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    Rice feeds half the world. Climate change’s droughts and floods put it at risk

    Under a midday summer sun in California’s Sacramento Valley, rice farmer Peter Rystrom walks across a dusty, barren plot of land, parched soil crunching beneath each step.

    In a typical year, he’d be sloshing through inches of water amid lush, green rice plants. But today the soil lies naked and baking in the 35˚ Celsius (95˚ Fahrenheit) heat during a devastating drought that has hit most of the western United States. The drought started in early 2020, and conditions have become progressively drier.

    Low water levels in reservoirs and rivers have forced farmers like Rystrom, whose family has been growing rice on this land for four generations, to slash their water use.

    Rystrom stops and looks around. “We’ve had to cut back between 25 and 50 percent.” He’s relatively lucky. In some parts of the Sacramento Valley, depending on water rights, he says, farmers received no water this season.

    California is the second-largest U.S. producer of rice, after Arkansas, and over 95 percent of California’s rice is grown within about 160 kilometers of Sacramento. To the city’s east rise the peaks of the Sierra Nevada, which means “snowy mountains” in Spanish. Rice growers in the valley below count on the range to live up to its name each winter. In spring, melting snowpack flows into rivers and reservoirs, and then through an intricate network of canals and drainages to rice fields that farmers irrigate in a shallow inundation from April or May to September or October.

    If too little snow falls in those mountains, farmers like Rystrom are forced to leave fields unplanted. On April 1 this year, the date when California’s snowpack is usually at its deepest, it held about 40 percent less water than average, according to the California Department of Water Resources. On August 4, Lake Oroville, which supplies Rystrom and other local rice farmers with irrigation water, was at its lowest level on record.

    Drought in the Sacramento Valley has forced Peter Rystrom and other rice farmers to leave swaths of land barren.N. Ogasa

    Not too long ago, the opposite — too much rain — stopped Rystrom and others from planting. “In 2017 and 2019, we were leaving ground out because of flood. We couldn’t plant,” he says. Tractors couldn’t move through the muddy, clay-rich soil to prepare the fields for seeding.

    Climate change is expected to worsen the state’s extreme swings in precipitation, researchers reported in 2018 in Nature Climate Change. This “climate whiplash” looms over Rystrom and the other 2,500 or so rice producers in the Golden State. “They’re talking about less and less snowpack, and more concentrated bursts of rain,” Rystrom says. “It’s really concerning.”

    Farmers in China, India, Bangladesh, Indonesia, Vietnam — the biggest rice-growing countries — as well as in Nigeria, Africa’s largest rice producer — also worry about the damage climate change will do to rice production. More than 3.5 billion people get 20 percent or more of their calories from the fluffy grains. And demand is increasing in Asia, Latin America and especially in Africa.

    To save and even boost production, rice growers, engineers and researchers have turned to water-saving irrigation routines and rice gene banks that store hundreds of thousands of varieties ready to be distributed or bred into new, climate-resilient forms. With climate change accelerating, and researchers raising the alarm about related threats, such as arsenic contamination and bacterial diseases, the demand for innovation grows.

    “If we lose our rice crop, we’re not going to be eating,” says plant geneticist Pamela Ronald of the University of California, Davis. Climate change is already threatening rice-growing regions around the world, says Ronald, who identifies genes in rice that help the plant withstand disease and floods. “This is not a future problem. This is happening now.”

    Saltwater woes

    Most rice plants are grown in fields, or paddies, that are typically filled with around 10 centimeters of water. This constant, shallow inundation helps stave off weeds and pests. But if water levels suddenly get too high, such as during a flash flood, the rice plants can die.

    Striking the right balance between too much and too little water can be a struggle for many rice farmers, especially in Asia, where over 90 percent of the world’s rice is produced. Large river deltas in South and Southeast Asia, such as the Mekong River Delta in Vietnam, offer flat, fertile land that is ideal for farming rice. But these low-lying areas are sensitive to swings in the water cycle. And because deltas sit on the coast, drought brings another threat: salt.

