Aurelia Butler

Changes in atmospheric density after sunset can cause hot pockets of gas called ‘plasma bubbles’ to form over the Earth’s equator, resulting in communication disruptions between satellites and the Earth. New AI models are now helping scientists to predict plasma bubble events and create a forecast. The work was presented this week at the National Astronomy Meeting (NAM 2022) by Sachin Reddy, a PhD student at University College London.
Shortly after sunset, pockets of super-heated gas called ‘plasma bubbles’ form in the upper atmosphere and stretch into space (up to 900km above the Earth’s surface). These bubbles start small and grow rapidly — from the size of a football pitch to that of a small country in just a couple of hours. As the bubbles grow bigger, they can prevent satellites from communicating with the Earth by blocking and warping their radio signals.
To predict plasma bubbles, a team of researchers has collated 8 years of data from the SWARM satellite mission. The spacecraft has an automatic bubble detector on-board called the Ionospheric Bubble Index. This compares changes in the density of electrons and the magnetic field strength to check if bubbles are present: a strong correlation between the two indicates the presence of a plasma bubble.
The satellite flies at an altitude of 460km (about 30x higher than a commercial plane) through the middle of most plasma bubbles. The model combines the data collection from SWARM with a machine learning approach to make predictions on the likelihood of a plasma bubble event occurring at any time.
The results show that the number of plasma bubble events varies from season to season, just like the weather, and that the number of events increases with solar activity. Despite this, the model finds location to be a far more crucial element in predicting plasma bubbles than the time of year, with most events occurring over a region in the Atlantic called the South Atlantic Anomaly. The AI model predicts events with an accuracy of 91% across different tests.
Reddy says: “Just like the weather forecast on earth, we need to be able to forecast bubbles to prevent major disruptions to satellite services. Our aim is to be able to say something like: “At 8pm tomorrow there is a 30% chance of a bubble appearing over the Horn of Africa.” This kind of information is extremely useful for spacecraft operators and for people who depend on satellite data every day, just like you and me.”
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Materials provided by Royal Astronomical Society. Note: Content may be edited for style and length. More

Researchers at Nanyang Technological University, Singapore (NTU Singapore) have found that the increased use of videoconferencing platforms during the COVID-19 pandemic contributed to a higher level of fatigue, as reported by workers.
Following work-from-home orders issued by governments worldwide during the pandemic, many employees attended meetings virtually using technologies such as Zoom or Microsoft Teams, instead of meeting face-to-face.
In a survey conducted in December 2020, the NTU research team found that 46.2% of all respondents reported feelings of fatigue or being overwhelmed, tired, or drained from the use of videoconferencing applications.
The researchers derived the results through an analysis of a survey of 1,145 Singapore residents in full-time employment and who had indicated that they use videoconferencing apps frequently.
The researchers from the NTU Wee Kim Wee School of Communication and Information (WKWSCI) and its Centre for Information Integrity and the Internet (IN-cube), published their findings in the journal Computers in Human Behavior Reports in June 2022.
Assistant Professor Benjamin Li, from NTU’s WKWSCI, who led the study, said: “We were motivated to conduct our study after hearing of increasing reports of fatigue from the use of videoconferencing applications during the pandemic. We found that there was a clear relation between the increased use of videoconferencing and fatigue in Singaporean workers. Our findings are even more relevant in today’s context, as the use of videoconferencing tools is here to stay, due to flexible work arrangements being a continuing trend.” He is also a member of IN-cube. More

