Computers Math

Magnetism, one of the oldest technologies known to humans, is at the forefront of new-age materials that could enable next-generation lossless electronics and quantum computers. Researchers led by Penn State and the university of California, San Diego have discovered a new ‘knob’ to control the magnetic behavior of one promising quantum material, and the findings could pave the way toward novel, efficient and ultra-fast devices.
“The unique quantum mechanical make-up of this material — manganese bismuth telluride — allows it to carry lossless electrical currents, something of tremendous technological interest,” said Hari Padmanabhan, who led the research as a graduate student at Penn State. “What makes this material especially intriguing is that this behavior is deeply connected to its magnetic properties. So, a knob to control magnetism in this material could also efficiently control these lossless currents.”
Manganese bismuth telluride, a 2D material made of atomically thin stacked layers, is an example of a topological insulator, exotic materials that simultaneously can be insulators and conductors of electricity, the scientists said. Importantly, because this material is also magnetic, the currents conducted around its edges could be lossless, meaning they do not lose energy in the form of heat. Finding a way to tune the weak magnetic bonds between the layers of the material could unlock these functions.
Tiny vibrations of atoms, or phonons, in the material may be one way to achieve this, the scientists reported April 8 in the journal Nature Communications.
“Phonons are tiny atomic wiggles — atoms dancing together in various patterns, present in all materials,” Padmanabhan said. “We show that these atomic wiggles can potentially function as a knob to tune the magnetic bonding between the atomic layers in manganese bismuth telluride.”
The scientists at Penn State studied the material using a technique called magneto-optical spectroscopy — shooting a laser onto a sample of the material and measuring the color and intensity of the reflected light, which carries information on the atomic vibrations. The team observed how the vibrations changed as they altered the temperature and magnetic field. More

Testing shortages, long waits for results, and an over-taxed health care system have made headlines throughout the COVID-19 pandemic. These issues can be further exacerbated in small or rural communities in the US and globally. Additionally, respiratory symptoms of COVID-19 such as fever and cough are also associated with the flu, which complicates non-lab diagnoses during certain seasons. A new study by College of Health and Human Services researchers is designed to help identify which symptoms are more likely to indicate COVID during flu season. This is the first study to take seasonality into account.
Farrokh Alemi, principal investigator and professor of Health Administration and Policy, and other Mason researchers predict the probability that a patient has COVID-19, flu, or another respiratory illness prior to testing, depending on the season. This can help clinicians triage patients who are most suspected of having COVID-19.
“When access to reliable COVID testing is limited or test results are delayed, clinicians, especially those who are community-based, are more likely to rely on signs and symptoms than on laboratory findings to diagnose COVID-19,” said Alemi, who observed these challenges at points throughout the pandemic. “Our algorithm can help health care providers triage patient care while they are waiting on lab testing or help prioritize testing if there are testing shortages.”
The findings suggest that community-based health care providers should follow different signs and symptoms for diagnosing COVID depending on the time of year. Outside of flu season, fever is an even stronger predictor of COVID than during flu season. During flu season, a person with a cough is more likely to have the flu than COVID. The study showed that assuming anyone with a fever during flu season has COVID would be incorrect. The algorithm relied on different symptoms for patients in different age and gender. The study also showed that symptom clusters are more important in diagnosis of COVID-19 than symptoms alone.
The algorithms were created by analyzing the symptoms reported by 774 COVID patients in China and 273 COVID patients in the United States. The analysis also included 2,885 influenza and 884 influenza-like illnesses in US patients. “Modeling the Probability of COVID-19 Based on Symptom Screening and Prevalence of Influenza and Influenza-Like Illnesses” was published in Quality Management in Health Care’s April/June 2022 issue. The rest of the research team is also from Mason: Professor of Global Health and Epidemiology Health Amira Roess, Affiliate Faculty Jee Vang, and doctoral candidate Elina Guralnik.
“Though helpful, the algorithms are too complex to expect clinicians to perform these calculations while providing care. The next step is to create an AI, web-based, calculator that can be used in the field. This would allow clinicians to arrive at a presumed diagnosis prior to the visit,” said Alemi. From there, clinicians can make triage decisions on how to care for the patient while waiting for official lab results.
The study does not include any COVID-19 patients without respiratory symptoms, which includes asymptomatic people. Additionally, the study did not differentiate between the first and second week of onset of symptoms, which can vary.
This research was a prototype of how existing data can be used to find signature symptoms of a new disease. The methodology may have relevance beyond this pandemic.
“When there is a new outbreak, collecting data is time consuming. Rapid analysis of existing data can reduce the time to differentiate presentation of new diseases from illnesses with overlapping symptoms. The method in this paper is useful for rapid response to the next pandemic,” said Alemi.
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Materials provided by George Mason University. Original written by Mary Cunningham. Note: Content may be edited for style and length. More

