A team of researchers used Stampede2 and Bridges simulations to analyze the stability of the Ebola virus's nucleocapsid, a protein shell that protects its genetic material. The study found that RNA helps stabilize the nucleocapsid through electrostatic interactions with its nucleoproteins, providing potential targets for new therapeutics.
Researchers have developed new computational tools to speed up molecular dynamics simulations, enabling novel studies on protein behavior. The tools are being used to predict the 3D shape of unknown proteins in COVID-19 viruses, which could lead to new drug developments.
Researchers used XSEDE-allocated supercomputers to simulate the structure of the blue whirl, a new type of flame that consists of four separate flames. The simulations revealed the three types of flames that make up the bright rim of the blue whirl, which can be used to burn fuels more cleanly.
Researchers use advanced simulation to model large mass ratio black hole merger, predicting characteristics of ultimate merged black hole and its speed. The simulation's success could help plan future gravitational wave detectors and answer mysteries about black holes.
Researchers from the University of Texas at Austin and Georgia Tech used supercomputers to model the formation of the first stars, known as Population III or Pop III stars. Their simulations showed that these ancient stars forged heavier elements, such as carbon, which seeded the next generation of stars.
A joint UC Berkeley-ITU team uses molecular dynamics simulations and single molecule experiments to identify the processes that happen when the virus binds to human cells. They discover intermediate states and specific amino acids that stabilize each state, which may lead to targeted treatments.
The University of Texas at Dallas has gained access to high-performance computing resources through the donation of systems like Stampede1, increasing its research capabilities. The introduction of Ganymede has enabled researchers to conduct complex projects like seismic imaging and data science, reducing computation time by up to 5 days.
Biological condensates, previously known as membrane-less organelles, have been found to play a crucial role in DNA repair and aging. Researchers used the Frontera supercomputer to study their behavior and recruitment of molecules.
The NASA JPL team is using deep learning to develop software for future Mars rovers, which will enable them to travel farther and explore more of the planet. The team has been training machine learning models on the Maverick2 supercomputer and developing novel capabilities such as Drive-By Science and Energy-Optimal Autonomous Navigation.
A researcher at Catholic University of America uses computer models to understand the structure of viruses, aiming to create effective antibodies against COVID-19. His findings suggest that mutated versions of existing antibodies could be produced to neutralize the virus.
Researchers from Caltech and institutions like Northwestern University used deep learning and supercomputing to identify Nyx, a product of a long-ago galaxy merger. The discovery provides the first indication that a dwarf galaxy merged with the Milky Way disk.
Researchers designed millions of protein therapeutics targeting SARS-CoV-2, with over 2,000 showing binding signals. The approach has shown promise in identifying highly promising leads for the spike protein binder.
The COMPASS experiment at CERN is analyzing the proton's inner structure using particle collisions and complex algorithms. The team confirmed a theoretically expected sign change in the Sivers function, which relates to quark orbital motion inside the proton.
Research by Liao Chen reveals significant differences between simple and realistic models of aquaporins and glucose transporters, leading to a better understanding of their biological functions. The study's findings have implications for diseases such as de Vivo's syndrome and multiple forms of cancer.
Scientists developed a computational pipeline to screen drugs for induced arrhythmias, distinguishing between proarrhythmic agents and safe ones. The pipeline uses multi-scale computer simulation data to predict proarrhythmia vulnerability, enabling the identification of potentially toxic compounds.
A team of computational scientists, medicinal chemists, biochemists, and virologists have coalesced to rapidly identify drug-like molecules that inhibit SARS-CoV-19 replication. They are leveraging powerful supercomputers to screen millions of small molecules against all the major non-structural proteins of SARS-CoV-19.
The TACC COVID-19 Twitter dataset enables researchers to analyze social media communications and identify trends in pandemic responses. The dataset, which contains over 40 million tweets, can be used for topic modeling, entity analysis, and event detection, facilitating discoveries about the spread of misinformation and racist messaging.
Researchers at the University of Utah and Texas Advanced Computing Center used powerful supercomputers to rapidly generate molecular models of compounds relevant for COVID-19. They applied their approach, developed previously for Ebola virus research, to identify promising peptides that can disrupt the coronavirus.
A team of researchers is using artificial intelligence to screen small molecules against SARS-CoV-2 proteins, reducing the number from a billion to just a few thousand promising candidates. The approach uses deep learning and physics-based simulations to identify effective treatments, accelerating the discovery process.
A study using supercomputers found that the US can double or quadruple its installed wind turbine capacity without significantly affecting local climate. The researchers used simulations to model the impact of wind turbines on local climates, finding that adding more turbines in a given area would have minimal effects.
The Texas Advanced Computing Center has announced that the National Science Foundation has approved allocations of supercomputing time on Frontera to 49 science projects. These projects will utilize a total of 54 million node hours and constitute approximately 65% of the total time on the system being allocated for this year.
Researchers developed CALM model to simulate pedestrian movement and predict disease spread on airplanes. The model produces results almost 60 times faster than SPED, enabling real-time decisions in emergency situations.
Researchers have created a massive computer model of the coronavirus, which will help design new drugs and vaccines. The model, built by Rommie Amaro's team, contains 200 million atoms and simulates the virus's interaction with human cells.
Researchers used supercomputer simulations to study viral reproduction and DNA replication mechanisms. They discovered that twisting stress in protein filaments plays a key role in creating membrane deformations, which is crucial for virus release and cellular processes.
Scientists used XSEDE-allocated supercomputers to study ion transport through nanoporous membranes. Advanced path sampling techniques captured the kinetics of solute transport, revealing a previously unknown mechanism called induced charge anisotropy that affects ion movement.
