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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.

 

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

 

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.

 

Scientists turned out at rallies from coast to coast and in Europe to “Stand Up for Science,” revitalizing a movement to defend scientific integrity that started during Trump’s first term.

 

Ambition is a predictor of success. But according to a new study, the motives behind it can also lead to lying and cheating.

 

Lifestyle improvements like adopting a healthy diet or quitting smoking can slow biological aging processes.

 

Large language models (LLMs) have shown remarkable advancements in chemistry and biomedical research, acting as versatile foundation models for various tasks. We introduce AMP-Designer, an LLM-based approach, for swiftly designing antimicrobial peptides (AMPs) with desired properties. Within 11 days, AMP-Designer achieved the de novo design of 18 AMPs with broad-spectrum activity against Gram-negative bacteria. In vitro validation revealed a 94.4% success rate, with two candidates demonstrating exceptional antibacterial efficacy, minimal hemotoxicity, stability in human plasma, and low potential to induce resistance, as evidenced by significant bacterial load reduction in murine lung infection experiments. The entire process, from design to validation, concluded in 48 days. AMP-Designer excels in creating AMPs targeting specific strains despite limited data availability, with a top candidate displaying a minimum inhibitory concentration of 2.0 micrograms per milliliter against Propionibacterium acnes. Integrating advanced machine learning techniques, AMP-Designer demonstrates remarkable efficiency, paving the way for innovative solutions to antibiotic resistance.

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