New insight into yam disease defense: how leaf traits and ABA stop anthracnose
Peer-Reviewed Publication
Updates every hour. Last Updated: 19-Dec-2025 11:11 ET (19-Dec-2025 16:11 GMT/UTC)
A research team has isolated the anthracnose pathogen infecting greater yam and identified it as Colletotrichum alatae—the first report of this species in yam.
Carlos Moreno Yruela, who is currently a researcher at the Swiss Federal Institute of Technology in Lausanne, Switzerland, was selected in the ERC Starting Grants call to develop the CHEMTUBIO project at the Institute for Bioengineering of Catalonia (IBEC). The project will study the chemistry of enzymes that erase microtubule modifications. These enzymes are essential for the functioning of our cells and have shown great promise as potential therapeutic targets for treating cancer, heart disease and neurological disorders.
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.