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Researchers present an optical flow-based learning process that allows robots to estimate distances through the visual appearance (shape, color, texture) of the objects in view. This artificial intelligence (AI)-based learning strategy increases the navigation skills of small flying drones and entails a new hypothesis on insect intelligence. More
Quantum nanowires — which have length but no width or height-provide a unique environment for the formation and detection of a quasiparticle known as a Majorana zero mode.
A new UNSW-led study overcomes previous difficulty detecting the Majorana zero mode, and produces a significant improvement in device reproducibility.
Potential applications for Majorana zero modes include fault-resistant topological quantum computers, and topological superconductivity.
MAJORANA FERMIONS IN 1D WIRES
A Majorana fermion is a composite particle that is its own antiparticle.
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Antimatter explainer: Every fundamental particle has a corresponding antimatter particle, with the same mass but opposite electrical charge. For example, the antiparticle of an electron (charge -1) is a positron (charge +1)
Such unusual particle’s interest academically and commercially comes from their potential use in a topological quantum computer, predicted to be immune to the decoherence that randomises the precious quantum information.
Majorana zero modes can be created in quantum wires made from special materials in which there is a strong coupling between their electrical and magnetic properties.
In particular, Majorana zero modes can be created in one-dimensional semiconductors (such as semiconductor nanowires) when coupled with a superconductor.
In a one-dimensional nanowire, whose dimensions perpendicular to length are small enough not to allow any movement of subatomic particles, quantum effects predominate.
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NEW METHOD FOR DETECTING NECESSARY SPIN-ORBIT GAP
Majorana fermions, which are their own antiparticle, have been theorised since 1937, but have only been experimentally observed in the last decade. The Majorana fermion’s ‘immunity’ to decoherence provides potential use for fault-tolerant quantum computing.
One-dimensional semiconductor systems with strong spin-orbit interaction are attracting great attention due to potential applications in topological quantum computing.
The magnetic ‘spin’ of an electron is like a little bar magnet, whose orientation can be set with an applied magnetic field.
In materials with a ‘spin-orbit interaction’ the spin of an electron is determined by the direction of motion, even at zero magnetic field. This allows for all electrical manipulation of magnetic quantum properties.
Applying a magnetic field to such a system can open an energy gap such that forward -moving electrons all have the same spin polarisation, and backward-moving electrons have the opposite polarisation. This ‘spin-gap’ is a pre-requisite for the formation of Majorana zero modes.
Despite intense experimental work, it has proven extremely difficult to unambiguously detect this spin-gap in semiconductor nanowires, since the spin-gap’s characteristic signature (a dip in its conductance plateau when a magnetic field is applied) is very hard to distinguish from unavoidable the background disorder in nanowires.
The new study finds a new, unambiguous signature for the spin-orbit gap that is impervious to the disorder effects plaguing previous studies.
“This signature will become the de-facto standard for detecting spin-gaps in the future,” says lead author Dr Karina Hudson.
REPRODUCIBILITY
The use of Majorana zero modes in a scalable quantum computer faces an additional challenge due to the random disorder and imperfections in the self-assembled nanowires that host the MZM.
It has previously been almost impossible to fabricate reproducible devices, with only about 10% of devices functioning within desired parameters.
The latest UNSW results show a significant improvement, with reproducible results across six devices based on three different starting wafers.
“This work opens a new route to making completely reproducible devices,” says corresponding author Prof Alex Hamilton UNSW). More
More people could be protected from life-threatening rabies thanks to an agile approach to dog vaccination using smart phone technology to spot areas of low vaccination coverage in real time. The work could help save the lives of children worldwide. More
Mount Sinai researchers have published one of the first studies using a machine learning technique called “federated learning” to examine electronic health records to better predict how COVID-19 patients will progress. The study was published in the Journal of Medical Internet Research — Medical Informatics on January 18.
The researchers said the emerging technique holds promise to create more robust machine learning models that extend beyond a single health system without compromising patient privacy. These models, in turn, can help triage patients and improve the quality of their care.
Federated learning is a technique that trains an algorithm across multiple devices or servers holding local data samples but avoids clinical data aggregation, which is undesirable for reasons including patient privacy issues. Mount Sinai researchers implemented and assessed federated learning models using data from electronic health records at five separate hospitals within the Health System to predict mortality in COVID-19 patients. They compared the performance of a federated model against ones built using data from each hospital separately, referred to as local models. After training their models on a federated network and testing the data of local models at each hospital, the researchers found the federated models demonstrated enhanced predictive power and outperformed local models at most of the hospitals.
