Computers Math

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

By adding some magnetic flair to an exotic quantum experiment, physicists produced an ultra-stable one-dimensional quantum gas with never-before-seen ‘scar’ states – a feature that could someday be useful for securing quantum information. More

Researchers have used machine learning techniques to predict how proteins, particularly those implicated in neurological diseases, completely change their shapes in a matter of microseconds. More

A new study says that despite a record drop in global carbon emissions in 2020, a pandemic-driven shift to remote work and more at-home entertainment still presents significant environmental impact due to how internet data is stored and transferred around the world. More

Compared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their architecture, according to a new study in Nature Communications led by Georgia State University.
Advanced biomedical technologies such as structural and functional magnetic resonance imaging (MRI and fMRI) or genomic sequencing have produced an enormous volume of data about the human body. By extracting patterns from this information, scientists can glean new insights into health and disease. This is a challenging task, however, given the complexity of the data and the fact that the relationships among types of data are poorly understood.
Deep learning, built on advanced neural networks, can characterize these relationships by combining and analyzing data from many sources. At the Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State researchers are using deep learning to learn more about how mental illness and other disorders affect the brain.
Although deep learning models have been used to solve problems and answer questions in a number of different fields, some experts remain skeptical. Recent critical commentaries have unfavorably compared deep learning with standard machine learning approaches for analyzing brain imaging data.
However, as demonstrated in the study, these conclusions are often based on pre-processed input that deprive deep learning of its main advantage — the ability to learn from the data with little to no preprocessing. Anees Abrol, research scientist at TReNDS and the lead author on the paper, compared representative models from classical machine learning and deep learning, and found that if trained properly, the deep-learning methods have the potential to offer substantially better results, generating superior representations for characterizing the human brain.
“We compared these models side-by-side, observing statistical protocols so everything is apples to apples. And we show that deep learning models perform better, as expected,” said co-author Sergey Plis, director of machine learning at TReNDS and associate professor of computer science.

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Plis said there are some cases where standard machine learning can outperform deep learning. For example, diagnostic algorithms that plug in single-number measurements such as a patient’s body temperature or whether the patient smokes cigarettes would work better using classical machine learning approaches.
“If your application involves analyzing images or if it involves a large array of data that can’t really be distilled into a simple measurement without losing information, deep learning can help,” Plis said.. “These models are made for really complex problems that require bringing in a lot of experience and intuition.”
The downside of deep learning models is they are “data hungry” at the outset and must be trained on lots of information. But once these models are trained, said co-author Vince Calhoun, director of TReNDS and Distinguished University Professor of Psychology, they are just as effective at analyzing reams of complex data as they are at answering simple questions.
“Interestingly, in our study we looked at sample sizes from 100 to 10,000 and in all cases the deep learning approaches were doing better,” he said.
Another advantage is that scientists can reverse analyze deep-learning models to understand how they are reaching conclusions about the data. As the published study shows, the trained deep learning models learn to identify meaningful brain biomarkers.
“These models are learning on their own, so we can uncover the defining characteristics that they’re looking into that allows them to be accurate,” Abrol said. “We can check the data points a model is analyzing and then compare it to the literature to see what the model has found outside of where we told it to look.”
The researchers envision that deep learning models are capable of extracting explanations and representations not already known to the field and act as an aid in growing our knowledge of how the human brain functions. They conclude that although more research is needed to find and address weaknesses of deep-learning models, from a mathematical point of view, it’s clear these models outperform standard machine learning models in many settings.
“Deep learning’s promise perhaps still outweighs its current usefulness to neuroimaging, but we are seeing a lot of real potential for these techniques,” Plis said. More

