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Researchers develop flexible fiber material for self-powered health-monitoring sensors

Could clothing monitor a person’s health in real time, because the clothing itself would be a self-powered sensor? A new material created through electrospinning, which is a process that draws out fibers using electricity, brings this possibility one step closer.

A team led by researchers at Penn State has developed a new fabrication approach that optimizes the internal structure of electrospun fibers to improve their performance in electronic applications. The team has published its findings in the Journal of Applied Physics.

This novel electrospinning approach could open the door to more efficient, flexible and scalable electronics for wearable sensors, health monitoring and sustainable energy harvesting, according to Guanchun Rui, a visiting postdoctoral student in the Department of Electrical Engineering and the Materials Research Institute and co-lead author of the study.

Cyberpunk: Envisioning Possible Futures Through Cinema

Paris theater — cyberpunk: envisioning possible futures through cinema.

This series was programmed to accompany the museum’s exhibition Cyberpunk: Envisioning Possible Futures Through Cinema.

Tickets are available below for purchase by the public, while Academy members can request tickets at membership.oscars.org.

New computer program mimics cell behavior for faster medical discoveries

Using mathematical analysis of patterns of human and animal cell behavior, scientists say they have developed a computer program that mimics the behavior of such cells in any part of the body. Led by investigators at Indiana University, Johns Hopkins Medicine, the University of Maryland School of Medicine and Oregon Health & Science University, the new work was designed to advance ways of testing and predicting biological processes, drug responses and other cell dynamics before undertaking more costly experiments with live cells.

With further work on the program, the researchers say it could eventually serve as a “digital twin” for testing any drug’s effect on cancer or other conditions, gene environment interactions during brain development, or any number of dynamic cellular molecular processes in people where such studies are not possible.

Funded primarily by the Jayne Koskinas Ted Giovanis Foundation and the National Institutes of Health, and leveraging prior knowledge and data funded by the Lustgarten Foundation and National Foundation for Cancer Research, the new study and examples of cell simulations are described online July 25 in the journal Cell.

Not Big Bang, new theory uses ‘Gravity’ and ‘Quantum Physics’ to explain the universe’s birth

A groundbreaking study from the Universities of Barcelona and Padua challenges the inflation theory, suggesting the universe began from a stable De Sitter space, driven by gravity and quantum mechanics alone. This model explains the formation of cosmic structures through quantum fluctuations evolving into gravitational waves, offering a simpler, testable alternative to the Big Bang’s fiery start.

Building electronics that don’t die: Columbia’s breakthrough at CERN

Deep beneath the Swiss-French border, the Large Hadron Collider unleashes staggering amounts of energy and radiation—enough to fry most electronics. Enter a team of Columbia engineers, who built ultra-rugged, radiation-resistant chips that now play a pivotal role in capturing data from subatomic particle collisions. These custom-designed ADCs not only survive the hostile environment inside CERN but also help filter and digitize the most critical collision events, enabling physicists to study elusive phenomena like the Higgs boson.

Natural Molecule Shows Remarkable Anti-Aging Results After Just 28 Days

A clinical study shows that pterostilbene, a natural compound, significantly improves signs of aging in the skin, pointing to a new direction in skincare science. As interest in anti-aging skincare continues to surge, scientists in China have conducted a clinical trial to test the effectiveness o

EpInflammAge: Epigenetic-Inflammatory Clock for Disease-Associated Biological Aging Based on Deep Learning

We present EpInflammAge, an explainable deep learning tool that integrates epigenetic and inflammatory markers to create a highly accurate, disease-sensitive biological age predictor. This novel approach bridges two key hallmarks of aging—epigenetic alterations and immunosenescence. First, epigenetic and inflammatory data from the same participants was used for AI models predicting levels of 24 cytokines from blood DNA methylation. Second, open-source epigenetic data (25 thousand samples) was used for generating synthetic inflammatory biomarkers and training an age estimation model. Using state-of-the-art deep neural networks optimized for tabular data analysis, EpInflammAge achieves competitive performance metrics against 34 epigenetic clock models, including an overall mean absolute error of 7 years and a Pearson correlation coefficient of 0.85 in healthy controls, while demonstrating robust sensitivity across multiple disease categories. Explainable AI revealed the contribution of each feature to the age prediction. The sensitivity to multiple diseases due to combining inflammatory and epigenetic profiles is promising for both research and clinical applications. EpInflammAge is released as an easy-to-use web tool that generates the age estimates and levels of inflammatory parameters for methylation data, with the detailed report on the contribution of input variables to the model output for each sample.