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• Researchers designed a gene-editing system that replaced up to a quarter of the main type of cell in the mouse liver with edited cells.
• The technique shows promise for treating genetic diseases that require the correction or replacement of defective genes in the liver.

Researchers shocked to see that bacteria’s antimicrobial resistance is strengthened when exposed to plastic particles and point to a potential outsized impact on refugees

Concentrations of lead in the air remain substantial in developed and developing countries, largely the result of industrial emissions. Even with the decline in lead in gasoline, developed countries, including the United States, still have substantial air lead emissions and in part as a result, blood lead levels in children are high worldwide.

In a new study, researchers used data on lead emissions in the air to estimate the effects of air lead concentration on infant mortality. The study found a statistically significant relation between air lead concentration and infant mortality.

A team of scientists at the Indiana University School of Medicine has identified that an eye condition affecting the retina, the light-sensing tissue in the back of the eye, may serve as an early indicator for Alzheimer's disease. Their findings, published in Alzheimer's & Dementia, offer new insights into the potential use of retinal changes as early biomarkers for Alzheimer's, which could improve diagnosis and treatment of neurodegenerative disease.

Researchers racing to develop bird flu vaccines for humans have turned to a cutting-edge technology that enabled the rapid development of lifesaving covid shots.

There’s a catch: The mRNA technology faces growing doubts among Republicans, including people around President Donald Trump.

Researchers showed that bioactive glass administered with gallium oxide in the laboratory can reduce cancer cells and leave noncancerous cells unharmed.

Green lights flickered on the wireless router in Barbara Williams’ kitchen. Just one bar lit up — a weak signal connecting her to the world beyond her home in the Alabama Black Belt.

It affects your mood, your sleep, even your motivation to exercise. There's convincing evidence that it's the starting point for Parkinson's disease and could be responsible for long COVID's cognitive effects. And it sits about 2 feet below your brain.

The gut plays an obvious role in our health by digesting what we eat and extracting nutrients. But there's a growing appreciation among scientists that our digestive systems affect our general well-being in a much broader fashion. One fascinating aspect of the gut's widespread impact on health is its direct influence on and communication with the brain, a conduit known as the gut-brain axis.

Through direct signals from the vagus nerve, connects the brain and the gut, as well as through molecules secreted into the bloodstream from our gut microbes and immune cells that traffic from the gut to the rest of the body, our brains and our digestive tracts are in constant communication. And when that communication goes off the rails, diseases and disorders can result.

Test-time inference has emerged as a powerful paradigm for enabling language models to ``think'' longer and more carefully about complex challenges, much like skilled human experts. While reinforcement learning (RL) can drive self-improvement in language models on verifiable tasks, some models exhibit substantial gains while others quickly plateau. For instance, we find that Qwen-2.5-3B far exceeds Llama-3.2-3B under identical RL training for the game of Countdown. This discrepancy raises a critical question: what intrinsic properties enable effective self-improvement? We introduce a framework to investigate this question by analyzing four key cognitive behaviors -- verification, backtracking, subgoal setting, and backward chaining -- that both expert human problem solvers and successful language models employ. Our study reveals that Qwen naturally exhibits these reasoning behaviors, whereas Llama initially lacks them. In systematic experimentation with controlled behavioral datasets, we find that priming Llama with examples containing these reasoning behaviors enables substantial improvements during RL, matching or exceeding Qwen's performance. Importantly, the presence of reasoning behaviors, rather than correctness of answers, proves to be the critical factor -- models primed with incorrect solutions containing proper reasoning patterns achieve comparable performance to those trained on correct solutions. Finally, leveraging continued pretraining with OpenWebMath data, filtered to amplify reasoning behaviors, enables the Llama model to match Qwen's self-improvement trajectory. Our findings establish a fundamental relationship between initial reasoning behaviors and the capacity for improvement, explaining why some language models effectively utilize additional computation while others plateau.