    Salt’s impact is glaringly apparent in the Mekong River Delta. When the river runs low, saltwater from the South China Sea encroaches upstream into the delta, where it can creep into the soils and irrigation canals of the delta’s rice fields.

    In Vietnam’s Mekong River Delta, farmers pull dead rice plants from a paddy that was contaminated by saltwater intrusion from the South China Sea, which can happen during a drought.HOANG DINH NAM/AFP VIA GETTY IMAGES

    “If you irrigate rice with water that’s too salty, especially at certain [growing] stages, you are at risk of losing 100 percent of the crop,” says Bjoern Sander, a climate change specialist at the International Rice Research Institute, or IRRI, who is based in Vietnam.

    In a 2015 and 2016 drought, saltwater reached up to 90 kilometers inland, destroying 405,000 hectares of rice paddies. In 2019 and 2020, drought and saltwater intrusion returned, damaging 58,000 hectares of rice. With regional temperatures on the rise, these conditions in Southeast Asia are expected to intensify and become more widespread, according to a 2020 report by the Economic and Social Commission for Asia and the Pacific.

    Then comes the whiplash: Each year from around April to October, the summer monsoon turns on the faucet over swaths of South and Southeast Asia. About 80 percent of South Asia’s rainfall is dumped during this season and can cause destructive flash floods.

    Bangladesh is one of the most flood-prone rice producers in the region, as it sits at the mouths of the Ganges, Brahmaputra and Meghna rivers. In June 2020, monsoon rains flooded about 37 percent of the country, damaging about 83,000 hectares of rice fields, according to Bangladesh’s Ministry of Agriculture. And the future holds little relief; South Asia’s monsoon rainfall is expected to intensify with climate change, researchers reported June 4 in Science Advances.

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    A hot mess

    Water highs and lows aren’t the entire story. Rice generally grows best in places with hot days and cooler nights. But in many rice-growing regions, temperatures are getting too hot. Rice plants become most vulnerable to heat stress during the middle phase of their growth, before they begin building up the meat in their grains. Extreme heat, above 35˚ C, can diminish grain counts in just weeks, or even days. In April in Bangladesh, two consecutive days of 36˚ C destroyed thousands of hectares of rice.

    In South and Southeast Asia, such extreme heat events are expected to become common with climate change, researchers reported in July in Earth’s Future. And there are other, less obvious, consequences for rice in a warming world.

    One of the greatest threats is bacterial blight, a fatal plant disease caused by the bacterium Xanthomonas oryzae pv. oryzae. The disease, most prevalent in Southeast Asia and rising in Africa, has been reported to have cut rice yields by up to 70 percent in a single season.

    “We know that with higher temperature, the disease becomes worse,” says Jan Leach, a plant pathologist at Colorado State University in Fort Collins. Most of the genes that help rice combat bacterial blight seem to become less effective when temperatures rise, she explains.

    And as the world warms, new frontiers may open for rice pathogens. An August study in Nature Climate Change suggests that as global temperatures rise, rice plants (and many other crops) at northern latitudes, such as those in China and the United States, will be at higher risk of pathogen infection.

    Meanwhile, rising temperatures may bring a double-edged arsenic problem. In a 2019 study in Nature Communications, E. Marie Muehe, a biogeochemist at the Helmholtz Centre for Environmental Research in Leipzig, Germany, who was then at Stanford University, showed that under future climate conditions, more arsenic will infiltrate rice plants. High arsenic levels boost the health risk of eating the rice and impair plant growth.

    Arsenic naturally occurs in soils, though in most regions the toxic element is present at very low levels. Rice, however, is particularly susceptible to arsenic contamination, because it is grown in flooded conditions. Paddy soils lack oxygen, and the microbes that thrive in this anoxic environment liberate arsenic from the soil. Once the arsenic is in the water, rice plants can draw it in through their roots. From there, the element is distributed throughout the plants’ tissues and grains.

