Lifeboat Foundation

Data is power. According to Dinesh Bharadia, an associate professor at UC San Diego in the Department of Electrical and Computer Engineering with an affiliate appointment in the Department of Computer Science and Engineering and the Qualcomm Institute (QI), “data will be the next decade’s ‘silicon.’”

The rapid growth of the Internet of Things means that data is more readily available and easily accessible than ever. Sensors, “smart” devices and software connect our world to the cloud, gathering information and enabling new types of data sharing and analysis. However, most of these tools are battery-powered and have difficulty sensing changes in real time.

Now, the tide is turning.

Recent research reveals that the immune system interacts with the body’s internal clock, influencing both fat storage and temperature regulation.

The discovery hints at why shift workers and others with irregular work, eating, or sleep patterns driven by the demands of modern life fall out of metabolic sync, and may hold potential for developing therapies to address obesity and prevent wasting.

The key finding—that an immune molecule within adipose (fat) tissue, known as interleukin-17A (IL-17A), plays a regulatory role in fat storage—holds significant therapeutic potential for addressing obesity, preventing wasting, and mitigating other metabolic disorders. By targeting this molecule, drug developers may gain a valuable new pathway for creating treatments aimed at these conditions.

KAIST researchers have developed a groundbreaking single-atom editing technology using light-powered “molecular scissors” to convert oxygen atoms into nitrogen in drug compounds, simplifying drug development and boosting efficacy.

In the field of pioneering drug development, a groundbreaking new technology that enables the precise and rapid editing of key atoms critical to drug efficacy has been hailed as a transformative and “dream” innovation, revolutionizing the process of discovering potential drug candidates. Researchers at KAIST have achieved a world-first by successfully developing single-atom editing technology designed to maximize drug efficacy.

On October 8th, KAIST (represented by President Kwang-Hyung Lee) announced that Professor Yoonsu Park’s research team from the Department of Chemistry successfully developed technology that enables the easy editing and correction of oxygen atoms in furan compounds into nitrogen atoms, directly converting them into pyrrole frameworks, which are widely used in pharmaceuticals.

Gamma radiation converts methane into glycine and other complex molecules. Gamma radiation can convert methane into a wide variety of products at room temperature, including hydrocarbons, oxygen-containing molecules, and amino acids, reports a research team in the journal Angewandte Chemie. This type of reaction probably plays an important role in the formation of complex organic molecules in the universe — and possibly in the origin of life. They also open up new strategies for the industrial conversion of methane into high value-added products under mild conditions.

With these research results, the team led by Weixin Huang at the University of Science and Technology of China (Hefei) has contributed to our fundamental understanding of the early development of molecules in the universe.

“Gamma rays, high-energy photons commonly existing in cosmic rays and unstable isotope decay, provide external energy to drive chemical reactions of simple molecules in the icy mantles of interstellar dust and ice grains,” states Huang.

Databricks is in talks to raise up to $8 billion from investors that would value the data analytics company at $55 billion, in one of the largest fundraises in Silicon Valley.

For every yellow star like our own there are ten times as many smaller stars. Red Dwarfs are the most common type of star, outnumbering all the others combined, and as we head out into interstellar space to colonize the galaxy, the exoplanets around these red alien suns may be the most common home for settlers.

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Continue reading “Colonizing Red Dwarfs” »

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Continue reading “The Core Equation Of Neuroscience” »

Large language models, a type of AI that analyzes text, can predict the results of proposed neuroscience studies more accurately than human experts, finds a study led by UCL (University College London) researchers.

The findings, published in Nature Human Behaviour, demonstrate that large language models (LLMs) trained on vast datasets of text can distill patterns from scientific literature, enabling them to forecast scientific outcomes with superhuman accuracy.

The researchers say this highlights their potential as powerful tools for accelerating research, going far beyond just knowledge retrieval.

Recent studies using advanced supercomputing have focused on the dynamics within copper-based superconductors, aiming to develop materials that are efficient at higher temperatures and could improve electronic devices significantly.

Over the past 35 years, scientists have been studying a remarkable class of materials known as superconductors. When cooled to specific temperatures, these materials allow electricity to flow without any resistance.

A research team utilizing the Summit supercomputer has been delving into the behavior of these superconductors, particularly focusing on how negatively charged particles interact with the smallest units of light within the material. This interaction triggers sudden and dramatic changes in the material’s properties and holds the key to understanding how certain copper-based superconductors function.