Computers Math

Bristol scientists have demonstrated a new virtual reality [VR] technique which should help in developing drugs against the SARS-CoV-2 virus — and enable researchers to share models and collaborate in new ways. The innovative tool, created by University of Bristol researchers, and published in the Journal of Chemical Information and Modeling, will help scientists around the world identify anti-viral drug leads more rapidly.
A SARS-CoV-2 enzyme known as the main protease (Mpro) is a promising target in the search for new anti-viral treatments. Molecules that stop the main protease from working — called enzyme inhibitors — stop the virus reproducing, and so could be effective drugs. Researchers across the world are working to find such molecules. A key predictor of a drug’s effectiveness is how tightly it binds to its target; knowing how a drug fits into the protein helps researchers design changes to its structure to make it bind more tightly.
Professor Adrian Mulholland from Bristol’s School of Chemistry and the study’s lead author explained: “We’ve shown that interactive virtual reality can model how viral proteins and inhibitors bind to the enzyme. Researchers can use this tool to help understand how the enzyme works, and also to see how potential drugs fit into the enzyme. This should help design and test new potential drug leads. We are sharing these models with the whole community.”
The Bristol team have developed a virtual framework for interactive ‘molecular dynamics’ simulations. It is an open source software framework, called Narupa, which uses readily available VR equipment.
In this study, the Bristol team created a 3D model structure of the SARS-CoV-2 Mpro and used interactive molecular dynamics simulations in VR (iMD-VR) to ‘step inside’ it and visualise molecules binding to the enzyme, in atomic detail. Results showed that users were able to show how a drug molecule fits within the enzyme.
Professor Mulholland added: “There are currently many efforts globally aimed at identifying drug leads for COVID-19. Our iMD-VR tools will be a valuable resource, enabling virtual collaboration for the international drug discovery community, helping to predict how potential drug leads bind to SARS-CoV-2 targets. An exciting aspect is that it also allows researchers to collaborate in new ways: using cloud computing, they can tackle a drug discovery problem together at the same time when in they are in different locations — potentially even in different countries — working simultaneously in the same virtual molecular environment.”
“Computational modelling of how drugs bind to the SARS-CoV-2 spike protein has been critical in advancing the global fight against the pandemic. Narupa takes that modelling to an entirely new level with molecular dynamics simulations in virtual reality,” said Alison Derbenwick Miller, Vice President, Oracle for Research. “We are delighted that Oracle’s high-performance cloud infrastructure supported the development of this innovative framework, and is now helping to advance globally-connected efforts to defeat COVID-19. Growing a connected community of cloud-powered researchers is exactly what Oracle for Research was designed to do.”
The study was supported by grants from the EPSRC, the Royal Society and the British Society for Antimicrobial Chemotherapy. Cloud credits were provided by Oracle for Research.

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A new study outlines ways colleges and universities can update their curricula to prepare the workforce for a new wave of quantum technology jobs. Three researchers, including Rochester Institute of Technology Associate Professor Ben Zwickl, suggested steps that need to be taken in a new paper in Physical Review Physics Education Research after interviewing managers at more than 20 quantum technology companies across the U.S.
The study’s authors from University of Colorado Boulder and RIT set out to better understand the types of entry-level positions that exist in these companies and the educational pathways that might lead into those jobs. They found that while the companies still seek employees with traditional STEM degrees, they want the candidates to have a grasp of fundamental concepts in quantum information science and technology.
“For a lot of those roles, there’s this idea of being ‘quantum aware’ that’s highly desirable,” said Zwickl, a member of RIT’s Future Photon Initiative and Center for Advancing STEM Teaching, Learning and Evaluation. “The companies told us that many positions don’t need to have deep expertise, but students could really benefit from a one- or two-semester introductory sequence that teaches the foundational concepts, some of the hardware implementations, how the algorithms work, what a qubit is, and things like that. Then a graduate can bring in all the strength of a traditional STEM degree but can speak the language that the company is talking about.”
The authors said colleges and universities should offer introductory, multidisciplinary courses with few prerequisites that will allow software engineering, computer science, physics, and other STEM majors to learn the core concepts together. Zwickl said providing quantum education opportunities to students across disciplines will be important because quantum technology has the opportunity to disrupt a wide range of fields.
“It’s a growing industry that will produce new sensors, imaging, communication, computing technologies, and more,” said Zwickl. “A lot of the technologies are in a research and development phase, but as they start to move toward commercialization and mass production, you will have end-users who are trying to figure out how to apply the technology. They will need technical people on their end that are fluent enough with the ideas that they can make use of it.”
Zwickl’s participation in the project was supported in part by funding RIT received from the NSF’s Quantum Leap Challenge Institutes program. As a co-PI and lead on the education and workforce development for the proposal, he said he is hoping to apply many of the lessons learned from the study to RIT’s curriculum. He is in the process of developing two new introductory RIT courses in quantum information and science as well as an interdisciplinary minor in the field.

