A $3 million gift from the Norman and Mary Pattiz Foundation will support promising research for Alzheimer’s disease at the Keck School of Medicine of USC, including novel drug discovery and other efforts aimed at early detection and prevention. The newly created Norman and Mary Pattiz Alzheimer’s Research Fund will provide pilot funding and accelerate priority projects of the USC Alzheimer’s Disease Research Center (ADRC). The gift will help fund an interdisciplinary team at the USC Center for Personalized Brain Health (CPBH) which is targeting a family of enzymes that can increase the risk of inflammation in the brains of people with the APOE4 gene variation—the most significant genetic risk factor for late-onset Alzheimer’s—as a way to prevent the disease. In addition to drug targeting which includes imaging modality development, and the use of artificial intelligence to pinpoint small molecules involved in brain inflammation, the fund will also support efforts to modernize the center’s brain pathology library and look at brain inflammation markers in patients with Alzheimer's. Also made possible by the funding will be a plan to create a registry for early detection of individuals at high risk for neuro-inflammation based on the APOE4 genetic variant and cardiovascular risk factors. 

Degraded face videos have long plagued digital communication—from glitchy video calls to blurry archival footage. 

In 1999, the Alfred Wegener Institute launched an ambitious project: the HAUSGARTEN observatory in the Fram Strait between northeast Greenland and Svalbard. Spanning ice-covered waters and extending to depths of more than 5,500 metres, this unique research infrastructure has become a key site for monitoring the impact of climate change on polar marine ecosystems. A special issue of the journal Deep-Sea Research Part II: Topical Studies in Oceanography now brings together key findings from 25 years of research at HAUSGARTEN, underscoring the urgent need for global climate action.

Researchers from the South Dakota School of Mines and Technology have developed a smart detection system that automatically identifies surface defects on aluminum gas meter lids. By combining three different deep learning models, the ensemble approach achieved over 97% accuracy in detecting cracks and cold flows, offering a faster, more reliable alternative to manual inspection that could improve safety and reduce waste in manufacturing.