Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Get the latest international news and world events from around the world.

Log in for authorized contributors

AI based study demonstrates stimulation of brown fat by blood-pressure drug

Scientists have discovered that the drug losartan, commonly prescribed for high blood pressure, boosts brown adipose tissue activity in mice, enhancing their metabolism. This indicates that repurposing losartan—an angiotensin receptor blocker (ARB) with a well-known safety profile—may be a clinically viable method of stimulating brown fat, which has been linked to improved cardiovascular health and, reported for the first time in this study, psychiatric health.

In a paper published in Nature, researchers found that across all diagnoses, those conditions were less prevalent in individuals with brown fat.

The researchers initially discovered the connection between brown fat and ARBs through an analysis of medical records from 100,000 patients. The work builds on discoveries in the lab made in 2021 in which they characterized roughly 50,000 patients, finding that people with more brown fat had significantly lower odds of cardiometabolic diseases, including coronary artery disease and type II diabetes.

Optical system maps multiple infrared fields onto one small detector

Researchers from the Changchun Institute of Optics, Fine Mechanics and Physics of the Chinese Academy of Sciences have developed an optical system that focuses light from several subfields onto a single small-format detector. By analyzing field-dependent spot shapes, the system identifies which subfield an isolated infrared target comes from. In sequence-level tests, it identified the correct field in about 89% of cases. The study is published in Optics and Laser Technology.

Wide-field infrared imaging typically uses large focal-plane arrays, where each part of the scene occupies a distinct detector region. Larger arrays increase demands on detector fabrication, cooling, electronic readout and system integration. The researchers pursued another route by reusing the same detector area for several viewing regions. This enables the optical field to expand without increasing the detector format in direct proportion, which may reduce the hardware burden of wide-field systems designed for sparse point-like targets.

In scenes with only a few isolated targets, much of a large array may carry no useful target signal at a given moment. The new design uses that sparsity by mapping several regions to shared detector pixels.

A likely supermassive neutron star might shed light on the mass gap between neutron stars and black holes

A team of astronomers recently identified a red giant star with an unseen binary companion after examining data from the Large Sky Area MultiObject Fiber Spectroscopic Telescope (LAMOST) and the Apache Point Observatory Galactic Evolution Experiment (APOGEE). Although the companion object can’t be observed visually, astronomers can see the gravitational effects it has on the red giant star.

All of this is nothing new—researchers often discover binary systems with effectively invisible companions thanks to gravitational effects. However, the inferred mass of the unseen object, along with the system’s other characteristics, makes it a bit of an anomaly. The research on this pair, published in Science Advances, suggests the unseen companion is likely an unusually massive neutron star that is pushing the boundaries of how massive neutron stars can get.

Unstable plasma heats itself with waves it generates, experiments reveal

Fusion power is expected to be a sustainable energy source that emits no carbon dioxide during electricity generation. To achieve this, research is underway worldwide to confine high-temperature plasma using magnetic fields. To initiate a fusion reaction, high-energy particles or waves must be injected from outside to heat the plasma to extremely high temperatures.

Once the fusion reaction begins, “self-heating” occurs, in which high-energy particles generated by the reaction further heat the plasma, allowing the high-temperature state to be maintained even if external heating is reduced.

This self-heating process is complex, and large-scale experimental facilities such as ITER are necessary to demonstrate it in its entirety. Demonstration of self-heating is expected around 2040. However, one of the key processes underpinning self-heating—the phenomenon in which “waves generated by plasma instabilities heat the plasma itself”—can be verified even with current experimental facilities.

AI models can answer differently when told they are being watched

Large language models (LLMs), the AI systems underpinning the functioning of ChatGPT, Gemini and other similar platforms, are now widely used worldwide. While the responses provided by these models can be useful in many situations, they can also change under specific conditions.

Researchers at TCS Research and KIIT Bhubaneswar in India recently carried out an experiment with 10 different LLMs to understand the extent to which these models follow rules under different conditions. Their findings, reported in an arXiv preprint, suggest that some LLMs can change their responses when they are told that their answers might be monitored, yet how much their responses vary depends on the model and the value it is asked to set aside.