    Muehe and her team grew a Californian variety of rice in a local low-arsenic soil inside climate-controlled greenhouses. Increasing the temperature and carbon dioxide levels to match future climate scenarios enhanced the activity of the microbes living in the rice paddy soils and increased the amount of arsenic in the grains, Muehe says. And importantly, rice yields diminished. In the low-arsenic Californian soil under future climate conditions, rice yield dropped 16 percent.

    According to the researchers, models that forecast the future production of rice don’t account for the impact of arsenic on harvest yields. What that means, Muehe says, is that current projections are overestimating how much rice will be produced in the future.

    Managing rice’s thirst

    From atop an embankment that edges one of his fields, Rystrom watches water gush from a pipe, flooding a paddy packed with rice plants. “On a year like this, we decided to pump,” he says.

    Able to tap into groundwater, Rystrom left only about 10 percent of his fields unplanted this growing season. “If everybody was pumping from the ground to farm rice every year,” he admits, it would be unsustainable.

    One widely studied, drought-friendly method is “alternate wetting and drying,” or intermittent flooding, which involves flooding and draining rice paddies on one- to 10-day cycles, as opposed to maintaining a constant inundation. This practice can cut water use by up to 38 percent without sacrificing yields. It also stabilizes the soil for harvesting and lowers arsenic levels in rice by bringing more oxygen into the soils, disrupting the arsenic-releasing microbes. If tuned just right, it may even slightly improve crop yields.

    But the water-saving benefits of this method are greatest when it is used on highly permeable soils, such as those in Arkansas and other parts of the U.S. South, which normally require lots of water to keep flooded, says Bruce Linquist, a rice specialist at the University of California Cooperative Extension. The Sacramento Valley’s clay-rich soils don’t drain well, so the water savings where Rystrom farms are minimal; he doesn’t use the method.

    Building embankments, canal systems and reservoirs can also help farmers dampen the volatility of the water cycle. But for some, the solution to rice’s climate-related problems lies in enhancing the plant itself.

    Fourth-generation rice farmer Peter Rystrom (left) stands with his grandfather Don Rystrom (middle) and his father Steve Rystrom (right).CALIFORNIA RICE COMMISSION, BRIAN BAER

    Better breeds

    The world’s largest collection of rice is stored near the southern rim of Laguna de Bay in the Philippines, in the city of Los Baños. There, the International Rice Genebank, managed by IRRI, holds over 132,000 varieties of rice seeds from farms around the globe.

    Upon arrival in Los Baños, those seeds are dried and processed, placed in paper bags and moved into two storage facilities — one cooled to 2˚ to 4˚ C from which seeds can be readily withdrawn, and another chilled to –20˚ C for long-term storage. To be extra safe, backup seeds are kept at the National Center for Genetic Resources Preservation in Fort Collins, Colo., and the Svalbard Global Seed Vault tucked inside a mountain in Norway.

    All this is done to protect the biodiversity of rice and amass a trove of genetic material that can be used to breed future generations of rice. Farmers no longer use many of the stored varieties, instead opting for new higher-yield or sturdier breeds. Nevertheless, solutions to climate-related problems may be hidden in the DNA of those older strains. “Scientists are always looking through that collection to see if genes can be discovered that aren’t being used right now,” says Ronald, of UC Davis. “That’s how Sub1 was discovered.”

    Over 132,000 varieties of rice seeds fill the shelves of the climate-controlled International Rice Genebank. Breeders from around the world can use the seeds to develop new climate-resilient rice strains.IRRI/FLICKR (CC BY-NC-SA 2.0)

    The Sub1 gene enables rice plants to endure prolonged periods completely submerged underwater. It was discovered in 1996 in a traditional variety of rice grown in the Indian state of Orissa, and through breeding has been incorporated into varieties cultivated in flood-prone regions of South and Southeast Asia. Sub1-wielding varieties, called “scuba rice,” can survive for over two weeks entirely submerged, a boon for farmers whose fields are vulnerable to flash floods.

    Some researchers are looking beyond the genetic variability preserved in rice gene banks, searching instead for useful genes from other species, including plants and bacteria. But inserting genes from one species into another, or genetic modification, remains controversial. The most famous example of genetically modified rice is Golden Rice, which was intended as a partial solution to childhood malnutrition. Golden Rice grains are enriched in beta-carotene, a precursor to vitamin A. To create the rice, researchers spliced a gene from a daffodil and another from a bacterium into an Asian variety of rice.

