Aurelia Butler

Northwestern University researchers have invented a new high-resolution camera that can see the unseen — including around corners and through scattering media, such as skin, fog or potentially even the human skull.
Called synthetic wavelength holography, the new method works by indirectly scattering coherent light onto hidden objects, which then scatters again and travels back to a camera. From there, an algorithm reconstructs the scattered light signal to reveal the hidden objects. Due to its high temporal resolution, the method also has potential to image fast-moving objects, such as the beating heart through the chest or speeding cars around a street corner.
The study will be published on Nov. 17 in the journal Nature Communications.
The relatively new research field of imaging objects behind occlusions or scattering media is called non-line-of-sight (NLoS) imaging. Compared to related NLoS imaging technologies, the Northwestern method can rapidly capture full-field images of large areas with submillimeter precision. With this level of resolution, the computational camera could potentially image through the skin to see even the tiniest capillaries at work.
While the method has obvious potential for noninvasive medical imaging, early-warning navigation systems for automobiles and industrial inspection in tightly confined spaces, the researchers believe potential applications are endless.
“Our technology will usher in a new wave of imaging capabilities,” said Northwestern’s Florian Willomitzer, first author of the study. “Our current sensor prototypes use visible or infrared light, but the principle is universal and could be extended to other wavelengths. For example, the same method could be applied to radio waves for space exploration or underwater acoustic imaging. It can be applied to many areas, and we have only scratched the surface.”
Willomitzer is a research assistant professor of electrical and computer engineering at Northwestern’s McCormick School of Engineering. Northwestern co-authors include Oliver Cossairt, associate professor of computer science and electrical and computer engineering, and former Ph.D. student Fengqiang Li. The Northwestern researchers collaborated closely with Prasanna Rangarajan, Muralidhar Balaji and Marc Christensen, all researchers at Southern Methodist University. More

Physicists from Exeter and Zaragoza have created a theory describing how non-reciprocity can be induced at the quantum level, paving the way for non-reciprocal transport in the next generation of nanotechnology.
A pair of theoretical physicists, from the University of Exeter (United Kingdom) and the University of Zaragoza (Spain), have developed a quantum theory explaining how to engineer non-reciprocal flows of quantum light and matter. The research may be important for the creation of quantum technologies which require the directional transfer of energy and information at small scales.
Reciprocity, going the same way backward as forward, is a ubiquitous concept in physics. A famous example may be found in Newton’s Law: for every action there is an equal and opposite reaction. The breakdown of such a powerful notion as reciprocity in any area of physics, from mechanics to optics to electromagnetism, is typically associated with surprises which can be exploited for technological application. For example, a nonreciprocal electric diode allows current to pass in forwards but not backwards and forms a building block of the microelectronics industry.
In their latest research, Downing and Zueco provide a quantum theory of non-reciprocal transport around a triangular cluster of strongly interacting quantum objects. Inspired by the physics of quantum rings, they show that by engineering an artificial magnetic field one may tune the direction of the energy flow around the cluster. The theory accounts for strong particle interactions, such that directionality appears at a swathe of energies, and considers the pernicious effect of dissipation for the formation of non-reciprocal quantum currents.
The research may be useful in the development of quantum devices requiring efficient, directional transportation, as well for further studies of strongly interacting quantum phases, synthetic magnetic fields and quantum simulators.
Charles Downing from the University of Exeter explains: “Our calculations provide insight into how one may instigate directional transport in closed nanoscopic lattices of atoms and photons with strong interactions, which may lead to the development of novel devices of a highly directional character.”
“Non-reciprocal population dynamics in a quantum trimer” is published in Proceedings of the Royal Society A, a historic journal which has been publishing scientific research since 1905.
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UT Southwestern and University of Washington researchers led an international team that used artificial intelligence (AI) and evolutionary analysis to produce 3D models of eukaryotic protein interactions. The study, published in Science, identified more than 100 probable protein complexes for the first time and provided structural models for more than 700 previously uncharacterized ones. Insights into the ways pairs or groups of proteins fit together to carry out cellular processes could lead to a wealth of new drug targets.
