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    New super-conductors could take data beyond zeroes and ones

    Remember flip-phones? Our smartphones may one day look just as obsolete thanks to spintronics, an incipient field of research promising to revolutionize the way our electronic devices send and receive signals.
    In most current technologies, data is encoded as a zero or a one, depending on the number of electrons that reach a capacitor. With spintronics, data is also transferred according to the direction in which these electrons spin.
    In a new study appearing this week in the Proceedings of the National Academy of Sciences, a team of Duke University and Weizmann Institute researchers led by Michael Therien, professor of Chemistry at Duke, report a keystone achievement in the field: the development of a conducting system that controls the spin of electrons and transmits a spin current over long distances, without the need for the ultra-cold temperatures required by typical spin-conductors.
    “The structures we present here are exciting because they define new strategies to generate large magnitude spin currents at room temperature,” said Chih-Hung Ko, first author of the paper and recent Duke chemistry Ph.D.
    Electrons are like spinning tops. Spin-up electrons rotate clockwise, and spin-down electrons rotate counter-clockwise. Electrons with opposite spins can occupy the same volume, but electrons that spin in the same direction repel themselves, like magnets of the same polarity.
    By controlling the way that electrons spin along a current, scientists can encode a new layer of information into an electric signal. More

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    How Omicron escapes from antibodies

    A new study from MIT suggests that the dozens of mutations in the spike protein of the Omicron variant help it to evade all four of the classes of antibodies that can target the SARS-CoV-2 virus that causes Covid-19.
    This includes antibodies generated by vaccinated or previously infected people, as well as most of the monoclonal antibody treatments that have been developed, says Ram Sasisekharan, the Alfred H. Caspary Professor of Biological Engineering and Health Sciences and Technology (HST) at MIT.
    Using a computational approach that allowed them to determine how mutated amino acids of the viral spike protein influence nearby amino acids, the researchers were able to get a multidimensional view of how the virus evades antibodies. According to Sasisekharan, the traditional approach of only examining changes in the virus’ genetic sequence reduces the complexity of the spike protein’s three-dimensional surface and doesn’t describe the multidimensional complexity of the protein surfaces that antibodies are attempting to bind to.
    “It is important to get a more comprehensive picture of the many mutations seen in Omicron, especially in the context of the spike protein, given that the spike protein is vital for the virus’s function, and all the major vaccines are based on that protein,” he says. “There is a need for tools or approaches that can rapidly determine the impact of mutations in new virus variants of concern, especially for SARS-CoV-2.”
    Sasisekharan is the senior author of the study, which appears this week in Cell Reports Medicine. The lead author of the paper is MIT HST graduate student Nathaniel Miller. Technical associate Thomas Clark and research scientist Rahul Raman are also authors of the paper.
    Even though Omicron is able to evade most antibodies to some degree, vaccines still offer protection, Sasisekharan says. More

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    Researchers develop highly accurate modeling tool to predict COVID-19 risk

    As new coronavirus variants emerge and quickly spread around the globe, both the public and policymakers are faced with a quandary: maintaining a semblance of normality, while also minimizing infections. While digital contact tracing apps offered promise, the adoption rate has been low, due in part to privacy concerns.
    At USC, researchers are advocating for a new approach to predict the chance of infection from Covid-19: combining anonymized cellphone location data with mobility patterns — broad patterns of how people move from place to place.
    To produce “risk scores” for specific locations and times, the team used a large dataset of anonymous, real-world location signals from cell phones across the US in 2019 and 2020. The system shows a 50% improvement in accuracy compared to current systems, said the researchers.
    “Our results show that it is possible to predict and target specific areas that are high-risk, as opposed to putting all businesses under one umbrella. Such risk-targeted policies can be significantly more effective, both for controlling Covid-19 and economically,” said lead author Sepanta Zeighami, a computer science Ph.D. student advised by Professor Cyrus Shahabi.
    “It’s also unlikely that Covid-19 will be the last pandemic in human history, so if we want to avoid the chaos of 2020 and the tragic losses while keeping daily life as unaffected as possible when the next pandemic happens, we need such data-driven approaches.”
    To address privacy concerns, the mobility data comes in an aggregated format, allowing the researchers to see patterns without identifying individual users. The data is not being used for contact tracing, identifying infected individuals, or where they are going, said the researchers. More

