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Single-cell resolution functional networks during unconsciousness are segregated into spatially intermixed modules

The common neural mechanisms underlying the reduction of consciousness during sleep and anesthesia remain unclear. Previous studies have examined changes in network structure by only using recordings with limited spatial resolution, which has hindered the investigation of the critical spatial scales for the reduction of consciousness. To address this issue, we recorded calcium signals from approximately 10,000 neurons across multiple cortical regions in awake, sleeping, and anesthetized mice and compared network structure at different spatial scales by leveraging single-cell resolution and wide-field two-photon microscopy. At the single-cell scale, both sleep and anesthesia exhibit higher network modularity than an awake state, indicating a segregated network, but modules are spatially intermixed in all three states.

Beyond the hours slept: inconsistent sleep routines threaten mental health in 100,000 UK Biobank participants

Sleep duration has a well-established effect on mental health and well-being, with durations of 7 to 9 hours being the general recommendation. Here, we analyze the significance of sleep patterns and find that a consistent routine reduces the risk of developing mental disorders far more than simply ensuring a certain average sleep duration.

We analyzed the sleep behavior of 100,000 adults for one week using motion data from wrist-worn devices. We modeled sleep behavior using multivariate generalized additive Cox proportional hazard models, incorporating a smooth 2D interaction effect of sleep duration and routine sleep hours. We calculated C-statistics and E-values to evaluate model performance and assess the robustness against hidden confounders. We also stratified analyses by age and gender.

Most participants slept for 7 to 9 hours as recommended, yet they consistently only slept during the same 4.8 hours each night. We found that an average sleep duration around 8 hours minimizes the risk of future mental disorders—but only if integrated into a rigorous sleep routine spanning at least the same 7 hours each night. Our study provides evidence that adopting such sleep behavior could reduce the population incidence rate of mental disorders by 23% (HR: 0.79, \(p0.0001\), for the average participant). The models showed a strong fit (C-statistics: 0.63), robustness to hidden confounders (E-value: 1.8), and stability under age-and gender-based stratification. We identified weekend behavior as a frequent reason for low sleep routines, with over 25% of the population disrupting their weekly sleep routine during weekend nights—raising the risk of future mental disorders by 10%.

AI-guided micromachining advances next-generation biocompatible titanium alloys

Researchers have developed a new machine-learning-assisted approach to optimize micro-electro-discharge machining (µ-EDM) of a next-generation biocompatible titanium alloy, potentially improving the manufacturing of advanced medical and aerospace components.

The work is published in the journal Scientific Reports.

Titanium alloys are widely used in biomedical implants, aerospace systems, and automotive engineering due to their strength, corrosion resistance, and low weight. However, the commonly used alloy Ti–6Al–4V contains aluminum and vanadium, elements associated with long-term toxicity risks in biomedical applications.

AI and brain control: New system identifies animal behavior and silences responsible neurons in real time

A male fruit fly in a laboratory chamber extends his wings and vibrates them to produce his species’ version of a love song. A female fly stays nearby listening. Suddenly, a green light flashes across the chamber for a fraction of a second. The male’s song cuts off mid-note and his wings fold. The female, not impressed by the interrupted serenade, walks away. The culprit? An AI system that watched the male begin his courtship dance and shut down his song-producing brain cells.

Developed by scientists at Nagoya University and their collaborators from Osaka University and Tohoku University, the AI can watch and recognize animal behaviors and control the specific brain circuits that drive them.

Published in Science Advances, the study presents an advanced AI system that can identify which animal performs a behavior in a group and selectively target only that animal’s brain cells during social interactions.

Why the economics of orbital AI are so brutal

He’s not alone. xAI’s head of compute has reportedly bet his counterpart at Anthropic that 1% of global compute will be in orbit by 2028. Google (which has a significant ownership stake in SpaceX) has announced a space AI effort called Project Suncatcher, which will launch prototype vehicles in 2027. Starcloud, a startup that has raised $34 million backed by Google and Andreessen Horowitz, filed its own plans for an 80,000 satellite constellation last week. Even Jeff Bezos has said this is the future.

But behind the hype, what will it actually take to get data centers into space?

In a first analysis, today’s terrestrial data centers remain cheaper than those in orbit. Andrew McCalip, a space engineer, has built a helpful calculator comparing the two models. His baseline results show that a 1 GW orbital data center might cost $42.4 billion — almost 3x its ground-bound equivalent, thanks to the up-front costs of building the satellites and launching them to orbit.

Studies test whether gene-editing can fix high cholesterol. For now, take your medicine

WASHINGTON (AP) — Scientists are testing an entirely new way to fight heart disease: a gene-editing treatment that might offer a one-time fix for high cholesterol.

It’s very early stage research, tried in only a few dozen people so far. But gene-editing approaches being developed by two companies show hints that switching off certain genes could dramatically lower artery-clogging cholesterol, raising hopes of one day being able to prevent heart attacks without having to take pills.

“People want a fix, not a bandage,” said Dr. Luke Laffin, a preventive cardiologist at the Cleveland Clinic. After co-authoring a promising study published in the New England Journal of Medicine, he said he was flooded with queries about how to participate in the next clinical trial.

What honey bee brain chemistry tells us about human learning

A multi-institutional team of researchers led by Virginia Tech’s Fralin Biomedical Research Institute at VTC has for the first time identified specific patterns of brain chemical activity that predict how quickly individual honey bees learn new associations, offering important insights into the biological basis of learning and decision-making. The study, published in Science Advances, found that the balance between the neurotransmitters octopamine and tyramine can predict whether a bee will learn quickly, slowly, or not at all, as they associate an odor with a reward.

Because the same ancient brain chemicals that guide learning in bees also shape attention and learning in people, the findings may help scientists better understand why individual humans learn at different speeds—and how those processes may go awry in a variety of brain disorders.

Specific patterns of brain chemical activity appear before learning begins and again when a learned behavior first emerges, signaling how quickly an individual bee will learn. The research can help explain how chemicals in the brain drive attention and reinforce learning, with implications for fundamental biology, medicine, and agriculture.

The surprise in physics: two electrons do not become entangled “all at once,” but rather the correlation forms first and then the temporal signature appears in the leak

Physicists show electron entanglement forms over attoseconds, with correlations appearing before timing signatures emerge.

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