A new experiment settles a controversy over proton and neutron energies in light nuclei.
Earth was mostly devoid of oxygen for much of its 4.5 billion year lifetime. That is, until certain processes started to allow for the eventual buildup of oxygen up to the levels we have now (around 21% of the atmosphere). While scientists have found evidence of the approximate timescales of rises in oxygen over time and are aware of some of the mechanisms behind it, the main driver behind Earth’s long-term oxygenation is still unclear.
A new study explores whether changes in subduction style—how tectonic plates sink—influenced oxygen levels over time. The study, published in Proceedings of the National Academy of Sciences, points to a process called cold subduction as the main driving factor behind Earth’s rise in oxygen levels, which ultimately led to a more habitable Earth.
The world’s urban population increased by 785 million people between 2000 and 2020, but that tells only part of the story. Now, a research team including an expert from the University of Michigan has dug into the demographics of more than 10,000 individual cities to obtain insights that can be lost in the aggregate. The findings are published in the journal Nature Cities.
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Iqbal et al. show that HflK/C conformational dynamics regulate bacterial adaptation to aminoglycoside stress. Using disulfide crosslinking to constrain the closed state, they demonstrate that stabilizing a closed HflK/C assembly impairs stress recovery and reveal a stress-induced conformation with dual openings that may facilitate FtsH-dependent membrane proteolysis.
New work explaining the inner workings of artificial intelligence could provide a way around the threat of AI “model collapse,” potentially averting growing numbers of AI hallucinations in the future.
First coined in 2024, “model collapse” refers to a scenario where an AI model trained on AI-produced data ceases to provide accurate results, instead producing inaccurate “gibberish” because of the poor quality of its training data.
Some have warned that high-quality text data to train systems like Large Language Models (LLMs) is set to run out as early as this year, and so data produced by models themselves has taken a larger training role—inviting the threat of model collapse.