“Machine learning models in health care often require diverse and large-scale data to be robust and translatable outside the patient population they were trained on,” said the study’s corresponding author, Benjamin Glicksberg, PhD, Assistant Professor of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai, and member of the Hasso Plattner Institute for Digital Health at Mount Sinai and the Mount Sinai Clinical Intelligence Center. “Federated learning is gaining traction within the biomedical space as a way for models to learn from many sources without exposing any sensitive patient data. In our work, we demonstrate that this strategy can be particularly useful in situations like COVID-19.”
Machine learning models built within a hospital are not always effective for other patient populations, partially due to models being trained on data from a single group of patients which is not representative of the entire population.
“Machine learning in health care continues to suffer a reproducibility crisis,” said the study’s first author, Akhil Vaid, MD, postdoctoral fellow in the Department of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai, and member of the Hasso Plattner Institute for Digital Health at Mount Sinai and the Mount Sinai Clinical Intelligence Center. “We hope that this work showcases benefits and limitations of using federated learning with electronic health records for a disease that has a relative dearth of data in an individual hospital. Models built using this federated approach outperform those built separately from limited sample sizes of isolated hospitals. It will be exciting to see the results of larger initiatives of this kind.”
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Materials provided by The Mount Sinai Hospital / Mount Sinai School of Medicine. Note: Content may be edited for style and length. More
A student infected with COVID-19 returning home from university for Christmas would, on average, have infected just less than one other household member with the virus, according to a new model devised by mathematicians at Cardiff University and published in Health Systems.
Professor Paul Harper and colleagues defined an equation to predict the number of secondary household infections using variables for prevalence of the virus, the probability of secondary transmission, the number of household occupants and the total number of students returning home.
The model predicts that each infected student allowed to return home would produce, on average, 0.94 secondary infections.
“With the potential movement of over 1 million UK students for the Christmas vacation, even a modest 1% infection level (meaning 10 in 1,000 students are infected, perhaps many of them without symptoms at the time of travel) would equate to 9,400 new secondary household cases across the country,” says Professor Harper.
As the study does not consider transmission to the students’ wider home communities or include the journey home — which may give rise to a larger number of cases, particularly if public transport is taken — the numbers are a lower bound on the likely impact of transmissions and new cases.
However, although the indicative levels of secondary infections are potentially very large, multiple strategies can be adopted to help reduce the number of students taking Covid-19 home, the authors say. These include strongly advising students not to mix in the days leading up to departure, implementing staggered departure times and facilitating mass testing of students before they head home.
The authors have provided computer code and an online app to allow anyone to rerun and adapt the simulations. “The code and app are quick to run with a focus on accessibility so that a user can rapidly change the input probabilities to suit their data, thereby generating their own results based on localised parameters,” the authors say.
Their results have been presented to the Welsh Government and have informed policy in relation to the two-week firebreak in Wales in October/November and for the forthcoming vacation. The data has also been communicated across the governments of England, Scotland and Northern Ireland.
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In groundbreaking materials research, a team led by University of Minnesota Professor K. Andre Mkhoyan has made a discovery that blends the best of two sought-after qualities for touchscreens and smart windows — transparency and conductivity.
The researchers are the first to observe metallic lines in a perovskite crystal. Perovskites abound in the Earth’s center, and barium stannate (BaSnO3) is one such crystal. However, it has not been studied extensively for metallic properties because of the prevalence of more conductive materials on the planet like metals or semiconductors. The finding was made using advanced transmission electron microscopy (TEM), a technique that can form images with magnifications of up to 10 million.
The research is published in Science Advances.
“The conductive nature and preferential direction of these metallic line defects mean we can make a material that is transparent like glass and at the same time very nicely directionally conductive like a metal,” said Mkhoyan, a TEM expert and the Ray D. and Mary T. Johnson/Mayon Plastics Chair in the Department of Chemical Engineering and Materials Science at the University of Minnesota’s College of Science and Engineering. “This gives us the best of two worlds. We can make windows or new types of touch screens transparent and at the same time conductive. This is very exciting.”
Defects, or imperfections, are common in crystals — and line defects (the most common among them is the dislocation) are a row of atoms that deviate from the normal order. Because dislocations have the same composition of elements as the host crystal, the changes in electronic band structure at the dislocation core, due to symmetry-reduction and strain, are often only slightly different than that of the host. The researchers needed to look outside the dislocations to find the metallic line defect, where defect composition and resulting atomic structure are vastly different.
