A UT Arlington chemist doing National Science Foundation-funded research on enzymes that regulate human biology has uncovered characteristics that could be used to identify predisposition to conditions such as heart disease, diabetic ulcers and some types of cancer.
Shorebird's Beak Inspires UT Arlington Research on Water Collection
A UT Arlington engineering professor and his doctoral student have designed a device based on a shorebird's beak that can accumulate water collected from fog and dew. The device could provide water in drought-stricken areas of the world or deserts around the globe.
UT Arlington Research Uses Nanotechnology to Help Cool Electrons with No External Sources
A team of researchers has discovered a way to cool electrons to −228 °C without external means and at room temperature, an advancement that could enable electronic devices to function with very little energy. The process involves passing electrons through a quantum well to cool them and keep them from heating.
Teens Living with 2 College-educated Parents Less Likely to Use Alcohol And Marijuana
ARLINGTON, Texas -- A high school senior who lives with two college-educated parents is significantly less likely to drink alcohol or smoke marijuana than a teenager who lives with one parent, a new University of Texas at Arlington study has found. For example, teens living with their mother only are 54 percent more likely to use alcohol, and 58 percent more likely to smoke if they live only with their father.
Older Coral Species More Hardy, UT Arlington Biologists Say
New research indicates older species of coral have more of what it takes to survive a warming and increasingly polluted climate, according to biologists from the University of Texas at Arlington and the University of Puerto Rico – Mayagüez. The researchers examined 140 samples of 14 species of Caribbean corals for a study published by the open-access journal PLOS ONE on Aug. 18.
UT Arlington Team's Work Could Lead to Earlier Diagnosis, Treatment of Mental Diseases
A computer science and engineering associate professor and her doctoral student graduate are using a genetic computer network inference model that eventually could predict whether a person will suffer from bipolar disorder, schizophrenia or another mental illness.