Researchers at The University of Texas MD Anderson Cancer Center have developed a new bioinformatics platform that predicts optimal treatment combinations for a given group of patients based on co-occurring tumor alterations. In retrospective validation studies, the tool selected combinations that resulted in improved patient outcomes across both pre-clinical and clinical studies.
The findings were presented today at the American Association for Cancer Research (AACR) Annual Meeting 2022 by principal investigator Anil Korkut, Ph.D., assistant professor of Bioinformatics and Computational Biology. The study results also were published today in Cancer Discovery.
The platform, called REcurrent Features LEveraged for Combination Therapy (REFLECT), integrates machine learning and cancer informatics algorithms to analyze biological tumor features — including genetic mutations, copy number changes, gene expression and protein expression aberrations — and identify frequent co-occurring alterations that could be targeted by multiple drugs.
“Our ultimate goal is to make precision oncology more effective and create meaningful patient benefit,” Korkut said. “We believe REFLECT may be one of the tools that can help overcome some of the current challenges in the field by facilitating both the discovery and the selection of combination therapies matched to the molecular composition of tumors.”
Targeted therapies have improved clinical outcomes for many patients with cancer, but monotherapies against a single target often lead to treatment resistance. Cancer cells frequently rely on co-occurring alterations, such as mutations in two signaling pathways, to drive tumor progression. Increasing evidence suggests that identifying and targeting both alterations simultaneously could increase durable responses, Korkut explained.
Led by Korkut and postdoctoral fellow Xubin Li, Ph.D., the researchers built and used the REFLECT tool to develop a systematic and unbiased approach to match patients with optimal combination therapies. More

Medical imaging is an important part of modern healthcare, enhancing both the precision, reliability and development of treatment for various diseases. Artificial intelligence has also been widely used to further enhance the process.
However, conventional medical image diagnosis employing AI algorithms require large amounts of annotations as supervision signals for model training. To acquire accurate labels for the AI algorithms — radiologists, as part of the clinical routine, prepare radiology reports for each of their patients, followed by annotation staff extracting and confirming structured labels from those reports using human-defined rules and existing natural language processing (NLP) tools. The ultimate accuracy of extracted labels hinges on the quality of human work and various NLP tools. The method comes at a heavy price, being both labour intensive and time consuming.
An engineering team at the University of Hong Kong (HKU) has developed a new approach “REFERS” (Reviewing Free-text Reports for Supervision), which can cut human cost down by 90%, by enabling the automatic acquisition of supervision signals from hundreds of thousands of radiology reports at the same time. It attains a high accuracy in predictions, surpassing its counterpart of conventional medical image diagnosis employing AI algorithms.
The innovative approach marks a solid step towards realizing generalized medical artificial intelligence. The breakthrough was published in Nature Machine Intelligence in the paper titled “Generalized radiograph representation learning via cross-supervision between images and free-text radiology reports.”
“AI-enabled medical image diagnosis has the potential to support medical specialists in reducing their workload and improving the diagnostic efficiency and accuracy, including but not limited to reducing the diagnosis time and detecting subtle disease patterns,” said Professor YU Yizhou, leader of the team from HKU’s Department of Computer Science under the Faculty of Engineering.
“We believe abstract and complex logical reasoning sentences in radiology reports provide sufficient information for learning easily transferable visual features. With appropriate training, REFERS directly learns radiograph representations from free-text reports without the need to involve manpower in labelling.” Professor Yu remarked.
For training REFERS, the research team uses a public database with 370,000 X-Ray images, and associated radiology reports, on 14 common chest diseases including atelectasis, cardiomegaly, pleural effusion, pneumonia and pneumothorax. The researchers managed to build a radiograph recognition model using 100 radiographs only, and attains 83% accuracy in predictions. When the number was increased to 1,000, their model exhibits amazing performance with an accuracy of 88.2%, which surpasses its counterpart trained with 10,000 radiologist annotations (accuracy at 87.6%). When 10,000 radiographs were used, the accuracy is at 90.1%. In general, an accuracy above 85% in predictions is useful in real-world clinical applications.
REFERS achieves the goal by accomplishing two report-related tasks, i.e., report generation and radiograph-report matching. In the first task, REFERS translates radiographs into text reports by first encoding radiographs into an intermediate representation, which is then used to predict text reports via a decoder network. A cost function is defined to measure the similarity between predicted and real report texts, based on which gradient-based optimization is employed to train the neural network and update its weights.
As for the second task, REFERS first encodes both radiographs and free-text reports into the same semantic space, where representations of each report and its associated radiographs are aligned via contrastive learning.
“Compared to conventional methods that heavily rely on human annotations, REFERS has the ability to acquire supervision from each word in the radiology reports. We can substantially reduce the amount of data annotation by 90% and the cost to build medical artificial intelligence. It marks a significant step towards realizing generalized medical artificial intelligence, ” said the paper’s first author Dr ZHOU Hong-Yu.
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Materials provided by The University of Hong Kong. Note: Content may be edited for style and length. More

