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Blog – Page 26

Understanding the electrical activity of neurons is key to unlocking insights into neurological diseases. Yale researchers have unveiled a high-throughput automated method that captures the electrical activity of large numbers of neurons simultaneously and without bias.

This cutting-edge approach provides a powerful “functional fingerprint” of neuron populations in their natural state, opening new doors to understanding and treating neurological diseases. The work was published June 13 in Nature Protocols.

The patch-clamp technique has long been a gold standard for studying the electrical activity of neurons, the fundamental units of the nervous system. However, the manual execution of this approach is slow and labor-intensive. Recent advances in robotic patch-clamp technologies have improved speed and efficiency, but they are limited to artificially grown neurons rather than neurons in their native unmanipulated state.

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The melting of crystals is the process by which an increase in temperature induces the disruption of the ordered crystalline lattice, leading to the disordered structure and highly fluctuating dynamic behavior of liquids. At the glass transition, where an amorphous solid (a glass) turns into a liquid, there is no obvious change in structure, and only the dynamics of the atoms change, going from strongly localized dynamics in space (in the glass state) to the highly fluctuating (diffusive) dynamics in the liquid.

The search for the atomic-scale mechanism of 3D crystal melting has a long history in physics, and famous physicists such as Max Born, Neville Mott and Frederick Lindemann proposed different ways to look at it. I have always had the impression that we still do not understand the melting of 3D crystals, which is a highly complicated cooperative process involving nonlinearly coupled dynamics of a huge number of atoms. This complexity I always found very fascinating.

Comparatively, the melting of 2D solids, mediated by dislocations-unbinding, is much better understood, and the theory that describes it led to the 2017 Nobel prize in physics for Kosterlitz and Thouless.

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Hey! If anyone’s interested in attending the Viva Frontier Tower Longevity Summit in SF this weekend (Aubrey de Grey and Irina Conboy, plus a ton of others, are speaking) I have a couple 60% coupons I can share. Shoot me a DM!

On June 22–23, the Longevity Summit hosted during the 6-week Viva Frontier Tower Pop-up Village (Jun 20 — Aug 4) will serve two purposes:

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Binary neutron star mergers, cosmic collisions between two very dense stellar remnants made up predominantly of neutrons, have been the topic of numerous astrophysics studies due to their fascinating underlying physics and their possible cosmological outcomes. Most previous studies aimed at simulating and better understanding these events relied on computational methods designed to solve Einstein’s equations of general relativity under extreme conditions, such as those that would be present during neutron star mergers.

Researchers at the Max Planck Institute for Gravitational Physics (Albert Einstein Institute), Yukawa Institute for Theoretical Physics, Chiba University, and Toho University recently performed the longest simulation of binary neutron star mergers to date, utilizing a framework for modeling the interactions between magnetic fields, high-density matter and neutrinos, known as the neutrino-radiation magnetohydrodynamics (MHD) framework.

Their simulation, outlined in Physical Review Letters, reveals the emergence of a magnetically dominated jet from the , followed by the collapse of the binary neutron star system into a black hole.

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Back in 2019, the Event Horizon Telescope (EHT) team revealed the first-ever image of a supermassive black hole in the galaxy M87. In 2022, they followed up with the iconic image of Sagittarius A at the heart of the Milky Way. While these images were groundbreaking, the data behind them held even deeper insights that were hard to decode.

Neural Networks Meet Black Hole Physics

Previous studies by the EHT Collaboration used only a handful of realistic synthetic data files. Funded by the National Science Foundation (NSF) as part of the Partnership to Advance Throughput Computing (PATh) project, the Madison-based CHTC enabled the astronomers to feed millions of such data files into a so-called Bayesian neural network, which can quantify uncertainties. This allowed the researchers to make a much better comparison between the EHT data and the models.

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IN A NUTSHELL 🌟 Anthropic is on a mission to develop artificial general intelligence that remains ethical and benevolent. 🎯 The company’s strategy, known as the Race to the Top, aims to set global standards for safe AI development. 🤖 Claude, Anthropic’s AI model, is designed to embody ethical principles and serve as a constant.

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A newly discovered mechanism that leads to liver dysfunction may be a key factor in type 2 diabetes and other metabolic disorders in individuals with obesity, according to a new study led by Harvard T.H. Chan School of Public Health.

The dysfunction identified—dysregulated hepatic coenzyme Q metabolism—leads to excessive reactive oxygen species (ROS) produced by mitochondria at a single specific site in an enzyme called complex I. The researchers say the discovery offers a potential path for new, precise treatments for metabolic diseases.

“Our findings provide the first step toward solving a complex problem in the field of metabolic disease research that has stood for three decades,” said corresponding author Gökhan S. Hotamisligil, James Stevens Simmons Professor of Genetics and Metabolism.

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