    Three decades have passed since its initial development, and only a handful of countries have deemed Golden Rice safe for consumption. On July 23, the Philippines became the first country to approve the commercial production of Golden Rice. Abdelbagi Ismail, principal scientist at IRRI, blames the slow acceptance on public perception and commercial interests opposed to genetically modified organisms, or GMOs (SN: 2/6/16, p. 22).

    Looking ahead, it will be crucial for countries to embrace GM rice, Ismail says. Developing nations, particularly those in Africa that are becoming more dependent on the crop, would benefit greatly from the technology, which could produce new varieties faster than breeding and may allow researchers to incorporate traits into rice plants that conventional breeding cannot. If Golden Rice were to gain worldwide acceptance, it could open the door for new genetically modified climate- and disease-resilient varieties, Ismail says. “It will take time,” he says. “But it will happen.”

    Climate change is a many-headed beast, and each rice-growing region will face its own particular set of problems. Solving those problems will require collaboration between local farmers, government officials and the international community of researchers.

    “I want my kids to be able to have a shot at this,” Rystrom says. “You have to do a lot more than just farm rice. You have to think generations ahead.” More

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    Scientists develop artificial intelligence method to predict anti-cancer immunity

    Researchers and data scientists at UT Southwestern Medical Center and MD Anderson Cancer Center have developed an artificial intelligence technique that can identify which cell surface peptides produced by cancer cells called neoantigens are recognized by the immune system.
    The pMTnet technique, detailed online in Nature Machine Intelligence, could lead to new ways to predict cancer prognosis and potential responsiveness to immunotherapies.
    “Determining which neoantigens bind to T cell receptors and which don’t has seemed like an impossible feat. But with machine learning, we’re making progress,” said senior author Dr. Tao Wang, Ph.D., Assistant Professor of Population and Data Sciences, and with the Harold C. Simmons Comprehensive Cancer Center and the Center for Genetics of Host Defense at UT Southwestern.
    Mutations in the genome of cancer cells cause them to display different neoantigens on their surfaces. Some of these neoantigens are recognized by immune T cells that hunt for signs of cancer and foreign invaders, allowing cancer cells to be destroyed by the immune system. However, others seem invisible to T cells, allowing cancers to grow unchecked.
    “For the immune system, the presence of neoantigens is one of the biggest differences between normal and tumor cells,” said Tianshi Lu, first co-author with Ze Zhang, doctoral students in the Tao Wang lab, which uses state-of-the-art bioinformatics and biostatistics approaches to study the implications of tumor immunology for tumorigenesis, metastasis, prognosis, and treatment response in a variety of cancers. “If we can figure out which neoantigens stimulate an immune response, then we may be able to use this knowledge in a variety of different ways to fight cancer,” Ms. Lu said.
    Being able to predict which neoantigens are recognized by T cells could help researchers develop personalized cancer vaccines, engineer better T cell-based therapies, or predict how well patients might respond to other types of immunotherapies. But there are tens of thousands of different neoantigens, and methods to predict which ones trigger a T cell response have proven to be time-consuming, technically challenging, and costly.
    Searching for a better technique with support of grants from the National Institutes of Health (NIH) and Cancer Prevention and Research Institute of Texas (CPRIT), the research team looked to machine learning. They trained a deep learning-based algorithm that they named pMTnet using data from known binding or nonbinding combinations of three different components: neoantigens; proteins called major histocompatibility complexes (MHCs) that present neoantigens on cancer cell surfaces; and the T cell receptors (TCRs) responsible for recognizing the neoantigen-MHC complexes. They then tested the algorithm against a dataset developed from 30 different studies that had experimentally identified binding or nonbinding neoantigen T cell-receptor pairs. This experiment showed that the new algorithms had a high level of accuracy.
    The researchers used this new tool to gather insights on neoantigens cataloged in The Cancer Genome Atlas, a public database that holds information from more than 11,000 primary tumors. pMTnet showed that neoantigens generally trigger a stronger immune response compared with tumor-associated antigens. It also predicted which patients had better responses to immune checkpoint blockade therapies and had better overall survival rates.
    “As an immunologist, the most significant hurdle currently facing immunotherapy is the ability to determine which antigens are recognized by which T cells in order to leverage these pairings for therapeutic purposes,” said corresponding author Alexandre Reuben, Ph.D., Assistant Professor of Thoracic-Head & Neck Medical Oncology at MD Anderson. “pMTnet outperforms its current alternatives and brings us significantly closer to this objective.”
    Other UTSW researchers who contributed to this study include James Zhu, Yunguan Wang, Xue Xiao, and Lin Xu. Other MD Anderson scientists who contributed to this work include Peixin Jiang, Chantale Bernatchez, John V. Heymach, and Don L. Gibbons. Dr. Jun Wang from NYU Langone Health also contributed to this work.
    UT Southwestern’s Simmons Cancer Center and MD Anderson Cancer Center are among the exclusive 51 designated comprehensive centers with the National Cancer Institute, which includes a joint effort with the National Human Genome Research Institute to oversee The Cancer Genome Atlas project. The study was supported by the NIH (grants 5P30CA142543/TW and R01CA258584/TW), CPRIT (RP190208/TW), MD Anderson (Lung Cancer Moon Shot), the University Cancer Foundation at MD Anderson, the Waun Ki Hong Lung Cancer Research Fund, Exon 20 Group, and Rexanna’s Foundation for Fighting Lung Cancer. More

