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.