A major new review by the Cochrane collaboration -- an independent network of researchers -- evaluated 73 randomized controlled trials involving about 5,000 people with depression and found that exercise matched the effectiveness of both pharmacological treatments and psychological therapies. The biological mechanisms overlap considerably with antidepressants. "Exercise can help improve neurotransmitter function, like serotonin as well as dopamine and endorphins," said Dr. Stephen Mateka, medical director of psychiatry at Inspira Health. Dr. Nicholas Fabiano of the University of Ottawa added that exercise triggers the release of brain-derived neurotrophic factor, or BDNF, which he calls "Miracle-Gro for the brain." Exercise has been adopted as a first-line treatment in depression guidelines globally, though Fabiano noted it remains underutilized. The meta-analysis found that combining aerobic exercise and resistance training appeared more effective than aerobic exercise alone, and that 13 to 36 workouts led to improvements in depressive symptoms. Light to moderate exercise proved as beneficial as vigorous workouts, at least initially.

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An R&D lab under America's Energy Department annnounced this week that "Neuromorphic computers, inspired by the architecture of the human brain, are proving surprisingly adept at solving complex mathematical problems that underpin scientific and engineering challenges." Phys.org publishes the announcement from Sandia National Lab: In a paper published in Nature Machine Intelligence, Sandia National Laboratories computational neuroscientists Brad Theilman and Brad Aimone describe a novel algorithm that enables neuromorphic hardware to tackle partial differential equations, or PDEs — the mathematical foundation for modeling phenomena such as fluid dynamics, electromagnetic fields and structural mechanics. The findings show that neuromorphic computing can not only handle these equations, but do so with remarkable efficiency. The work could pave the way for the world's first neuromorphic supercomputer, potentially revolutionizing energy-efficient computing for national security applications and beyond... "We're just starting to have computational systems that can exhibit intelligent-like behavior. But they look nothing like the brain, and the amount of resources that they require is ridiculous, frankly," Theilman said.For decades, experts have believed that neuromorphic computers were best suited for tasks like recognizing patterns or accelerating artificial neural networks. These systems weren't expected to excel at solving rigorous mathematical problems like PDEs, which are typically tackled by traditional supercomputers. But for Aimone and Theilman, the results weren't surprising. The researchers believe the brain itself performs complex computations constantly, even if we don't consciously realize it. "Pick any sort of motor control task — like hitting a tennis ball or swinging a bat at a baseball," Aimone said. "These are very sophisticated computations. They are exascale-level problems that our brains are capable of doing very cheaply..." Their research also raises intriguing questions about the nature of intelligence and computation. The algorithm developed by Theilman and Aimone retains strong similarities to the structure and dynamics of cortical networks in the brain. "We based our circuit on a relatively well-known model in the computational neuroscience world," Theilman said. "We've shown the model has a natural but non-obvious link to PDEs, and that link hasn't been made until now — 12 years after the model was introduced." The researchers believe that neuromorphic computing could help bridge the gap between neuroscience and applied mathematics, offering new insights into how the brain processes information. "Diseases of the brain could be diseases of computation," Aimone said. "But we don't have a solid grasp on how the brain performs computations yet." If their hunch is correct, neuromorphic computing could offer clues to better understand and treat neurological conditions like Alzheimer's and Parkinson's.

