Tyler O'Neal, Staff Editor ASTRONOMY

Two NRL research physicists named 2020 citation laureates 'of nobel class'

Two research physicists from the U.S. Naval Research Laboratory were named Citation Laureates "Researchers of Nobel Class" by Clarivate on Sept. 23 - Thomas L. Carroll and Louis M. Pecora - for research in nonlinear dynamics including synchronization of chaotic systems.

They were selected out of 24 world-class researchers from six countries identified as Citation Laureates. The figure shows two chaotic systems starting in very different places in their orbits, but soon synchronize and do a {module INSIDE STORY}

Science's highest honor, the Nobel Prize in Physics, will be awarded by The Royal Swedish Academy of Sciences in Stockholm, Sweden, Tuesday, Oct. 6 at 11:45 (CEST) via live stream at http://www.nobelprize.org. To date, 54 Citation Laureates listed in the Hall of Citation Laureates have gone on to receive a Nobel Prize. 

Carroll and Pecora's paper is cited over 7,000 times out of more than 50 million articles and proceedings indexed in the Web of Science since 1970, only 5,700 or .01 percent have been cited 2,000 or more times.

Authors from this group are identified and selected as Citation Laureates. They are individuals whose research reports are highly cited and whose contributions to science have been extremely influential, even transformative.

"It is an honor to have so many other scientists using our work," Carroll said. "As scientists, we all want to do something that makes a difference, and this award shows we have succeeded."

Carroll and Pecora's basic research in nonlinear dynamics is the study of how to model, analyze, and measure systems evolving in time, sometimes in complicated ways, including the motion now called "chaos."

Synchronization of chaotic systems started as a basic scientific idea, but the concept of chaotic systems has been widely applied to biology, communications, machine learning, and radar.Thomas L. Carroll, a research physicist from the U.S. Naval Research Laboratory's Center for Computational Materials Science, was recognized as a Citation Laureate

"Chaotic signals look like noise to an uninformed observer, but with the right knowledge they can carry information, which can be decoded by chaotic synchronization," Carroll said. "Recent work has even used chaotic signals to combine communications with radar on the same signal, leading to less spectrum congestion."

Many machine-learning techniques such as reservoir computing depend on the principles of chaotic synchronization.

"Neither Tom or I thought this would happen, in a lot of scientific breakthroughs serendipity plays a role," Pecora said. "We came along when there was just enough known about nonlinear dynamics to take to the next step and produce something interesting and, hopefully, useful."

Carroll and Pecora in 1990 developed a method to synchronize chaotic systems, which they confirmed with simulations and experiments. This greatly stimulated research into the uses of chaos for communication and resulted in a Physical Review Letter entitled "Synchronization in Chaotic Systems," which is now the 11th most cited paper in Physical Review Letters.

"The research described in the paper was a breakthrough on how to construct a new dynamical system in which two chaotic systems came into complete synchrony," Pecora said. "This caused an avalanche of research, leading to another ground-breaking paper by Carroll and Pecora on synchronizing dynamical systems in arbitrarily structured networks."

What might the U.S. Navy want with chaotic motion?

"This caused us to consider what might be done with chaotic motion in some type of system," Pecora said. "What we thought about was that it might be possible to generate chaotic or rather complicated signals and use them to send messages."

More recently, their ideas have shown up in other areas of research. Several concepts developed in their work now underlie the description and design of reservoir computers and an active area of artificial intelligence. This is in addition to guiding research in the dynamics of power grids, computer networks, and modeling how neurons might interact in some brain functions.

A chaotic motion has a rigorous mathematical description. It's a pattern of motion extremely complicated due to instabilities inherent in the system doing the motion. No outside noise or interference causes complicated motions. These instabilities make the motion hard to predict far into the future.

"This is not just some mathematical quirk of an unusual model," Pecora said. "The weather is chaotic in the technical sense and that's what makes it hard to predict. Even our solar system is somewhat chaotic making predictions of planetary trajectories for long times into the future very difficult."

