Yang Zhao receives agInnovation Research Award of Excellence
Grant and Award Announcement
Updates every hour. Last Updated: 15-Aug-2025 15:11 ET (15-Aug-2025 19:11 GMT/UTC)
Yang Zhao, associate professor of animal science and University of Tennessee AgResearch Guthrie Endowed Professor in Precision Livestock Farming, is the winner of the regional 2025 Agricultural Research Innovation Award of Excellence.
The award was presented at the Southern Mini Land-Grant Conference on June 10 in Fayetteville, Arkansas, a meeting for agInnovation South, the coalition of directors of state agricultural experiment stations in Southern states. The group is a regional coalition of the national Association for Public and Land-grant Universities (APLU).
This article provides an overview of recent advancements in tissue engineering and regenerative medicine, highlighting various innovations in biomaterials, therapeutic strategies, and diagnostic technologies. It covers topics such as the development of minimally invasive implantable materials for bone regeneration, the construction of photo-responsive implant materials, the application of artificial ligaments in ACL reconstruction, and the exploration of active components in traditional Chinese medicine for treating osteoporosis.
MIT engineers found that fluid between cells plays a major role in how tissues respond when squeezed, pressed, or physically deformed, potentially influencing how they adapt to conditions such as aging, cancer, diabetes, and certain neuromuscular diseases.
· Cases of bowel cancer are on the rise, and the chemotherapy drugs used to treat most patients haven’t changed in almost 50 years. These drugs eventually stop working for many patients.
· Until now, scientists haven’t understood how resistance to chemotherapy develops.
· New machine learning technology can determine how resistance has developed, which will accelerate the design of new drugs to keep patients well for longer
Researchers from Nanjing University of Science and Technology have developed a novel computational method called BlastGraphNet, which uses graph neural networks to predict the distribution of blast loads on complex 3D buildings. This data-driven approach offers significant improvements in accuracy and efficiency compared to traditional methods, with potential applications in structural design, civil defense, and safety assessments. The study, published in Engineering, demonstrates the model’s ability to provide rapid and precise predictions of blast loads, supporting more effective risk management and engineering solutions.