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Scientists are putting Einstein's claim that the speed of light is constant to the test. While researchers found no evidence that light's speed changes with energy, this null result dramatically tightens the constraints on quantum-gravity theories that predict even the tiniest violations. ScienceDaily reports: Special relativity rests on the principle that the laws of physics remain the same for all observers, regardless of how they are moving relative to one another. This idea is known as Lorentz invariance. Over time, Lorentz invariance became a foundational assumption in modern physics, especially within quantum theory. [...] One prediction shared by several Lorentz-invariance-violating quantum gravity models is that the speed of light may depend slightly on a photon's energy. Any such effect would have to be tiny to match existing experimental limits. However, it could become detectable at the highest photon energies, specifically in very-high-energy gamma rays. A research team led by former UAB student Merce Guerrero and current IEEC PhD student at the UAB Anna Campoy-Ordaz set out to test this idea using astrophysical observations. The team also included Robertus Potting from the University of Algarve and Markus Gaug, a lecturer in the Department of Physics at the UAB who is also affiliated with the IEEC. Their approach relies on the vast distances light travels across the universe. If photons of different energies are emitted at the same time from a distant source, even minuscule differences in their speeds could build up into measurable delays by the time they reach Earth. Using a new statistical technique, the researchers combined existing measurements of very-high-energy gamma rays to examine several Lorentz-invariance-violating parameters favored by theorists within the Standard Model Extension (SME). The goal was ambitious. They hoped to find evidence that Einstein's assumptions might break down under extreme conditions. Once again, Einstein's predictions held firm. The study did not detect any violation of Lorentz invariance. Even so, the results are significant. The new analysis improves previous limits by an order of magnitude, sharply narrowing where new physics could be hiding.

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alternative_right shares a report from ScienceAlert: At the Experimental Advanced Superconducting Tokamak (EAST), physicists successfully exceeded what is known as the Greenwald limit, a practical density boundary beyond which plasmas tend to violently destabilize, often damaging reactor components. For a long time, the Greenwald limit was accepted as a given and incorporated into fusion reactor engineering. The new work shows that precise control over how the plasma is created and interacts with the reactor walls can push it beyond this limit into what physicists call a 'density-free' regime. [...] A team led by physicists Ping Zhu of Huazhong University of Science and Technology and Ning Yan of the Chinese Academy of Sciences designed an experiment to take this theory further, based on a simple premise: that the density limit is strongly influenced by the initial plasma-wall interactions as the reactor starts up. In their experiment, the researchers wanted to see if they could deliberately steer the outcome of this interaction. They carefully controlled the pressure of the fuel gas during tokamak startup and added a burst of heating called electron cyclotron resonance heating. These changes altered how the plasma interacts with the tokamak walls through a cooler plasma boundary, which dramatically reduced the degree to which wall impurities entered the plasma. Under this regime, the researchers were able to reach densities up to about 65 percent higher than the tokamak's Greenwald limit. This doesn't mean that magnetically confined plasmas can now operate with no density limits whatsoever. However, it does show that the Greenwald limit is not a fundamental barrier and that tweaking operational processes could lead to more effective fusion reactors. The findings have been published in Science Advances.

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Researchers at Stanford University have created a programmable synthetic "skin" that can independently change color and texture, "a feat previously only available within the animal kingdom," reports the Register. From the report: The technique employs electron beams to write patterns and add optical layers that create color effects. When exposed to water, the film swells to reveal texture and colors independently, depending on which side of the material is exposed, according to a paper published in the scientific journal Nature this week. In an accompanying article, University of Stuttgart's Benjamin Renz and Na Liu said the researchers' "most striking achievement was a photonic skin in which color and texture could be independently controlled, mirroring the separate regulation... in octopuses." The research team used the polymer PEDOT:PSS, which can swell in water, as the basis for their material. Its reaction to water can be controlled by irradiating it with electrons, creating textures and patterns in the film. By adding thin layers of gold, the researchers turned surface texture into tunable optical effects. A single layer could be used to scatter light, giving the shiny metal a matte, textured appearance. To control color, a polymer film was sandwiched between two layers of gold, forming an optical cavity, which selectively reflects light.