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    The past’s extreme ocean heat waves are now the new normal

    Yesterday’s scorching ocean extremes are today’s new normal. A new analysis of surface ocean temperatures over the past 150 years reveals that in 2019, 57 percent of the ocean’s surface experienced temperatures rarely seen a century ago, researchers report February 1 in PLOS Climate.

    To provide context for the frequency and duration of modern extreme heat events, marine ecologists Kisei Tanaka, now at the National Oceanographic and Atmospheric Administration in Honolulu, and Kyle Van Houtan, now at the Loggerhead Marinelife Center in Juno Beach, Fla., analyzed monthly sea-surface temperatures from 1870 through 2019, mapping where and when extreme heat events occurred decade to decade.

    Looking at monthly extremes rather than annual averages revealed new benchmarks in how the ocean is changing. More and more patches of water hit extreme temperatures over time, the team found. Then, in 2014, the entire ocean hit the “point of no return,” Van Houtan says. Beginning that year, at least half of the ocean’s surface waters saw temperatures hotter than the most extreme events from 1870 to 1919.

    Marine heat waves are defined as at least five days of unusually high temperatures for a patch of ocean. Heat waves wreak havoc on ocean ecosystems, leading to seabird starvation, coral bleaching, dying kelp forests, and migration of fish, whales and turtles in search of cooler waters (SN: 1/15/20; SN: 8/10/20).

    In May 2020, NOAA announced that it was updating its “climate normals” — what the agency uses to put daily weather events in historical context — from the average 1981–2010 values to the higher 1991–2020 averages (SN: 5/26/21). 

    This study emphasizes that ocean heat extremes are also now the norm, Van Houtan says. “Much of the public discussion now on climate change is about future events, and whether or not they might happen,” he says. “Extreme heat became common in our ocean in 2014. It’s a documented historical fact, not a future possibility.”

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    The power of chaos: A robust and low-cost cryptosystem for the post-quantum era