“Our results represent a significant advance in the new era in structural biology in which computation plays a fundamental role,” said Qian Cong, Ph.D., Assistant Professor in the Eugene McDermott Center for Human Growth and Development with a secondary appointment in Biophysics.
Dr. Cong led the study with David Baker, Ph.D., Professor of Biochemistry and Dr. Cong’s postdoctoral mentor at the University of Washington prior to her recruitment to UT Southwestern. The study has four co-lead authors, including UT Southwestern Computational Biologist Jimin Pei, Ph.D.
Proteins often operate in pairs or groups known as complexes to accomplish every task needed to keep an organism alive, Dr. Cong explained. While some of these interactions are well studied, many remain a mystery. Constructing comprehensive interactomes — or descriptions of the complete set of molecular interactions in a cell — would shed light on many fundamental aspects of biology and give researchers a new starting point on developing drugs that encourage or discourage these interactions. Dr. Cong works in the emerging field of interactomics, which combines bioinformatics and biology.
Until recently, a major barrier for constructing an interactome was uncertainty over the structures of many proteins, a problem scientists have been trying to solve for half a century. In 2020 and 2021, a company called DeepMind and Dr. Baker’s lab independently released two AI technologies called AlphaFold (AF) and RoseTTAFold (RF) that use different strategies to predict protein structures based on the sequences of the genes that produce them.
In the current study, Dr. Cong, Dr. Baker, and their colleagues expanded on those AI structure-prediction tools by modeling many yeast protein complexes. Yeast is a common model organism for fundamental biological studies. To find proteins that were likely to interact, the scientists first searched the genomes of related fungi for genes that acquired mutations in a linked fashion. They then used the two AI technologies to determine whether these proteins could be fit together in 3D structures. More

Imagine your car starting the moment you get in because it recognizes the jacket you’re wearing. Consider the value of a hospital gown that continuously measures and transmits a patient’s vital signs. These are just two applications made possible by a new “body area network”-enabling fabric invented by engineers at the University of California, Irvine.
In a paper published recently in Nature Electronics, researchers in UCI’s Henry Samueli School of Engineering detail how they integrated advanced metamaterials into flexible textiles to create a system capable of battery-free communication between articles of clothing and nearby devices.
“If you’ve held your smartphone or charge card close to a reader to pay for a purchase, you have taken advantage of near-field signaling technologies. Our fabrics work on the same principle, but we’ve extended the range significantly,” said co-author Peter Tseng, UCI assistant professor of electrical engineering & computer science. “This means you could potentially keep your phone in your pocket, and just by brushing your body against other textiles or readers, power and information can be transferred to and from your device.”
Lead author Amirhossein Hajiaghajani, a UCI Ph.D. student in electrical engineering & computer science, said the invention enables wearers to digitally interact with nearby electronic devices and make secure payments with a single touch or swipe of a sleeve.
“With our fabric, electronics establish signaling as soon as you hover your clothes over a wireless reader, so you can share information with a simple high-five or handshake,” he said. “You would no longer need to manually unlock your car with a key or separate wireless device, and your body would become the badge to open facility gates.”
Tseng likens the technology to a railway that transmits power and signals as it crisscrosses a garment. The system allows new segments to be added readily, and separate pieces of clothing can be paired to “talk” with one another.
The near-field communications protocol has enabled the growth in applications such as wireless device charging and powering of battery-free sensors, but a drawback of NFC has been its limited range of only a couple of inches. The UCI researchers extended the signal reach to more than 4 feet using passive magnetic metamaterials based on etched foils of copper and aluminum.
The team’s innovation was designed to be highly flexible and tolerant of bodily motion. Because signals travel in the UCI-invented system via magnetic induction — versus the continuous hard-wire connections that had been state-of-the-art in smart fabrics — it’s possible to coordinate separate pieces of clothing. In athletic gear, pants can measure leg movements while communicating with tops that track heart rate and other stats.