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    Peering into the Moon's shadows with AI

    The Moon’s polar regions are home to craters and other depressions that never receive sunlight. Today, a group of researchers led by the Max Planck Institute for Solar System Research (MPS) in Germany presents the highest-resolution images to date covering 17 such craters in the journal Nature Communications.
    Craters of this type could contain frozen water, making them attractive targets for future lunar missions, and the researchers focused further on relatively small and accessible craters surrounded by gentle slopes. In fact, three of the craters have turned out to lie within the just-announced mission area of NASA’s Volatiles Investigating Polar Exploration Rover (VIPER), which is scheduled to touch down on the Moon in 2023. Imaging the interior of permanently shadowed craters is difficult, and efforts so far have relied on long exposure times resulting in smearing and lower resolution. By taking advantage of reflected sunlight from nearby hills and a novel image processing method, the researchers have now produced images at 1-2 meters per pixel, which is at or very close to the best capability of the cameras.
    The Moon is a cold, dry desert. Unlike the Earth, it is not surrounded by a protective atmosphere and water which existed during the Moon’s formation has long since evaporated under the influence of solar radiation and escaped into space. Nevertheless, craters and depressions in the polar regions give some reason to hope for limited water resources. Scientists from MPS, the University of Oxford and the NASA Ames Research Center have now taken a closer look at some of these regions.
    “Near the lunar north and south poles, the incident sunlight enters the craters and depressions at a very shallow angle and never reaches some of their floors,” MPS-scientist Dr. Valentin Bickel, first author of the new paper, explains. In this “eternal night,” temperatures in some places are so cold that frozen water is expected to have lasted for millions of years. Impacts from comets or asteroids could have delivered it, or it could have been outgassed by volcanic eruptions, or formed by the interaction of the surface with the solar wind. Measurements of neutron flux and infrared radiation obtained by space probes in recent years indicate the presence of water in these regions. Eventually, NASA’s Lunar Crater Observation and Sensing Satellite (LCROSS) provided direct proof: twelve years ago, the probe fired a projectile into the shadowed south pole crater Cabeus. As later analysis showed, the dust cloud emitted into space contained a considerable amount of water.
    However, permanently shadowed regions are not only of scientific interest. If humans are to ever spend extended periods of time on the Moon, naturally occurring water will be a valuable resource — and shadowed craters and depressions will be an important destination. NASA’s uncrewed VIPER rover, for example, will explore the South Pole region in 2023 and enter such craters. In order to get a precise picture of their topography and geology in advance — for mission planning purposes, for example — images from space probes are indispensable. NASA’s Lunar Reconnaissance Orbiter (LRO) has been providing such images since 2009.
    However, capturing images within the deep darkness of permanently shadowed regions is exceptionally difficult; after all, the only sources of light are scattered light, such as that reflecting off the Earth and the surrounding topography, and faint starlight. “Because the spacecraft is in motion, the LRO images are completely blurred at long exposure times,” explains Ben Moseley of the University of Oxford, a co-author of the study. At short exposure times, the spatial resolution is much better. However, due to the small amounts of light available, these images are dominated by noise, making it hard to distinguish real geological features.
    To address this problem, the researchers have developed a machine learning algorithm called HORUS (Hyper-effective nOise Removal U-net Software) that “cleans up” such noisy images. It uses more than 70,000 LRO calibration images taken on the dark side of the Moon as well as information about camera temperature and the spacecraft’s trajectory to distinguish which structures in the image are artifacts and which are real. This way, the researchers can achieve a resolution of about 1-2 meters per pixel, which is five to ten times higher than the resolution of all previously available images.
