Lifeboat Foundation: Safeguarding Humanity
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Blog – Page 6

Aging depletes the brain’s protective sugar shield, weakening defenses and fueling cognitive decline, but restoring key sugars may reverse these effects.

What if a critical piece of the puzzle of brain aging has been hiding in plain sight? While neuroscience has traditionally focused on proteins and DNA

DNA, or deoxyribonucleic acid, is a molecule composed of two long strands of nucleotides that coil around each other to form a double helix. It is the hereditary material in humans and almost all other organisms that carries genetic instructions for development, functioning, growth, and reproduction. Nearly every cell in a person’s body has the same DNA. Most DNA is located in the cell nucleus (where it is called nuclear DNA), but a small amount of DNA can also be found in the mitochondria (where it is called mitochondrial DNA or mtDNA).

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(Yicai) March 3 — China Fusion Energy, a state-owned pioneer in an experimental technology to produce unlimited amounts of clean energy by replicating processes of the sun, has gained almost CNY1.8 billion (USD240.3 million) in investment from two major power companies.

China Nuclear Power, another affiliate of Beijing-headquartered China National Nuclear Corporation, invested CNY1 billion into Fusion Energy, while Zhejiang province-based thermal power giant Zheneng Electric Power allocated CNY750 million (USD102.8 million), the two investors announced recently.

After these transactions, CNNC remains the largest shareholder of Fusion Energy, which is expected to receive more investment from state-owned enterprises in the future.

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Materials are known to interact with electromagnetic fields in different ways, which reflect their structures and underlying properties. The Lyddane-Sachs-Teller relation is a physics construct that describes the relationship between a material’s static and dynamic dielectric constant (i.e., values indicating a system’s behavior in the presence or absence of an external electric field, respectively) and the vibrational modes of the material’s crystal lattice (i.e., resonance frequencies).

This construct, first introduced by physicists Lyddanne, Sachs and Teller in 1941, has since been widely used to conduct solid-state physics research and materials science studies. Ultimately, it has helped better explain and delineate the properties of various materials, which were then used to create new electronic devices.

Researchers at Lund University recently extended the Lyddane-Sachs-Teller relation to magnetism, showing that a similar relation links a material’s static permeability (i.e., its non-oscillatory response to a ) to the frequencies at which it exhibits a . Their paper, published in Physical Review Letters, opens new exciting possibilities for the study of magnetic materials.

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Researchers at the Arc Institute, Stanford University, and NVIDIA have developed Evo 2, an advanced AI model capable of predicting genetic variations and generating genomic sequences across all domains of life.

Testing shows that Evo 2 accurately predicts the functional effects of mutations across prokaryotic and eukaryotic genomes. It also successfully annotated the woolly mammoth genome from raw without a direct training reference, showing an ability to generalize function from the sequence alone.

Current genomic models struggle with predicting functional impacts of mutations across diverse biological systems, particularly for eukaryotic genomes. Machine learning approaches have demonstrated some success in modeling and prokaryotic genomes. The complexity of eukaryotic DNA, with its long-range interactions and regulatory elements, presents more of a challenge.

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Google’s X company is working on the next generation of Taara, a silicon photonics technology designed to bring fast broadband speeds to some underdeveloped areas of the world. According to statements by Taara general manager Mahesh Krishnaswamy, this light-based solution could offer unprecedented connectivity opportunities in any part of the world – and beyond.

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AI-powered precision in medicine is helping to enhance the accuracy, efficiency, and personalization of medical treatments and healthcare interventions. Machine learning models analyze vast datasets, including genetic information, disease pathways, and past clinical outcomes, to predict how drugs will interact with biological targets. This not only speeds up the identification of promising compounds but also helps eliminate ineffective or potentially harmful options early in the research process.

Researchers are also turning to AI to improve how they evaluate a drug’s effectiveness across diverse patient populations. By analyzing real-world data, including electronic health records and biomarker responses, AI can help researchers identify patterns that predict how different groups may respond to a treatment. This level of precision helps refine dosing strategies, minimize side effects, and support the development of personalized medicine where treatments are tailored to an individual’s genetic and biological profile.

AI is having a positive impact on the pharmaceutical industry helping to reshape how drugs are discovered, tested, and brought to market. From accelerating drug development and optimizing research to enhancing clinical trials and manufacturing, AI is reducing costs, improving efficiency, and ultimately delivering better treatments to patients.

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A Shenzhen-based humanoid robot maker said it has deployed “dozens of robots” in an electric vehicle (EV) factory where they work together on complicated tasks, offering a peek into the future of Made-in-China tech as artificial intelligence (AI) and robotics technologies are applied to empower manufacturing.

Hong Kong-listed UBTech Robotics said on Monday that it has completed a test to deploy dozens of its Walker S1 robots in the Zeekr EV factory in the Chinese port city of Ningbo for “multitask” and “multi site” operations.

According to photos and videos provided by UBTech, the human-shaped robots work as a team to complete tasks such as lifting heavy boxes and handling soft materials.

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