Computers Math

Scientists at Fred Hutchinson Cancer Research Center in Seattle have shown that a potent antibody from a COVID-19 survivor interferes with a key feature on the surface of the coronavirus’s distinctive spikes and induces critical pieces of those spikes to break off in the process.
The antibody — a tiny, Y-shaped protein that is one of the body’s premier weapons against pathogens including viruses — was isolated by the Fred Hutch team from a blood sample received from a Washington state patient in the early days of the pandemic.
The team led by Drs. Leo Stamatatos, Andrew McGuire and Marie Pancera previously reported that, among dozens of different antibodies generated naturally by the patient, this one — dubbed CV30 — was 530 times more potent than any of its competitors.
Using tools derived from high-energy physics, Hutch structural biologist Pancera and her postdoctoral fellow Dr. Nicholas Hurlburt have now mapped the molecular structure of CV30. They and their colleagues published their results online today in the journal Nature Communications.
The product of their research is a set of computer-generated 3D images that look to the untrained eye as an unruly mass of noodles. But to scientists they show the precise shapes of proteins comprising critical surface structures of antibodies, the coronavirus spike and the spike’s binding site on human cells. The models depict how these structures can fit together like pieces of a 3D puzzle.
“Our study shows that this antibody neutralizes the virus with two mechanisms. One is that it overlaps the virus’s target site on human cells, the other is that it induces shedding or dissociation of part of the spike from the rest,” Pancera said.

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On the surface of the complex structure of the antibody is a spot on the tips of each of its floppy, Y-shaped arms. This infinitesimally small patch of molecules can neatly stretch across a spot on the coronavirus spike, a site that otherwise works like a grappling hook to grab onto a docking site on human cells.
The target for those hooks is the ACE2 receptor, a protein found on the surfaces of cells that line human lung tissues and blood vessels. But if CV30 antibodies cover those hooks, the coronavirus cannot dock easily with the ACE2 receptor. Its ability to infect cells is blunted.
This very effective antibody not only jams the business end of the coronavirus spike, it apparently causes a section of that spike, known as S1, to shear off. Hutch researcher McGuire and his laboratory team performed an experiment showing that, in the presence of this antibody, there is reduction of antibody binding over time, suggesting the S1 section was shed from the spike surface.
The S1 protein plays a crucial role in helping the coronavirus to enter cells. Research indicates that after the spike makes initial contact with the ACE2 receptor, the S1 protein swings like a gate to help the virus fuse with the captured cell surface and slip inside. Once within a cell, the virus hijacks components of its gene and protein-making machinery to make multiple copies of itself that are ultimately released to infect other target cells.
The incredibly small size of antibodies is difficult to comprehend. These proteins are so small they would appear to swarm like mosquitos around a virus whose structure can only be seen using the most powerful of microscopes. The tiny molecular features Pancera’s team focused on the tips of the antibody protein are measured in nanometers — billionths of a meter.

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Yet structural biologists equipped with the right tools can now build accurate 3D images of these proteins, deduce how parts of these structures fit like puzzle pieces, and even animate their interactions.
Fred Hutch structural biologists developed 3D images of an antibody fished from the blood of an early COVID-19 survivor that efficiently neutralized the coronavirus.
Dr. Nicholas Hurlburt, who helped develop the images, narrates this short video showing how that antibody interacts with the notorious spikes of the coronavirus, blocking their ability to bind to a receptor on human cells that otherwise presents a doorway to infection.
Key to building models of these nanoscale proteins is the use of X-ray crystallography. Structural biologists determine the shapes of proteins by illuminating frozen, crystalized samples of these molecules with extremely powerful X-rays. The most powerful X-rays come from a gigantic instrument known as a synchrotron light source. Born from atom-smashing experiments dating back to the 1930s, a synchrotron is a ring of massively powerful magnets that are used to accelerate a stream of electrons around a circular track at close to the speed of light. Synchrotrons are so costly that only governments can build and operate them. There are only 40 of them in the world.
Pancera’s work used the Advanced Photon Source, a synchrotron at Argonne National Laboratory near Chicago, which is run by the University of Chicago and the U.S. Department of Energy. Argonne’s ring is 1,200 feet in diameter and sits on an 80-acre site.
As the electrons whiz around the synchrotron ring, they give off enormously powerful X-rays — far brighter than the sun but delivered in flashes of beams smaller than a pinpoint.
Structural biologists from around the world rely on these brilliant X-ray beamlines to illuminate frozen crystals of proteins. They reveal their structure in the way these bright beams are bent as they pass though the molecules. It takes powerful computers to translate the data readout from these synchrotron experiments into the images of proteins that are eventually completed by structural biologists.
The Fred Hutch team’s work on CV30 builds on that of other structural biologists who are studying a growing family of potent neutralizing antibodies against the coronavirus. The goal of most coronavirus vaccine candidates is to stimulate and train the immune system to make similar neutralizing antibodies, which can recognize the virus as an invader and stop COVID-19 infections before they can take hold.
Neutralizing antibodies from the blood of recovered COVID-19 patients may also be infused into infected patients — an experimental approach known as convalescent plasma therapy. The donated plasma contains a wide variety of different antibodies of varying potency. Although once thought promising, recent studies have cast doubt on its effectiveness.
However, pharmaceutical companies are experimenting with combinations of potent neutralizing antibodies that can be grown in a laboratory. These “monoclonal antibody cocktails” can be produced at industrial scale for delivery by infusion to infected patients or given as prophylactic drugs to prevent infection. After coming down with COVID-19, President Trump received an experimental monoclonal antibody drug being tested in clinical trials by the biotech company Regeneron, and he attributes his apparently quick recovery to the advanced medical treatment he received.
The Fred Hutch research team holds out hope that the protein they discovered, CV30, may prove to be useful in the prevention or treatment of COVID-19. To find out, this antibody, along with other candidate proteins their team is studying, need to be tested preclinically and then in human trials.
“It is too early to tell how good they might be,” Pancera said.
This work was supported by donations to the Fred Hutch COVID-19 Research Fund. More