    Using this method, the researchers have now re-evaluated images of 17 shadowed regions from the lunar south pole region which measure between 0.18 and 54 square kilometers in size. In the resulting images, small geological structures only a few meters across can be discerned much more clearly than before. These structures include boulders or very small craters, which can be found everywhere on the lunar surface. Since the Moon has no atmosphere, very small meteorites repeatedly fall onto its surface and create such mini-craters.
    “With the help of the new HORUS images, it is now possible to understand the geology of lunar shadowed regions much better than before,” explains Moseley. For example, the number and shape of the small craters provide information about the age and composition of the surface. It also makes it easier to identify potential obstacles and hazards for rovers or astronauts. In one of the studied craters, located on the Leibnitz Plateau, the researchers discovered a strikingly bright mini-crater. “Its comparatively bright color may indicate that this crater is relatively young,” says Bickel. Because such a fresh scar provides fairly unhindered insight into deeper layers, this site could be an interesting target for future missions, the researchers suggest.
    The new images do not provide evidence of frozen water on the surface, such as bright patches. “Some of the regions we’ve targeted might be slightly too warm,” Bickel speculates. It is likely that lunar water does not exist as a clearly visible deposit on the surface at all — instead, it could be intermixed with the regolith and dust, or may be hidden underground.
    To address this and other questions, the researchers’ next step is to use HORUS to study as many shadowed regions as possible. “In the current publication, we wanted to show what our algorithm can do. Now we want to apply it as comprehensively as possible,” says Bickel.
    This work has been enabled by the Frontier Development Lab (FDL.ai). FDL is a co-operative agreement between NASA, the SETI Institute and Trillium Technologies Inc, in partnership with the Luxembourg Space Agency and Google Cloud. More

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    Mathematical constructions of COVID virus activity could provide new insight for vaccines, treatment

    Mathematical constructions of the action of SARS-CoV-2 and its multiple spikes, which enable its success at infecting cells, can give vaccine developers and pharmaceutical companies alike a more precise picture of what the virus is doing inside us and help fine tune prevention and treatment, mathematical modelers say.
    Mathematical construction enables examination of the activity of individual virus particles including the emergence of new spikes — and more severe infection potential — that can result when a single virus particle infects a human cell, says Dr. Arni S.R. Rao, director of the Laboratory for Theory and Mathematical Modeling in the Section of Infectious Diseases at the Medical College of Georgia.
    The number of spikes and the way they are distributed on a virus particle are believed to be key in the spread of the virus, Rao and his colleague Dr. Steven G. Krantz, professor of mathematics at Washington University in St. Louis, Missouri, write in the Journal of Mathematical Analysis and Applications.
    Laboratory experiments on virus particles and their bonding, or infection, of cells more typically are done on a group of viruses, they write.
    “Right now, we don’t know when a spike bonds with a cell, what happens with that virus particle’s other spikes,” says Rao, the study’s corresponding author. “How many new infected cells are being produced has never been studied for the coronavirus. We need quantification because ultimately the vaccine or pharmaceutical industry needs to target those infected cells,” he says of the additional insight their mathematical methodology, which also enables the study of the growth of the virus’ spikes over time, provides.
    Viral load is considered one of the strong predictors of the severity of sickness and risk of death and the number of spikes successfully bonding with cells is an indicator of the viral load, Rao says. PCR, or polymerase chain reaction, which is used for COVID testing, for example, provides viral load by assessing the amount of the virus’ RNA present in a test sample. More

