Aurelia Butler

Since the beginning of the pandemic, researchers worldwide have been looking for ways to treat COVID-19. And while the COVID-19 vaccines represent the best measure to prevent the disease, therapies for those who do get infected remain in short supply. A new groundbreaking study from U-M reveals several drug contenders already in use for other purposes — including one dietary supplement — that have been shown to block or reduce SARS-CoV2 infection in cells.
The study, published recently in the Proceedings of the National Academy of Science, uses artificial intelligence-powered image analysis of human cell lines during infection with the novel coronavirus. The cells were treated with more than 1,400 individual FDA-approved drugs and compounds, either before or after viral infection, and screened, resulting in 17 potential hits. Ten of those hits were newly recognized, with seven identified in previous drug repurposing studies, including remdesivir, which is one of the few FDA-approved therapies for COVID-17 in hospitalized patients.
“Traditionally, the drug development process takes a decade — and we just don’t have a decade,” said Jonathan Sexton, Ph.D., Assistant Professor of Internal Medicine at the U-M Medical School and one of the senior authors on the paper. “The therapies we discovered are well positioned for phase 2 clinical trials because their safety has already been established.”
The team validated the 17 candidate compounds in several types of cells, including stem-cell derived human lung cells in an effort to mimic SARS-CoV2 infection of the respiratory tract. Nine showed anti-viral activity at reasonable doses, including lactoferrin, a protein found in human breastmilk that is also available over the counter as a dietary supplement derived from cow’s milk.
“We found lactoferrin had remarkable efficacy for preventing infection, working better than anything else we observed,” Sexton said. He adds that early data suggest this efficacy extends even to newer variants of SARS-CoV2, including the highly transmissible Delta variant.
The team is soon launching clinical trials of the compound to examine its ability to reduce viral loads and inflammation in patients with SARS-CoV2 infection. More

A study led by researchers at North Carolina State University developed a new method that enables quantum computers to measure the thermodynamic properties of systems by calculating the zeros of the partition function.
“We’ve illustrated a new way to get at thermodynamic properties of a system, such as free energy, entropy, and other properties that are too complex to currently be measured via traditional or quantum computing,” says Lex Kemper, associate professor of physics at NC State and corresponding author of a paper describing the work. “By calculating partition function zeros we are on the way to solving the problem of scaling to larger numbers of qubits when trying to calculate free energies and entropies in a given system.”
Quantum computers are often used to study complicated systems due to their ability to handle large computations beyond the reach of conventional computers. However, some problems, such as measuring the thermodynamics or free energy in a system (which involves calculating its entropy), are still too big for even these computers to handle efficiently.
A partition function describes the statistical properties of a system in thermodynamic equilibrium. The total energy, free energy, entropy, or pressure of a system can be expressed mathematically in terms of the partition function or its derivatives.
Kemper and his colleagues used a quantum computer to measure the partition function zeros, rather than the entropy, of a spin model as it is tuned across a phase transition.
“Our method skips the part where we calculate the entropy in favor of looking at the partition function,” Kemper says. “That’s because the partition function is a generating function — a function that you can perform operations on to get at other thermodynamic information such as the internal energy and the entropy.
“We measure the partition function by determining where it is zero. Once you know all the zeros of a function, you know the whole function. Since the zeros lie in the complex plane, we used a mapping between having a complex magnetic field and time evolution to find them.”
The researchers calculated the partition function on both a standard and a trapped ion quantum computer in the laboratory of Norbert Linke at the University of Maryland. The results from both compared favorably.
“This is a way to use a quantum computer to get at all the thermodynamic properties of a system without necessitating huge numbers of quantum computations,” Kemper says.
The research appears in Science Advances and is supported by the Department of Energy (grant DE-SC0019469). First author of the paper Akhil Francis is a graduate student at NC State. Norbert Linke and Chris Monroe from the University of Maryland; Jim Freericks from Georgetown University; and Sonika Johri from IonQ also contributed to the work.
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Materials provided by North Carolina State University. Original written by Tracey Peake. Note: Content may be edited for style and length. More

Since the early days of the COVID pandemic, scientists have aggressively pursued the secrets of the mechanisms that allow severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to enter and infect healthy human cells.
