First global analysis to combine individual child data with household poverty finds that in a third of poor families, some children are undernourished while others are not, and some attend school while others miss out

In a first-of-its-kind collaboration, the World Health Summit Academic Alliance and leading medical journal The Lancet have launched a joint commission that will examine and strengthen the core responsibilities of academic institutions to health research and to society, and work to restore public trust in scientific institutions. The 30-member commission includes Dr. Adnan Hyder, dean and Robert A. Knox Professor at Boston University School of Public Health, who coauthored a new commentary in The Lancet to announce this initiative and call for international, cross-sector collaborations to combat health and science misinformation.

The role of AI in libraries, American history as told to middle-grade students, and library services for older adults are the topics of projects by faculty at the University of Illinois School of Information Sciences that received funding by the Institute of Museum and Library Sciences.

The University at Buffalo is leading a $2 million National Science Foundation project to improve wildfire resilience. The three-year REKINDLE initiative combines artificial intelligence, infrastructure modeling, Earth-observation data, and social science to understand cascading impacts such as debris flows, flash floods, and failures in power, water, and transportation systems. Researchers will study communities in California, develop predictive tools, and examine household responses. The project aims to guide infrastructure investments, emergency planning, recovery efforts, education, and workforce development.

The University of Cincinnati is part of Ohio's $18 million behavioral health scholarship program, offering graduate students in selected fields full tuition in exchange for serving underserved communities after graduation.

The Family Heart Foundation, a leading research, education and advocacy organization, today announced the publication of a new peer-reviewed study in JACC: Advances demonstrating how its FIND Lp(a)^® Machine Learning Model can identify people with atherosclerotic cardiovascular disease (ASCVD) who are more likely to have high lipoprotein(a) — or Lp(a). The model has already been successfully deployed at five large U.S. healthcare systems participating in the FIND Lp(a) Program that aims to catalyze Lp(a) screening. The model’s high level of precision supports targeted Lp(a) screening as a practical approach for healthcare systems working toward implementation of guideline-recommended universal screening.