IsoBank aims to provide a centralized repository for stable isotope data, addressing the need for accessible and organized datasets. The database will facilitate comparison across time, space, and subject, enabling researchers to build on existing knowledge.
A team of researchers, led by Uri Manor at the Salk Institute, used deep learning to develop a new approach for super-resolution microscopy. By training a neural network on high-resolution images, they were able to improve the resolution of microscope images, enabling better understanding of brain cells and their behavior.
Scientists have determined the structure of the B-Raf protein, which is responsible for about 50% of melanomas. The study reveals an asymmetric organization of the complex, enabling asymmetric activation of the B-Raf dimer, a mechanism that explains the origin of paradoxical activation by small molecule inhibitors.
University of Texas and MIT researchers develop predictive digital twins for UAVs, using physics-based models to capture the details of their behavior. The system can make real-time predictions of a UAV's health, enabling autonomous decision-making and reducing the risk of crashes.
The Texas Advanced Computing Center will develop a meta-portal to provide access to analytics tools and data sets for researchers studying the biological characteristics underlying acute-to-chronic pain transition. Researchers aim to identify biomarkers that can predict chronic pain for medical and public health use.
The Tapis Project aims to simplify access to powerful supercomputers and manage data from diverse sources. The platform offers event-driven computing, streamlining workflows and enabling hands-free analysis.
Frontera, the fastest academic supercomputer in the world, is set to revolutionize scientific research in the US. With its leadership-class computing capability, Frontera will support complex science applications in fields like black hole physics, climate modeling, and drug design.
Researchers used Stampede2 to study shock turbulence interactions at high turbulence intensities, exploring amplification factors, shock jumps and turbulent Mach number. The study aims to improve understanding of turbulent flows interacting with shock waves, enabling advancements in supersonic aircraft design and supernova research.
A new standard, BIDS, allows researchers to compare and combine studies in an apples-to-apples way. This enables the sharing and analysis of large brain imaging datasets, improving the field's ability to understand brain function.
Frontera, located at the University of Texas at Austin, achieved the highest scale and data analysis capabilities ever deployed at a university in the US. The system supports dozens of research teams aiming to solve massive computational problems, including climate simulations and machine learning-enabled cancer studies.
Researchers used supercomputers to simulate complex earthquake ruptures, documenting interactions between faults and analyzing results with advanced visualization software. The model helps understand how faults interact during earthquake rupture, enabling scientists to study past earthquakes and possible future scenarios.
The GRACE mission has provided unprecedented insight into global water resources, measuring polar ice loss and ocean currents. The data has also highlighted the impact of drought and aquifer depletion worldwide.
Scientists have been exploring new materials to harness thermoelectric power from waste heat, with researchers at the University of Texas using supercomputers to optimize material configurations. The team has made promising initial findings, showing that certain cobalt oxides can convert heat into electricity.
A team of researchers unveiled an image of the shadow cast by a black hole at the center of galaxy Messier 87, using the Event Horizon Telescope and supercomputers like Stampede1 and Stampede2. The image confirms that supermassive black holes exist and match the appearance expected from simulations.
Researchers designed proteins that can assemble into complex structures using supercomputers and artificial charges. The stacked octamer structure consists of 16 proteins, resembling a braided ring with highly ordered and specific interactions.
Researchers from UT Austin developed an LSTM-based language model that predicts brain activity with greater accuracy than ever before when listening to stories. The model incorporates context up to 20 words, improving predictions even with minimal context.
Researchers used supercomputer simulations to measure atomic-scale stress tensor of materials with dislocations and phase boundaries. They developed a new approach to calculate stress at the atomic level, addressing limitations of classical continuum mechanics.
The University of Texas at Austin will build the nation's fastest academic supercomputer with a $60 million NSF grant, expected to enable major scientific discoveries in fields like astrophysics and zoology. The system, known as Frontera, will begin operations in 2019 and be twice as powerful as its predecessor.
University of Texas at San Antonio researchers have developed improved computer models to predict the dispersal of chemical plumes, enabling more accurate evacuations. The models can simulate real-world conditions despite limited information, providing critical insights into the spread of toxic agents like sarin gas.
Scientists used supercomputers to model HIV-1 replication and identified inositol hexakisphosphate as a key molecule promoting assembly and maturation. This discovery opens a door for developing new treatments and therapeutics.
A new tool called DesignSafe is helping researchers improve their ability to predict hurricanes, move people out of harm's way, and build homes that can survive the worst nature can throw at them. The platform provides critical information about natural hazards and enables engineers to design safer structures.
The Atesins used supercomputers at the Texas Advanced Computing Center to study organometallic compounds and understand the structure of a palladium catalyst. Their research revealed that the most stable form of the molecule is chair-shaped, and repulsion between this conformation and the substrate dictates the final product.
Researchers at UCSD designed a two-dimensional protein crystal that can toggle between states of varying porosity and density. The material's structural dynamics were simulated using all-atom molecular dynamics, revealing new insights into the emergence of complex properties in biomolecules. Control over the opening and closing of pore...
A new AI method developed by MD Anderson researchers uses deep neural networks to automate the contouring of high-risk clinical target volumes in head and neck cancers. The model achieves comparable results to trained oncologists, offering a potential solution to reduce inter-physician variability and improve treatment efficiency.
Researchers used supercomputer simulations to study the behavior of mantle plumes, a key factor in volcanic formation. The study provided new insights into how plumes interact with seismic waves and could help guide future experiments on the ocean floor.