DUG’s Post

‘The inaugural public exascale supercomputer, Frontier, which was deployed by the Oak Ridge National Laboratory in late 2021, coupled with the rapid proliferation of artificial intelligence tools tailored for biophysics, exemplifies the profound strides being made to seamlessly bridge simulation with actual observation. The momentum gained by computational biophysics signifies a monumental shift. As biophysical research progresses, the seamless integration of experimental and computational efforts is expected to redefine the frontiers of knowledge, laying the groundwork for unprecedented discoveries that could reshape our understanding of the biological world.’ https://lnkd.in/gjzsWy2r

Supercomputers are fundamentally altering the realm of biophysics. Scientists at Auburn University delve into how advanced high-performance computing (HPC) is revolutionizing biophysics, enabling intricate biological processes to be simulated with unparalleled accuracy. They highlight how computational biophysicists can transcend the constraints of experimentation, simulating processes like the precise binding of pathogenic bacteria to humans at an atomic level. This integration of computational modeling and experimental biophysics is set to redefine the parameters of biological knowledge, yielding pioneering discoveries that reshape our comprehension of the biological domain. The advent of exascale supercomputers and the proliferation of AI tools designed for biophysics play a pivotal role in this transformative shift.

📣 Publication Alert 📣 Our research paper as contribution to medical physics and AI during COVID-19 pandemic has been published last February 16, 2024 in PUP Journal of Science and Technology. This publication is the output from the PUP RIST research pitching competition. Link: https://lnkd.in/eng2nUAu #medicalphysics #diagnostics #deeplearning #artificialintelligence #research

Machine Learning Applications in Medicine and Biology From the Editor's web site: This book combines selected papers from the 2022 IEEE Signal Processing in Medicine and Biology Symposium (IEEE SPMB) held at Temple University. The symposium presents multidisciplinary research in the life sciences. Topics covered include:    Signal and image analysis (EEG, ECG, MRI)    Machine learning    Data mining and classification    Big data resources Applications of particular interest at the 2022 symposium included digital pathology, computational biology, and quantum computing. The book features tutorials and examples of successful applications that will appeal to a wide range of professionals and researchers in signal processing, medicine, and biology. https://lnkd.in/dxDhK6TV

We congratulate Tollef Jahren for successfully defending his PhD thesis "Deep learning for detecting valvular events and suppressing reverberations in cardiac ultrasound" for the degree Philosophiae Doctor at Department of Informatics, University of Oslo 🤖🧠 🤖 Jahren's thesis introduced a new method for delineating heart cycles directly in spectral Doppler images. This innovation eliminates the need to record ECGs imultaneously, which means clinicians don't need to attach ECG pads, saving valuable time. 🧠 Jahren's research also tackles the issue of poor image quality in patients who are difficult to scan, addressing the problem of reverberational clutter that can obscure important cardiac structures in the images. The effectiveness of this method is demonstrated using in-vivo data. Read more on our web page: https://lnkd.in/dW_P2kp6 Photo: Sarina Thomas Robert Jenssen | Line Eikvil | Anne Solberg | Inger Solheim | Petter Bjørklund | UiT- The Arctic University of Norway | Norsk Regnesentral | University of Oslo | Elisabeth Wetzer | Erik Steen

A recent journal article by Rafael C. Bernardi – an assistant professor of Biophysics at Auburn University – sheds light on how biophysicists are using high-performance computing (HPC) to make revolutionary discoveries in biology. “The new exascale computers allow computational biophysicists to go beyond what can be done experimentally and simulate biological processes with a much higher level of detail,” he writes. “For instance, we can now understand how pathogenic bacteria bind to humans during infection at an atomistic level, generating data for AI models and opening new roads of exploration.” We love to see it! This is just one more example of how high-performance computing can be applied to every industry, sector, and area of our lives. Read more here: https://lnkd.in/e8UV7cmX #highperformancecomputing #hpc #dataprocessing #knowledgeshare

Curious about the #kuleuven Open Science Discovery training for #PhD Researchers on 24 October 2023? Find out which topics will be discussed, and why you should you participate here: https://lnkd.in/ek6H-MnJ #youreca #phd #postdoc #openscience #openresearch

Biology, psychology, physics, neuroscience, material science, network science, nonlinear dynamics — all these fields of study hang on electrical engineering to gain fundamental insights into topics such as signal processing, consciousness, criticality, and neuroplasticity of nervous systems. This highly interdisciplinary project is organized by the Christian-Albrechts-University of Kiel and has 16 subprojects. The project is funded by Collaborative Research Center under SFB 1461. Inspired by neurophysiological processes in human synapses, our scientists work on in-memory-computing architectures using memristive devices. Such architectures combine memory and processing units, which makes them faster and more energy-efficient to solve artificial intelligence tasks. To be able to adjust the coupling strength between the artificial synapses in finer gradations, several RRAM cells are connected in series, just like in the human nervous system. These RRAM devices are co-integrated with transistors in a 130 nm BiCMOS process. For their characterization, IHP has made a breakthrough: a compact programming unit, just 1 mm² in size, so there are no limitations related to space in the lab and to programming the cells. The next step within the project at IHP is to implement a compact artificial neural network as a mixed-signal IC and get insights into the functions of neural structure in hardware and outlook for future computers and applications. Read more about the project: https://lnkd.in/dSPKbVqj #futuretech #neuralnetworks #neuroscience