Physicists at Washington University in St. Louis have discovered how to locally add electrical charge to an atomically thin graphene device by layering flakes of another thin material, alpha-RuCl3, on top of it.
A paper published in the journal Nano Letters describes the charge transfer process in detail. Gaining control of the flow of electrical current through atomically thin materials is important to potential future applications in photovoltaics or computing.
“In my field, where we study van der Waals heterostructures made by custom-stacking atomically thin materials together, we typically control charge by applying electric fields to the devices,” said Erik Henriksen, assistant professor of physics in Arts & Sciences and corresponding author of the new study, along with Ken Burch at Boston College. “But here it now appears we can just add layers of RuCl33. It soaks up a fixed amount of electrons, allowing us to make ‘permanent’ charge transfers that don’t require the external electric field.”
Jesse Balgley, a graduate student in Henriksen’s laboratory at Washington University, is second author of the study. Li Yang, professor of physics, and his graduate student Xiaobo Lu, also both at Washington University, helped with computational work and calculations, and are also co-authors.
Physicists who study condensed matter are intrigued by alpha-RuCl3 because they would like to exploit certain of its antiferromagnetic properties for quantum spin liquids.
In this new study, the scientists report that alpha-RuCl3 is able to transfer charge to several different types of materials — not just graphene, Henriksen’s personal favorite.
They also found that they only needed to place a single layer of alpha-RuCl3 on top of their devices to create and transfer charge. The process still works, even if the scientists slip a thin sheet of an electrically insulating material between the RuCl3 and the graphene.
“We can control how much charge flows in by varying the thickness of the insulator,” Henriksen said. “Also, we are able to physically and spatially separate the source of charge from where it goes — this is called modulation doping.”
Adding charge to a quantum spin liquid is one mechanism thought to underlie the physics of high-temperature superconductivity.
“Anytime you do this, it could get exciting,” Henriksen said. “And usually you have to add atoms to bulk materials, which causes lots of disorder. But here, the charge flows right in, no need to change the chemical structure, so it’s a ‘clean’ way to add charge.”

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Materials provided by Washington University in St. Louis. Original written by Talia Ogliore. Note: Content may be edited for style and length. More

There are things in life that can be predicted reasonably well. The tides rise and fall. The moon waxes and wanes. A billiard ball bounces around a table according to orderly geometry.
And then there are things that defy easy prediction: The hurricane that changes direction without warning. The splashing of water in a fountain. The graceful disorder of branches growing from a tree.
These phenomena and others like them can be described as chaotic systems, and are notable for exhibiting behavior that is predictable at first, but grows increasingly random with time.
Because of the large role that chaotic systems play in the world around us, scientists and mathematicians have long sought to better understand them. Now, Caltech’s Lihong Wang, the Bren Professor in the Andrew and Peggy Cherng department of Medical Engineering, has developed a new tool that might help in this quest.
In the latest issue of Science Advances, Wang describes how he has used an ultrafast camera of his own design that recorded video at one billion frames per second to observe the movement of laser light in a chamber specially designed to induce chaotic reflections.
“Some cavities are non-chaotic, so the path the light takes is predictable,” Wang says. But in the current work, he and his colleagues have used that ultrafast camera as a tool to study a chaotic cavity, “in which the light takes a different path every time we repeat the experiment.”
The camera makes use of a technology called compressed ultrafast photography (CUP), which Wang has demonstrated in other research to be capable of speeds as fast as 70 trillion frames per second. The speed at which a CUP camera takes video makes it capable of seeing light — the fastest thing in the universe — as it travels.

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But CUP cameras have another feature that make them uniquely suited for studying chaotic systems. Unlike a traditional camera that shoots one frame of video at a time, a CUP camera essentially shoots all of its frames at once. This allows the camera to capture the entirety of a laser beam’s chaotic path through the chamber all in one go.
That matters because in a chaotic system, the behavior is different every time. If the camera only captured part of the action, the behavior that was not recorded could never be studied, because it would never occur in exactly the same way again. It would be like trying to photograph a bird, but with a camera that can only capture one body part at a time; furthermore, every time the bird landed near you, it would be a different species. Although you could try to assemble all your photos into one composite bird image, that cobbled-together bird would have the beak of a crow, the neck of a stork, the wings of a duck, the tail of a hawk, and the legs of a chicken. Not exactly useful.
Wang says that the ability of his CUP camera to capture the chaotic movement of light may breathe new life into the study of optical chaos, which has applications in physics, communications, and cryptography.
“It was a really hot field some time ago, but it’s died down, maybe because we didn’t have the tools we needed,” he says. “The experimentalists lost interest because they couldn’t do the experiments, and the theoreticians lost interest because they couldn’t validate their theories experimentally. This was a fun demonstration to show people in that field that they finally have an experimental tool.”
The paper describing the research, titled “Real-time observation and control of optical chaos,” appears in the January 13 issue of Science Advances. Co-authors are Linran Fan, formerly of Caltech, now an assistant professor at Wyant College of Optical Sciences at the University of Arizona; and Xiaodong Yan and Han Wang, of the University of Southern California.
Funding for the research was provided by the Army Research Office Young Investigator Program, the Air Force Office of Scientific Research, the National Science Foundation, and the National Institutes of Health. More