Facing a growing migration crisis in 2021, the Polish government declared a state of emergency along its border with Belarus, suspending basic civil liberties for citizens. A new study in The Journal of Politics finds evidence that the action avoided politcal backlash from voters, indicating citizens will tolerate civil restrictions if they feel it is worth the outcome.

A team of researchers led by two University of Mississippi professors is working to unlock patterns and causes of the No. 1 killer of Mississippians: heart disease.

True or false?

“It is safe to take an over-the-counter medicine to help you sleep, even if you are drunk on alcohol.”

“Driving while high on THC (cannabis) is safe.”

“Using psychedelics is safe for everyone.”

              None of those statements is true. But young men who take a passive approach to news and information—consuming whatever flows over their social media transoms—were likely to believe them in a national survey conducted by Washington State University researchers.

Recent climate-related crises — from severe storms and flooding to extreme heat — have raised new questions about how local governments communicate the risk of these crises and what they are doing to keep their citizens safe. To better understand what this communication looks like at local level, and the factors that may be shaping it, researchers from Drexel University analyzed climate resilience planning information available on the public-facing websites of 24 coastal communities in New Jersey that are contending with the effects of sea level rise. Their report, recently published in the Journal of Extreme Events, found wide variation in the number and extent of mitigation actions taken and how the websites describe causes of coastal hazards — for example, only half of the communities are acknowledging sea level rise as a contributing factor to these hazards.

About The Study: Transparency of evidence supporting Food and Drug Administration (FDA) authorization of artificial intelligence (AI)- and machine learning -based devices for Alzheimer disease and related dementias was limited, precluding effective evaluation of training and validation dataset representativeness. Disease status (i.e., dementia type and distribution), age, and sex were reported for fewer than half of devices, while race and ethnicity data were rarely disclosed, raising uncertainty about real-world generalizability and clinical accuracy in intended populations.