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Illustration of the intricate organization of the PKA structure, wherein different parts of the protein are connected through elaborate hydrogen bonding networks (dashed yellow lines), glued together by the hydrophobic assemblies (light blue and orange volumes)—all working together to build the functional active site. Insert shows protonation of the transferred phosphoryl group (cyan mesh) and its many interactions with water and the active site amino acid residues. Credit: Jill Hemman/ORNL

OAK RIDGE, Tenn., March 20, 2019—Direct observations of the structure and catalytic mechanism of a prototypical kinase enzyme—protein kinase A or PKA—will provide researchers and drug developers with significantly enhanced abilities to understand and treat fatal diseases and neurological disorders such as cancer, diabetes, and cystic fibrosis.

(From left) ORNL Associate Laboratory Director for Computing and Computational Sciences Jeff Nichols; ORNL Health Data Sciences Institute Director Gina Tourassi; DOE Deputy Under Secretary for Science Thomas Cubbage; ORNL Task Lead for Biostatistics Blair Christian; and ORNL Research Scientist Ioana Danciu were invited to the White House to showcase an ORNL-developed digital tool aimed at better matching cancer patients with clinical trials.

OAK RIDGE, Tenn., March 4, 2019—A team of researchers from the Department of Energy’s Oak Ridge National Laboratory Health Data Sciences Institute have harnessed the power of artificial intelligence to better match cancer patients with clinical trials.

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While studying the genes in poplar trees that control callus formation, scientists at Oak Ridge National Laboratory have uncovered genetic networks at the root of tumor formation in several human cancers.

The Consortium for Advanced Simulation of Light Water Reactors uses its Virtual Environment for Reactor Applications (VERA) software for the modeling and simulation of various nuclear reactors, such as the Westinghouse AP1000 pressurized water reactor.

The Department of Energy’s Oak Ridge National Laboratory is collaborating with industry on six new projects focused on advancing commercial nuclear energy technologies that offer potential improvements to current nuclear reactors and move new reactor designs closer to deployment.

Radiochemical technicians David Denton and Karen Murphy use hot cell manipulators at Oak Ridge National Laboratory during the production of actinium-227.

The Department of Energy’s Oak Ridge National Laboratory is now producing actinium-227 (Ac-227) to meet projected demand for a highly effective cancer drug through a 10-year contract between the U.S. DOE Isotope Program and Bayer.

From left, ORNL’s Rick Lowden, Chris Bryan and Jim Kiggans were troubled that target discs of a material needed to produce Mo-99 using an accelerator could deform after irradiation and get stuck in their holder.

“Made in the USA.” That can now be said of the radioactive isotope molybdenum-99 (Mo-99), last made in the United States in the late 1980s. Its short-lived decay product, technetium-99m (Tc-99m), is the most widely used radioisotope in medical diagnostic imaging. Tc-99m is best known ...

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For the past six years, some 140 scientists from five institutions have traveled to the Arctic Circle and beyond to gather field data as part of the Department of Energy-sponsored NGEE Arctic project. This article gives insight into how scientists gather the measurements that inform t...

Arjun Shankar

The field of “Big Data” has exploded in the blink of an eye, growing exponentially into almost every branch of science in just a few decades. Sectors such as energy, manufacturing, healthcare and many others depend on scalable data processing and analysis for continued in...

Scientists will use ORNL’s computing resources such as the Titan supercomputer to develop deep learning solutions for data analysis. Credit: Jason Richards/Oak Ridge National Laboratory, U.S. Dept. of Energy.

A team of researchers from Oak Ridge National Laboratory has been awarded nearly $2 million over three years from the Department of Energy to explore the potential of machine learning in revolutionizing scientific data analysis. The Advances in Machine Learning to Improve Scient...