Johns Hopkins APL Releases First-Ever Global Estimates for Road Transportation Greenhouse Emissions Leveraging Artificial Intelligence and Satellite Images

APL scientists have leveraged the global coverage of satellite imagery and the strengths of machine learning to create the first automated approach for estimating greenhouse gas emissions from the road transportation sector.

New machine-learning simulations reduce energy need for mask fabrics, other materials

A new computational effort between Argonne and 3M promises to reduce energy consumption without sacrificing material quality in the production of nonwoven plastics, commonly used in surgical masks.

AI model using daily step counts predicts unplanned hospitalizations during cancer therapy

An artificial intelligence (AI) model developed by researchers can predict the likelihood that a patient may have an unplanned hospitalization during their radiation treatments for cancer. The machine-learning model uses daily step counts as a proxy to monitor patients’ health as they go through cancer therapy, offering clinicians a real-time method to provide personalized care. Findings will be presented today at the American Society for Radiation Oncology (ASTRO) Annual Meeting.

5 big strides from Argonne towards nuclear energy’s future

Nuclear energy is an exciting carbon-free energy source. Recent work at Argonne National Laboratory shows how nuclear energy can improve and why it is such an enticing resource in the fight against climate change.

Machine Learning Takes Hold in Nuclear Physics

Scientists have begun turning to new tools offered by machine learning to help save time and money. In the past several years, nuclear physics has seen a flurry of machine learning projects come online, with many papers published on the subject. Now, 18 authors from 11 institutions summarize this explosion of artificial intelligence-aided work in “Machine Learning in Nuclear Physics,” a paper recently published in Reviews of Modern Physics.

AACN Research Grants Influence Nursing Practice

The American Association of Critical-Care Nurses announces the newest recipients of its annual research grants and invites clinicians and researchers to submit projects online by Oct. 28, 2022, for the next application cycle. AACN will award up to three $50,000 Impact Research Grants in 2023, as well as co-sponsoring the AACN-Sigma Critical Care Grant, with up to $10,000 in funding.

Scientists use machine learning to accelerate materials discovery

Scientists at Argonne National Laboratory have recently demonstrated an automated process for identifying and exploring promising new materials by combining machine learning (ML) and high performance computing.

Helping companies improve energy efficiency through high performance computing

The U.S. Department of Energy (DOE) has awarded DOE’s Argonne National Laboratory with $600,000 in federal funding to work on two new projects that will advance cutting edge manufacturing and clean energy technologies.

Machine learning creates opportunity for new personalized therapies

Researchers at the University of Michigan Rogel Cancer Center have developed a computational platform that can predict new and specific metabolic targets in ovarian cancer, suggesting opportunities to develop personalized therapies for patients that are informed by the genetic makeup of their tumors. The study appeared in Nature Metabolism.

Mutational signature linking bladder cancer and tobacco smoking found with new AI tool

UC San Diego researchers have for the first time discovered a pattern of DNA mutations that links bladder cancer to tobacco smoking. The work could help researchers identify what environmental factors, such as exposure to tobacco smoke and UV radiation, cause cancer in certain patients. It could also lead to more customized treatments for a patient’s specific cancer.

Mount Sinai Researchers Use Artificial Intelligence to Uncover the Cellular Origins of Alzheimer’s Disease and Other Cognitive Disorders

Deep learning models represent “an entirely new paradigm for studying dementia”

Researchers combine data science and machine learning techniques to improve traditional MRI image reconstruction

University of Minnesota Twin Cities researchers have found a way to improve the performance of traditional Magnetic Resonance Imaging (MRI) reconstruction techniques, allowing for faster MRIs without relying on the use of newer deep learning methods.

Breakthrough Reported in Machine Learning-Enhanced Quantum Chemistry

The equations of quantum mechanics require too much computer time and power when used to predict behavior in large systems. Researchers have now shown that machine learning models can mimic the basic structure from first principles, which can be very difficult to simulate directly. The result is predictions that are easy to compute and are accurate in a wide range of chemical systems.