The applications in medicine are countless, Hajiaghajani said, such as freeing hospital staff from the task of applying numerous patient sensors, as they can all be integrated into metamaterial-equipped gowns.
The materials involved in the system are low-cost and easy to fabricate and customize, he noted, and varying lengths and branches of the metamaterial “rails” can be heat-pressed onto wearers’ existing clothing — no need to go out and buy a brand-new high-tech tracksuit.
“Our textiles are simple to make and can be integrated with interesting wearable designs,” Hajiaghajani said. “We want to create designs that not only are cool and inexpensive but can reduce the burden that modern electronics can bring to our lives.”
Support for this project was provided by the National Science Foundation. The team also included Fadi Kurdahi, UCI professor of electrical engineering & computer science, and graduate students Amir Hosein Afandizadeh Zargari, Manik Dautta and Abel Jimenez.
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Using a new model of brain activity, Indiana University computational neuroscientists Maria Pope, Richard Betzel and Olaf Sporns are exploring striking bursts of activity in the human brain that have not been examined before. These bursts may have potential to serve as biomarkers for brain disease and conditions such as depression, schizophrenia, dementia, and ADHD.
While analyzing human neuroimaging data, the IU research team discovered short bursts of activity that form ongoing “events” in the brain and are always taking place no matter the activity or state of the brain. In the course of a 10-minute brain scan, these events will occur roughly 10 to 20 times, each lasting for just a few seconds, the researchers found.
“What people had not seen is that how brain regions talk to each other is punctuated by these brief moments that are just a few seconds long during which there’s a lot happening,” said Olaf Sporns, who is Distinguished Professor and Robert H. Shaffer Chair in the College of Arts and Sciences Department of Psychological and Brain Sciences at IU Bloomington.
“Now that we see them, we’ve focused on those moments to get a picture of how specific brain regions link up and talk to each other during these events.”
To begin investigating the workings of these mysterious events, the team built a computational model. Led by Maria Pope, a graduate student in Sporns’ lab and a dual Ph.D. candidate in neuroscience and informatics, the group used neuroimaging data of a human brain to build a model replicating its connections. The model was then simulated in a state similar to the resting brain to create synthetic MRI signals, using mathematical equations that reenact neuronal activity.
The model showed burst-like events just like those seen in human brain recordings. More

Human behavior drives the evolution of biological organisms in ways that can profoundly adversely impact human welfare. Understanding people’s incentives when they do so is essential to identify policies and other strategies to improve evolutionary outcomes. In a new study publishing November 16thin the open access journal, PLOS Biology, researchers led by Troy Day at Queens University and David McAdams at Duke University bring the tools of economics and game theory to evolution management.
From antibiotic-resistant bacteria that endanger our health to control-resistant crop pests that threaten to undermine global food production, we are now facing the harmful consequences of our failure to efficiently manage the evolution of the biological world. As Day explains, “By modelling the joint economic and evolutionary consequences of people’s actions we can determine how best to incentivize behavior that is evolutionarily desirable.”
The centerpiece of the new analysis is a simple mathematical formula that determines when physicians, farmers, and other “evolution managers” will have sufficient incentive to steward the biological resources that are under their control, trading off the short-term costs of stewardship against the long-term benefits of delaying adverse evolution.
For instance, when a patient arrives in an urgent-care facility, screening them to determine if they are colonized by a dangerous superbug is costly, but protects future patients by allowing superbug carriers to be isolated from others. Whether the facility itself gains from screening patients depends on how it weighs these costs and benefits.
The researchers take the mathematical model further by implementing game theory, which analyzes how individuals’ decisions are interconnected and can impact each other — such as physicians in the same facility whose patients can infect each other or corn farmers with neighboring fields. Their game-theoretic analysis identifies conditions under which outcomes can be improved through policies that change incentives or facilitate coordination.
“In the example of antibiotic-resistant bacteria, hospitals could go above and beyond to control the spread of superbugs through methods like community contact tracing,” McAdams says. “This would entail additional costs and, alone, a hospital would likely not have an incentive to do so. But if every hospital took this additional step, they might all collectively benefit from slowing the spread of these bacteria. Game theory gives you a systematic way to think through those possibilities and maximize overall welfare.”
