There are a half dozen refrigerator-sized cabinets inside a lab at Stanford University's Precourt Institute for energy that is designed for killing bacteria as quickly as possible. Each contains around 100 lithium-ion cells in trays in which the batteries could be charged and discharged dozens of times each day. The batteries used in these electrochemical torture chambers would normally be found in electronics or electric vehicles. Instead, energy is transported in and out of these cells as quickly as possible, generating reams of performance data that artificial intelligence can use to learn how to make a better battery. To estimate how a battery would perform in the future, AI would require data from a battery after it had begun to degrade. It could take months to cycle the battery enough times to get the required data.

Whether for squats or sit-ups, the software created by the start-up VAY alerts exercisers about incorrect movements via a smartphone screen.

Scientists at Case Western Reserve University have used artificial intelligence (AI) to identify new biomarkers for breast cancer that can predict whether the cancer will return after treatment -- and which can be identified from routinely acquired tissue biopsy samples of early-stage breast cancer. The key to that initial determination is collagen, a common protein found throughout the body, including in breast tissue. Previous research had suggested that the collagen network, or arrangement of the fibers, relates strongly to breast cancer aggressiveness. But this work by Case Western Reserve researchers definitively demonstrated collagen's critical role -- using only standard tissue biopsy slides and AI. The researchers, using machine-learning technology to analyze a dataset of digitized tissue samples from breast cancer patients, were able to prove that a well-ordered arrangement of collagen is a key prognostic biomarker for an aggressive tumor and a likely recurrence.

Scientists at Case Western Reserve University have used Artificial Intelligence (AI) to identify new biomarkers for breast cancer that can predict whether the cancer will return after treatment--and which can be identified from routinely acquired tissue biopsy samples of early-stage breast cancer. The key to that initial determination is collagen, a common protein found throughout the body, including in breast tissue. Previous research had suggested that the collagen network, or arrangement of the fibers, relates strongly to breast cancer aggressiveness. But this work by Case Western Reserve researchers definitively demonstrated collagen's critical role--using only standard tissue biopsy slides and AI. The researchers, using machine-learning technology to analyze a dataset of digitized tissue samples from breast cancer patients, were able to prove that a well-ordered arrangement of collagen is a key prognostic biomarker for an aggressive tumor and a likely recurrence.

They used AI to establish cause and effect of edge erosion of a turbine blade, simulated potential fixes and determined the best solution. The researchers then used 3D printing to create a material capable of hardening under mechanical stress and which is more resistant to edge erosion. Leading edge erosion, which can be caused by rain and sea water, affects turbine production. Repairs and replacements are costly and result in long periods of downtime. "People's ability to perceive is not enough to see all the dimensions involved in optimizing the material solution.

An artificial intelligence program developed by Weill Cornell Medicine and NewYork-Presbyterian researchers can distinguish types of cancer from images of cells with almost 100 percent accuracy, according to a new study. This new technology has the potential to augment cancer diagnosis techniques that currently require the human eye. Currently, cancer is diagnosed by visual examination of tissue samples under a microscope. Pathologists consider variables like cell shape, number, mass and appearance when determining whether tissue appears malignant or benign. While accurate analysis is critical to making the right diagnosis, the process can become complicated. "The diversity among cancer cells is very high," said co-senior author Dr. Olivier Elemento, director of the Caryl and Israel Englander Institute for Precision Medicine at Weill Cornell Medicine, who also leads joint precision medicine efforts at Weill Cornell Medicine and NewYork-Presbyterian/Weill Cornell Medical Center.

An international team of scientists are using data on genetic material, cell surface texture and typical facial features derived by artificial intelligence methods to simulate disease models for deficiencies in the molecule glycosylphosphatidylinositol (GPI) anchor, which is known to cause various diseases. One of the diseases is Mabry syndrome, a rare disease that is triggered by a change in a single gene, causing mental retardation. "This disease belongs to a group that we describe as GPI anchor deficiencies and which includes more than 30 genes," physician and physicist Dr. Peter Krawitz from the Institute for Genome Statistics and Bioinformatics of the University Hospital Bonn, said in a statement. GPI anchors attach specific proteins to the cell membrane and if they do not properly function due to a gene mutation, signal transmission and further steps in the cell-cell communication are impaired. The researchers investigated how a diagnosis of GPI anchor deficiencies can be improved with modern and fast DNA sequencing methods, cell surface analysis and computer aided image recognition.

An international team of scientists are using data on genetic material, cell surface texture and typical facial features derived by artificial intelligence methods to simulate disease models for deficiencies in the molecule glycosylphosphatidylinositol (GPI) anchor, which is known to cause various diseases.