In a new proof-of-concept study led by Dr. Mark Walker at the University of Ottawa’s Faculty of Medicine, researchers are pioneering the use of a unique Artificial Intelligence-based deep learning model as an assistive tool for the rapid and accurate reading of ultrasound images.
The goal of the team’s study was to demonstrate the potential for deep-learning architecture to support early and reliable identification of cystic hygroma from first trimester ultrasound scans. Cystic hygroma is an embryonic condition that causes the lymphatic vascular system to develop abnormally. It’s a rare and potentially life-threatening disorder that leads to fluid swelling around the head and neck.
The birth defect can typically be easily diagnosed prenatally during an ultrasound appointment, but Dr. Walker — co-founder of the OMNI Research Group (Obstetrics, Maternal and Newborn Investigations) at The Ottawa Hospital — and his research group wanted to test how well AI-driven pattern recognition could do the job.
“What we demonstrated was in the field of ultrasound we’re able to use the same tools for image classification and identification with a high sensitivity and specificity,” says Dr. Walker, who believes their approach might be applied to other fetal anomalies generally identified by ultrasonography.
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Materials provided by University of Ottawa. Note: Content may be edited for style and length. More

As our devices become smaller, faster, more energy efficient, and capable of holding larger amounts of data, spintronics may continue that trajectory. Whereas electronics is based on the flow of electrons, spintronics is based on the spin of electrons.
An electron has a spin degree of freedom, meaning that it not only holds a charge but also acts like a little magnet. In spintronics, a key task is to use an electric field to control electron spin and rotate the north pole of the magnet in any given direction.
The spintronic field effect transistor harnesses the so-called Rashba or Dresselhaus spin-orbit coupling effect, which suggests that one can control electron spin by electric field. Although the method holds promise for efficient and high-speed computing, certain challenges must be overcome before the technology reaches its true, miniature but powerful, and eco-friendly, potential.
For decades, scientists have been attempting to use electric fields to control spin at room temperature but achieving effective control has been elusive. In research recently published in Nature Photonics, a research team led by Jian Shi and Ravishankar Sundararaman of Rensselaer Polytechnic Institute and Yuan Ping of the University of California at Santa Cruz took a step forward in solving the dilemma.
“You want the Rashba or Dresselhaus magnetic field to be large to make the electron spin precess quickly,” said Dr. Shi, associate professor of materials science and engineering. “If it’s weak, the electron spin precesses slowly and it would take too much time to turn the spin transistor on or off. However, often a larger internal magnetic field, if not arranged well, leads to poor control of electron spin.”
The team demonstrated that a ferroelectric van der Waals layered perovskite crystal carrying unique crystal symmetry and strong spin-orbit coupling was a promising model material to understand the Rashba-Dresselhaus spin physics at room temperature. Its nonvolatile and reconfigurable spin-related room temperature optoelectronic properties may inspire the development of important design principles in enabling a room-temperature spin field effect transistor.
Simulations revealed that this material was particularly exciting, according to Dr. Sundararaman, associate professor of materials science and engineering. “The internal magnetic field is simultaneously large and perfectly distributed in a single direction, which allows the spins to rotate predictably and in perfect concert,” he said. “This is a key requirement to use spins for reliably transmitting information.”
“It’s a step forward toward the practical realization of a spintronic transistor,” Dr. Shi said.
The first authors of this article include graduate student Lifu Zhang and postdoctoral associate Jie Jiang from Dr. Shi’s group, as well as graduate student Christian Multunas from Dr. Sundararaman’s group.
This work was supported by the United States Army Research Office (Physical Properties of Materials program by Dr. Pani Varanasi), the Air Force Office of Scientific Research, and the National Science Foundation.
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Materials provided by Rensselaer Polytechnic Institute. Original written by Katie Malatino. Note: Content may be edited for style and length. More