To control an epidemic, authorities will often impose varying degrees of lockdown. In a paper in the journal Chaos, by AIP Publishing, scientists have discovered, using mathematics and computer simulations, why dividing a large population into multiple subpopulations that do not intermix can help contain outbreaks without imposing contact restrictions within those local communities.
“The key idea is that, at low infection numbers, fluctuations can alter the course of the epidemics significantly, even if you expect an exponential increase in infection numbers on average,” said author Ramin Golestanian.
When infection numbers are high, random effects can be ignored. But subdividing a population can create communities so small that the random effects matter.
“When a large population is divided into smaller communities, these random effects completely change the dynamics of the full population. Randomness causes peak infection numbers to be brought way down,” said author Philip Bittihn.
To tease out the way randomness affects an epidemic, the investigators first considered a so-called deterministic model without random events. For this test, they assumed that individuals in each subpopulation encounter others at the same rate they would have in the large population. Even though subpopulations are not allowed to intermix, the same dynamics are observed in the subdivided population as in the initial large population.
If, however, random effects are included in the model, dramatic changes ensue, even though the contact rate in the subpopulations is the same as in the full one.
A population of 8 million individuals with 500 initially infected ones was studied using an infectious contact rate seen for COVID-19 with mild social distancing measures in place. With these parameters, the disease spreads exponentially with infections doubling every 12 days.
“If this population is allowed to mix homogeneously, the dynamics will evolve according to the deterministic prediction with a peak around 5% infected individuals,” said Bittihn.
However, if the population is split into 100 subpopulations of 80,000 people each, the peak percentage of infected individuals drops to 3%. If the community is split up even further to 500 subgroups of 16,000 each, the infection peaks at only 1% of the initial population.
The main reason subdividing the population works is because the epidemic is completely extinguished in a significant fraction of the subgroups. This “extinction effect” occurs when infection chains spontaneously terminate.
Another way subdividing works is by desynchronizing the full population. Even if outbreaks occur in the smaller communities, the peaks may come at different times and cannot synchronize and add up to a large number.
“In reality, subpopulations cannot be perfectly isolated, so local extinction might only be temporary,” Golestanian said. “Further study is ongoing to take this and suitable countermeasures into account.”