    Fast algorithms on quantum computers could easily break many widely used cryptosystems, necessitating more innovative solutions for digital security. In a recent study, a team of scientists designed a stream cipher consisting of three cryptographic primitives based on independent mathematical models of chaos. The resulting cryptographic approach is robust to attacks from large-scale quantum computers and can be implemented on low-cost computers, paving the way to secure digital communications in the post-quantum era.
    While for most of us cryptographic systems are things that just run “under the hood,” they are an essential element in the world of digital communications. However, the upcoming rise of quantum computers could shake the field of cryptography to its core. Fast algorithms running on these machines could break some of the most widely used cryptosystems, rendering them vulnerable. Well aware of this looming threat, cryptography researchers worldwide are working on novel encryption methods that can withstand attacks from quantum computers.
    Chaos theory is actively being studied as a basis for post-quantum era cryptosystems. In mathematics, chaos is a property of certain dynamic systems that makes them extremely sensitive to initial conditions. While technically deterministic (non-random), these systems evolve in such complex ways that predicting their long-term state with incomplete information is practically impossible, since even small rounding errors in the initial conditions yield diverging results. This unique characteristic of chaotic systems can be leveraged to produce highly secure cryptographic systems, as a team of researchers from Ritsumeikan University, Japan, showed in a recent study published in IEEE Transactions on Circuits and Systems I.
    Led by Professor Takaya Miyano, the team developed an unprecedented stream cipher consisting of three cryptographic primitives based on independent mathematical models of chaos. The first primitive is a pseudorandom number generator based on the augmented Lorenz (AL) map. The pseudorandom numbers produced using this approach are used to create key streams for encrypting/decrypting messages, which take the stage in the second and perhaps most remarkable primitive — an innovative method for secret-key exchange.
    This novel strategy for exchanging secret keys specifying the AL map is based on the synchronization of two chaotic Lorenz oscillators, which can be independently and randomly initialized by the two communicating users, without either of them knowing the state of the other’s oscillator. To conceal the internal states of these oscillators, the communicating users (the sender and the receiver) mask the value of one of the variables of their oscillator by multiplying it with a locally generated random number. The masked value of the sender is then sent to receiver and vice-versa. After a short time, when these back-and-forth exchanges cause both oscillators to sync up almost perfectly to the same state in spite of the randomization of the variables, the users can mask and exchange secret keys and then locally unmask them with simple calculations.
    Finally, the third primitive is a hash function based on the logistic map (a chaotic equation of motion), which allows the sender to send a hash value and, in turn, allows the receiver to ensure that the received secret key is correct, i.e., the chaotic oscillators were synchronized properly.
    The researchers showed that a stream cipher assembled using these three primitives is extremely secure and resistant to statistical attacks and eavesdropping since it is mathematically impossible to synchronize their own oscillator to either the sender’s or the receiver’s ones. This is an unprecedented achievement, as Prof. Miyano states: “Most chaos-based cryptosystems can be broken by attacks using classical computers within a practically short time. In contrast, our methods, especially the one for secret-key exchange, appear to be robust against such attacks and, more importantly, even hard to break using quantum computers.”
    In addition to its security, the proposed key exchange method is applicable to existing block ciphers, such as the widely used Advanced Encryption Standard (AES). Moreover, the researchers could implement their chaos-based stream cipher on the Raspberry Pi 4, a small-scale computer, using Python 3.8. They even used it to securely transmit a famous painting by Johannes Vermeer between Kusatsu and Sendai, two places in Japan 600 km apart. “The implementation and running costs of our cryptosystem are remarkably low compared with those of quantum cryptography,” highlights Prof. Miyano, “Our work thus provides a cryptographic approach that guarantees the privacy of daily communications between people all over the world in the post-quantum era.”
    With such power of chaos-based cryptography, we may not have much to worry about the dark sides of quantum computing.
    Story Source:
    Materials provided by Ritsumeikan University. Note: Content may be edited for style and length. More

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    On the spot drug delivery with light-controlled organic microswimmers

    Science Fiction novelists couldn’t have come up with a crazier plot: microrobots streaming through blood or through other fluids in our body which are driven by light, can carry drugs to cancer cells and drop off the medication on the spot. What sounds like a far-fetched fantasy, is however the short summary of a research project published in the journal Science Robotics. The microswimmers presented in the work bear the potential to one day perform tasks in living organisms or biological environments that are not easily accessible otherwise. Looking even further ahead, the swimmers could perhaps one day help treat cancer or other diseases.
    In their paper “Light-driven carbon nitride microswimmers with propulsion in biological and ionic media and responsive on-demand drug delivery,” a team of scientists from the Max Planck Institute for Intelligent Systems (MPI-IS) and its neighboring institute, the Max Planck Institute for Solid State Research (MPI-FKF), demonstrate organic microparticles that can steer through biological fluids and dissolved blood in an unprecedented way. Even in very salty liquids, the microswimmers can be propelled forward at high speed by visible light, either individually or as a swarm. Additionally, they are partially biocompatible and can take up and release cargo on demand. At MPI-IS, scientists from the Physical Intelligence Department led by Metin Sitti were involved and at MPI-FKF, scientists from the Nanochemistry Department led by Bettina Lotsch.
    Designing and fabricating such highly advanced microswimmers seemed impossible up until now. Locomotion by light energy is hindered by the salts found in water or the body. This requires a sophisticated design that is difficult to scale up. Additionally, controlling the robots from the outside is challenging and often costly. Controlled cargo uptake and on-the-spot delivery is another supreme discipline in the field of nanorobotics.
    The scientists used a porous two-dimensional carbon nitride (CNx) that can be synthesized from organic materials, for instance, urea. Like the solar cells of a photovoltaic panel, carbon nitride can absorb light which then provides the energy to propel the robot forward when light illuminates the particle surface.
    High ion tolerance
    “The use of light as the energy source of propulsion is very convenient when doing experiments in a petri dish or for applications directly under the skin,” says Filip Podjaski, a group leader in the Nanochemistry Department at MPI-FKF. “There is just one problem: even tiny concentrations of salts prohibit light-controlled motion. Salts are found in all biological liquids: in blood, cellular fluids, digestive fluids etc. However, we have shown that our CNx microswimmers function in all biological liquids — even when the concentration of salt ions is very high. This is only possible due to a favorable interplay of different factors: efficient light energy conversion as the driving force, as well as the porous structure of the nanoparticles, which allows ions to flow through them, reducing the resistance created by salt, so to speak. In addition, in this material, light favors the mobility of ions — making the particle even faster.”
    Having shown the swimmers are salt-tolerant, the team then tackled the challenge to use them as drug carriers. “This is also possible due to the material’s porosity,” Varun Sridhar explains. He is a postdoctoral researcher at MPI-IS and the first author of the publication. He and his team loaded the small pores of the swimmers with the anti-cancer drug Doxorubicin. “The particles adsorbed the drug like a sponge, up to unprecedentedly high amounts of 185% of the carrier mass while staying stably bound to the carbon nitride — even longer than a month. We then showed that controlled release of the drug is possible in a fluid with an acidic pH level. In addition, we were able to illuminate the microswimmers and thus release the drug, regardless of a change in pH. And even when loaded to full capacity, the swimmer did not slow down significantly, which is great.” More