Carroll and Pecora both hail from the Magnetic Materials and Nonlinear Dynamics Section at NRL where they continue research into networks and individual systems such as radars, radios, and structures. They help solve signal-processing problems in radar and communications.

Penn researchers win grant to use AI to improve heart transplant outcomes

Tyler O'Neal, Staff Editor ASTRONOMY

The collaborative team will utilize automated intelligence to determine the likelihood of acceptance or rejection of donor's hearts

A team of researchers in the Perelman School of Medicine at the University of Pennsylvania, Case Western Reserve University, Cleveland Clinic, and Cedars-Sinai Medical Center, were recently awarded a $3.2 million grant from the National Institutes of Health (NIH) to enhance research for improving heart transplant outcomes for patients. The four-year grant will fund a project exploring the use of artificial intelligence (AI)-driven analysis to determine the likelihood of cardiac patients accepting or rejecting a new heart.

One of the most significant risks of heart transplantation is the patient's body rejecting the donor organ. The body's immune system may see the donor heart as a foreign object and try to reject it, which can then damage the organ. Rejections occur in 30 to 40 percent of patients during the first year after transplant. However, it is widely appreciated that the current rejection grading standard has poor diagnostic accuracy, and has limited ability to discern the mechanism of rejection. These limitations expose patients to risks of both over- and under-treatment.

The team will utilize AI to analyze cardiac biopsy tissue images to distinguish potential cardiac rejection grades and detect patterns of immune cells that reveal the mechanism of rejection. Improved diagnostic accuracy may allow for earlier recognition of serious rejection and also may promote reduced rates of infection and other complications of immune-suppressing drugs taken by transplant patients. Better identification of rejection mechanisms will allow for more precise targeting of the medications. The team also hopes to identify patterns to help predict how patients will do over the long-term, and allow fewer biopsies of the heart.

Penn Medicine, Case Western, Cleveland Clinic, and Cedars-Sinai Medical Center will provide the data--digitized images of biopsies from patients who have already had transplants. The principal investigator, Kenneth B. Margulies, MD, a professor of Cardiovascular Medicine at Penn, in partnership with Anant Madabhushi, Ph.D., a professor of Biomedical Engineering at Case Western Reserve and director of the Center for Computational Imaging and Personalized Diagnostics, will apply the AI techniques to the data to see whether the initial biopsy images could have more accurately predicted which patients would accept or reject the new heart.

"This research is focused on a critical component of heart transplantation--improving patient outcomes. Unfortunately, the number of patients with end-stage heart failure is increasing. But research like this is another step in the right direction for improving survival and quality of life for heart failure patients," Margulies said. {module INSIDE STORY}

In addition, the research team will compare the relative performance of the AI analysis against human pathologists to compare their accuracy in identifying serious rejection. Previous research has shown that supercomputers were more accurate than their human counterparts in diagnostic ability. However, the team believes pathologists will not be replaced by computers; instead, Margulies asserts that "[super]computer-aided tissue diagnostics will serve as a decision support tool for pathologists, consistently and efficiently identifying subtle features that will increase the value of the diagnostic procedure and ultimately improve patient outcomes."

Stockholm researchers run simulations with the Amazon rainforest shrinking by 40 percent

Tyler O'Neal, Staff Editor ASTRONOMY

A larger part of the Amazon rainforest is at risk of crossing a tipping point where it could become a savanna-type ecosystem than previously thought, according to new research. The research, based on supercomputer models is published in an academic journal.

Rainforests are very sensitive to changes that affect rainfall for extended periods. If rainfall drops below a certain threshold, areas may shift into a savanna state.

“In around 40 percent of the Amazon, the rainfall is now at a level where the forest could exist in either state – rainforest or savanna, according to our findings,” says lead author Arie Staal, formerly a postdoctoral researcher at the Stockholm Resilience Centre and the Copernicus Institute of Utrecht University.

