A groundbreaking study reveals how neurons sense and transmit mechanical forces across their membranes—a key to understanding touch, movement, and development.
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Why Some 80-Year-Olds Have the Memory of 50-Year-Olds
For 25 years, scientists have studied “SuperAgers”—people aged 80 and above whose memory rivals those decades younger. Research reveals that their brains either resist Alzheimer’s-related plaques and tangles or remain resilient despite having them.
These individuals maintain a youthful brain structure, with a thicker cortex and unique neurons linked to memory and social skills. Insights from their biology and behavior could inspire new strategies to protect cognitive health into late life.
For the past 25 years, researchers at Northwestern Medicine have been examining people aged 80 and older, known as “SuperAgers,” to uncover why their minds stay so sharp.
Ultracompact semiconductor could power next-gen AI and 6G chips
A research team, led by Professor Heein Yoon in the Department of Electrical Engineering at UNIST has unveiled an ultra-small hybrid low-dropout regulator (LDO) that promises to advance power management in advanced semiconductor devices. This innovative chip not only stabilizes voltage more effectively, but also filters out noise—all while taking up less space—opening new doors for high-performance system-on-chips (SoCs) used in AI, 6G communications, and beyond.
The new LDO combines analog and digital circuit strengths in a hybrid design, ensuring stable power delivery even during sudden changes in current demand—like when launching a game on your smartphone—and effectively blocking unwanted noise from the power supply.
What sets this development apart is its use of a cutting-edge digital-to-analog transfer (D2A-TF) method and a local ground generator (LGG), which work together to deliver exceptional voltage stability and noise suppression. In tests, it kept voltage ripple to just 54 millivolts during rapid 99 mA current swings and managed to restore the voltage to its proper level in just 667 nanoseconds. Plus, it achieved a power supply rejection ratio (PSRR) of −53.7 dB at 10 kHz with a 100 mA load, meaning it can effectively filter out nearly all noise at that frequency.
Fruit flies offer new insights into how Alzheimer’s disease risk genes affect the brain
Scientists have identified hundreds of genes that may increase the risk of developing Alzheimer’s disease but the roles these genes play in the brain are poorly understood. This lack of understanding poses a barrier to developing new therapies, but in a study published in the American Journal of Human Genetics, researchers at Baylor College of Medicine and the Jan and Dan Duncan Neurological Research Institute (Duncan NRI) at Texas Children’s Hospital offer new insights into how Alzheimer’s disease risk genes affect the brain.
“We studied fruit fly versions of 100 human Alzheimer’s disease risk genes,” said first author Dr. Jennifer Deger, a neuroscience graduate in Baylor’s Medical Scientist Training Program (M.D./Ph. D.), mentored by Drs. Joshua Shulman and Hugo Bellen.
“We developed fruit flies with mutations that ‘turned off’ each gene and determined how this affected the fly’s brain structure, function and stress resilience as the flies aged.”
Machine learning enables real-time analysis of iron oxide thin film growth in reactive magnetron sputtering
Researchers at University of Tsukuba have developed a technology for real-time estimation of the valence state and growth rate of iron oxide thin films during their formation. This novel technology was realized by analyzing the full-wavelength data of plasma emission spectra generated during reactive sputtering using machine learning. It is expected to enable high-precision control of the film deposition process.
Metal oxide and nitride thin films are commonly used in electronic devices and energy materials. Reactive sputtering is a versatile technique for depositing thin films by reacting a target metal with gases such as oxygen or nitrogen. A challenge with this process is the transitioning of the target surface between metallic and compound states, causing large fluctuations in film growth rate and composition. At present, there are limited effective methods for real-time monitoring of a material’s chemical state and deposition rate during film formation.
A machine learning technique based on principal component analysis was employed to examine massive emission spectra generated within a reactive sputter plasma. This analysis focused on assessing the state of thin film formation. The results, published in Science and Technology of Advanced Materials: Methods, indicated that the valence state of iron oxide thin films was accurately identified using only the first and second principal components of the spectra. In addition, the film growth rate was predicted with high precision.