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    New power transfer technology provides unprecedented freedom for wireless charging

    A new power transfer technology makes it possible to conveniently charge devices without using any wires or plugs. Warehouse robots, kitchen appliances, and even phones or laptops can receive power anywhere over the charging area, and because the power transfer continues even while the device is in motion, this technology could one day power electric vehicles while they’re on the go.
    The basics of wireless power transfer have been in place for some time, but existing systems are not able to charge devices placed anywhere within a large area. Using a single large transmitter to cover the entire area leads to unwanted electromagnetic exposure and means that the power flow to individual devices cannot be controlled. If many small transmitters are used, the receiving devices must be in a known position, and the transmitter and receiver have to be precisely aligned. This means the system either has to use fixed charging locations or incorporate position sensors, communication protocols, and processing to track the location of each receiver.
    Researchers at Aalto University have tackled these problems, developing a power transfer technology that works regardless of the position and orientation of the transmitter and receiver. The key idea is to arrange the transmitters in a grid with the current in neighbouring transmitters running in opposite directions — for example, a clockwise loop in one transmitter and counter-clockwise loops in its neighbours.
    This creates a chessboard-like grid of ‘positive’ and ‘negative’ transmitting coils with a magnetic flux between them. A receiver above the grid of transmitters captures the magnetic flux between positive and negative transmitters, which generates an electric current to charge the device.
    ‘The beauty of our method is that it’s very simple yet quite sophisticated,’ says Prasad Jayathurathnage, the postdoctoral researcher who led the project. ‘We don’t need a high-end processor or lots of computations to make the transmitters intelligent. At the end of the day, it’s all an electromagnetic system, and our approach was to figure out how we could detect the receiver’s presence and position electromagnetically.’
    Because the presence of a receiver triggers the power transfer, the system can work without any positional tracking and communication between the receivers and transmitters. This also means that power is only transferred to the receiver, rather than the entire area being energised, and it makes it possible for several devices to be charged simultaneously.
    Tiling transmitters together produces a charging area of the desired size and shape. A subset of the transmitters is then activated at lower power. ‘That’s basically a search — the transmitters are listening for a receiver,’ explains Shamsul Al Mahmud, a doctoral student in the project. If power transfer to a receiver begins, the neighbouring transmitters switch from being off into an alert mode, primed to transfer power if the receiver appears over them.
    ‘With this configuration, we had almost constant efficiency and constant power received regardless of the receiver’s position and orientation,’ says Ishtiaque Panhwar, a researcher involved in the project, and the power transfer continued smoothly even as the receiving device moved around.
    The technology has been tested with commercial warehouse robots in cooperation with Finnish firm Solteq Robotics, and Jayathurathnage also leads the project Parkzia, a project funded by Business Finland. The project aims to commercialize this new technology for industry and transport. ‘Taking this technology out of the lab and seeing it work in the warehouse was an exciting moment for me personally,’ says Jayathurathnage. ‘I was finally bringing the product of ten years of research out of the lab.’
    More familiar applications can also improve our daily life. ‘Take kitchen appliances, for example,’ says Jayathurathnage. ‘At the moment, you need to put a rice cooker or a blender at a particular spot for it to get energy. But with our technology we can make the whole kitchen counter a source of power for appliances or even phones, but the electromagnetic field is only generated under the devices.’
    Although the technology is essentially ready for real-world applications, it still needs commercial packaging and certification. In the meantime, Jayathurathnage’s team will continue to refine and improve this technology. One of their goals is to boost the power levels from about 1 kW to around 20 kW so that the technology could be used to charge electric vehicles. ‘There are pilot projects on electrifying roads across the world,’ says Jayathurathnage. ‘Electric vehicles are a really great application of this technology.’
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    Materials provided by Aalto University. Note: Content may be edited for style and length. More