Early in the pandemic, University of California San Diego’s Rommie Amaro, a computational biophysical chemist, helped develop a detailed visualization of the SARS-CoV-2 spike protein that efficiently latches onto our cell receptors.
Now, Amaro and her research colleagues from UC San Diego, University of Pittsburgh, University of Texas at Austin, Columbia University and University of Wisconsin-Milwaukee have discovered how glycans — molecules that make up a sugary residue around the edges of the spike protein — act as infection gateways.
Published August 19 in the journal Nature Chemistry, a research study led by Amaro, co-senior author Lillian Chong at the University of Pittsburgh, first author and UC San Diego graduate student Terra Sztain and co-first author and UC San Diego postdoctoral scholar Surl-Hee Ahn, describes the discovery of glycan “gates” that open to allow SARS-CoV-2 entry.
“We essentially figured out how the spike actually opens and infects,” said Amaro, a professor of chemistry and biochemistry and a senior author of the new study. “We’ve unlocked an important secret of the spike in how it infects cells. Without this gate the virus basically is rendered incapable of infection.”
Amaro believes the research team’s gate discovery opens potential avenues for new therapeutics to counter SARS-CoV-2 infection. If glycan gates could be pharmacologically locked in the closed position, then the virus is effectively prevented from opening to entry and infection. More

‘Growing’ electronic components directly onto a semiconductor block avoids messy, noisy oxidation scattering that slows and impedes electronic operation.
A UNSW study out this month shows that the resulting high-mobility components are ideal candidates for high-frequency, ultra-small electronic devices, quantum dots, and for qubit applications in quantum computing.
Smaller Means Faster, but Also Noisier
Making computers faster requires ever-smaller transistors, with these electronic components now only a handful of nanometres in size. (There are around 12 billion transistors in the postage-stamp sized central chip of modern smartphones.)
However, in even smaller devices, the channel that the electrons flow through has to be very close to the interface between the semiconductor and the metallic gate used to turn the transistor on and off. Unavoidable surface oxidation and other surface contaminants cause unwanted scattering of electrons flowing through the channel, and also lead to instabilities and noise that are particularly problematic for quantum devices.
“In the new work we create transistors in which an ultra-thin metal gate is grown as part of the semiconductor crystal, preventing problems associated with oxidation of the semiconductor surface,” says lead author Yonatan Ashlea Alava. More

Scientists from SANKEN at Osaka University demonstrated the readout of spin-polarized multielectron states composed of three or four electrons on a semiconductor quantum dot. By making use of the spin filtering caused by the quantum Hall effect, the researchers were able to improve upon previous methods that could only easily resolve two electrons. This work may lead to quantum computers based on the multielectron high-spin states.
Despite the almost unimaginable increase in the power of computers over the last 75 years, even the fastest machines available today run on the same basic principle as the original room-sized collection of vacuum tubes: information is still processed by herding electrons through circuits based on their electric charge. However, computer manufacturers are rapidly reaching the limit of how much they can readily achieve with charge alone, and new methods, such as quantum computing, are not ready yet to take their place. One promising approach is to utilize the intrinsic magnetic moment of electrons, called “spin,” but controlling and measuring these values has proven to be very challenging.
Now, a team of researchers led by Osaka University showed how to read out the spin state of multiple electrons confined to a tiny quantum dot fabricated from gallium and arsenic. Quantum dots act like artificial atoms with properties that can be tuned by scientists by changing their size or composition. However, the gaps in energy levels generally becomes smaller and harder to resolve as the number of trapped electrons increases.
To overcome this, the team took advantage of a phenomenon called the quantum Hall effect. When electrons are confined to two dimensions and subjected to a strong magnetic field, their states become quantized, so their energy levels can only take on certain specific values. “Previous spin readout methods could only handle one or two electrons, but using the quantum Hall effect, we were able to resolve up to four spin-polarized electrons,” first author Haruki Kiyama says. To prevent disturbances from thermal fluctuations, the experiments were performed at extremely low temperatures, around 80 millikelvin. “This readout technique may pave the way toward faster and higher-capacity spin-based quantum information processing devices with multielectron spin states,” senior author Akira Oiwa says.
