Epilepsy is one of the most common neurological diseases globally, affecting around 50 million people worldwide. Fortunately, up to 70 percent of people with epilepsy could live seizure-free if properly diagnosed and treated, and a reliable technique to monitor the onset of seizures could improve the quality of life of patients who are constantly facing the fear of random seizure attacks. The scalp-based EEG test, despite being the gold standard for diagnosing epilepsy, is costly, necessitates hospitalization, demands skilled professionals for operation, and is discomforting for users. In this paper, we propose EarSD, a novel lightweight, unobtrusive, and socially acceptable ear-worn system to detect epileptic seizure onsets by measuring the physiological signals from behind the user's ears. EarSD includes an integrated custom-built sensing, computing, and communication PCB to collect and amplify the signals of interest, remove the noises caused by motion artifacts and environmental impacts, and stream the data wirelessly to the computer or mobile phone nearby, where data are uploaded to the host computer for further processing. We conducted both in-lab and in-hospital experiments with epileptic seizure patients who were hospitalized for seizure studies. The preliminary results confirm that EarSD can detect seizures with up to 95.3 percent accuracy by just using classical machine learning algorithms.

Nineteen protesters were arrested at the Kentucky Capitol on Wednesday amid a protest against a measure that would ban certain gender care for minors. Documents obtained by Fox News Digital show that University of Texas Southwestern medical students are being taught that gender is independent of physical structure. Fox News Digital obtained the documents via a FOIA request from Do No Harm, a national association of medical professionals that combats "woke" activism in the healthcare system. According to the University of Texas Southwestern Medical Center's Human Structure curriculum, they "explicitly acknowledge the differentiation between the terms sex and gender." RACHEL LEVINE SAYS CHANGING KIDS' GENDERS WILL SOON BE FULLY EMBRACED: 'WHEELS WILL TURN ON THIS' "The latter is a psychological, social, and cultural construct, including self-identification. Gender is independent of physical structure, chromosomes, or genes," curriculum materials read.

Scientist are now combining recent advances in evolutionary analysis and deep learning to build three-dimensional models of how most proteins in eukaryotes interact. The research effort has implications for understanding the biochemical processes that are common to all animals, plants, and fungi. The open-access work appears Nov. 11 in Science. As part of a multi-institutional collaboration, the lab of David Baker at the UW Medicine Institute for Protein Design helped guide this new development. "To really understand the cellular conditions that give rise to health and disease, it's essential to know how different proteins in a cell work together," Baker said.

A team of researchers from Ben Gurion University and the University of Texas Southwestern Medical Center at Dallas have developed a method to determine which tumor cells are most likely to metastasize efficiently to distant sites in the body. An interdisciplinary research team headed by one of BGU's newest faculty members, Dr. Assaf Zaritsky of the Department of Software and Information Systems Engineering, and Gaudenz Danuser of the University of Texas Southwestern Medical Center (UTSW) at Dallas, has developed a groundbreaking method based on artificial intelligence to identify melanoma cells that are likely to metastasize to other parts of the body. The method, called "quantitative live cell histology", was presented at the American Society for Cell Biology / EMBO conference in San Diego last December. It was developed during Dr. Zaritsky's postdoctoral studies at UTSW. The approach calls for filming live cancer cells with microscopic cameras and using artificial intelligence to analyze the video sequence and identify cells' appearance and behavioral patterns that associate with metastatic potential.