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The functional genomics field, which looks at the activities of the genome and levels of gene expression rather than particular gene mutations, generally relies on aggregating information from many samples for its statistical power. This means that broadly sharing raw data is vital; however, sharing these data currently is challenging because of the privacy concerns of individuals within those datasets, leading to these data being largely inaccessible behind firewalls.
In a study publishing November 12 in the journal Cell, a team of investigators demonstrates that it’s possible to de-identify those data to ensure patient privacy. They also demonstrate how these raw data could be linked back to specific individuals through their gene variants by something as simple as an abandoned coffee cup if these sanitation measures are not put in place.
“The purpose of this study is to come up with practical ways to broadly share the raw data without creating undue privacy concerns,” says senior author Mark Gerstein, a professor of bioinformatics at Yale University.
Functional genomics research is frequently tied to a specific disease. For example, an investigation into a particular psychiatric condition might look at the expression of certain genes in a type of neuron. And, by nature of having their genetic material included in such a study, an individual’s medical status with regard to that condition could inadvertently be revealed.
This can happen through what’s known as a quasi-identifier. The way a quasi-identifier works is that if someone has enough individual data points about you, even if those data on their own are not sensitive or unique, they can be combined to create an identifier that is unique to you. In a non-genetic setting, this means if someone has your zip code, birthday, the model of car you drive, and other similar data that might not be considered private or sensitive on their own, they might eventually be able to combine them and create a unique profile that would link you to other data that you wouldn’t want public — data like financial records that were collected when you applied for a car loan. The same thing could happen if someone were able to obtain some of your genetic variants and link those variants to the presence of your genetic material in a study on a particular disease. This could in turn reveal a diagnosis, such as HIV status or an inherited cancer predisposition, that you would prefer to keep private.
In their study, the researchers constructed a “linkage attack” scenario to demonstrate how someone could make these kinds of connections from functional genomics studies’ data by using DNA obtained from a discarded coffee cup. After adding samples from two consenting participants to a functional genomics database, the researchers gathered used coffee cups from the same individuals. They sequenced genetic material left on the cups and were able to successfully match that material to the samples in the database and infer sensitive health information about the participants. The researchers were also able to use DNA information “stolen” from a genotyping database to match the identities of 421 people with phenotypic information found in a test functional-genomics dataset that the researchers constructed for 436 people.
However, the researchers also identified steps that can be taken to thwart these kinds of linkage attacks and safeguard participants’ health information when functional genomics datasets are shared. “Functional genomics is special because variants are usually not needed for data processing,” says first author Gamze Gürsoy, a postdoctoral researcher at the Gerstein lab. “Because of this, we can sanitize the variants to prevent data being linked back to the private information connected to the phenotypes included in these studies, while still retaining the utility of the data.”
To achieve this balance between privacy and data usefulness, the researchers propose a file-format manipulation that will allow raw functional genomics data to be shared while largely reducing sensitive information leakage by generalizing information about phenotypic variants. The file format is based on a widely used standard file-format system, is compatible with a range of software and pipelines, and when tested, showed little loss of utility. The researchers have also developed a framework with which other researchers can tune the level of privacy and utility balance they want to achieve with the file format based on the policies and consents of the donors.
“As more data are released for these kinds of functional genomics studies, concerns about security and privacy shouldn’t be lost,” Gerstein says. “At the dawn of the Internet, people didn’t realize how important their online activities would become. Now that type of digital privacy has become so important to us. If we move into an era where getting your genome sequenced becomes routine, we don’t want these worries about health privacy to become dominating.”
This work was supported by the National Institutes of Health, the AL Williams Professorship fund, and the Chan Zuckerberg Initiative Donor-Advised Fund.