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A joint research group led by Prof. Jens Eisert of Freie Universität Berlin and Helmholtz-Zentrum Berlin (HZB) has shown a way to simulate the quantum physical properties of complex solid state systems. This is done with the help of complex solid state systems that can be studied experimentally. The study was published in the journal Proceedings of the National Academy of Sciences (PNAS).
“The real goal is a robust quantum computer that generates stable results even when errors occur and corrects these errors,” explains Jens Eisert, professor at Freie Universität Berlin and head of a joint research group at HZB. So far, the development of robust quantum computers is still a long way off, because quantum bits react extremely sensitively to the smallest fluctuations in environmental parameters.
But now a new approach could promise success: two postdocs from the group around Jens Eisert, Maria Laura Baez and Marek Gluza have taken up an idea of Richard Feynman, a brilliant US-American physicist of the post-war period. Feynman had proposed to use real systems of atoms with their quantum physical properties to simulate other quantum systems. These quantum systems can consist of atoms strung together like pearls in a string with special spin properties, but could also be ion traps, Rydberg atoms, superconducting Qbits or atoms in optical lattices. What they have in common is that they can be created and controlled in the laboratory. Their quantum physical properties could be used to predict the behaviour of other quantum systems. But which quantum systems would be good candidates? Is there a way to find out in advance?
Eisert’s team has now investigated this question using a combination of mathematical and numerical methods. In fact, the group showed that the so-called dynamic structure factor of such systems is a possible tool to make statements about other quantum systems. This factor indirectly maps how spins or other quantum quantities behave over time, it is calculated by a Fourier transformation.
“This work builds a bridge between two worlds,” explains Jens Eisert. “On the one hand, there is the Condensed Matter Community, which studies quantum systems and gains new insights from them — and on the other hand there is Quantum Informatics — which deals with quantum information. We believe that great progress will be possible if we bring the two worlds together,” says the scientist.

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A new artificial intelligence-based score considers multiple factors to predict the prognosis of individual patients with COVID-19 seen at urgent care clinics or emergency departments. The tool, which was created by investigators at Massachusetts General Hospital (MGH), can be used to rapidly and automatically determine which patients are most likely to develop complications and need to be hospitalized.
The impetus for the study began early during the U.S. epidemic when Massachusetts experienced frequent urgent care visits and hospital admissions. While working as an infectious diseases physician and as part of the MGH Biothreats team, Gregory Robbins, MD, recognized the need for a more sophisticated method to identify outpatients at greatest risk for experiencing negative outcomes.
As described in The Journal of Infectious Diseases, a team of experts in neurology, infectious disease, critical care, radiology, pathology, emergency medicine and machine learning designed the COVID-19 Acuity Score (CoVA) based on input from information on 9,381 adult outpatients seen in MGH’s respiratory illness clinics and emergency department between March 7 and May 2, 2020. “The large sample size helped ensure that the machine learning model was able to learn which of the many different pieces of data available allow reliable predictions about the course of COVID-19 infection,” said M. Brandon Westover, MD, PhD, an investigator in the Department of Neurology and director of Data Science at the MGH McCance Center for Brain Health. Westover is one of three co-senior authors of the study, along with Robbins and Shibani Mukerji, MD, PhD, associate director of MGH’s Neuro-Infectious Diseases Unit.
CoVA was then tested in another 2,205 patients seen between May 3 and May 14. “Testing the model prospectively helped us to verify that the CoVA score actually works when it sees ‘new’ patients, in the real world,” said first author Haoqi Sun, PhD, an investigator in the Department of Neurology and a research faculty member in the MGH Clinical Data Animation Center (CDAC). In this prospective validation group, 26.1 percent, 6.3 percent and 0.5 percent of patients experienced hospitalization, critical illness or death, respectively, within seven days. CoVA demonstrated excellent performance in predicting which patients would fall into these categories.
Among 30 predictors — which included demographics like age and gender, COVID-19 testing status, vital signs, medical history and chest X-ray results (when available) — the top five were age, diastolic blood pressure, blood oxygen saturation, COVID-19 testing status and respiratory rate.
“While several other groups have developed risk scores for complications of COVID-19, ours is unique in being based on such a large patient sample, being prospectively validated, and in being specifically designed for use in the outpatient setting, rather than for patients who are already hospitalized,” Mukerji said. “CoVA is designed so that automated scoring could be incorporated into electronic medical record systems. We hope that it will be useful in case of future COVID-19 surges, when rapid clinical assessments may be critical.”

