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New protein interaction map sheds light on how brain cell communication breaks down in Alzheimer’s disease

A new study led by the Icahn School of Medicine at Mount Sinai offers one of the most comprehensive views yet of how brain cells interact in Alzheimer’s disease, mapping protein networks that reveal communication failures and point to new therapeutic opportunities.

Published online in Cell, the study analyzed in brain tissue from nearly 200 individuals.

The researchers discovered that disruptions in communication between neurons and supporting called glia—specifically astrocytes and microglia—are closely linked to the progression of Alzheimer’s disease. One protein in particular, called AHNAK, was identified as a top driver of these harmful interactions.

“This Appears To Be A Universal Law”: 50-Year-Old Mystery About Our Sun’s Storms May Have Been Solved

For around half a century, scientists have been puzzled by the odd spectral lines produced by solar flares. Now we may have some answers. Anew study looking at solar flares may have solved a 50-year-old mystery about our host star, finding that solar flares may be far hotter than we realized.

Functional connectomics reveals general wiring rule in mouse visual cortex

The MICrONS mouse visual cortex dataset shows that neurons with similar response properties preferentially connect, a pattern that emerges within and across brain areas and layers, and independently emerges in artificial neural networks where these ‘like-to-like’ connections prove important for task performance.

Study of the world’s longest-lived person reveals rare genes and good bacteria are among the keys to a long life

What is the secret of supercentenarians? While there is no magical “elixir of life” that allows us to live forever, this incredibly rare group of people who live to be 110 years or older appears to have some biological advantage. To identify the factors that underlie extreme longevity, scientists conducted a comprehensive study of Maria Branyas, who was the world’s oldest verified living person at the time of the study.

Chatbot connections: New study reveals the truth about AI boyfriends

Advances in AI technology have ushered in a new era of digital romance, where people are forming intimate emotional connections with chatbots. For many, these AI companions are a crucial lifeline, helping to combat feelings of loneliness. Yet, despite a rapidly evolving social trend that has attracted widespread interest, it has been largely understudied by researchers.

A new analysis of the popular Reddit community, r/MyBoyfriendIsAI, is addressing the gap by providing the first in-depth insights into how intimate human–AI relationships begin, evolve and affect users.

Researchers from the Massachusetts Institute of Technology (MIT) studied 1,506 of the most popular posts from this Reddit community, which has more than 27,000 members. First, they used AI tools to read all the conversations and sorted them into six main themes, such as coping with loss. Then they used custom-built AI classifiers to review the posts again and measure specific details within them.

Compact camera uses 25 color channels for high-speed, high-definition hyperspectral video

A traditional digital camera splits an image into three channels—red, green and blue—mirroring how the human eye perceives color. But those are just three discrete points along a continuous spectrum of wavelengths. Specialized “spectral” cameras go further by sequentially capturing dozens, or even hundreds, of these divisions across the spectrum.

This process is slow, however, meaning that hyperspectral cameras can only take still images, or videos with very low frame rates, or frames per second (fps). But what if a high-fps video camera could capture dozens of wavelengths at once, revealing details invisible to the naked eye?

Now, researchers at the University of Utah’s John and Marcia Price College of Engineering have developed a new way of taking a high-definition snapshot that encodes spectral data into images, much like a traditional camera encodes color. Instead of a filter that divides light into three color channels, their specialized filter divides it into 25. Each pixel stores compressed spectral information along with its , which computer algorithms can later reconstruct into a “cube” of 25 separate images—each representing a distinct slice of the visible spectrum.

Atomic neighborhoods in semiconductors provide new avenue for designing microelectronics

Inside the microchips powering the device you’re reading this on, the atoms have a hidden order all their own. A team led by Lawrence Berkeley National Laboratory (Berkeley Lab) and George Washington University has confirmed that atoms in semiconductors will arrange themselves in distinctive localized patterns that change the material’s electronic behavior.

The research, published in Science, may provide a foundation for designing specialized semiconductors for quantum-computing and optoelectronic devices for defense technologies.

On the , semiconductors are crystals made of different elements arranged in repeating . Many semiconductors are made primarily of one element with a few others added to the mix in small quantities. There aren’t enough of these trace additives to cause a throughout the material, but how these atoms are arranged next to their immediate neighbors has long been a mystery.

A new approach to magnify wave functions when imaging interacting ultracold atoms

The precise imaging of many-body systems, which are comprised of many interacting particles, can help to validate theoretical models and better understand how individual particles in these systems influence each other. Ultracold quantum gases, collections of atoms cooled to temperatures close to absolute zero, are among the most promising experimental platforms for studying many-body interactions.

To study these gases, most physicists use a technique known as –resolved imaging, which allows them to detect individual atoms and probe correlations in their behavior. Despite its advantages, this imaging method has a relatively low resolution, thus it fails to pick up a system’s subtler features.

Researchers at Heidelberg University recently devised a new strategy to magnify atomic wave functions, offering a mathematical description of the system’s , which could help to overcome the limitations of conventional single-atom imaging techniques.

Theoretical model uses neuroimaging data to link brain alterations to schizophrenia

Schizophrenia is a chronic mental health disorder characterized by hallucinations, delusions, disorganized thinking and atypical movement or speech patterns. This psychiatric condition can be highly debilitating, and diagnosed individuals can report markedly different experiences.

Understanding the neurobiological basis of could be highly valuable, as it could inform the development of new interventions to reduce the risk of its emergence or treat its symptoms. The results of many neuroimaging studies carried out so far, however, were inconsistent or inconclusive, failing to clearly delineate the processes and brain regions implicated in its clinical expression.

In a recent paper published in Nature Mental Health, researchers at Taipei Medical University analyzed meta-analyses summarizing the most consistent findings of schizophrenia-related neuroimaging studies. Drawing on the results of this analysis, they developed a new theoretical model that delineates characteristic brain alterations linked to the psychiatric disorder.

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