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Scientists have pointed out how to create a time crystal in an intriguing class of materials, the high-temperature superconductors. They propose to drive these superconducting materials into a time crystalline state by inducing Higgs excitations via light.
When you cool down liquid water, it crystallizes into ice. Consider a bucket filled with water, for example. When the water is liquid, the water molecules can be anywhere inside the bucket. In this sense, every point inside the bucket is equivalent. Once the water freezes, however, the water molecules occupy well-defined positions in space. Thus, not every point inside the bucket is equivalent anymore. Physicists refer to this phenomenon as spontaneous symmetry breaking. Here the translation symmetry in space is broken by the formation of the crystal.
Is it possible for crystals to form in time instead of space? While it appears like an outlandish notion, it turns out that a time crystal may emerge when a physical system of many interacting particles is periodically driven. The defining feature of a time crystal is that a macroscopic observable, such as the electric current in a solid, oscillates at a frequency that is smaller than the driving frequency.
So far, time crystals have been realized in artificial model systems. But now, what about real systems? A piece of a high-temperature superconductor is such a real system — you can buy it online. It is not much to look at, with its brownish, rusty color. Yet its frictionless electron flow at temperatures up to 100 K ( 173 °C) constitutes one of the most spectacular phenomena of material science.
“We propose to turn a high-temperature superconductor into a time crystal by shining a laser on it,” explains first author Guido Homann from the Department of Physics at Universität Hamburg. The frequency of the laser needs to be tuned to the sum resonance of two fundamental excitations of the material. One of these excitations is the elusive Higgs mode, which is conceptually related to the Higgs boson in particle physics. The other excitation is the plasma mode, corresponding to an oscillatory motion of electron pairs, which are responsible for superconductivity.
Co-author Dr. Jayson Cosme from Universität Hamburg, now University of the Philippines, adds that “the creation of a time crystal in a high-temperature superconductor is an important step because it establishes this genuine dynamical phase of matter in the domain of solid-state physics.” Controlling solids by light is not only fascinating from a scientific perspective but also technologically relevant, as emphasized by group leader Prof. Dr. Ludwig Mathey. “The ultimate goal of our research is to design quantum materials on demand.” With their novel proposal, this fascinating endeavor is now advanced towards dynamical states of matter, rather than the usual static states of matter, by laying out a strategy to design time crystals instead of regular crystals, which opens up a new and surprising direction of material design.

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The development of functional nanomaterials has been a major landmark in the history of materials science. Nanoparticles with diameters ranging from 5 to 500 nm have unprecedented properties, such as high catalytic activity, compared to their bulk material counterparts. Moreover, as particles become smaller, exotic quantum phenomena become more prominent. This has enabled scientists to produce materials and devices with characteristics that had been only dreamed of, especially in the fields of electronics, catalysis, and optics.
But what if we go smaller? Sub-nanoparticles (SNPs) with particle sizes of around 1 nm are now considered a new class of materials with distinct properties due to the predominance of quantum effects. The untapped potential of SNPs caught the attention of scientists from Tokyo Tech, who are currently undertaking the challenges arising in this mostly unexplored field. In a recent study published in the Journal of the American Chemical Society, a team of scientists from the Laboratory of Chemistry and Life Sciences, led by Dr Takamasa Tsukamoto, demonstrated a novel molecular screening approach to find promising SNPs.
As one would expect, the synthesis of SNPs is plagued by technical difficulties, even more so for those containing multiple elements. Dr Tsukamoto explains: “Even SNPs containing just two different elements have barely been investigated because producing a system of subnanometer scale requires fine control of the composition ratio and particle size with atomic precision.” However, this team of scientists had already developed a novel method by which SNPs could be made from different metal salts with extreme control over the total number of atoms and the proportion of each element.
Their approach relies on dendrimers, a type of symmetric molecule that branches radially outwards like trees sprouting form a common center. Dendrimers serve as a template on which metal salts can be accurately accumulated at the base of the desired branches. Subsequently, through chemical reduction and oxidation, SNPs are precisely synthesized on the dendrimer scaffold. The scientists used this method in their most recent study to produce SNPs with various proportions of indium and tin oxides and then explored their physicochemical properties.
One peculiar finding was that unusual electronic states and oxygen content occurred at an indium-to-tin ratio of 3:4. These results were unprecedented even in studies of nanoparticles with controlled size and composition, and the scientists ascribed them to physical phenomena exclusive to the sub-nanometer scale. Moreover, they found that the optical properties of SNPs with this elemental proportion were different not only from those of SNPs with other ratios, but also of nanoparticles with the same ratio. The SNPs with this ratio were yellow instead of white and exhibited green photoluminescence under ultraviolet irradiation.
Exploring material properties at the sub-nanometer scale will most likely lead to their practical application in next-generation electronics and catalysts. This study, however, is just the beginning in the field of sub-nanometer materials, as Dr Tsukamoto concludes: “Our study marks the first-ever discovery of unique functions in SNPs and their underlying principles through a sequential screening search. We believe our findings will serve as the initial step toward the development of as-yet-unknown quantum sized materials.” The sub-nanometric world awaits!