The Effects of Copper, Patient-Relevant Tissue Models, and Artificial Intelligence Featured in Sept. 2022 Issue of ToxSci

A new ToxPoint articles argues that “Copper Is the New Showstopper,” while a commentary calls for patient-relevant tissue models in the September 2022 issue of Toxicological Sciences (ToxSci), the official journal of the Society of Toxicology (SOT). Other featured articles…

Low-Cost Disease Diagnosis by Mapping Heart Sounds

In the Journal of Applied Physics, researchers develop a method to identify aortic valve dysfunction using complex network analysis that is accurate, simple to use, and low-cost. They used heart sound data to create a complex network of connected points, which was split into sections, and each part was represented with a node. If the sound in two portions was similar, a line was drawn between them. In a healthy heart, the graph showed two distinct clusters of points, with many nodes unconnected. A heart with aortic stenosis contained many more correlations and edges.

Preventing Pressure Injuries Among ICU Patients With COVID-19 Requires Extra Vigilance

Patients who are critically ill with COVID-19 are at exceptionally high risk for developing healthcare-associated pressure injuries, especially those related to medical devices, and clinicians must consider additional factors beyond those assessed with common classification tools.

Precision health perspectives

In February, UCI launched the Institute for Precision Health, a campus-wide, interdisciplinary endeavor that merges UCI’s powerhouse health sciences, engineering, machine learning, artificial intelligence, clinical genomics and data science capabilities. The objective is to identify, create and deliver the most effective health and wellness strategy for each individual person and, in doing so, confront the linked challenges of health equity and the high cost of care.

Rensselaer Researchers to Address Big Data Challenges

Dr. Yangyang Xu, assistant professor of mathematical sciences at Rensselaer Polytechnic Institute, has received a $250,000 grant from the National Science Foundation (NSF) to research challenges associated with distributed big data in machine learning.Machine learning algorithms allow computers to make decisions, predictions, and recommendations on the basis of input training data without being explicitly told what information to look for in the data.

Get More from Your Lunch Break with Bite-Size Science

Take a break for lunch and nourish your brain with the latest in scientific discussions, presented by experts at Jefferson Lab. The second season of the lab’s summer series, Bite-Size Science, is now underway. The Bite-Size Science lunchtime lecture series features half-hour, live-streamed presentations on lab-related science, engineering and technology topics and presented by leaders in their fields. The presentations are tailored to non-scientists and are brief, free, and feature a chat feature for Q&A with the presenters.

UCLA researchers use artificial intelligence tools to speed critical information on drug overdose deaths

Fast data processing of overdose deaths, which have increased in recent years, is crucial to developing a rapid public health response. But the system now in place lacks precision and takes months. To correct that, UCLA researchers have developed an automated process that reduces data collection to a few weeks.

JMIR Biomedical Engineering | Using Machine Learning to Reduce Treatment Burden

JMIR Publications recently published “Reducing Treatment Burden Among People With Chronic Conditions Using Machine Learning: Viewpoint” in JMIR Biomedical Engineering which reported that the COVID-19 pandemic has illuminated multiple challenges within the health care system and is unique to those living with chronic conditions.

SLAC expands and centralizes computing infrastructure to prepare for data challenges of the future

A computing facility at the Department of Energy’s SLAC National Accelerator Laboratory is doubling in size, preparing the lab for new scientific endeavors that promise to revolutionize our understanding of the world from atomic to cosmic scales but also require handling unprecedented data streams.

Machine Learning Paves Way for Smarter Particle Accelerators

Scientists have developed a new machine-learning platform that makes the algorithms that control particle beams and lasers smarter than ever before. Their work could help lead to the development of new and improved particle accelerators that will help scientists unlock the secrets of the subatomic world.

Global Expert Panel Identifies 5 Areas Where Machine Learning Could Enhance Health Economics and Outcomes Research

Value in Health, the official journal of ISPOR—the professional society for health economics and outcomes research, announced today the publication of new guidance for health economics and outcomes research and decision makers in the use of an important class of artificial intelligence techniques.

Designed to identify wildlife by sound, the BirdNET app opens new avenues for citizen science

The BirdNET app, a free machine-learning powered tool that can identify more than 3,000 birds by sound alone, generates reliable scientific data and makes it easier for people to contribute citizen-science data on birds by simply recording sounds. Results of tests to measure the app’s accuracy are published in the open access journal PLOS Biology.