One way in which scientists are studying how the human body grows and ages is by creating artificial organs in the laboratory. The most popular of these organs is currently the organoid, a miniaturized organ made from stem cells. Organoids have been used to model a variety of organs, but brain organoids are the most clouded by controversy.
Current brain organoids are different in size and maturity from normal brains. More importantly, they do not produce any behavioral output, demonstrating they are still a primitive model of a real brain. However, as research generates brain organoids of higher complexity, they will eventually have the ability to feel and think. In response to this anticipation, Associate Professor Takuya Niikawa of Kobe University and Assistant Professor Tsutomu Sawai of Kyoto University’s Institute for the Advanced Study of Human Biology (WPI-ASHBi), in collaboration with other philosophers in Japan and Canada, have written a paper on the ethics of research using conscious brain organoids. The paper can be read in the academic journal Neuroethics.
Working regularly with both bioethicists and neuroscientists who have created brain organoids, the team has been writing extensively about the need to construct guidelines on ethical research. In the new paper, Niikawa, Sawai and their coauthors lay out an ethical framework that assumes brain organoids already have consciousness rather than waiting for the day when we can fully confirm that they do.
“We believe a precautionary principle should be taken,” Sawai said. “Neither science nor philosophy can agree on whether something has consciousness. Instead of arguing about whether brain organoids have consciousness, we decided they do as a precaution and for the consideration of moral implications.”
To justify this assumption, the paper explains what brain organoids are and examines what different theories of consciousness suggest about brain organoids, inferring that some of the popular theories of consciousness permit them to possess consciousness.
Ultimately, the framework proposed by the study recommends that research on human brain organoids follows the ethical principles similar to those for animal experiments. Therefore, recommendations include using the minimum number of organoids possible and doing the upmost to prevent pain and suffering while considering the interests of the public and patients.
“Our framework was designed to be simple and is based on valence experiences and the sophistication of those experiences,” said Niikawa.
This, the paper explains, provides guidance on how strict the conditions for experiments should be. These conditions should be decided based upon several criteria, which include the physiological state of the organoid, the stimuli to which it responds, the neural structures it possesses, and its cognitive functions.
Moreover, the paper argues that this framework is not exclusive to brain organoids. It can be applied to anything that is perceived to hold consciousness, such as fetuses, animals and even robots.
“Our framework depends on the precautionary principle. Something that we believe does not have consciousness today may, through the development of consciousness studies, be found to have consciousness in the future. We can consider how we ought to treat these entities based on our ethical framework,” conclude Niikawa and Sawai.
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Materials provided by Kyoto University. Note: Content may be edited for style and length. More

A new spin on one of the 20th century’s smallest but grandest inventions, the transistor, could help feed the world’s ever-growing appetite for digital memory while slicing up to 5% of the energy from its power-hungry diet.
Following years of innovations from the University of Nebraska-Lincoln’s Christian Binek and University at Buffalo’s Jonathan Bird and Keke He, the physicists recently teamed up to craft the first magneto-electric transistor.
Along with curbing the energy consumption of any microelectronics that incorporate it, the team’s design could reduce the number of transistors needed to store certain data by as much as 75%, said Nebraska physicist Peter Dowben, leading to smaller devices. It could also lend those microelectronics steel-trap memory that remembers exactly where its users leave off, even after being shut down or abruptly losing power.
“The implications of this most recent demonstration are profound,” said Dowben, who co-authored a recent paper on the work that graced the cover of the journal Advanced Materials.
Many millions of transistors line the surface of every modern integrated circuit, or microchip, which itself is manufactured in staggering numbers — roughly 1 trillion in 2020 alone — from the industry-favorite semiconducting material, silicon. By regulating the flow of electric current within a microchip, the tiny transistor effectively acts as a nanoscopic on-off switch that’s essential to writing, reading and storing data as the 1s and 0s of digital technology.
But silicon-based microchips are nearing their practical limits, Dowben said. Those limits have the semiconductor industry investigating and funding every promising alternative it can. More

