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The U.S. National Science Foundation today announced a new funding opportunity to support research and technology development that will improve the next generation of wireless communication systems known as NextG.     In collaboration with industry, other government agencies, and international partners, the NSF Verticals-enabling Intelligent NEtwork  Systems (NSF VINES) program will invest up to $100 million to accelerate performance and capabilities of next-generation (NextG) advanced intelligent network systems  spanning the user-edge-core-cloud continuum. 

“NSF VINES will enhance U.S. competitiveness in advanced telecommunications technologies, including NextG wireless telecommunications and emerging potential NextG vertical industries, and prepare the American workforce for jobs available now and in the future,” said Brian Stone, performing the duties of the NSF Director.

“This important investment from NSF, in collaboration with industry and other government agencies, will help strengthen U.S. leadership and ensure the American people reap the benefits in areas such as self-driving cars, advanced manufacturing, energy infrastructure, and beyond,” said Dr. Lynne Parker, Principal Deputy Director of The White House Office of Science and Technology Policy. 

NSF VINES is in partnership with several major industry organizations and U.S. federal agencies, including Ericsson, Intel, Qualcomm, the U.S. Department of Homeland Security, U.S. Department of Defense Office of the Under Secretary for Research and Engineering, and U.S. Department of Commerce National Institute of Standards and Technology, as well as international partners from

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RSS feed source: National Science Foundation

Researchers supported by the U.S. National Science Foundation have provided a new understanding of how and where learning occurs in the brain. The two-part finding has implications for understanding and treating neurodegenerative diseases like Alzheimer’s and other dementias, which impact more than 7 million people in the United States and account for $384 billion in health and long-term care costs, as well as for enhancing neural networks.

“Identifying how the brain actually forms new connections and learns is a question at the frontier of neuroscience,” said Paul Forlano, program officer in the NSF Directorate for Biological Sciences. “Knowing that influences our understanding of how we interact with our environment and pick up on and respond to cues, which opens the door to a range of new fundamental and applied research.”

The researchers, led by Kishore Kuchibhotla, assistant professor at Johns Hopkins University, used brain imaging to determine when mice learned a new skill. The imaging reinforced previous work, showing that mice learned quickly and that those that continued to make errors weren’t still learning; they were experimenting. The difference between mistakes and testing the rules was evident in changes in the neural activity that the researchers saw in the mice.

Kuchibhotla said the distinction between the brain dynamics in learning and the dynamics involved in using that skill could be mimicked in having a memory

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Cardiovascular diseases cause one death every 33 seconds in America. Diagnosing these conditions, which account for approximately 20% of all deaths annually, can be difficult because the overlaying and natural fluorescence of cardiac tissue complicate diagnostic images. A new algorithm, developed by researchers supported by the U.S. National Science Foundation and described in Nature Cardiovascular Research, could lead to clearer images, earlier diagnoses and better outcomes.

“Enhancing visualization of cardiac systems is just one application of this new tool,” said Eric Lyons, a program director in the NSF Directorate for Biological Sciences. “This could also help advance live-cell imaging in other parts of the body, like the brain, and drive insights into fundamental biological processes and systems.”

Current forms of imaging each have drawbacks, being limited by how broad or deep they can visualize, the ability to visualize small scales like molecules or the frame rate of cameras and speed of data acquisition and processing. The algorithm addresses many of these challenges and allows for simultaneous viewing of multiple parameters and measurement of the volume of heart chambers.

The tool uses an approach known as multiscale recursive decomposition, where images are broken down into smaller parts across multiple scales. This allows for the precise extraction of dynamic cardiovascular signals, which could allow physicians and others to diagnose cardiovascular disease earlier and more precisely. Better diagnoses

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