“Evolutionary change in response to human interventions, such as the evolution of resistance in response to drug treatment or evolutionary change in response to harvesting, can have significant economic repercussions,” Day adds. “We determine the conditions under which it is economically beneficial to employ costly strategies that limit evolution and thereby preserve the value of biological resources for longer.”
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Turbulence makes many people uneasy or downright queasy. And it’s given researchers a headache, too. Mathematicians have been trying for a century or more to understand the turbulence that arises when a flow interacts with a boundary, but a formulation has proven elusive.
Now an international team of mathematicians, led by UC Santa Barbara professor Björn Birnir and the University of Oslo professor Luiza Angheluta, has published a complete description of boundary layer turbulence. The paper appears in Physical Review Research, and synthesizes decades of work on the topic. The theory unites empirical observations with the Navier-Stokes equation — the mathematical foundation of fluid dynamics — into a mathematical formula.
This phenomenon was first described around 1920 by Hungarian physicist Theodore von Kármán and German physicist Ludwig Prandtl, two luminaries in fluid dynamics. “They were honing in on what’s called boundary layer turbulence,” said Birnir, director of the Center for Complex and Nonlinear Science. This is turbulence caused when a flow interacts with a boundary, such as the fluid’s surface, a pipe wall, the surface of the Earth and so forth.
Prandtl figured out experimentally that he could divide the boundary layer into four distinct regions based on proximity to the boundary. The viscous layer forms right next to the boundary, where turbulence is damped by the thickness of the flow. Next comes a transitional buffer region, followed by the inertial region, where turbulence is most fully developed. Finally, there is the wake, where the boundary layer flow is least affected by the boundary, according to a formula by von Kármán.
The fluid flows quicker farther from the boundary, but its velocity changes in a very specific manner. Its average velocity increases in the viscous and buffer layers and then transitions to a logarithmic function in the inertial layer. This “log law,” found by Prandtl and von Kármán, has perplexed researchers, who worked to understand where it came from and how to describe it.
The flow’s variation — or deviation from the mean velocity — also displayed peculiar behavior across the boundary layer. Researchers sought to understand these two variables and derive formulas that could describe them. More

Researchers at Georgia State University have created lightning-fast computer software that can help nations track and analyze pandemics, like the one caused by COVID-19, before they spread like wildfire around the globe.
The group of computer science and mathematics researchers says its new software is several orders of magnitude faster than existing computer programs and can process more than 200,000 novel virus genomes in less than two hours. The software then builds a clear visual tree of the strains and where they are spreading. This provides information that can be invaluable for countries making early decisions about lockdowns, quarantines, social distancing and testing during infectious disease outbreaks.
“The future of infectious outbreaks will no doubt be heavily data driven,” said Alexander Zelikovsky, a Georgia State computer science professor who worked on the project.
The new software was co-created with Pavel Skums, assistant professor of computer science, Mark Grinshpon, principal senior lecturer of mathematics and statistics, Daniel Novikov, a computer science Ph.D. student, and two former Georgia State Ph.D. students — Sergey Knyazev (now a postdoctoral scholar at the University of California at Los Angeles) and Pelin Icer (now a postdoctoral scholar at Swiss Federal Institute of Technology, ETH Zürich).
Their paper describing the new approach, “Scalable Reconstruction of SARS-CoV-2 Phylogeny with Recurrent Mutations,” was published in the Journal of Computational Biology.
“The COVID-19 pandemic has been an unprecedented challenge and opportunity for scientists,” said Skums, who noted that never before have researchers around the world sequenced so many complete genomes of any virus. The strains of SARS-CoV-2 are uploaded onto the free global GISAID database (https://www.gisaid.org/hcov19-variants/), where they can be data-mined and studied by any scientist. Zelikovsky, Skums and their colleagues analyzed more than 300,000 different GISAID strains for their new work. More