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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

AI — Revealing hidden biology

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. 

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

Three ORNL scientists elected AAAS fellows

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.

ORNL Corporate Research Fellow and Geospatial Science and Human Security Division Director Budhu Bhaduri has been elected as a fellow of the American Association of Geographers. The honor recognizes Bhaduri for his “innovation, mentorship and wide-ranging leadership” in geographic sciences. Credit: ORNL, U.S. Dept. of Energy

Budhu Bhaduri elected fellow of the American Association of Geographers

ORNL’s Budhendra “Budhu” Bhaduri has been elected a fellow of the American Association of Geographers. The honor recognizes Bhaduri as “a world leader in innovation, development and application of research in human dynamics, geographic data science, remote sensing and scalable geocomputation.”

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

Scientists use Summit supercomputer, deep learning to predict protein functions at genome scale

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.