Lifeboat Foundation: Safeguarding Humanity
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Cells function through an intricate network of proteins, each designed for specific tasks like metabolism, tissue repair, and immune defense. These proteins are built using genetic blueprints in our DNA. A process called alternative splicing enables a single gene to generate multiple mRNA transcripts — molecules carrying genetic instructions — allowing for protein diversity.

In healthy cells, this process maintains balance. Cancer cells, however, disrupt that process to fuel their unchecked growth by disabling proteins that regulate cell proliferation.

The researchers focused on a genetic element known as a poison exon. This natural “off switch” prevents the production of certain proteins by marking their RNA messages for destruction before they can be translated. Cancer cells suppress the poison exon in a key gene called TRA2β. Without this regulation, TRA2β levels rise, promoting tumor growth and making cancer cells more aggressive.

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Rutgers researchers found that increased brown fat improves longevity and exercise capacity in mice. They aim to develop a drug that replicates these benefits in humans.

Rutgers Health researchers have made discoveries about brown fat that could pave the way for helping people stay physically fit as they age.

A team from Rutgers New Jersey Medical School found that mice lacking a specific gene developed an unusually potent form of brown fat tissue, which extended lifespan and increased exercise capacity by approximately 30%. The team is now working on a drug that could replicate these effects in humans.

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Dark matter could be an entire dark sector of the universe, with its own particles and forces.

By Kathryn Zurek edited by Clara Moskowitz

Have you ever stood by the sea and been overwhelmed by its vastness, by how quickly it could roll in and swallow you? Evidence suggests that we are suspended in a cosmic sea of dark matter, a mysterious substance that shapes galaxies and large structures in the universe but is transparent to photons, the carriers of the electromagnetic force. Our galactic home, the Milky Way, is submerged in dark matter, but this hidden body but does not devour us, because its forces cannot touch the regular matter we’re made of.

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A collaborative team of researchers from Imperial College London and Queen Mary University of London has achieved a significant milestone in sustainable energy technology, as detailed in their latest publication in Nature Energy.

The study unveils a pioneering approach to harnessing sunlight for efficient and stable hydrogen production using cost-effective organic materials, potentially transforming the way we generate and store clean energy.

The research tackles a longstanding challenge in the development of solar-to-hydrogen systems: the instability of organic materials such as polymers and small molecules in water and the inefficiencies caused by energy losses at critical interfaces. To address this, the research team introduced a multi-layer device architecture that integrates an organic photoactive layer with a protective graphite sheet functionalized with a nickel-iron catalyst.

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Distributed acoustic sensing (DAS) systems represent cutting-edge technology in infrastructure monitoring, capable of detecting minute vibrations along fiber optic cables spanning tens of kilometers. These systems have proven invaluable for applications ranging from earthquake detection and oil exploration to railway monitoring and submarine cable surveillance.

However, the massive amounts of data generated by these systems create a significant bottleneck in processing speed, limiting their effectiveness for real-time applications where immediate responses are crucial.

Machine learning techniques, particularly neural networks, have emerged as a promising solution for processing DAS data more efficiently. While the processing capabilities of traditional electronic computing using CPUs and GPUs have massively improved over the past decades, they still face fundamental limitations in speed and energy efficiency. In contrast, photonic neural networks, which use light instead of electricity for computations, offer a revolutionary alternative, potentially achieving much higher processing speeds at a fraction of the power.

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Snap a photo of your meal, and artificial intelligence instantly tells you its calorie count, fat content, and nutritional value—no more food diaries or guesswork.

This futuristic scenario is now much closer to reality, thanks to an AI system developed by NYU Tandon School of Engineering researchers that promises a new tool for the millions of people who want to manage their weight, diabetes and other diet-related health conditions.

The technology, detailed in a paper presented at the 6th IEEE International Conference on Mobile Computing and Sustainable Informatics, uses advanced deep-learning algorithms to recognize food items in images and calculate their nutritional content, including calories, protein, carbohydrates and fat.

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When someone is traumatically injured, giving them blood products before they arrive at the hospital—such as at the scene or during emergency transport—can improve their likelihood of survival and recovery. But patients with certain traumatic injuries have better outcomes when administered specific blood components.

University of Pittsburgh School of Medicine and UPMC scientist-surgeons report in Cell Reports Medicine that giving that has been separated from other parts of donated blood improves outcomes in patients with (TBI) or shock, whereas giving unseparated or “whole” blood may be best for patients with traumatic bleeding.

Together, Pitt and UPMC have become home to the largest clinical trials research consortium for early trauma care in the U.S., allowing the research to benefit both soldiers and civilians.

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An AI-powered robot that can prepare cups of coffee in a busy kitchen could usher in the next generation of intelligent machines, a study suggests.

The research, published in the journal Nature Machine Intelligence, was led by Ruaridh Mon-Williams, a Ph.D. student jointly at the University of Edinburgh, Massachusetts Institute of Technology and Princeton University.

Using a combination of cutting-edge AI, sensitive sensors and fine-tuned motor skills, the robot can interact with its surroundings in more human-like ways than ever before, researchers say.

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