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The quest for high throughput intelligent computing paradigms — for big data and artificial intelligence — and the ever-increasing volume of digital information has led to an intensified demand for high-speed and low-power consuming next-generation electronic devices. The “forgotten” world of antiferromagnets (AFM), a class of magnetic materials, offers promise in future electronic device development and complements present-day ferromagnet-based spintronic technologies.
Formidable challenges for AFM-based functional spintronic device development are high-speed electrical manipulation (recording), detection (retrieval), and ensuring the stability of the recorded information — all in a semiconductor industry-friendly material system.
Researchers at Tohoku University, University of New South Wales (Australia), ETH Zürich (Switzerland), and Diamond Light Source (United Kingdom) successfully demonstrated current-induced switching in a polycrystalline metallic antiferromagnetic heterostructure with high thermal stability. The established findings show potential for information storage and processing technologies.
The research group used a Mn-based metallic AFM (PtMn)/heavy metal (HM) heterostructure — attractive because of its significant antiferromagnetic anisotropy and its compatibility with PtMn Silicon-based electronics. Electrical recording of resistance states (1 or 0) was obtained through the spin-orbit interaction of the HM layer; a charge current in the adjacent HM resulted in spin-orbit torques acting on the AFM, leading to a change in the resistance level down to a microsecond regime.
“Interestingly, the switching degree is controllable by the strength of the current in the HM layer and shows long-term data retention capabilities,” said Samik DuttaGupta, corresponding author of the study. “The experimental results from electrical measurements were supplemented by a magnetic X-ray imaging, helping to clarify the reversible nature of switching dynamics localized within nm-sized AFM domains.”
The results are the first demonstration of current-induced switching of an industry-compatible AFM down to the microsecond regime within the field of metallic antiferromagnetic spintronics. These findings are expected to initiate new avenues for research and encourage further investigations towards the realization of functional devices using metallic AFMs for information storage and processing technologies.

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Current research and applications in the field of artificial intelligence (AI) include several key challenges. These include: (a) A priori estimation of the required dataset size to achieve a desired test accuracy. For example, how many handwritten digits does a machine have to learn before being able to predict a new one with a success rate of 99%? Similarly, how many specific types of circumstances does an autonomous vehicle have to learn before its reaction will not lead to an accident? (b) The achievement of reliable decision-making under a limited number of examples, where each example can be trained only once, i.e., observed only for a short period. This type of realization of fast on-line decision making is representative of many aspects of human activity, robotic control and network optimization.
In an article published today in the journal Scientific Reports, researchers show how these two challenges are solved by adopting a physical concept that was introduced a century ago to describe the formation of a magnet during a process of iron bulk cooling.
Using a careful optimization procedure and exhaustive simulations, a group of scientists from Bar-Ilan University has demonstrated the usefulness of the physical concept of power-law scaling to deep learning. This central concept in physics, which arises from diverse phenomena, including the timing and magnitude of earthquakes, Internet topology and social networks, stock price fluctuations, word frequencies in linguistics, and signal amplitudes in brain activity, has also been found to be applicable in the ever-growing field of AI, and especially deep learning.
“Test errors with online learning, where each example is trained only once, are in close agreement with state-of-the-art algorithms consisting of a very large number of epochs, where each example is trained many times. This result has an important implication on rapid decision making such as robotic control,” said Prof. Ido Kanter, of Bar-Ilan’s Department of Physics and Gonda (Goldshmied) Multidisciplinary Brain Research Center, who led the research. “The power-law scaling, governing different dynamical rules and network architectures, enables the classification and hierarchy creation among the different examined classification or decision problems,” he added.
“One of the important ingredients of the advanced deep learning algorithm is the recent new bridge between experimental neuroscience and advanced artificial intelligence learning algorithms,” said PhD student Shira Sardi, a co-author of the study. Our new type of experiments on neuronal cultures indicate that an increase in the training frequency enables us to significantly accelerate the neuronal adaptation process. “This accelerated brain-inspired mechanism enables building advanced deep learning algorithms which outperform existing ones,” said PhD student Yuval Meir, another co-author.
The reconstructed bridge from physics and experimental neuroscience to machine learning is expected to advance artificial intelligence and especially ultrafast decision making under limited training examples as to contribute to the formation of a theoretical framework of the field of deep learning.