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With a training technique commonly used to teach dogs to sit and stay, Johns Hopkins University computer scientists showed a robot how to teach itself several new tricks, including stacking blocks. With the method, the robot, named Spot, was able to learn in days what typically takes a month.
By using positive reinforcement, an approach familiar to anyone who’s used treats to change a dog’s behavior, the team dramatically improved the robot’s skills and did it quickly enough to make training robots for real-world work a more feasible enterprise. The findings are newly published in a paper called, “Good Robot!”
“The question here was how do we get the robot to learn a skill?” said lead author Andrew Hundt, a PhD student working in Johns Hopkins’ Computational Interaction and Robotics Laboratory. “I’ve had dogs so I know rewards work and that was the inspiration for how I designed the learning algorithm.”
Unlike humans and animals that are born with highly intuitive brains, computers are blank slates and must learn everything from scratch. But true learning is often accomplished with trial and error, and roboticists are still figuring out how robots can learn efficiently from their mistakes.
The team accomplished that here by devising a reward system that works for a robot the way treats work for a dog. Where a dog might get a cookie for a job well done, the robot earned numeric points.
Hundt recalled how he once taught his terrier mix puppy named Leah the command “leave it,” so she could ignore squirrels on walks. He used two types of treats, ordinary trainer treats and something even better, like cheese. When Leah was excited and sniffing around the treats, she got nothing. But when she calmed down and looked away, she got the good stuff. “That’s when I gave her the cheese and said, ‘Leave it! Good Leah!'”
Similarly, to stack blocks, Spot the robot needed to learn how to focus on constructive actions. As the robot explored the blocks, it quickly learned that correct behaviors for stacking earned high points, but incorrect ones earned nothing. Reach out but don’t grasp a block? No points. Knock over a stack? Definitely no points. Spot earned the most by placing the last block on top of a four-block stack.

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The training tactic not only worked, it took just days to teach the robot what used to take weeks. The team was able to reduce the practice time by first training a simulated robot, which is a lot like a video game, then running tests with Spot.
“The robot wants the higher score,” Hundt said. “It quickly learns the right behavior to get the best reward. In fact, it used to take a month of practice for the robot to achieve 100% accuracy. We were able to do it in two days.”
Positive reinforcement not only worked to help the robot teach itself to stack blocks, with the point system the robot just as quickly learned several other tasks — even how to play a simulated navigation game. The ability to learn from mistakes in all types of situations is critical for designing a robot that could adapt to new environments.
“At the start the robot has no idea what it’s doing but it will get better and better with each practice. It never gives up and keeps trying to stack and is able to finish the task 100% of the time,” Hundt said.
The team imagines these findings could help train household robots to do laundry and wash dishes — tasks that could be popular on the open market and help seniors live independently. It could also help design improved self-driving cars.
“Our goal is to eventually develop robots that can do complex tasks in the real world — like product assembly, caring for the elderly and surgery,” Hager said. “We don’t currently know how to program tasks like that — the world is too complex. But work like this shows us that there is promise to the idea that robots can learn how to accomplish such real-world tasks in a safe and efficient way.”

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Engineers at Rice University and Texas A&M University have found a 2D material that could make computers faster and more energy-efficient.
Their material is a derivative of perovskite — a crystal with a distinctive structure — that has the surprising ability to enable the valleytronics phenomenon touted as a possible platform for information processing and storage.
The lab of materials scientist Jun Lou of Rice’s Brown School of Engineering synthesized a layered compound of cesium, bismuth and iodine that is adept at storing the valley states of electrons, but only in the structure’s odd layers.
These bits can be set with polarized light, and the even layers appear to protect the odd ones from the kind of field interference that bedevils other perovskites, according to the researchers.
Best of all, the material appears to be scalable.
“This is not a new material, but we figured out a way to make it without solution processing or exfoliating it from bulk,” Lou said. “What’s novel is that we can produce it (via chemical vapor deposition) in a few layers, and all the way down to a monolayer. That enabled us to probe its nonlinear optical properties.”
The discovery is detailed in Advanced Materials.

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Valleytronics are a cousin to spintronics, in which memory bits are defined by an electron’s quantum spin state. In valleytronics, electrons have degrees of freedom in the multiple momentum states — or valleys — they occupy. These states can be read as bits.
“In a transistor, if you put an electron there, it represents a state, and if you take it out, that represents another state,” said co-principal investigator Hanyu Zhu of Rice. “In valleytronics, the electrons are always present, and are in either of two different quantum wavefunctions with opposite momenta. These two wavefunctions interact with different light polarization, so the momentum state can be resolved optically.”
A close look at the inorganic, lead-free material through an electron microscope showed molecules in the odd layer are asymmetric. “That lack of symmetry is missing in the even layers — that’s how we differentiate between them — and it gives rise to the properties we see,” Lou said. “That’s just the nature of this crystal structure.”
The lab tested the material with up to 11 layers and found a lack of transparency doesn’t seem to affect how well light triggered a response. “Even a thicker material behaves like it’s still a single layer,” Lou said. “That’s quite important.”
“Thicker 2D transition metal dichalcogenides lose unique properties like valleytronics,” he said. “All the behaviors are gone. That’s not the case for this material.”
Lou said calculations by co-principal investigator Xiaofeng Qian of Texas A&M University provided the necessary theoretical evidence.

