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Two key protein structures in the body are being visualized for the first time, thanks in part to the latest technology in the University of Cincinnati’s Center for Advanced Structural Biology—potentially opening the door for better designed therapeutics.

The research of a trio of UC structural biologists was published today in the Proceedings of the National Academy of Sciences (PNAS).

It’s the first publication to come out of the Seegar Lab at UC. Tom Seegar, Ph.D., Ohio Eminent Scholar and assistant professor in the Department of Molecular and Cellular Biosciences in the College of Medicine, serves as corresponding author of the study.

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A rare and bewildering intermediate between crystal and glass can be the most stable arrangement for some combinations of atoms, according to a study from the University of Michigan.

The findings come from the first quantum-mechanical simulations of quasicrystals—a type of solid that scientists once thought couldn’t exist. While the atoms in quasicrystals are arranged in a lattice, as in a crystal, the pattern of atoms doesn’t repeat like it does in conventional crystals. The new simulation method suggests quasicrystals—like crystals—are fundamentally , despite their similarity to disordered solids like glass that form as a consequence of rapid heating and cooling.

“We need to know how to arrange atoms into specific structures if we want to design materials with desired properties,” said Wenhao Sun, the Dow Early Career Assistant Professor of Materials Science and Engineering, and the corresponding author of the paper published today in Nature Physics. “Quasicrystals have forced us to rethink how and why certain materials can form. Until our study, it was unclear to scientists why they existed.”

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In a study published in Science Advances, researchers from Technical University of Denmark and Universidad Politécnica de Madrid demonstrate a new device called an acoustic rainbow emitter (ARE) that takes in broadband white-noise signals from a point source that radiates sound equally in all directions and scatters it up so that different sound frequencies or pitches are emitted.

Similar to how a prism splits into a , the ARE device steers each in different directions, creating an acoustic rainbow.

In nature, some animals—like humans, bats, and dolphins—have evolved intricate ears (pinnae) that can catch, shape and direct sound in amazing ways, helping them sense and navigate their surroundings.

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Single-atom catalysts (SACs) are materials consisting of individual metal atoms dispersed on a substrate (i.e., supporting surface). Recent studies have highlighted the promise of these catalysts for the efficient conversion and storage of energy, particularly when deployed in fuel cells and water electrolyzers.

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Vision-language models (VLMs) are advanced computational techniques designed to process both images and written texts, making predictions accordingly. Among other things, these models could be used to improve the capabilities of robots, helping them to accurately interpret their surroundings and interact with human users more effectively.

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A new platform for engineering chiral electron pathways offers potential fresh insights into a quantum phenomenon discovered by chemists—and exemplifies how the second quantum revolution is fostering transdisciplinary collaborations that bridge physics, chemistry, and biology to tackle fundamental questions.

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New research from the University of Minnesota Medical School suggests that different genetic forms of autism may lead to similar patterns in brain activity and behavior. The findings were recently published in Nature Neuroscience.

Using brain-recording technology, the research team observed neurons across the entire brain to explore whether different genetic forms of autism share patterns and establish commonalities in neural responses. They found that, despite , various forms may show a similar unique pattern of —also known as a brain signature.

“We hope this research will serve as a stepping stone linking genetic differences and behavioral atypicalities,” said Jean-Paul Noel, Ph.D., an assistant professor at the University of Minnesota Medical School.

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Engineers at the University of California San Diego have developed a new cooling technology that could significantly improve the energy efficiency of data centers and high-powered electronics. The technology features a specially engineered fiber membrane that passively removes heat through evaporation. It offers a promising alternative to traditional cooling systems like fans, heat sinks and liquid pumps. It could also reduce the water use associated with many current cooling systems.

The advance is detailed in a paper published on June 13 in the journal Joule.

As (AI) and cloud computing continue to expand, the demand for data processing—and the heat it generates—is skyrocketing. Currently, accounts for up to 40% of a data center’s total energy use. If trends continue, for cooling could more than double by 2030.

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Color, as the way light’s wavelength is perceived by the human eye, goes beyond a simple aesthetic element, containing important scientific information like a substance’s composition or state.

Spectrometers are that analyze by decomposing light into its constituent wavelengths, and they are widely used in various scientific and industrial fields, including material analysis, chemical component detection, and life science research.

Existing high-resolution spectrometers were large and complex, making them difficult for widespread daily use. However, thanks to the ultra-compact, high-resolution spectrometer developed by KAIST researchers, it is now expected that light’s color information can be utilized even within smartphones or wearable devices.

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In the world of particle physics, where scientists unravel the mysteries of the universe, artificial intelligence (AI) and machine learning (ML) are making waves with how they’re increasing understanding of the most fundamental particles. Central to this exploration are parton distribution functions (PDFs). These complex mathematical models are crucial for predicting outcomes of high energy physics experiments that test the Standard Model of particle physics.

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