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Whether a robot or a person delivers your package, the carbon footprint would essentially be the same, according to a University of Michigan study that could help inform the future of automated delivery as the pandemic fuels a dramatic rise in online shopping.
The researchers examined the environmental impacts of advanced residential package delivery scenarios that use electric and gas-powered autonomous vehicles and two-legged robots to ferry goods from delivery hubs to neighborhoods, and then to front doors. They compared those impacts with the traditional approach of a human driver who hand-delivers parcels.
They found that while robots and automation contribute less than 20% of a package’s footprint, most of the greenhouse gas emissions come from the vehicle. Vehicle powertrain and fuel economy are the key factors determining the package’s footprint. Switching to electric vehicles and reducing the carbon intensity of the electricity they run on could have the biggest impacts in sustainable parcel delivery, the researchers say.
Their study is a life cycle analysis of the cradle-to-grave greenhouse gas emissions for 12 suburban delivery scenarios. It’s unique in that it doesn’t just tally emissions from the delivery process. It also counts greenhouse gases from manufacturing the vehicles and robots, as well as disposing of them or recycling them at the end of their lives.
“We found that the energy and carbon footprints of this automated parcel delivery in suburban areas was similar to that of conventional human driven vehicles. The advantages of better fuel economy through vehicle automation were offset by greater electricity loads from automated vehicle power requirements,” said Gregory Keoleian, the Peter M. Wege Endowed Professor of Sustainable Systems at the U-M School for Environment and Sustainability and a professor of civil and environmental engineering.
“For all delivery systems studied, the vehicle-use phase is the single largest contributor to greenhouse gas emissions, highlighting the need for low-carbon fuels for sustainable parcel delivery. It is critically important to decarbonize grids while deploying electrified vehicles.”
Optimizing ‘the last mile’ in a surging package delivery market More

An international study led by the University of Bonn has found evidence of a long-sought effect in accelerator data. The so-called “triangle singularity” describes how particles can change their identities by exchanging quarks, thereby mimicking a new particle. The mechanism also provides new insights into a mystery that has long puzzled particle physicists: Protons, neutrons and many other particles are much heavier than one would expect. This is due to peculiarities of the strong interaction that holds the quarks together. The triangle singularity could help to better understand these properties. The publication is now available in Physical Review Letters.
In their study, the researchers analyzed data from the COMPASS experiment at the European Organization for Nuclear Research CERN in Geneva. There, certain particles called pions are brought to extremely high velocities and shot at hydrogen atoms.
Pions consist of two building blocks, a quark and an anti-quark. These are held together by the strong interaction, much like two magnets whose poles attract each other. When magnets are moved away from each other, the attraction between them decreases successively. With the strong interaction it is different: It increases in line with the distance, similar to the tensile force of a stretching rubber band.
However, the impact of the pion on the hydrogen nucleus is so strong that this rubber band breaks. The “stretching energy” stored in it is released all at once. “This is converted into matter, which creates new particles,” explains Prof. Dr. Bernhard Ketzer of the Helmholtz Institute for Radiation and Nuclear Physics at the University of Bonn. “Experiments like these therefore provide us with important information about the strong interaction.”
Unusual signal
In 2015, COMPASS detectors registered an unusual signal after such a crash test. It seemed to indicate that the collision had created an exotic new particle for a few fractions of a second. “Particles normally consist either of three quarks — this includes the protons and neutrons, for example — or, like the pions, of one quark and one antiquark,” says Ketzer. “This new short-lived intermediate state, however, appeared to consist of four quarks.”
Together with his research group and colleagues at the Technical University of Munich, the physicist has now put the data through a new analysis. “We were able to show that the signal can also be explained in a different way, that is, by the aforementioned triangle singularity,” he stresses. This mechanism was postulated as early as the 1950s by the Russian physicist Lev Davidovich Landau, but has not yet been proven directly.