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“The valley polarization observed in both thin and thick layers is largely due to the weak interlayer electronic coupling, a unique feature of this perovskite derivative compared to other 2D materials when stacked together,” Qian said. “It also leads to persistent nonlinear optical responses in thicker samples.”
The material also seems less susceptible to environmental degradation, a common problem for hybrid perovskites developed for solar energy. “This material won’t give you very high conversion efficiency, but think of it like an all-around athlete in the Olympic Games,” said lead author and Rice postdoctoral fellow Jia Liang. “It may not be the best in each category, but if you consider its different aspects together, it will stand out,” he said.
The researchers suggested the already strong light-matter interaction they observed could be enhanced by further engineering the material’s band gap.
“I think it’s a breakthrough for using this type of material in information processing,” Lou said. “We’re really hoping this is the starting point.” More

A University of Central Florida researcher is working to make portable devices and electric vehicles stay charged longer by extending the life of the rechargeable lithium-ion batteries powering them.
Assistant Professor Yang Yang is doing this by making the batteries more efficient, with some of his latest work focusing on keeping an internal metal structure, the anode, from falling apart over time by applying a thin, film-like coating of copper and tin. The new technique is detailed in a recent study in the journal Advanced Materials.
An anode generates electrons that travel to a similar structure, the cathode, inside the battery, thus creating a current and power.
“Our work has shown that the rate of degradation of the anode can be reduced by more than 1,000 percent by using a copper-tin film compared to a tin film that is often used,” said Yang, who is with UCF’s NanoScience Technology Center.
Yang is an expert in battery improvement including making them safer and able to withstand extreme temperatures.
The technique is unique because of its use of the copper-tin alloy and is an important improvement in stabilizing rechargeable battery performance, Yang says.
It is also scalable for use in the smallest smartphone battery to larger batteries that power electric cars and trucks.
“We are motivated by our most recent research progress in alloyed materials for various applications,” he says. “Each alloy is unique in composition, structure and property.”

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Maths — it’s the subject some kids love to hate, yet despite its lack of popularity, mathematics is critical for a STEM-capable workforce and vital for Australia’s current and future productivity.
In a new study by the University of South Australia in collaboration with the Australian Council for Educational Research, researchers have been exploring the impact of anxiety on learning maths, finding that boosting student confidence, is pivotal to greater engagement with the subject.
Maths anxiety, or ‘mathemaphobia’, is the sense of fear, worry and nervousness that students may experience when participating in mathematical tasks.
In Australia a quarter to a third of Australian secondary students report feeling tense, nervous or helpless when doing maths, and it’s this reaction that’s influencing their decisions to study maths.
Lead researcher, Dr Florence Gabriel says maths anxiety is one of the biggest barriers to students choosing to study it, especially at senior school and tertiary levels.
“Many of us would have felt some sort of maths anxiety in the past — a sense of panic or worry, feelings of failure, or even a faster heart rate — all of which are associated with stress,” Dr Gabriel says.

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“Maths anxiety is essentially an emotional reaction, but it’s just like stress in other situations.
“When students experience maths anxiety, they’ll tend to hurry through maths questions, lose focus, or simply give up when it all seems too hard. Not surprisingly, these reactions compound and lead to poor maths achievement — and later a reluctance to engage with the subject at all.
“To break this cycle, our research shows that we need to build and grow student confidence in maths, especially before starting a new maths concept.
“This draws on the notion of self-regulated learning ¬- where students have the ability to understand, track and control their own learning.
“By drawing a student’s attention to instances where they’ve previously overcome a difficult maths challenge, or to a significant maths success, we’re essentially building their confidence and belief in their own abilities, and it’s this that will start to counteract negative emotions.”
The study assessed the responses of 4295 Australian 15-year-old students that participated in the 2012 cycle of the OECD’s Program for International Student Assessment (PISA).

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It focussed on the psychological factors of maths learning: motivation (the belief that maths is important and useful for their future); maths self-concept (the belief in their ability to do maths); maths anxiety (self-feelings when doing maths); perseverance (their willingness to continue to work on difficult problems); maths self-efficacy (their self-belief that they can successfully solve maths problems); and maths literacy (the ability to apply maths to the real world).
“Importantly, our research shows the domino effect that these variables have on one another,” Dr Gabriel says.
“Through structural equation modelling, our data shows that low motivation and self-concept will lead to maths anxiety, which in turn affects perseverance, self-efficacy and, ultimately, maths achievement.
“By developing a student’s ability to reflect on past successes — before maths anxiety sets in — we can break through some of the negative and emotional beliefs about maths and, hopefully, pave the way for students to accept and engage with maths in the future.” More