“We easily spotted these line defects in the high-resolution scanning transmission electron microscopy images of these BaSnO3 thin films because of their unique atomic configuration and we only saw them in the plan view,” said Hwanhui Yun, a graduate student in the Department of Chemical Engineering and Materials Science and a lead author of the study.
For this study, BaSnO3 films were grown by molecular beam epitaxy (MBE) — a technique to fabricate high-quality crystals — in a lab at the University of Minnesota Twin Cities. Metallic line defects observed in these BaSnO3 films propagate along film growth direction, which means researchers can potentially control how or where line defects appear — and potentially engineer them as needed in touchscreens, smart windows, and other future technologies that demand a combination of transparency and conductivity.
“We had to be creative to grow high-quality BaSnO3 thin films using MBE. It was exciting when these new line defects came into light in the microscope,” said Bharat Jalan, associate professor and Shell Chair in the Department of Chemical Engineering and Materials Science, who heads up the lab that grows a variety of perovskite oxide films by MBE.
Perovskite crystals (ABX3) contain three elements in the unit cell. This gives it freedom for structural alterations such as composition and crystal symmetry, and the ability to host a variety of defects. Because of different coordination and bonding angles of the atoms in the line defect core, new electronic states are introduced and the electronic band structure is modified locally in such a dramatic way that it turns the line defect into metal.
“It was fascinating how theory and experiment agreed with each other here,” said Turan Birol, assistant professor in the Department of Chemical Engineering and Materials Science and an expert in density functional theory (DFT). “We could verify the experimental observations of the atomic structure and electronic properties of this line defect with first principles DFT calculations.”
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Materials provided by University of Minnesota. Note: Content may be edited for style and length. More
A team of researchers lead by the University of Tsukuba have created a new theoretical model to understand the spread of vibrations through disordered materials, such as glass. They found that as the degree of disorder increased, sound waves traveled less and less like ballistic particles, and instead began diffusing incoherently. This work may lead to new heat- and shatter-resistant glass for smartphones and tablets.
Understanding the possible vibrational modes in a material is important for controlling its optical, thermal, and mechanical properties. The propagation of vibrations in the form of sound of a single frequency through amorphous materials can occur in a unified way, as if it was a particle. Scientists like to call these quasiparticles “phonons.” However, this approximation can break down if the material is too disordered, which limits our ability to predict the strength of glass under a wide range of circumstances.
Now, a team of scientists led by the University of Tsukuba have developed a new theoretical framework that explains the observed vibrations in glass with better agreement with experimental data. They demonstrate that thinking about vibrations as individual phonons is only justified in the limit of long wavelengths. On shorter length scales, disorder leads to increased scattering and the sound waves lose coherence. “We call these excitations ‘diffusions,’ because they represent the incoherent diffusion of vibrations, as opposed to the directed motion of phonons,” explains author Professor Tatsuya Mori. In fact, the equations for low frequencies start looking like those for hydrodynamics, which describe the behavior of fluids. The researchers compared the predictions of the model with data obtained from soda lime glass and showed that they proved a better fit compared with previously accepted equations.
“Our research supports the view that this phenomenon is not unique to acoustic phonons, but rather represents a general phenomenon that can occur with other kinds of excitations within disordered materials,” co-authors Professor Alessio Zaccone, University of Cambridge and Professor Matteo Baggioli, Instituto de Fisica Teorica UAM-CSIC say. Future work may involve utilizing the effects of disorder in order to improve the durability of glass for smart devices.
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Materials provided by University of Tsukuba. Note: Content may be edited for style and length. More
One reason it’s so difficult to produce effective vaccines against some viruses, including influenza and HIV, is that these viruses mutate very rapidly. This allows them to evade the antibodies generated by a particular vaccine, through a process known as “viral escape.”
MIT researchers have now devised a new way to computationally model viral escape, based on models that were originally developed to analyze language. The model can predict which sections of viral surface proteins are more likely to mutate in a way that enables viral escape, and it can also identify sections that are less likely to mutate, making them good targets for new vaccines.
“Viral escape is a big problem,” says Bonnie Berger, the Simons Professor of Mathematics and head of the Computation and Biology group in MIT’s Computer Science and Artificial Intelligence Laboratory. “Viral escape of the surface protein of influenza and the envelope surface protein of HIV are both highly responsible for the fact that we don’t have a universal flu vaccine, nor do we have a vaccine for HIV, both of which cause hundreds of thousands of deaths a year.”