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Think about your favorite t-shirt, the one you’ve worn a hundred times, and all the abuse you’ve put it through. You’ve washed it more times than you can remember, spilled on it, stretched it, crumbled it up, maybe even singed it leaning over the stove once.
We put our clothes through a lot and if the smart textiles of the future are going to survive all that we throw at them, their components are going to need to be resilient.
Now, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences and the Wyss Institute for Biologically Inspired Engineering have developed an ultra-sensitive, seriously resilient strain sensor that can be embedded in textiles and soft robotic systems.
The research is published in Nature.
“Current soft strain gauges are really sensitive but also really fragile,” said Oluwaseun Araromi, a Research Associate in Materials Science and Mechanical Engineering at SEAS and the Wyss Institute and first author of the paper. “The problem is that we’re working in an oxymoronic paradigm — highly sensitivity sensors are usually very fragile and very strong sensors aren’t usually very sensitive. So, we needed to find mechanisms that could give us enough of each property.”
In the end, the researchers created a design that looks and behaves very much like a Slinky.

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“A Slinky is a solid cylinder of rigid metal but if you pattern it into this spiral shape, it becomes stretchable,” said Araromi. “That is essentially what we did here. We started with a rigid bulk material, in this case carbon fiber, and patterned it in such a way that the material becomes stretchable.”
The pattern is known as a serpentine meander, because its sharp ups and downs resemble the slithering of a snake. The patterned conductive carbon fibers are then sandwiched between two prestrained elastic substrates.
The overall electrical conductivity of the sensor changes as the edges of the patterned carbon fiber come out of contact with each other, similar to the way the individual spirals of a slinky come out of contact with each other when you pull both ends. This process happens even with small amounts of strain, which is the key to the sensor’s high sensitivity.
Unlike current highly sensitive stretchable sensors, which rely on exotic materials such as silicon or gold nanowires, this sensor doesn’t require special manufacturing techniques or even a clean room. It could be made using any conductive material.
The researchers tested the resiliency of the sensor by stabbing it with a scalpel, hitting it with a hammer, running it over with a car, and throwing it in a washing machine ten times. The sensor emerged from each test unscathed.

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To demonstrate its sensitivity, the researchers embedded the sensor in a fabric arm sleeve and asked a participant to make different gestures with their hand, including a fist, open palm, and pinching motion. The sensors detected the small changes in the subject’s forearm muscle through the fabric and a machine learning algorithm was able to successfully classify these gestures.
“These features of resilience and the mechanical robustness put this sensor in a whole new camp,” said Araromi.
Such a sleeve could be used in everything from virtual reality simulations and sportswear to clinical diagnostics for neurodegenerative diseases like Parkinson’s Disease.
Harvard’s Office of Technology Development has filed to protect the intellectual property associated with this project.
“The combination of high sensitivity and resilience are clear benefits of this type of sensor,” said Robert Wood, the Charles River Professor of Engineering and Applied Sciences at SEAS and senior author of the study. “But another aspect that differentiates this technology is the low cost of the constituent materials and assembly methods. This will hopefully reduce the barriers to get this technology widespread in smart textiles and beyond.”
“We are currently exploring how this sensor can be integrated into apparel due to the intimate interface to the human body it provides,” says Conor Walsh, the Paul A. Maeder Professor of Engineering and Applied Sciences at SEAS and co-author of the study. “This will enable exciting new applications by being able to make biomechanical and physiological measurements throughout a person’s day, not possible with current approaches.”
The research was co-authored by Moritz A. Graule, Kristen L. Dorsey, Sam Castellanos, Jonathan R. Foster, Wen-Hao Hsu, Arthur E. Passy, James C. Weaver, Senior Staff Scientist at SEAS and Joost J. Vlassak, the Abbott and James Lawrence Professor of Materials Engineering at SEAS.
It was funded through the university’s strategic research alliance with Tata. The 6-year, $8.4M alliance was established in 2016 to advance Harvard innovation in fields including robotics, wearable technologies, and the internet of things (IoT). More