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Media Contacts
![A new process developed by Oak Ridge National Laboratory leverages deep learning techniques to study cell movements in a simulated environment, guided by simple physics rules similar to video-game play. Credit: MSKCC and UTK](/sites/default/files/styles/list_page_thumbnail/public/2022-01/Observed%20data%20AI%20story%20tip.jpg?h=8e5dac0a&itok=wrAOsfIs)
Scientists have developed a novel approach to computationally infer previously undetected behaviors within complex biological environments by analyzing live, time-lapsed images that show the positioning of embryonic cells in C. elegans, or roundworms. Their published methods could be used to reveal hidden biological activity.
![Trucks deliver concrete to the VENUS instrument construction site at the Spallation Neutron Source. Credit: ORNL/Carlos Jones](/sites/default/files/styles/list_page_thumbnail/public/2022-01/2021-P00073-Cement%20Trucks%20at%20VENUS%20cleared_0.jpg?h=c6980913&itok=FHH93zn3)
Cement trucks entering and exiting the Spallation Neutron Source are a common sight as construction of the VENUS neutron imaging beamline progresses. Slated for completion and commissioning in 2024-2025, VENUS is the twentieth neutron instrument at SNS and will offer many new capabilities.
![Three ORNL scientists have been elected fellows of the American Association for the Advancement of Science, or AAAS, the world’s largest general scientific society and publisher of the Science family of journals. Credit: ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/AAAS_fellows_resize_0.jpg?h=e91a75a9&itok=y20mbH61)
Three ORNL scientists have been elected fellows of the American Association for the Advancement of Science, or AAAS, the world’s largest general scientific society and publisher of the Science family of journals.
![This protein drives key processes for sulfide use in many microorganisms that produce methane, including Thermosipho melanesiensis. Researchers used supercomputing and deep learning tools to predict its structure, which has eluded experimental methods such as crystallography. Credit: Ada Sedova/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/thermosipho_collabfold2_0.jpg?h=3432ff3c&itok=4xhLbjKZ)
A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.