Scientists have discovered a direct cause-and-effect link between faulty mitochondria and the memory loss seen in neurodegenerative diseases. By creating a novel tool to boost mitochondrial activity in mouse models, researchers restored memory performance, suggesting mitochondria could be a powerful new target for treatments. The findings not only shed light on the early drivers of brain cell degeneration but also open possibilities for slowing or even preventing diseases like Alzheimer’s.
IN A NUTSHELL 🔍 NASA monitors the South Atlantic Anomaly, a region of weakened magnetic intensity impacting satellite operations. 🛰️ The anomaly poses risks to technological systems in spacecraft due to exposure to solar particles. 🧭 The anomaly’s evolution involves dynamic changes and a potential split into two distinct cells. 🌌 Ongoing research explores the
What happened to GUT grand unified theory.
Is our missing antimatter hiding in a mirror universe?
Some scientists think a time-reversed anti-universe exists alongside ours — a place where antimatter rules and their “forward” is our “backwards.” If true, it could solve one of physics’ biggest mysteries.
In this video: the antimatter imbalance, CPT symmetry, and what life in a mirror reality might be like.
Could our missing antimatter be hiding in a parallel, time-reversed universe?
Physicists have long puzzled over one of the biggest mysteries in cosmology: why our universe is made almost entirely of matter, when the Big Bang should have created equal amounts of matter and antimatter. Some theories suggest that the answer lies in a mirror universe — a realm where antimatter dominates and time flows in the opposite direction to ours.
In this episode of Stellar Stories, we explore:
Einstein set the speed of light as the cosmic speed limit, and nothing we do here overturns that. What changed is that researchers—some formerly at NASA, some now in academia and nonprofits—have mapped pieces of a path where spacetime itself does the moving, not the spacecraft.
That’s the idea behind a warp metric: compress space in front, expand it behind, and let the ship surf inside a bubble without breaking local limits. The trick sounds simple until you look at the bill. Classic calculations require negative energy, a substance no lab can supply in macroscopic amounts.
Traditional drug development methods involve identifying a target protein (e.g., a cancer cell receptor) that causes disease, and then searching through countless molecular candidates (potential drugs) that could bind to that protein and block its function. This process is costly, time-consuming, and has a low success rate.
KAIST researchers have developed an AI model that, using only information about the target protein, can design optimal drug candidates without any prior molecular data—opening up new possibilities for drug discovery. The research is published in the journal Advanced Science.
The research team led by Professor Woo Youn Kim in the Department of Chemistry has developed an AI model named BInD (Bond and Interaction-generating Diffusion model), which can design and optimize drug candidate molecules tailored to a protein’s structure alone—without needing prior information about binding molecules. The model also predicts the binding mechanism (non-covalent interactions) between the drug and the target protein.