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.
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.
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.
Researchers at the University of Colorado Anschutz Medical Campus, Montefiore Einstein Comprehensive Cancer Center (MECCC), and Utrecht University have found the first direct evidence that common respiratory infections, including COVID-19 and influenza, can awaken dormant breast cancer cells that have spread to the lungs, setting the stage for new metastatic tumors. The findings published today in Nature, obtained in mice, were supported by research showing increases in death and in metastatic lung disease among cancer survivors infected with SARS-CoV-2, the virus that causes COVID-19.
Half a billion years ago, nature evolved a remarkable trick: generating vibrant, shimmering colors via intricate, microscopic structures in feathers, wings and shells that reflect light in precise ways. Now, researchers from Trinity have taken a major step forward in harnessing it for advanced materials science.
A team led by Professor Colm Delaney from Trinity’s School of Chemistry and AMBER, the Research Ireland Center for Advanced Materials and BioEngineering Research, has developed a pioneering method, inspired by nature, to create and program structural colors using a cutting-edge microfabrication technique.
The work could have major implications for environmental sensing, biomedical diagnostics, and photonic materials. The research is published in the journal Advanced Materials.
Scientists at Columbia Engineering have developed an injectable hydrogel made from yogurt-derived extracellular vesicles (EVs) that could revolutionize regenerative medicine. These EVs serve both as healing agents and as structural components, eliminating the need for added chemicals. The innovation leverages everyday dairy products like yogurt to create a biocompatible material that mimics natural tissue and enhances healing.
It happens only once every 100 million decays of the Σ+ particle.
npj Science of Learning — A qualitative systematic review on AI empowered self-regulated learning (SRL) in higher education. Aiming to synthesize empirical studies, we employed a qualitative approach to scrutinize AI’s role in supporting SRL processes. Through a meticulous selection process adhering to PRISMA guidelines, we identified 14 distinct studies that leveraged AI applications, including chatbots, adaptive feedback systems, serious games, and e-textbooks, to support student autonomy. Our findings reveal a nuanced landscape where AI demonstrates potential in facilitating SRL’s forethought, performance, and reflection phases, yet also highlights whether the agency is human-centered or AI-centered leading to variations in the SRL model. This review underscores the imperative for balanced AI integration, ensuring technological advantages are harnessed without undermining student self-efficacy. The implications suggest a future where AI is a thoughtfully woven thread in the SRL fabric of higher education, calling for further research to optimize this synergy.
