Selective oxidation plays a key role in the production of compounds widely used throughout the chemical industry. Now, according to a new study using advanced computational resources including SDSC’s Comet supercomputer, these materials and other compounds, such as those used to make polyester resins, could undergo a new catalysis process that uses less energy and generates fewer by-products than current methods.
A team of biologists and biomedical researchers at UC San Diego has developed a new method to determine if bacteria are susceptible to antibiotics within a few hours, an advance that could slow the appearance of drug resistance and allow doctors to more rapidly identify the appropriate treatment for patients with life threatening bacterial infections.
Patty Maysent has accepted the position as CEO of UC San Diego Health, the region’s only academic health system. Her appointment was approved by the UC Board of Regents and will commence January 21, 2016.
The Health Cyberinfrastructure (CI) Division of the San Diego Supercomputer Center is participating in a multi-million dollar project with City of Hope, a National Cancer Institute (NCI) designated Comprehensive Cancer Center, and other organizations to create a research cyberinfrastructure that includes a secure, cloud-based data management platform.
Using a new computer science approach, researchers at University of California, San Diego School of Medicine, Columbia University and Stanford University discovered a distinctive molecular feature — a biomarker — that identified colon cancer patients who were most likely to remain disease-free up to five years after surgery. The biomarker, a protein called CDX2, also helped the researchers identify Stage II colon cancer patients who are most likely to benefit from chemotherapy after surgery.
Researchers at University of California, San Diego School of Medicine and collaborators developed GenomeSpace, a cloud-based, biologist-friendly platform that connects more than 20 bioinformatics software packages and resources for genomic data analysis. The team is now developing and crowdsourcing “recipes” — step-by-step workflows — to better enable non-programming researchers to interpret their genomic data. The work is described in a paper published January 18, 2016 in Nature Methods.
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