A new study from Tel Aviv University enables developers, for the first time in the world, to determine vaccine safety via smart sensors that measure objective physiological parameters. According to the researchers, most clinical trials testing the safety of new vaccines. including COVID-19 vaccines, rely on participants’ subjective reports, which can lead to biased results. In contrast, objective physiological data, obtained through sensors attached to the body, is clear and unambiguous.
The study was led by Dr. Yftach Gepner of the Department of Epidemiology and Preventive Medicine at TAU’s Sackler Faculty of Medicine, together with Dr. Dan Yamin and Dr. Erez Shmueli from TAU’s Fleischman Faculty of Engineering. The paper was published in Communications Medicine, a journal from the Nature portfolio.
Dr. Gepner: “In most methods used today, clinical trials designed to evaluate the safety of a new drug or vaccine employ self-report questionnaires, asking participants how they feel before and after receiving the treatment. This is clearly a totally subjective report. Even when Pfizer and Moderna developed their vaccines for the new COVID-19 virus, they used self-reports to prove their safety.”
In the current study, researchers from Tel Aviv University demonstrated that smart sensors can be used to test new vaccines. The study was conducted when many Israelis received their second dose of the COVID-19 vaccine. The researchers equipped volunteers with innovative, FDA-approved sensors developed by the Israeli company Biobeat. Attached to their chests, these sensors measured physiological reactions from one day before to three days after receiving the vaccine. The innovative sensors monitored 13 physiological parameters, such as: heart rate, breathing rate, saturation (blood oxygen levels), heartbeat volume, temperature, cardiac output, and blood pressure.
The surprising results: a significant discrepancy was found between subjective self-reports about side effects and actual measurements. That is, in nearly all objective measures, significant changes were identified after vaccination, even for subjects who reported having no reaction at all.
In addition, the study found that side effects escalate over the first 48 hours, and then parameters return to the level measured before vaccination. In other words: a direct assessment of the vaccine’s safety identified physiological reactions during the first 48 hours, with levels restabilizing afterwards.
“The message from our study is clear,” says Dr. Gepner. “In 2022 the time has come to conduct continual, sensitive, objective testing of the safety of new vaccines and therapies. There is no reason to rely on self-reports or wait for the occurrence of rare side effects like myocarditis, an inflammation of the heart muscle, which occurs in one of 10,000 patients. Preliminary signs that predict such conditions can be detected with advanced sensors, identifying normal vs. extreme alterations in physiological parameters and any risk of inflammation. Today trial participants are invited to the clinic for blood pressure testing, but often their blood pressure rises just because the situation is stressful. Continual monitoring at home solves these problems with simple, convenient, inexpensive, and accurate means. This is the kind of medicine we should strive for in 2022.”
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Materials provided by Tel-Aviv University. Note: Content may be edited for style and length. More

A trial in which trainee teachers who were being taught to identify pupils with potential learning difficulties had their work ‘marked’ by artificial intelligence has found the approach significantly improved their reasoning.
The study, with 178 trainee teachers in Germany, was carried out by a research team led by academics at the University of Cambridge and Ludwig-Maximilians-Universität München (LMU Munich). It provides some of the first evidence that artificial intelligence (AI) could enhance teachers’ ‘diagnostic reasoning’: the ability to collect and assess evidence about a pupil, and draw appropriate conclusions so they can be given tailored support.
During the trial, trainees were asked to assess six fictionalised ‘simulated’ pupils with potential learning difficulties. They were given examples of their schoolwork, as well as other information such as behaviour records and transcriptions of conversations with parents. They then had to decide whether or not each pupil had learning difficulties such as dyslexia or Attention Deficit Hyperactivity Disorder (ADHD), and explain their reasoning.
Immediately after submitting their answers, half of the trainees received a prototype ‘expert solution’, written in advance by a qualified professional, to compare with their own. This is typical of the practice material student teachers usually receive outside taught classes. The others received AI-generated feedback, which highlighted the correct parts of their solution and flagged aspects they might have improved.
After completing the six preparatory exercises, the trainees then took two similar follow-up tests — this time without any feedback. The tests were scored by the researchers, who assessed both their ‘diagnostic accuracy’ (whether the trainees had correctly identified cases of dyslexia or ADHD), and their diagnostic reasoning: how well they had used the available evidence to make this judgement.
The average score for diagnostic reasoning among trainees who had received AI feedback during the six preliminary exercises was an estimated 10 percentage points higher than those who had worked with the pre-written expert solutions. More