As electrodes, the harvester uses a carbon nanomaterial called graphene, layered with modified human proteins. The electrodes collect energy from the human body, relay it to the harvester, which then stores it for later use. Because graphene sheets can be drawn in sheets as thin as a few atoms, this allows for the creation of utra-thin supercapacitors that could be used as alternatives to classic batteries. For example, the bio-friendly supercapacitors researchers created are thinner than a human hair, and are also flexible, moving and twisting with the human body.
"It's suggesting that the child itself has something wrong with it, genetically, and that it has monetary value attached to it," Todd Kuiken, a senior research scholar with the Genetic Engineering and Society Center at North Carolina State University, told Gizmodo. "They attached damages to the genetic makeup of the child, rather than the mistake. That's the part that makes it uncomfortable. This can take you in all sort of fucked up directions."
But in a related story, Slashdot reader sciencehabit writes that four machine-learning algorithms all performed better than currently-used algorithm of the American College of Cardiology, according to newly-published research, which concludes that "machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others."
"I can't stress enough how important it is," one Stanford vascular surgeon told Science magazine, "and how much I really hope that doctors start to embrace the use of artificial intelligence to assist us in care of patients."