In a study appearing today in Science, Berger and her colleagues identified possible targets for vaccines against influenza, HIV, and SARS-CoV-2. Since that paper was accepted for publication, the researchers have also applied their model to the new variants of SARS-CoV-2 that recently emerged in the United Kingdom and South Africa. That analysis, which has not yet been peer-reviewed, flagged viral genetic sequences that should be further investigated for their potential to escape the existing vaccines, the researchers say.
Berger and Bryan Bryson, an assistant professor of biological engineering at MIT and a member of the Ragon Institute of MGH, MIT, and Harvard, are the senior authors of the paper, and the lead author is MIT graduate student Brian Hie.
The language of proteins
Different types of viruses acquire genetic mutations at different rates, and HIV and influenza are among those that mutate the fastest. For these mutations to promote viral escape, they must help the virus change the shape of its surface proteins so that antibodies can no longer bind to them. However, the protein can’t change in a way that makes it nonfunctional.
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The MIT team decided to model these criteria using a type of computational model known as a language model, from the field of natural language processing (NLP). These models were originally designed to analyze patterns in language, specifically, the frequency which with certain words occur together. The models can then make predictions of which words could be used to complete a sentence such as “Sally ate eggs for …” The chosen word must be both grammatically correct and have the right meaning. In this example, an NLP model might predict “breakfast,” or “lunch.”
The researchers’ key insight was that this kind of model could also be applied to biological information such as genetic sequences. In that case, grammar is analogous to the rules that determine whether the protein encoded by a particular sequence is functional or not, and semantic meaning is analogous to whether the protein can take on a new shape that helps it evade antibodies. Therefore, a mutation that enables viral escape must maintain the grammaticality of the sequence but change the protein’s structure in a useful way.
“If a virus wants to escape the human immune system, it doesn’t want to mutate itself so that it dies or can’t replicate,” Hie says. “It wants to preserve fitness but disguise itself enough so that it’s undetectable by the human immune system.”
To model this process, the researchers trained an NLP model to analyze patterns found in genetic sequences, which allows it to predict new sequences that have new functions but still follow the biological rules of protein structure. One significant advantage of this kind of modeling is that it requires only sequence information, which is much easier to obtain than protein structures. The model can be trained on a relatively small amount of information — in this study, the researchers used 60,000 HIV sequences, 45,000 influenza sequences, and 4,000 coronavirus sequences.
“Language models are very powerful because they can learn this complex distributional structure and gain some insight into function just from sequence variation,” Hie says. “We have this big corpus of viral sequence data for each amino acid position, and the model learns these properties of amino acid co-occurrence and co-variation across the training data.”
Blocking escape
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Once the model was trained, the researchers used it to predict sequences of the coronavirus spike protein, HIV envelope protein, and influenza hemagglutinin (HA) protein that would be more or less likely to generate escape mutations.
For influenza, the model revealed that the sequences least likely to mutate and produce viral escape were in the stalk of the HA protein. This is consistent with recent studies showing that antibodies that target the HA stalk (which most people infected with the flu or vaccinated against it do not develop) can offer near-universal protection against any flu strain.
The model’s analysis of coronaviruses suggested that a part of the spike protein called the S2 subunit is least likely to generate escape mutations. The question still remains as to how rapidly the SARS-CoV-2 virus mutates, so it is unknown how long the vaccines now being deployed to combat the Covid-19 pandemic will remain effective. Initial evidence suggests that the virus does not mutate as rapidly as influenza or HIV. However, the researchers recently identified new mutations that have appeared in Singapore, South Africa, and Malaysia, that they believe should be investigated for potential viral escape (these new data are not yet peer-reviewed).
In their studies of HIV, the researchers found that the V1-V2 hypervariable region of the protein has many possible escape mutations, which is consistent with previous findings, and they also found sequences that would have a lower probability of escape.
The researchers are now working with others to use their model to identify possible targets for cancer vaccines that stimulate the body’s own immune system to destroy tumors. They say it could also be used to design small-molecule drugs that might be less likely to provoke resistance, for diseases such as tuberculosis.
“There are so many opportunities, and the beautiful thing is all we need is sequence data, which is easy to produce,” Bryson says.
The research was funded by a National Defense Science and Engineering Graduate Fellowship from the Department of Defense and a National Science Foundation Graduate Research Fellowship. More
