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The process of separating useful molecules from mixtures of other substances accounts for 15% of the nation’s energy, emits 100 million tons of carbon dioxide and costs $4 billion annually.

Commercial manufacturers produce columns of porous materials to separate potential new drugs developed by the pharmaceutical industry, for example, and also for energy and chemical production, environmental science and making foods and beverages.

But in a new study, researchers at Case Western Reserve University have found these manufactured separation materials don’t function as intended because the pores are so packed with polymer they become blocked. That means the separations are inefficient and unnecessarily expensive.

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Neuronal dendrites must relay synaptic inputs over long distances, but the mechanisms by which activity-evoked intracellular signals propagate over macroscopic distances remain unclear. Here, we discovered a system of periodically arranged endoplasmic reticulum-plasma membrane (ER-PM) junctions tiling the plasma membrane of dendrites at ∼1 μm intervals, interlinked by a meshwork of ER tubules patterned in a ladder-like array. Populated with Junctophilin-linked plasma membrane voltage-gated Ca2+ channels and ER Ca2+-release channels (ryanodine receptors), ER-PM junctions are hubs for ER-PM crosstalk, fine-tuning of Ca2+ homeostasis, and local activation of the Ca2+/calmodulin-dependent protein kinase II.

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A new report from TechInsights breaks things down, suggesting we could be in for a closely matched competition.

When it comes to transistor density, TSMC’s N2 appears to take the lead. The publication’s data estimates N2’s high-density standard cell transistor density at an impressive 313 million transistors per square millimeter, outpacing Intel’s 18A at 238 million and Samsung’s SF3 at 231 million. Of course, density isn’t everything; chip designers use a mix of high-, standard-, and low-power cells. However, TSMC’s advantage in density could provide an edge for certain workloads.

The comparison becomes less clear when it comes to performance projections. Intel’s 18A may have an advantage over TSMC’s N2 and Samsung’s SF3, but these are still just estimates based on extrapolating from previous node improvements.

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In a paper published earlier this month in Physical Review Letters, a team of physicists led by Jonathan Richardson of the University of California, Riverside, showcases how new optical technology can extend the detection range of gravitational-wave observatories such as the Laser Interferometer Gravitational-Wave Observatory, or LIGO, and pave the way for future observatories.

Since 2015, observatories like LIGO have opened a new window on the universe. Plans for future upgrades to the 4-kilometer LIGO detectors and the construction of a next-generation 40-kilometer observatory, Cosmic Explorer, aim to push the gravitational-wave detection horizon to the earliest times in the history of the universe, before the first stars formed. However, realizing these plans hinges on achieving laser power levels exceeding 1 megawatt, far beyond LIGO’s capabilities today.

The research paper reports a breakthrough that will enable gravitational-wave detectors to reach extreme laser powers. It presents a new low-noise, high-resolution approach that can correct the limiting distortions of LIGO’s main 40-kilogram mirrors which arise with increasing laser power due to heating.

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The SYNGAP1 gene, which supports the production of a protein called SynGAP (Synaptic Ras GTPase-Activating Protein), is known to play a key role in supporting the development of synapses and neural circuits (i.e., connections between neurons). Mutations in this gene have been linked to various learning disabilities, including intellectual disabilities, speech and language delays, autism spectrum disorder (ASD), and epilepsy.

Researchers at the Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology recently carried out a study aimed at better understanding the via which the SYNGAP1 gene contributes to healthy cognitive function. Their findings, published in Nature Communications, suggest that the autonomous expression of this gene in the cortical excitatory neurons of mice promotes the animals’ cognitive abilities via the assembly of long-range integrating sensory and motor information.

“Our paper builds on our ongoing research into how major risk genes for mental health disorders, including autism, regulate brain organization and function,” Gavin Rumbaugh, senior author of the paper, told Medical Xpress. “The field knows the major risk genes that directly contribute to cognitive and behavioral impairments that lead to diagnosable forms of autism and related neuropsychiatric disorders in humans.

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ICEPS transplantation for LSCD was found to be safe throughout the study period. A larger clinical trial is planned to further investigate the efficacy of the procedure.

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Using a series of more than 1,000 X-ray snapshots of the shapeshifting of enzymes in action, researchers at Stanford University have illuminated one of the great mysteries of life—how enzymes are able to speed up life-sustaining biochemical reactions so dramatically. Their findings could impact fields ranging from basic science to drug discovery, and provoke a rethinking of how science is taught in the classroom.

“When I say enzymes speed up reactions, I mean as in a trillion-trillion times faster for some reactions,” noted senior author of the study, Dan Herschlag, professor of biochemistry in the School of Medicine. “Enzymes are really remarkable little machines, but our understanding of exactly how they work has been lacking.”

There are lots of ideas and theories that make sense, Herschlag said, but biochemists have not been able to translate those ideas into a specific understanding of the chemical and physical interactions responsible for enzymes’ enormous reaction rates. As a result, biochemists don’t have a basic understanding and, therefore, have been unable to predict rates or design new enzymes as well as nature does, an ability that would be impactful across industry and medicine.

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The words “optimal” and “optimize” derive from the Latin “optimus,” or “best,” as in “make the best of things.” Alessio Figalli, a mathematician at the university ETH Zurich, studies optimal transport: the most efficient allocation of starting points to end points. The scope of investigation is wide, including clouds, crystals, bubbles and chatbots.

Dr. Figalli, who was awarded the Fields Medal in math that is motivated by concrete problems found in nature. He also likes the discipline’s “sense of eternity,” he said in a recent interview. “It is something that will be here forever.” (Nothing is forever, he conceded, but math will be around for “long enough.”) “I like the fact that if you prove a theorem, you prove it,” he said. “There’s no ambiguity, it’s true or false. In a hundred years, you can rely on it, no matter what.”

The study of optimal transport was introduced almost 250 years ago by Gaspard Monge, a French mathematician and politician who was motivated by problems in military engineering. His ideas found broader application solving logistical problems during the Napoleonic Era — for instance, identifying the most efficient way to build fortifications, in order to minimize the costs of transporting materials across Europe.

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A team of microbiologists, chemists and pharmaceutical specialists at Shandong University, Guangzhou Medical University, Second Military Medical University and Qingdao University, all in China, has developed an AI model that generates antimicrobial peptide structures for screening against treatment-resistant microbes.

In their study published in the journal Science Advances, the group developed a compression method to reduce the number of elements needed in training data for an AI system, which helped to reduce diversification issues with current AI models.

Prior research has suggested that drug-resistant microbes are one of the most pressing problems in medical science. Researchers around the world have been looking for new ways to treat people infected with such microbes—one approach involves developing , which work by targeting bacterial membranes.

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