“Language models can follow a directive when they believe they are monitored and abandon it when they believe they are not, a pattern termed alignment faking,” wrote Manodeep Ray, Vivek Srivastava and their colleagues in their paper.

A perfect couple: Chromium keeps ruthenium grounded in water electrolysis

They say opposites attract, and many couples do fit this pattern. One is pragmatic and down-to-earth, while the other is a dreamer with their head in the clouds. Yet, these differences make them perfect for each other. According to researchers at ICFO, chemical interactions between atoms are not that different. Ruthenium, for instance, belongs to the “dreamer” class. It is commonly used in single-atom catalysts that speed up water electrolysis, which splits water to obtain green hydrogen. However, on its own, ruthenium tends to over-oxidize and dissolve during operation, like an idealist who takes flight without looking back.

In an article published in Nature Materials, ICFO researchers Tengyu Chen, Dr. Kaiwen Wang, Dr. Lu Xia, Dr. Wulyu Jiang, Dr. Linlin Yang, Dr. Kaiqi Zhao, Ranit Ram, Dr. Junmei Chen, Dr. Marinos Dimitropoulos, Dr. Viktoria Golovanova, Prathama Haldar, Dr. Barbara Polesso, Aparna M. Das and Dr. Sunil Kadam, led by ICFO professor F. Pelayo García de Arquer, now present the perfect partner to keep ruthenium grounded: chromium.

The work was carried out in close collaboration with the University of Bayreuth, Fujian Institute of Research on the Structure of Matter, the Institut de Recerca en Energia de Catalunya (IREC), the Catalan Institute of Nanoscience and Nanotechnology (ICN2), Chongqing University and the National Institute for Space Research (INPE).

Molecular sensor makes invisible traces of mercury easier to see

Mercury contamination presents an obvious challenge: it is invisible. A glass of water or a piece of fruit can seem perfectly normal to the naked eye, even when trace amounts of toxic mercury are present. Detecting such minute levels usually requires specialized laboratory tools and trained experts.

Researchers at Istanbul Technical University (ITU) have created a molecule that provides an alternative method to detect mercury. When mercury(II) ions are present, the sensor changes color and starts emitting near-infrared light, transforming an invisible contaminant into a measurable signal. The research is published in the journal Talanta.

Heat drives electron orbital currents, revealing first experimental evidence of orbital Nernst effect

Researchers at National Taiwan University (NTU) have reported the first experimental evidence of heat-current-driven transverse accumulation of electrons’ orbital angular momentum, a phenomenon known as the orbital Nernst effect. The study was recently published in Nature Communications.

The Hall and Nernst effects are fundamental phenomena in electric and thermal transport. They describe how information carriers, such as electrons or spins, can develop a transverse response when driven by an electric field or a temperature gradient. They provide insights into the material’s electronic band structure and hold the key to applications.

Among the Hall family, the spin Hall and orbital Hall effects have attracted considerable interest because they allow an electric current to generate transverse flows of spin and orbital angular momentum, respectively. These forms of angular momentum can be transferred, directly or indirectly, to the magnetic moments in a material. When sufficiently strong, they can even switch the magnetization, providing a promising mechanism for writing information in spintronic memory devices.

Self-learning AI separates particle jets from background noise in simulated heavy-ion collisions

A team of physicists and computer scientists led by researchers at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory developed a new artificial intelligence (AI) approach that learns on its own to separate important high-energy particle jet signals from the enormous background of particles recorded in relativistic heavy-ion collisions, where two beams of heavy ions collide at near the speed of light.

The advance could help scientists make more precise measurements of the quark-gluon plasma (QGP), a hot, dense state of matter that existed a microsecond after the Big Bang.

At Brookhaven Lab, physicists recreated early-universe conditions that produce the QGP, a “soup” of free quarks and gluons, in heavy-ion collisions at the Relativistic Heavy Ion Collider (RHIC), a DOE Office of Science user facility for nuclear physics research that completed operations in early 2026. Physicists use jets—collimated sprays of high-energy particles produced in relativistic heavy-ion collisions—as one of the best probes of the QGP to glean more about how quarks and gluons behave and about the strongest force in nature that binds them together.