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Media Contacts
![Alex Roschli in front of BAAM](/sites/default/files/styles/list_page_thumbnail/public/2019-03/2018-p09585.jpg?h=af53702d&itok=YVD6zmU4)
Alex Roschli is no stranger to finding himself in unique situations. After all, the early career researcher in ORNL’s Manufacturing Systems Research group bears a last name that only 29 other people share in the United States, and he’s certain he’s the only Roschli (a moniker that hails from Switzerland) with the first name Alex.
![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](/sites/default/files/styles/list_page_thumbnail/public/2019-03/19-G00204_MR_graphic_Kovalevsky_proof5_2.png?h=b7fbb1a9&itok=wrZFNX-o)
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.
![The concrete parts are installed in a residential and commercial tower (above center and below) on the site of the Domino Sugar Factory along the waterfront in Brooklyn. Windows in the tower resemble sugar crystals. Image credit: Gate Precast](/sites/default/files/styles/list_page_thumbnail/public/2019-03/392_4.jpg?h=2e111cc1&itok=PaciKdQX)
A residential and commercial tower under development in Brooklyn that is changing the New York City skyline has its roots in research at the Department of Energy’s Oak Ridge National Laboratory.
![Neutron scattering allowed direct observation of how aurein induces lateral segregation in the bacteria membranes, which creates instability in the membrane structure. This instability causes the membranes to fail, making harmful bacteria less effective.](/sites/default/files/styles/list_page_thumbnail/public/2019-03/Neutrons-FightingSuperbugs_0.jpg?h=e4b73f5a&itok=ebOQD-Mr)
As the rise of antibiotic-resistant bacteria known as superbugs threatens public health, Oak Ridge National Laboratory’s Shuo Qian and Veerendra Sharma from the Bhaba Atomic Research Centre in India are using neutron scattering to study how an antibacterial peptide interacts with and fights harmful bacteria.
![Transportation Energy Data Book Edition 37](/sites/default/files/styles/list_page_thumbnail/public/2019-03/Transportation-Logging_the_miles_ORNL_0.jpg?h=ade3edc7&itok=wGiEijHl)
Oak Ridge National Laboratory’s latest Transportation Energy Data Book: Edition 37 reports that the number of vehicles nationwide is growing faster than the population, with sales more than 17 million since 2015, and the average household vehicle travels more than 11,000 miles per year.
![(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.](/sites/default/files/styles/list_page_thumbnail/public/2019-03/TourassiWH%5B1%5D.png?h=26b5064d&itok=HUC2iYmE)
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.
![Laminations such as these are compiled to form the core of modern electric vehicle motors. ORNL has developed a software toolkit to speed the development of new motor designs and to improve the accuracy of their real-world performance.](/sites/default/files/styles/list_page_thumbnail/public/2019-02/Motors_OeRSTED_0.jpg?h=af53702d&itok=mT24R4WI)
Oak Ridge National Laboratory scientists have created open source software that scales up analysis of motor designs to run on the fastest computers available, including those accessible to outside users at the Oak Ridge Leadership Computing Facility.
![At the salt–metal interface, thermodynamic forces drive chromium from the bulk of a nickel alloy, leaving a porous, weakened layer. Impurities in the salt drive further corrosion of the structural material. Credit: Stephen Raiman/Oak Ridge National Labora At the salt–metal interface, thermodynamic forces drive chromium from the bulk of a nickel alloy, leaving a porous, weakened layer. Impurities in the salt drive further corrosion of the structural material. Credit: Stephen Raiman/Oak Ridge National Labora](/sites/default/files/styles/list_page_thumbnail/public/story%20tip%20image%20BW%20only.jpg?itok=Vbc0iTLt)
Oak Ridge National Laboratory scientists analyzed more than 50 years of data showing puzzlingly inconsistent trends about corrosion of structural alloys in molten salts and found one factor mattered most—salt purity.
![ORNL scientists used commuting behavior data from East Tennessee to demonstrate how machine learning models can easily accept new data, quickly re-train themselves and update predictions about commuting patterns. Credit: April Morton/Oak Ridge National La ORNL scientists used commuting behavior data from East Tennessee to demonstrate how machine learning models can easily accept new data, quickly re-train themselves and update predictions about commuting patterns. Credit: April Morton/Oak Ridge National La](/sites/default/files/styles/list_page_thumbnail/public/study_area_one_dest_2.jpg?itok=2cWFkQvW)
Oak Ridge National Laboratory geospatial scientists who study the movement of people are using advanced machine learning methods to better predict home-to-work commuting patterns.