8 Things Argonne is Doing to Save the Earth

Stepping into their superhero gear, Argonne scientists are using science and the world’s best technology to combat some of Earth’s toughest foes, from pollution to climate change.

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Software Package Enables Deeper Understanding of Cancer Immune Responses

Researchers at the Bloomberg Kimmel Institute for Caner Immunotherapy at the Johns Hopkins Kimmel Cancer Center have developed DeepTCR, a software package that employs deep-learning algorithms to analyze T-cell receptor (TCR) sequencing data. T-cell receptors are found on the surface of immune T cells. These receptors bind to certain antigens, or proteins, found on abnormal cells, such as cancer cells and cells infected with a virus or bacteria, to guide the T cells to attack and destroy the affected cells.

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April Snapshots

Science Snapshots from Berkeley Lab: X-rays accelerate battery R&D; infrared microscopy goes off grid; substrates support 2D tech

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Game on: Science Edition

UPTON, NY — Inspired by the mastery of artificial intelligence (AI) over games like Go and Super Mario, scientists at the National Synchrotron Light Source II (NSLS-II) trained an AI agent — an autonomous computational program that observes and acts — how to conduct research experiments at superhuman levels by using the same approach. The Brookhaven team published their findings in the journal Machine Learning: Science and Technology and implemented the AI agent as part of the research capabilities at NSLS-II.

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Argonne’s 2021 Maria Goeppert Mayer Fellows bring new energy, promise to their fields

The Department of Energy’s Argonne National Laboratory is proud to welcome five new FY21 Maria Goeppert Mayer Fellows to campus, each chosen for their incredible promise in their respective fields.

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New machine learning tool diagnoses electron beams in an efficient, non-invasive way

For the past few years, researchers at the Department of Energy’s SLAC National Accelerator Laboratory have been developing “virtual diagnostics” that use machine learning to obtain crucial information about electron beam quality in an efficient, non-invasive way. Now, a new virtual diagnostic approach incorporates additional information about the beam that allows the method to work in situations where conventional diagnostics have failed.

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U.S. Department of Energy Announces $34.5 Million for Data Science and Computation Tools to Advance Climate Solutions

The U.S. Department of Energy (DOE) today announced up to $34.5 million to harness cutting-edge research tools for new scientific discoveries, including clean energy and climate solutions. Two new funding opportunities will support researchers using data science and computation-based methods—including artificial intelligence and machine learning—to tackle basic science challenges, advance clean energy technologies, improve energy efficiency, and predict extreme weather and climate patterns.

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Virtual Argonne workshop provides guidance on using AI and supercomputing tools for science

The Argonne Leadership Computing Facility continues its efforts to build a community of scientists who can employ AI and data-intensive analysis at a scale that requires DOE supercomputers.

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NUS researchers harness AI to identify cancer cells by their acidity

Healthy and cancer cells can look similar under a microscope. One way of differentiating them is by examining the level of acidity, or pH level, inside the cells. Tapping on this distinguishing characteristic, a research team from the National University of Singapore (NUS) has developed a technique that uses artificial intelligence (AI) to determine whether a single cell is healthy or cancerous by analysing its pH. Each cancer test can be completed in under 35 minutes, and single cells can be classified with an accuracy rate of more than 95 per cent.

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Machine Learning Can Identify Cancerous Cells by Their Acidity

Researchers have developed a method, described in APL Bioengineering, that uses machine learning to determine whether a single cell is cancerous by detecting its pH. Their approach can discriminate cells originating from normal tissues from cells originating from cancerous tissues, as well as among different types of cancer, while keeping the cells alive. The method relies on treating the cells with bromothymol blue, a pH-sensitive dye that changes color depending on acidity.

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Sneak preview: New platform allows scientists to explore research environments virtually

The Department of Energy pledged $1.68 million to Argonne National Laboratory over three years so it can create a virtual platform or digital twin that will allow experimentalists to explore their proposed studies prior to visiting the labs.

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Argonne innovations and technology to help drive circular economy

In a collaborative effort to “recover, recycle and reuse,” Argonne strengthens research that addresses pollution, greenhouse gases and climate change and aligns with new policies for carbon emission reduction.

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Alexa, do I have an irregular heart rhythm? First AI system for contactless monitoring of heart rhythm using smart speakers

University of Washington researchers have developed a new skill for a smart speaker that for the first time monitors both regular and irregular heartbeats without physical contact.

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Scientists Use Supercomputers to Study Reliable Fusion Reactor Design, Operation

A team used two DOE supercomputers to complete simulations of the full-power ITER fusion device and found that the component that removes exhaust heat from ITER may be more likely to maintain its integrity than was predicted by the current trend of fusion devices.

