ERC starting grant for Anna Czarkwiani to study gravity sensing
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Updates every hour. Last Updated: 14-Jan-2026 20:11 ET (15-Jan-2026 01:11 GMT/UTC)
A research team presents the transcriptomic analysis of pearl millet, a highly resilient cereal, revealing how this crop adapts to high temperature, drought, and salt stress.
A research team identified nearly 7,000 phosphorylation sites in close to 2,800 proteins and revealed distinct regulatory patterns tied to growth or dormancy.
A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms—addressing a critical issue that can affect diagnostic accuracy and treatment decisions. The findings were published in the September 4 online issue of the Journal of Medical Internet Research [DOI: 10.2196/71757]. To tackle the problem, the investigators developed AEquity, a tool that helps detect and correct bias in health care datasets before they are used to train artificial intelligence (AI) and machine-learning models. The investigators tested AEquity on different types of health data, including medical images, patient records, and a major public health survey, the National Health and Nutrition Examination Survey, using a variety of machine-learning models. The tool was able to spot both well-known and previously overlooked biases across these datasets.
A new peer-reviewed study from the Environmental Working Group finds that advanced PFAS filtration systems not only remove toxic "forever chemicals" from drinking water but also significantly reduce other harmful contaminants. These include cancer-linked disinfection byproducts, agricultural nitrates, and heavy metals like arsenic and uranium. The study, published in ACS ES&T Water, analyzed data from 19 U.S. utilities and the EPA’s national monitoring program, showing that technologies like granular activated carbon, ion exchange, and reverse osmosis offer broader public health benefits than previously recognized.