Variability in sleep patterns in people experiencing pregnancy can effectively predict preterm birth, according to new research from WashU. The study was based on data from 665 patients in the first two trimesters of their pregnancy, all wearing a clinically validated wristwatch that allowed the research team to extract daily patterns in the length of sleep, what time the patients went to sleep and woke up, their movement during sleep and several other variables. A machine-learning model is better than a more statistical model for making predictions about preterm birth.

TUdi's DSTs are designed to help farmers monitor and assess soil health at the field level and they're now officially available! 

ITHACA, N.Y. – Taking carbon out of the atmosphere is essential for slowing global warming – and a team of Cornell University researchers has estimated “huge” potential for carbon capture using a method that is low-tech, sustainable and relatively simple: burying wood, especially the debris from managed forests.

When artificial intelligence (AI) analyses images, it can be difficult for humans to understand how the AI classifies the individual image elements. To understand, for example, why the AI diagnoses a discrepancy as a certain disease in medical images, it is helpful if the AI explains how it reached its conclusion. The computer scientist Zeynep Akata conducts research in the area of explainable AI and develops AI that combines visual, linguistic, and conceptual elements, and thus makes its decisions comprehensible to humans. In recognition of her achievements, the German National Academy of Sciences Leopoldina and the Commerzbank Foundation honour Akata with the “ZukunftsWissen” Award. The award, endowed with 50,000 euros, will be presented to her on Thursday, 25 September 2025 in Halle (Saale)/Germany at the Leopoldina Annual Assembly, which this year focuses on the topic of AI.