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An anonymous reader quotes a report from NPR: [I]t turns out that some genius dogs can learn a brand new word, like the name of an unfamiliar toy, by just overhearing brief interactions between two people. What's more, these "gifted" dogs can learn the name of a new toy even if they first hear this word when the toy is out of sight -- as long as their favorite human is looking at the spot where the toy is hidden. That's according to a new study in the journal Science. "What we found in this study is that the dogs are using social communication. They're using these social cues to understand what the owners are talking about," says cognitive scientist Shany Dror of Eotvos Lorand University and the University of Veterinary Medicine, Vienna. "This tells us that the ability to use social information is actually something that humans probably had before they had language," she says, "and language was kind of hitchhiking on these social abilities." [...] "There's only a very small group of dogs that are able to learn this differentiation and then can learn that certain labels refer to specific objects," she says. "It's quite hard to train this and some dogs seem to just be able to do it." [...] To explore the various ways that these dogs are capable of learning new words, Dror and some colleagues conducted a study that involved two people interacting while their dog sat nearby and watched. One person would show the other a brand new toy and talk about it, with the toy's name embedded into sentences, such as "This is your armadillo. It has armadillo ears, little armadillo feet. It has a tail, like an armadillo tail." Even though none of this language was directed at the dogs, it turns out the super-learners registered the new toy's name and were later able to pick it out of a pile, at the owner's request. To do this, the dogs had to go into a separate room where the pile was located, so the humans couldn't give them any hints. Dror says that as she watched the dogs on camera from the other room, she was "honestly surprised" because they seemed to have so much confidence. "Sometimes they just immediately went to the new toy, knowing what they're supposed to do," she says. "Their performance was really, really high." She and her colleagues wondered if what mattered was the dog being able to see the toy while its name was said aloud, even if the words weren't explicitly directed at the dog. So they did another experiment that created a delay between the dog seeing a new toy and hearing its name. The dogs got to see the unfamiliar toy and then the owner dropped the toy in a bucket, so it was out of sight. Then the owner would talk to the dog, and mention the toy's name, while glancing down at the bucket. While this was more difficult for dogs, overall they still could use this information to learn the name of the toy and later retrieve it when asked. "This shows us how flexible they are able to learn," says Dror. "They can use different mechanisms and learn under different conditions."

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Every human body contains a small population of cells that are not genetically its own -- cells that crossed the placenta during pregnancy and that persist for decades after birth. These "microchimeric" cells, named after the lion-goat-serpent hybrid of Greek mythology, have been found in every organ studied so far, though they are exceedingly rare: one such cell exists for every 10,000 to 1 million of a person's own cells. The cells were first noticed in the late 1800s when pathologist Georg Schmorl described placenta-like "giant cells" in the lungs of people who had died from eclampsia. In 1969, researchers detected Y-chromosome-containing white blood cells in people who would later give birth to boys. For more than two decades, scientists presumed these cells were temporary. That changed in 1993 when geneticist Diana Bianchi found Y-chromosome cells in women who had given birth to sons up to 27 years earlier. The cells appear to have regenerative properties, transforming into blood vessels or skin cells to promote wound healing. They also challenge a central assumption of immunology -- that the immune system classifies cells as either "self" or "non-self" and rejects foreign material. Microchimeric cells should trigger rejection but do not. Higher-than-typical concentrations have been found in people with autoimmune conditions including diabetes, lupus, and scleroderma.

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Mark Thomson, a professor of experimental particle physics at the University of Cambridge, takes over as CERN's director general this week, and one of his first major decisions during his five-year tenure will be shutting down the Large Hadron Collider for an extended upgrade. The shutdown starts in June to make way for the high-luminosity LHC -- a major overhaul involving powerful new superconducting magnets that will squeeze the collider's proton beams and increase their brightness. The upgrade will raise collisions tenfold and strengthen the detectors to better capture subtle signs of new physics. The machine won't restart until Thomson's term is nearly over. Thomson is far from disconsolate about the downtime. "The machine is running brilliantly and we're recording huge amounts of data," he told The Guardian. "There's going to be plenty to analyse over the period." Beyond the upgrade, Thomson must shepherd CERN's plans for the Future Circular Collider, a proposed 91km machine more than three times the size of the current collider. Member states vote on the project in 2028; the first phase carries an estimated price tag of 15 billion Swiss francs (nearly $19 billion).

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