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    A computer algorithm called 'Eva' may have saved lives in Greece

    A prescriptive computer program developed by the USC Marshall School of Business and Wharton School of Business of the University of Pennsylvania for Greece to identify asymptomatic, infected travelers may have slowed tCOVID-19’s spread through its borders, a new study in the journal Nature indicates.
    “It was a very high-impact artificial intelligence project, and I believe we saved lives by developing a cutting edge, novel system for targeted testing during the pandemic,” said Kimon Drakopoulos, a USC Marshall assistant professor of Data Sciences and Operations and one of the study’s authors.
    In July 2020, Greece largely reopened its borders to spare its tourism-dependent economy from the devastating impact of long-term shutdowns amid COVID-19.
    Greece collaborated with USC Marshall and Wharton to create “Eva,” an artificial intelligence algorithm that uses real-time data to identify high-risk visitors for testing. Evidence shows the algorithm caught nearly twice as many asymptomatic infected travelers as would have been caught if Greece had relied on only travel restrictions and randomized COVID testing.
    “Our work with Eva proves that carefully integrating real-time data, artificial intelligence and lean operations offers huge benefits over conventional, widely used approaches to managing the pandemic,” said Vishal Gupta, a USC Marshall associate professor of data science another of the study’s authors.
    The joint study was published Wednesday in the journal Nature. More

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    Switching on a superfluid

    We can learn a lot by studying microscopic and macroscopic changes in a material as it crosses from one phase to another, for example from ice to water to steam. A new Australian study examines systems transitioning from ‘normal’ fluid to a quantum state known as a superfluid, which can flow with zero friction, with a view to future, superfluid-based, quantum technologies, such as ultra-low energy electronics.We can learn a lot by studying microscopic and macroscopic changes in a material as it crosses from one phase to another, for example from ice to water to steam.
    But while these phase transitions are well understood in the case of water, much less is known about the dynamics when a system goes from being a normal fluid to a superfluid, which can flow with zero friction, ie without losing any energy.
    A new Swinburne study observing transition of an atomic gas from normal fluid to superfluid provides new insights into the formation of these remarkable states, with a view to future, superfluid-based, quantum technologies, such as ultra-low energy electronics.
    Superfluid formation was seen to involve a number of different timescales, associated with different dynamical processes that take place upon crossing the phase boundary.
    UNDERSTANDING DYNAMIC TRANSITIONS, TOWARDS FUTURE TECHNOLOGIES
    As a nonequilibrium, dynamic process, phase transitions are challenging to understand from a theoretical perspective, inside these fascinating and potentially useful states of matter. More

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    Artificial intelligence may be set to reveal climate-change tipping points

