Sleep isn't just a time for rest. Growing evidence suggests it may be one of the most powerful factors influencing how well we age, affecting everything from brain health and memory to hospital recovery and long-term independence. For more than two decades, Katie Stone, Ph.D., a sleep researcher at the Center for Aging Science of Sutter Health Research Institute has helped advance understanding of how sleep affects dementia, heart health, falls, fractures and healthy aging. Her research is helping to reshape how scientists think about sleep as a driver of long-term health.

Overhearable speech (speech that may occur when caregivers are talking to each other in the child’s presence), constitutes over half of children’s language exposure, but its role in language development remains underexplored. Researchers from the University of Chicago, The New School for Social Research and Princeton University examined when and how English-learning children growing up in the United States understand words that they overheard from their parents’ conversations at home. The present study focused on word learning and researchers compared children’s recognition of words that parents reported as primarily overhearable to children (i.e., “high-overheardness” words associated with adult talk such as pot, salad, and charger) with words that parents reported as primarily directed to children (i.e., “low-overheardness” words such as zipper, mirror and oatmeal). 

The findings show that 24- and 30-month-olds could recognize high-overheardness words and 18-month-olds were not able to recognize these words. However, 18-month-olds with larger vocabularies by 24 months showed better recognition of high-overheardness words and a smaller recognition gap between high- and low-overheardness words than peers with smaller vocabularies. This suggests that early world learning is dynamic and changes alongside children’s developing language abilities and social experiences. 

The research is featured in a new Child Development article “Developmental change in acquiring overhearable words from naturalistic contexts” by Yuchen Jin (University of Chicago), Ruthe Foushee (The New School for Social Research), Kennedy Casey (Princeton University) and Marisa Casillas (University of Chicago). 

When sudden rainstorms roll across Texas, flooding can erupt in a matter of minutes. From coastal bends facing storm surges to low-water crossings in San Antonio, flood risks often spike block by block long before regional alerts sound. That means some people could have ankle-deep water in their yard by the time a flash flood warning notification sounds on their phone.

But a better monitoring system could get the word out more swiftly — and also more accurately. Researchers at The University of Texas at San Antonio have developed a self-sustaining, artificial intelligence-powered flood warning system designed to spot dangerous water accumulation at the street level.

Led by Chen Pan, PhD, assistant professor of electrical engineering in the Margie and Bill Klesse College of Engineering and Integrated Design, the team engineered a field-ready prototype that combines solar energy harvesting, multi-sensor environmental tracking, long-range wireless radios and on-device machine learning.