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New machine learning theory that can be applied to fusion energy raises questions about the very nature of science

A novel computer algorithm, or set of rules, that accurately predicts the orbits of planets in the solar system could be adapted to better predict and control the behavior of the plasma that fuels fusion facilities designed to harvest on Earth the fusion energy that powers the sun and stars.

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The AI-driven initiative that’s hastening the discovery of drugs to treat COVID-19

Ten organizations have created a pipeline of artificial intelligence and simulation tools to narrow the search for drug candidates that can inhibit SARS-CoV-2.

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Federal COVID-19 response taps UCI Health as a model for delivering monoclonal antibody therapy

Irvine, Calif., Feb. 9, 2021 — Monoclonal antibodies are showing promise for improving outcomes for COVID-19 patients, but when a hospital is already beyond capacity, administering them can be a challenge. As hospitalizations soared across California, clinicians with UCI Health created a system for delivering monoclonal antibodies that is keeping hospital beds available for patients with the greatest need.

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Argonne Training Program on Extreme-Scale Computing seeks applications for 2021

ATPESC provides in-depth training on using supercomputers, including next-generation exascale systems, to facilitate breakthrough science and engineering.

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Machine learning algorithm may be the key to timely, inexpensive cyber-defense

Attacks on vulnerable computer networks and cyber-infrastructure — often called zero-day attacks — can quickly overwhelm traditional defenses, resulting in billions of dollars of damage and requiring weeks of manual patching work to shore up the systems after the intrusion. Now, a Penn State-led team of researchers used a machine learning approach, based on a technique known as reinforcement learning, to create an adaptive cyber defense against these attacks.

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Mount Sinai Researchers Build Models Using Machine Learning Technique to Enhance Predictions of COVID-19 Outcomes

Mount Sinai researchers have published one of the first studies using a machine learning technique called “federated learning” to examine electronic health records to better predict how COVID-19 patients will progress.

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Story Tips from Johns Hopkins Experts on COVID-19

Vaccines take time to work. After getting a COVID-19 vaccine, it takes a while for the immune system to fully respond and provide protection from the virus. For the Moderna and Pfizer COVID-19 vaccines, it takes up to two weeks after the second shot to become appropriately protected.

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Mount Sinai Researchers Build Models Using Machine Learning Technique to Enhance Predictions of COVID-19 Outcomes

Mount Sinai researchers have published one of the first studies using federated learning to examine electronic health records to better predict how COVID-19 patients will progress.

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Jefferson Lab Launches Virtual AI Winter School for Physicists

Artificial intelligence is a game-changer in nuclear physics, able to enhance and accelerate fundamental research and analysis by orders of magnitude. DOE’s Jefferson Lab is exploring the expanding synergy between nuclear physics and computer science as it co-hosts together with The Catholic University of America and the University of Maryland a virtual weeklong series of lectures and hands-on exercises Jan. 11-15 for graduate students, postdoctoral researchers and even “absolute beginners.”

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Fermilab receives DOE award to develop machine learning for particle accelerators

Fermilab scientists and engineers are developing a machine learning platform to help run Fermilab’s accelerator complex alongside a fast-response machine learning application for accelerating particle beams. The programs will work in tandem to boost efficiency and energy conservation in Fermilab accelerators.

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10 ways Argonne science is combatting COVID-19

Argonne scientists and research facilities have made a difference in the fight against COVID-19 in the year since the first gene sequence for the virus was published.

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UCI researchers use deep learning to identify gene regulation at single-cell level

Irvine, Calif., Jan. 5, 2021 — Scientists at the University of California, Irvine have developed a new deep-learning framework that predicts gene regulation at the single-cell level. Deep learning, a family of machine-learning methods based on artificial neural networks, has revolutionized applications such as image interpretation, natural language processing and autonomous driving.

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Machine Learning Improves Particle Accelerator Diagnostics

Operators of Jefferson Lab’s primary particle accelerator are getting a new tool to help them quickly address issues that can prevent it from running smoothly. The machine learning system has passed its first two-week test, correctly identifying glitchy accelerator components and the type of glitches they’re experiencing in near-real-time. An analysis of the results of the first field test of the custom-built machine learning system was recently published in the journal Physical Review Accelerators and Beams.

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UCI researchers create model to calculate COVID-19 health outcomes

Irvine, Calif., Dec. 17, 2020 —University of California, Irvine health sciences researchers have created a machine-learning model to predict the probability that a COVID-19 patient will need a ventilator or ICU care. The tool is free and available online for any healthcare organization to use. “The goal is to give an earlier alert to clinicians to identify patients who may be vulnerable at the onset,” said Daniel S.

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