Theoretical physicists at Johannes Gutenberg University Mainz have put Google’s artificial intelligence AlphaFold to the test and have found the most complex protein knots so far.
The question of how the chemical composition of a protein, the amino acid sequence, determines its 3D structure has been one of the biggest challenges in biophysics for more than half a century. This knowledge about the so-called “folding” of proteins is in great demand, as it contributes significantly to the understanding of various diseases and their treatment, among other things. For these reasons, Google’s DeepMind research team has developed AlphaFold, an artificial intelligence that predicts 3D structures.
A team consisting of researchers from Johannes Gutenberg University Mainz (JGU) and the University of California, Los Angeles, has now taken a closer look at these structures and examined them with respect to knots. We know knots primarily from shoelaces and cables, but they also occur on the nanoscale in our cells. Knotted proteins can not only be used to assess the quality of structure predictions but also raise important questions about folding mechanisms and the evolution of proteins.
The most complex knots as a test for AlphaFold
“We investigated numerically all — that is some 100,000 — predictions of AlphaFold for new protein knots,” said Maarten A. Brems, a PhD student in the group of Dr. Peter Virnau at Mainz University. The goal was to identify rare, high-quality structures containing complex and previously unknown protein knots to provide a basis for experimental verification of AlphaFold’s predictions. The study not only discovered the most complex knotted protein to date but also the first composite knots in proteins. The latter can be thought of as two separate knots on the same string. “These new discoveries also provide insight into the evolutionary mechanisms behind such rare proteins,” added Robert Runkel, a theoretical physicist also involved in the project. The results of this study were recently published in Protein Science.
Dr. Peter Virnau is pleased with the results: “We have already established a collaboration with our colleague Todd Yeates from UCLA to confirm these structures experimentally. This line of research will shape the biophysics community’s view of artificial intelligence — and we are fortunate to have an expert like Dr. Yeates involved.”
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Results from a University of Otago, Christchurch trial suggest fresh hope for the estimated one-in-twelve people worldwide suffering from a fear of flying, needles, heights, spiders and dogs.
The trial, led by Associate Professor Cameron Lacey, from the Department of Psychological Medicine, studied phobia patients using a headset and a smartphone app treatment programme — a combination of Virtual Reality (VR) 360-degree video exposure therapy and cognitive behavioural therapy (CBT).
Participants downloaded a fully self-guided smartphone app called “oVRcome,” developed by Christchurch tech entrepreneur Adam Hutchinson, aimed at treating patients with phobia and anxiety.
The app was paired with a headset to immerse participants in virtual environments to help treat their phobia.
The results from the trial, just published in the Australian and New Zealand Journal of Psychiatry, showed a 75 per cent reduction in phobia symptoms after six weeks of the treatment programme.
“The improvements they reported suggests there’s great potential for the use of VR and mobile phone apps as a means of self-guided treatment for people struggling with often-crippling phobias,” Associate Professor Lacey says. More

A new study from the Violence Prevention Research Program (VPRP) at UC Davis suggests machine learning, a type of artificial intelligence, may help identify handgun purchasers who are at high risk of suicide. It also identified individual and community characteristics that are predictive of firearm suicide. The study was published in JAMA Network Open.
Previous research has shown the risk of suicide is particularly high immediately after purchase, suggesting that acquisition itself is an indicator of elevated suicide risk.
Risk factors identified by the algorithm to be predictive of firearm suicide included: older age first-time firearm purchaser white race living in close proximity to the gun dealer purchasing a revolver”While limiting access to firearms among individuals at increased risk for suicide presents a critical opportunity to save lives, accurately identifying those at risk remains a key challenge. Our results suggest the potential utility of handgun records in identifying high-risk individuals to aid suicide prevention,” said Hannah S. Laqueur, an assistant professor in the Department of Emergency Medicine and lead author of the study.
In 2020, almost 48,000 Americans died by suicide, of which more than 24,000 were firearm suicides. Firearms are by far the most lethal method of suicide. Access to firearms has been identified as a major risk factor for suicideand is a potential focus for suicide prevention.
Methodology
To see if an algorithm could identify gun purchasers at risk of firearm suicide, the researchers looked at data from almost five million firearm transactions from the California Dealer Record of Sale database (DROS). The records, which spanned from 1996 to 2015, represented almost two million individuals. They also looked at firearm suicide data from California death records between 1996 and 2016. More