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UCF Prof del Barco's magnet research takes giant leap

Researchers pushing the limits of magnets as a means to create faster electronics published their proof of concept findings today, April 10, in the journal Science.

The University of Central Florida is the lead university in the multidisciplinary university research initiative (MURI) project, which is funded by a $7.5 million grant from the Department of Defense. The team exploring methods for creating machines that operate at trillions of cycles per second includes the University of California, Santa Cruz and Riverside, Ohio State University, Oakland University (Michigan) and New York University, among others.

Today's computers rely on ferromagnets (the same kind that sticks to your refrigerator) to align the binary 1s and 0s that process and store information. Anti-ferromagnets are much more powerful, but their natural state, displaying no net measurable magnetization, makes it difficult to harness their power.  {module INSIDE STORY} UCF Professor Enrique del Barco is leading the team lexploring methods for creating machines that operate at trillions of cycles per second.

The laboratory of Enrique del Barco, Ph.D., and collaborators at the University of California, the National High Magnetic Field Laboratory, the Norwegian University of Science and Technology and the Chinese Northeastern University are successfully overcoming that natural resistance using electrical currents passed through anti-ferromagnets on the nanoscale. The results are groundbreaking because they represent proof of concept showing that antiferromagnetic devices can operate on the terahertz level -- or calculations completed in a trillionth of a second. Not only does that hold potential for everything from guidance systems to communications, but it brings devices closer to mimicking the way the brain operates.

"What we're seeing now is that operating at this level is possible and doable," del Barco said.

The next steps will require close collaboration between the theory, experiment, and materials groups within the MURI. Creating devices on the nanoscale (with lateral dimensions below half a micron) takes a fundamental understanding of the appropriate materials. Both theoretical and experimental studies will follow this proof of concept with the intention of finding creative ways to scale down anti-ferromagnets.

UC Davis supercomputer model predicts how drugs affect heart rhythm

Tyler O'Neal, Staff Editor MANUFACTURING

UC Davis researchers develop an easy pre-clinical test of cardiac safety

UC Davis Health researchers have developed a supercomputer model to screen drugs for unintended cardiac side effects, especially arrhythmia risk.

Published in Circulation Research, the study was led by Colleen E. Clancy, professor of physiology and membrane biology, and Igor Vorobyov, assistant professor of physiology and membrane biology.

Clancy is a recognized leader in using high-performance computing to understand electrical changes in the heart.

"One main reason for a drug being removed from the market is potentially life-threatening arrhythmias," Clancy said. "Even drugs developed to treat arrhythmia have ended up actually causing them." Colleen E. Clancy with Pei-Chi Yang and Kevin DeMarco of her research team (from left to right).{module INSIDE STORY}

The problem, according to Clancy, is that there is no easy way to preview how a drug interacts with hERG-encoded potassium channels essential to normal heart rhythm.

"So far there has been no surefire way to determine which drugs will be therapeutic and which will harmful," Clancy said. "What we have shown is that we can now make this determination starting from the chemical structure of a drug and then predicting its impact on the heart rhythm."

Using a drug's chemical formula, the computer model reveals how that drug specifically interacts with hERG channels as well as cardiac cells and tissue. The outcomes can then be validated with comparisons to clinical data from electrocardiogram (ECG) results of patients. For the study, the researchers validated the model with ECGs of patients taking two drugs known to interact with hERG channels -- one with a strong safety profile and another known to increase arrhythmias. The results proved the accuracy of the model.

Clancy envisions the model will offer an essential pre-market test of cardiac drug safety. That test could ultimately be used for other organ systems such as the liver and brain.

"Every new drug needs to go through a screening for cardiac toxicity, and this could be an important first step to suggesting harm or safety before moving on to more expensive and extensive testing," Clancy said.

UK researchers create AI to speed up Covid-19 diagnosis through x-rays

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A new healthcare tool that applies artificial intelligence technology to improve the accuracy of COVID-19 detection in chest x-rays has been developed and shared by Birmingham City University researchers.

DeTraC, created by computer vision and data scientists Professor Mohamed Gaber and Dr Mohammed Abdelsamea from the School of Computing and Digital Technology, uses machine learning to assess and diagnose using large datasets of images from several hospitals across the world.

The technology is now publicly available for the World Health Organisation and the global medical community as an open-source program. chest x ray credit stillwaterising 132307484814796746 c7147

The announcement arrives on World Health Day, following a period of 10 days where the two academics – in collaboration with researcher Asmaa Abbas from Assiut University in Egypt - worked to adapt and deploy their diagnostic tool in response to updates from WHO tracking the spread of the virus. {module INSIDE STORY}

DeTraC, which stands for Decompose, Transfer and Compose, is a convolution neural network that can be trained using a limited number of medical images.

"We believe that our work will open the door for a number of other researchers to help medical professionals to improve their diagnosis by providing unbiased solutions directly from the images. It will boost artificial intelligence research in medical image processing and analysis – which could ultimately lead to a faster diagnosis of COVID-19," said Dr Mohammed Abdelsamea.

The Birmingham City University scientists’ work is the latest in a series of announcements made by the UK institution utilising the expertise, knowledge, resource and capacity of staff and students in order to contribute to the global fight against Coronavirus.