The conclusions are concerning because parts of the Amazon region are currently receiving less rain than previously and this trend is expected to worsen as the region warms due to rising greenhouse gas emissions.

Staal and colleagues focused on the stability of tropical rainforests in the Americas, Africa, Asia, and Oceania. With their approach, they were able to explore how rainforests respond to changing rainfall.

“By using the latest available atmospheric data and teleconnection models, we were able to simulate the downwind effects of the disappearance of forests for all tropical forests. By integrating these analyses over the entire tropics, the picture of the systematic stability of tropical forests emerged,” says Obbe Tuinenburg, former assistant professor at the Copernicus Institute of Utrecht University and visiting scientists at the Stockholm Resilience Centre.

The team explored the resilience of tropical rainforests by looking at two questions: what if all the forests in the tropics disappeared, where would they grow back? And it's inverse: what happens if rainforests covered the entire tropical region of Earth?

Such extreme scenarios could inform scientists about the resilience and stability of real tropical forests. They can also help us understand how forests will respond to the changing rainfall patterns as greenhouse gases in the atmosphere rise.

The researchers ran the simulations starting with no forests in the tropics across Africa, the Americas, Asia, and Australia. They watched forests emerge over time in the models. This allowed them to explore the minimum forest cover for all regions.

Staal said, “The dynamics of tropical forests are interesting. As forests grow and spread across a region this affects rainfall – forests create their own rain because leaves give off water vapor and this falls as rain further downwind. Rainfall means fewer fires leading to even more forests. Our simulations capture this dynamic.”

The team ran the models a second time, this time in a world where rainforests entirely covered the tropical regions of Earth. This is an unstable scenario because in many places there is not enough rainfall to sustain a rainforest. In many places, the forests shrank back due to a lack of moisture.

Staal says, “As forests shrink, we get less rainfall downwind and this causes drying leading to more fire and forest loss: a vicious cycle.“

Finally, the researchers explored what happens if emissions keep rising this century along a very high-emissions scenario used by the Intergovernmental Panel on Climate Change (IPCC).

Overall, the researchers found that as emissions grow, more parts of the Amazon lose their natural resilience, become unstable, and more likely to dry out and switch to become a savanna-type ecosystem. They note that even the most resilient part of the rainforest shrinks in the area. In other words, more of the rainforest is prone to crossing a tipping point as emissions of greenhouse gases reach very high levels.

“If we removed all the trees in the Amazon in a high-emissions scenario a much smaller area would grow back than would be the case in the current climate,” says co-author Lan Wang-Erlandsson of the Stockholm Resilience Centre.

The researchers conclude that the smallest area that can sustain a rainforest in the Amazon contracts a substantial 66% in the high-emissions scenario. Parts of the Amazon region are currently receiving less rain than previously and this trend is expected to worsen as the region warms due to rising greenhouse gas emissions. Photo: A. Staal{module INSIDE STORY}

In the Congo basin the team found that the forest remains at risk of changing state everywhere and will not grow back once gone, but that under a high emissions scenario part of the forest becomes less prone to crossing a tipping point. But Wang-Erlandsson adds ‘This area where natural forest regrowth is possible remains relatively small.”

“We understand now that rainforests on all continents are very sensitive to global change and can rapidly lose their ability to adapt,” says Ingo Fetzer of the Stockholm Resilience Centre. “Once gone, their recovery will take many decades to return to their original state. And given that rainforests host the majority of all global species, all this will be forever lost.”

The academics found that the minimal and maximal extents of the rainforests of Indonesia and Malaysia are relatively stable because their rainfall is more dependent on the ocean around them than on rainfall generated as a result of forest cover.

The study only explored the impacts of climate change on tropical forests. It did not assess the additional stress of deforestation in the tropics due to agricultural expansion and logging.