    Researchers are developing artificial intelligence that could assess climate change tipping points. The deep learning algorithm could act as an early warning system against runaway climate change.
    Chris Bauch, a professor of applied mathematics at the University of Waterloo, is co-author of a recent research paper reporting results on the new deep-learning algorithm. The research looks at thresholds beyond which rapid or irreversible change happens in a system, Bauch said.
    “We found that the new algorithm was able to not only predict the tipping points more accurately than existing approaches but also provide information about what type of state lies beyond the tipping point,” Bauch said. “Many of these tipping points are undesirable, and we’d like to prevent them if we can.”
    Some tipping points that are often associated with run-away climate change include melting Arctic permafrost, which could release mass amounts of methane and spur further rapid heating; breakdown of oceanic current systems, which could lead to almost immediate changes in weather patterns; or ice sheet disintegration, which could lead to rapid sea-level change.
    The innovative approach with this AI, according to the researchers, is that it was programmed to learn not just about one type of tipping point but the characteristics of tipping points generally.
    The approach gains its strength from hybridizing AI and mathematical theories of tipping points, accomplishing more than either method could on its own. After training the AI on what they characterize as a “universe of possible tipping points” that included some 500,000 models, the researchers tested it on specific real-world tipping points in various systems, including historical climate core samples.
    “Our improved method could raise red flags when we’re close to a dangerous tipping point,” said Timothy Lenton, director of the Global Systems Institute at the University of Exeter and one of the study’s co-authors. “Providing improved early warning of climate tipping points could help societies adapt and reduce their vulnerability to what is coming, even if they cannot avoid it.”
    Deep learning is making huge strides in pattern recognition and classification, with the researchers having, for the first time, converted tipping-point detection into a pattern-recognition problem. This is done to try and detect the patterns that occur before a tipping point and get a machine-learning algorithm to say whether a tipping point is coming.
    “People are familiar with tipping points in climate systems, but there are tipping points in ecology and epidemiology and even in the stock markets,” said Thomas Bury, a postdoctoral researcher at McGill University and another of the co-authors on the paper. “What we’ve learned is that AI is very good at detecting features of tipping points that are common to a wide variety of complex systems.”
    The new deep learning algorithm is a “game-changer for the ability to anticipate big shifts, including those associated with climate change,” said Madhur Anand, another of the researchers on the project and director of the Guelph Institute for Environmental Research.
    Now that their AI has learned how tipping points function, the team is working on the next stage, which is to give it the data for contemporary trends in climate change. But Anand issued a word of caution of what may happen with such knowledge.
    “It definitely gives us a leg up,” she said. “But of course, it’s up to humanity in terms of what we do with this knowledge. I just hope that these new findings will lead to equitable, positive change.”
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    Materials provided by University of Waterloo. Note: Content may be edited for style and length. More

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    Contact-tracing apps could improve vaccination strategies

    Mathematical modeling of disease spread suggests that herd immunity could be achieved with fewer vaccine doses by using Bluetooth-based contact-tracing apps to identify people who have more exposure to others — and targeting them for vaccination. Mark Penney, Yigit Yargic and their colleagues from the Perimeter Institute for Theoretical Physics in Ontario, Canada, present this approach in the open-access journal PLOS ONE on September 22, 2021.
    The COVID-19 pandemic has raised questions about how to best allocate limited supplies of vaccines for the greatest benefit against a disease. Mathematical models suggest that vaccines like those available for COVID-19 are most effective at reducing transmission when they are targeted to people who have more exposure to others. However, it can be challenging to identify such individuals.
    Penney and colleagues hypothesized that this challenge could be addressed by harnessing existing apps that anonymously alert users to potential COVID-19 exposure. These apps use Bluetooth technology to determine the duration of contact between any pair of individuals who both have the same app downloaded on their smart phones. The researchers wondered whether this technology could also be used to help identify and target vaccines to those with greater exposure — a strategy analogous to a wildfire-fighting practice called “hot-spotting,” which targets sites with intense fires.
    To explore the effectiveness of a hot-spotting approach to vaccination, Penney and colleagues used mathematical modeling to simulate how a disease would spread among a population with such a strategy in place. Specifically, they applied an analytical technique borrowed from statistical physics known as percolation theory.
    The findings suggest that a Bluetooth-based hot-spotting approach to vaccination could reduce the number of vaccine doses needed to achieve herd immunity by up to one half. The researchers found improvements even for simulations in which relatively few people use contact-tracing apps — a situation mirroring reality for COVID-19 in many regions.
    In the future, the modeling approach used for this study could be refined, such as by incorporating the effects of strains on the healthcare system. Nonetheless, the researchers note, the new findings highlight a technically feasible way to implement a strategy that previous research already supports.
    The authors add: “The technology underlying digital contact tracing apps has made it possible to implement novel decentralized and efficient vaccine strategies.”
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