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    New computational tool predicts cell fates and genetic perturbations

    Imagine a ball thrown in the air: it curves up, then down, tracing an arc to a point on the ground some distance away. The path of the ball can be described with a simple mathematical equation, and if you know the equation, you can figure out where the ball is going to land. Biological systems tend to be harder to forecast, but Whitehead Institute Member Jonathan Weissman, postdoc in his lab Xiaojie Qiu, and collaborators at the University of Pittsburgh School of Medicine are working on making the path taken by cells as predictable as the arc of a ball. Rather than looking at how cells move through space, they are considering how cells change with time.
    Weissman, Qiu, and collaborators Jianhua Xing, professor of computational and systems biology at the University of Pittsburgh School of Medicine, and Xing lab graduate student Yan Zhang have built a machine learning framework that can define the mathematical equations describing a cell’s trajectory from one state to another, such as its development from a stem cell into one of several different types of mature cell. The framework, called dynamo,can also be used to figure out the underlying mechanisms — the specific cocktail of gene activity — driving changes in the cell. Researchers could potentially use these insights to manipulate cells into taking one path instead of another, a common goal in biomedical research and regenerative medicine.
    The researchers describe dynamo in a paper published in the journal Cell on February 1. They explain the framework’s many analytical capabilities and use it to help understand mechanisms of human blood cell production, such as why one type of blood cell forms first (appears more rapidly than others).
    “Our goal is to move towards a more quantitative version of single cell biology,” Qiu says. “We want to be able to map how a cell changes in relation to the interplay of regulatory genes as accurately as an astronomer can chart a planet’s movement in relation to gravity, and then we want to understand and be able to control those changes.”
    How to map a cell’s future journey
    Dynamo uses data from many individual cells to come up with its equations. The main information that it requires is how the expression of different genes in a cell changes from moment to moment. The researchers estimate this by looking at changes in the amount of RNA over time, because RNA is a measurable product of gene expression. In the same way that knowing the starting position and velocity of a ball is necessary to understand the arc it will follow, researchers use the starting levels of RNAs and how those RNA levels are changing to predict the path of the cell. However, calculating changes in the amount of RNA from single cell sequencing data is challenging, because sequencing only measures RNA once. Researchers must then use clues like RNA-being-made at the time of sequencing and equations for RNA turnover to estimate how RNA levels were changing. Qiu and colleagues had to improve on previous methods in several ways in order to get clean enough measurements for dynamo to work. In particular, they used a recently developed experimental method that tags new RNA to distinguish it from old RNA, and combined this with sophisticated mathematical modeling, to overcome limitations of older estimation approaches. More