When researchers from The University of Texas at Austin went searching for microplastics in sediments pulled from the bottom of Matagorda Bay and its surrounding inlets, they didn’t find much. Most of their samples contained only tens to hundreds of microplastic particles for each kilogram of sediment. This is hundreds to thousands of times less than other bayside environments around the world.

Traditional methods of assessing damage after a disaster can take weeks or even months, delaying emergency response, insurance claims and long-term rebuilding efforts. New research from Texas A&M University might change that. Led by Dr. Maria Koliou, associate professor and Zachry Career Development Professor II in the Zachry Department of Civil and Environmental Engineering at Texas A&M, researchers have developed a new method that combines remote sensing, deep learning and restoration models to speed up building damage assessments and predict recovery times after a tornado. Once post-event images are available, the model can produce damage assessments and recovery forecasts in less than an hour.

Researchers at Toyohashi University of Technology in Japan, in collaboration with the Institute of Translational Medicine and Biomedical Engineering (IMTIB) in Argentina and the Indian Institute of Technology Madras, have advanced the "PDMS SlipChip," a versatile microfluidic device. By using a low-viscosity silicone oil and fine-tuning the fabrication process, they've made the SlipChip more reliable for cell-based experiments and simpler for creating concentration gradients. This breakthrough tackles previous issues like channel clogging and potential harm to cells, opening new avenues for biomedical research, including drug development and sophisticated cell studies.