Researchers at the University of Pittsburgh School of Medicine have now merged synthetic biology with a machine-learning algorithm that can create human liver organoids with blood- and bile-handling systems. When these were implemented in mice who had liver failure, it extended their life span. The researchers used machine learning and genetic engineering techniques to grow the livers at a much faster pace compared to previous development.
The IRST21® (Infrared Search and Track) sensor suite, named Legion-ES™ (Embedded System) is the updated sensor suite of the F-16 recently unveiled by Lockheed Martin. The tech is designed for the F-16 Block 70/72 and promises to further increase capabilities to detect and track airborne threats with weapon-quality accuracy.
Researchers at Rice University have identified printing speed as the most important metric in the development of 3-D-printed bio scaffolds that help injuries heal. The study shows that artificial intelligence can greatly improve techniques in developing bio scaffolds, the bonelike structures that serve as placeholders for injured tissue, and in the healing of craniofacial and musculoskeletal wounds. With the help of machine learning techniques, designing materials, and developing processes in creating implants can be faster and eliminate much trial and error. The study led by computer scientist Lydia Kavraki of Rice’s Brown School of Engineering has successfully shown that guided AI and controlled printing speed is critical in making high-quality implants.
Source: https://techxplore.com/news/2020-09-machine-bioscaffold.html
