Lifeboat Foundation

Scientists have built an artificial motor capable of mimicking the natural mechanisms that power life. Just like the proteins in our muscles, which convert chemical energy into power to allow us to perform daily tasks, these tiny rotary motors use chemical energy to generate force, store energy, and perform tasks in a similar way.

The finding, from The University of Manchester and the University of Strasbourg and published in the journal Nature, provides new insights into the fundamental processes that drive life at the molecular level and could open doors for applications in medicine, energy storage, and nanotechnology.

“Biology uses chemically powered molecular machines for every biological process, such as transporting chemicals around the cell, information processing or reproduction. By replicating nature at the nanoscale level, we can design entirely new materials with highly specific functions that don’t exist in the natural world. Building this outside of nature also gives us greater simplicity and control over its functions and uses,” said Professor David Leigh, lead researcher from The University of Manchester.

If Earth’s life survives the Anthropocene, it will eventually face another existential threat from space.

As the Sun brightens with age, it will inevitably interfere with our planet’s finicky carbon cycle, triggering a depletion of atmospheric carbon dioxide to the point where plants will starve.

Luckily, this won’t happen until at least 1.6 billion years from now, suggests new research from University of Chicago geophysicist RJ Graham and colleagues. That potentially doubles the projected lifespan of Earth’s plants and animals.

Cosmic voids, which act as bubbles in the cosmic web, help us read the universe better.

Researchers have found a link between a common virus and Alzheimer’s disease.

Alzheimer’s disease, which is the most common type of dementia, is a condition that affects the brain, makes changes in it that cause problems with memory, thinking, and behaviour.

Continue reading “Scientists discover common virus could be causing Alzheimer’s disease” »

Speaking on the sidelines of an event hosted by chip supplier Siliconware Precision Industries in Taichung, Taiwan, Huang explained the transition in Nvidia’s chip packaging requirements. “As we move into Blackwell, we will use largely CoWoS-L. Of course, we’re still manufacturing Hopper, and Hopper will use CoWoS-S. We will also transition the CoWoS-S capacity to CoWoS-L,” he stated.

Huang emphasized that this shift does not indicate a reduction in capacity but rather an increase in capacity for CoWoS-L technology. “So it’s not about reducing capacity. It’s actually increasing capacity into CoWoS-L,” he said.

CoWoS-L (Chip-on-Wafer-on-Substrate with Local Silicon Interconnect) represents a significant advancement over CoWoS-S in terms of performance and efficiency for high-end computing applications like AI and HPC.

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Continue reading “Gut Health Impacts The Thymus And Immune System During Aging: Niharika Duggal, PhD” »

Existing computer systems have separate data processing and storage devices, making them inefficient for processing complex data like AI. A KAIST research team has developed a memristor-based integrated system similar to the way our brain processes information. It is now ready for application in various devices, including smart security cameras, allowing them to recognize suspicious activity immediately without having to rely on remote cloud servers, and medical devices with which it can help analyze health data in real time.

The joint research team of Professor Shinhyun Choi and Professor Young-Gyu Yoon of the School of Electrical Engineering has developed the next-generation neuromorphic semiconductor-based ultra-small computing chip that can learn and correct errors on its own. The research is published in the journal Nature Electronics.

What is special about this computing chip is that it can learn and correct errors that occur due to non-ideal characteristics that were difficult to solve in existing neuromorphic devices. For example, when processing a video stream, the chip learns to automatically separate a moving object from the background, and it becomes better at this task over time.

An international team of researchers has made significant progress in understanding how gene expression is regulated across the human genome. In a recent study, they conducted a comprehensive analysis of cis-regulatory elements (CREs)—DNA sequences that control gene transcription. This research provides valuable insights into how CREs drive cell-specific gene expression and how mutations in these regions can impact health and contribute to disease.

CREs, such as enhancers and promoters, play a critical role in determining when and where genes are activated or silenced. Although their importance is well known, analyzing their activity on a large scale has been a longstanding challenge.

“The human genome contains a myriad of CREs, and mutations in these regions are thought to play a major role in human diseases and evolution,” explained Dr. Fumitaka Inoue, one of the co-first authors of the study. “However, it has been very difficult to comprehensively quantify their activity across the genome.”

For over 20 years, the Ihara research group at Ehime University has specialized in developing innovative methods for polymer synthesis using diazocarbonyl compounds as monomers.

They discovered that diazoacetate can be polymerized using a palladium (Pd)-based initiator to produce carbon–carbon (C–C) main-chain polymers, with each carbon atom in the backbone bonded to an alkoxycarbonyl (ester) group. Unlike traditional vinyl polymerization—where the polymer backbone is built from two-carbon units of vinyl monomers like ethylene and styrene—diazoacetate polymerization creates the C–C main chain from single-carbon units. This unique process, known as C1 polymerization, is a distinctive and significant feature of this synthesis method.

A new era in computing is emerging as researchers overcome the limitations of Moore’s Law through photonics.

This cutting-edge approach boosts processing speeds and slashes energy use, potentially revolutionizing AI and machine learning.

Machine learning is a subset of artificial intelligence (AI) that deals with the development of algorithms and statistical models that enable computers to learn from data and make predictions or decisions without being explicitly programmed to do so. Machine learning is used to identify patterns in data, classify data into different categories, or make predictions about future events. It can be categorized into three main types of learning: supervised, unsupervised and reinforcement learning.