According to this, the particle collision did not produce a tetraquark at all, but a completely normal quark-antiquark intermediate. This, however, disintegrated again straight away, but in an unusual manner: “The particles involved exchanged quarks and changed their identities in the process,” says Ketzer, who is also a member of the Transdisciplinary Research Area “Building Blocks of Matter and Fundamental Interactions” (TRA Matter). “The resulting signal then looks exactly like that from a tetraquark with a different mass.” This is the first time such a triangle singularity has been detected directly mimicking a new particle in this mass range. The result is also interesting because it allows new insights into the nature of the strong interaction.
Only a small fraction of the proton mass can be explained by Higgs mechanism
Protons, neutrons, pions and other particles (called hadrons) have mass. They get this from the so-called Higgs mechanism, but obviously not exclusively: A proton has about 20 times more mass than can be explained by the Higgs mechanism alone. “The much bigger part of the mass of hadrons is due to the strong interaction,” Ketzer explains. “Exactly how the masses of hadrons come about, however, is not yet clear. Our data help us to better understand the properties of the strong interaction, and perhaps the ways in which it contributes to the mass of particles.”
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Research led by a Wayne State University Department of Mathematics professor is aiding researchers in Wayne State’s Department of Psychiatry and Behavioral Neurosciences in analyzing fMRI data. fMRI is the preeminent class of signals collected from the brain in vivo and is irreplaceable in the study of brain dysfunction in many medical fields, including psychiatry, neurology and pediatrics.
Andrew Salch, Ph.D., associate professor of mathematics in Wayne State’s College of Liberal Arts and Sciences, is leading the multidisciplinary team that is investigating how concepts of topological data analysis, a subfield of mathematics, can be applied to recovering “hidden” structure in fMRI data.
“We hypothesized that aspects of the fMRI signal are not easily discoverable using many of the standard tools used for fMRI data analysis, which strategically reduce the number of dimensions in the data to be considered. Consequently, these aspects might be uncovered using concepts from the mathematical field of topological data analysis, also called TDA, which is intended for use on high-dimensional data sets,” said Salch. “The high dimensionality that characterizes fMRI data includes the three dimensions of space — that is, where in the brain the signal is being acquired — time — or how the signal varies as brain states change in time — and signal intensity — or how the strength of the fMRI signal changes in response to the task. When related to task-induced changes, the results reflect biologically meaningful aspects of brain function and dysfunction. This is a unique collaborative work focused on the complexities of both TDA and fMRI respectively, show how TDA can be applied to real fMRI data collected, and provide open access computational software we have developed for implementing the analyses.”
The research article, “From mathematics to medicine: A practical primer on topological data analysis and the development of related analytic tools for the functional discovery of latent structure in fMRI data,” appears in the Aug. 12 issue of PLOS ONE.
In it, the team used TDA to discover data structures in the anterior cingulate cortex, a critical control region in the brain. These structures — called non-contractible loops in TDA — appeared in specific conditions of the experiment, and were not identified using conventional techniques for fMRI analyses.
“We expect this work to become a citation classic,” said Vaibhav Diwadkar, Ph.D., professor of psychiatry and behavioral neurosciences and research collaborator. “Instead of merely applying TDA to fMRI, we provide a lucid argument for why medical researchers who use fMRI should consider using TDA, and why topologists should turn their attention to the study of complex fMRI data. Moreover, this important work provides readers with empirical demonstrations of such applications, and we provide potential users with the tools we used so they can in turn apply it to their own data.”
“Our ongoing research utilizing TDA with fMRI will provide a unique and complementary method for assessing brain function, and will give medical researchers greater flexibility in tackling complex properties in their data,” said Salch. “In particular, our work will help fMRI researchers become aware of the significant power of TDA that is designed to address complexity in data, and will enhance the value of using fMRI in neuroscience and medicine.”
In addition to Salch and Diwadkar, co-authors on the paper include Adam Regalski, Wayne State mathematics graduate student; Hassan Abdallah, Wayne State mathematics department alumni and current graduate student at the University of Michigan; and Michael Catanzaro, assistant professor of mathematics at Iowa State University and Wayne State mathematics department alumni.
This work is supported by the National Institutes of Health (MH111177 and MH059299), the Jack Dorsey Endowment, the Cohen Neuroscience Endowment, and the Lycaki-Young Funds from the State of Michigan. More