’s Google Cloud, Amazon.com Inc.’s Amazon Web Services, Microsoft Corp. and others.
Representatives from the consortium with backgrounds in areas such as high-performance computing, biology and epidemiology will approve the projects based on merit and the path to fastest impact. The goal is to grant researchers access to powerful computing resources days after they submit their projects.
LLNL biologists have found manipulating the gut microbiome with antibiotics alters the uptake and effectiveness of acetaminophen that is found in products like Tylenol.
The effectiveness of drug treatments can vary widely between individuals, which can lead to decreased efficacy or increased adverse reactions. Much of the variation can be contributed to genetics, but environmental factors such as nutritional status, disease state and gut bacterial composition also can influence the metabolic characteristics.
In recent years, the contribution of the gut microbiome on drug processing has been at the forefront of many studies investigating variations in drug response by the host.
In the new research, the LLNL team determined how changes in the gut microbiome can alter the pharmacokinetics and biodistribution of acetaminophen by looking at mice after treatment with the antibiotics ciprofloxacin, amoxicillin or a cocktail of ampicillin/neomycin.
The LLNL-developed Lawrence Livermore Microbial Detection Array was used to determine the gut composition after the antibiotics were administered. The analysis revealed that changes in microbe content in antibiotic-treated animals was associated with changes in acetaminophen biodisposition and metabolism.
Optical fibers form the vast pipeline through which nearly all voice, video and data communication flies almost instantaneously around the globe. The overwhelming majority of these hair-thin, flexible fibers are composed of just one material — silica glass. The extreme purity of silica fibers and the precision with which they’re made enable this otherwise common material to carry the world’s communications.
The demand of telecommunications and other industries for optical fibers that perform ideally at higher light intensities is driving fiber specialists and materials scientists to expand beyond conventional silica fibers.
Lawrence Livermore National Laboratory scientists undertook a comprehensive experimental and theoretical study to evaluate a large number of potential fiber materials, including the oxides of strontium, aluminum, barium and phosphorus. The team assessed each material’s propensity for triggering five deleterious nonlinear effects and produced a materials roadmap showing the properties’ complex interplay caused by blending those materials in various proportions.
Grain boundaries are one of the most prominent defects in engineering materials separating different crystallites, which determine their strength, corrosion resistance and failure. Typically, these interfaces are regarded as quasi two-dimensional defects, and controlling their properties remains one of the most challenging tasks in materials engineering.
However, more than 50 years ago the concept that grain boundaries can undergo phase transformations was established by thermodynamic concepts, but they have not been considered, since they could not be observed.
Until now. Researchers from Lawrence Livermore and the Max-Planck-Institut für Eisenforschung (MPIE), found a way to directly observe metal interfaces as they transform.