A planetary defense system would blunt solar storms with hundreds of tons of gas. Emerging heavy-lift rockets could deploy it in under two months.

Extreme space weather could wreak havoc on the satellites, communications networks, and electrical grids that modern society depends on. Researchers have now proposed an ambitious space-based planetary defense system that would weaken solar storms before they hit Earth.

The sun regularly emits massive pulses of radiation, energetic particles, and magnetic fields that interact with the Earth’s own magnetic field. This activity is the source of auroras like the northern lights, but the most violent eruptions can cause geomagnetic storms with the power to disrupt GPS and radio communications and fry electrical equipment.

While the impact of most of these events is limited, there is precedent for more catastrophic outcomes. In 1859, the Carrington Event, the most powerful solar storm ever recorded, knocked out telegraph lines across North America and Europe. In today’s highly electrified world, a similar event could cause between $2.4 and $3.4 trillion in damage to the power grid alone.

Now, researchers at Boston University and the University of Michigan have come up with a potential solution. In a paper published in Space Weather, they propose a constellation of satellites called StormWall that would release hundreds of tons of gas into orbit to blunt the force of an incoming solar storm.

“It’s as if you could install an airbag in the magnetosphere,” co-author Daniel Welling, a space physicist from the University of Michigan, told Science.

Solar storms have the potential to sow chaos because they weaken the magnetic shield protecting Earth from space radiation. Powerful enough storms disrupt the Earth’s magnetic field and cause it to reconnect to the sun’s, allowing energy from the solar storm to pour into the magnetosphere.

The Earth already has a natural defense against this—a doughnut-shaped reservoir of ionized gas, or plasma, sitting just above the atmosphere. When the planet’s magnetic field is disturbed, a plume of this plasma flows toward the sun and slows the rate at which the magnetic fields reconnect.

StormWall would turbocharge this process by releasing massive amounts of artificial plasma into the outer atmosphere. The researchers sketch out a system involving a constellation of satellites orbiting about 22,000 miles from Earth. The satellites would carry canisters of lithium, barium, or sodium gases to be ejected when a large solar storm is inbound. The gases, rapidly ionized by solar radiation, would add to the planet’s natural plasma shield.

Based on simulations, the researchers estimate that releasing around 400 tons of gas could reduce the strength of a major geomagnetic storm by over 50 percent. Crucially, the intervention would be swift and reversible. The plasma cloud could be in position by the time a storm hits, and it would dissipate just a few hours later.

Launching this much material into orbit would be a big undertaking, but the researchers say it could be within reach of emerging heavy-lift vehicles like SpaceX’s Starship or China’s Long March 9 rocket. They calculate that six launches could deploy the full constellation in under two months.

Outside experts have been broadly positive. Allison Jaynes, a space physicist at the University of Iowa, told Science the idea was “highly innovative and appears to be quite feasible in the near term.”

But getting the satellites into orbit is only part of the puzzle. Accurate and timely space weather forecasts would also be a prerequisite. And gaining international buy-in for a system that would drastically alter the near-Earth space environment, even if only temporarily, could be challenging.

The researchers flag potential side effects that need more study, including the generation of electromagnetic waves as the released material ionizes. Still, given the devastation a Carrington-sized event could unleash on the modern world, the potential downsides may be worth the risk.

The post Orbital Airbag Could Shield Earth From Devastating Solar Storms appeared first on SingularityHub.

Artificial Intelligence

Jeff Bezos Is Funding a Wild Hunt for the Brain’s ‘Core Algorithm’Steven Levy | Wired ($)

“The goal, Reardon tells me, is to build ‘a synthetic artificial intelligence brain that runs on 50 watts or less.’ It should adapt to its conditions, be as nimble as a human mind, and burn a tiny fraction of an LLM’s compute power and energy. The proof of concept is thriving inside our skulls.”

The post This Week’s Awesome Tech Stories From Around the Web (Through June 7) appeared first on SingularityHub.

The findings could lead to new treatments for multiple neurodegenerative diseases.

Huntington’s disease is tragically predictable. An inherited genetic mutation causes neurons to make distorted, sticky proteins. These proteins clump together and gradually overwhelm brain cells. The brain loses its ability to learn, remember, and make decisions.

This story is dogma in neuroscience. But decades of research and drugs targeting the clumps have had little success. Scientists are now wondering: Is there more to the story? In a twist, a team from the Hebrew University of Jerusalem and collaborators found that protein clumps may be a neuron’s first line of defense against damage.

The misfolded or malfunctioning proteins are quarantined inside bubbly hubs called “inclusion bodies.” Often considered detrimental to cell health, disrupting their formation unexpectedly led to cells becoming more sensitive to stressors often seen in neurodegenerative diseases.

Physical separation played just one part. Inclusion bodies also changed the activity of genes involved in neuroinflammation—even in the absence of immune cells. Scouting the genetic landscape of cells derived from patients with severe Huntington’s disease, the team homed in on a “master regulator” gene, ATF3, that orchestrates immune responses. Removing the gene lessened inclusion bodies’ protective effects against damage in cultured cells.

To be clear, the findings are only for a cell model of Huntington’s disease in a petri dish. And inclusion bodies could be a double-edged sword: protective in the beginning and detrimental later on. Still, acknowledging them as a more complicated villain could better inform strategies for disorders that take over our minds like Huntington’s.

“Our results reveal…that these structures are not merely byproducts of disease, but a central factor in the cell’s ability to mount a protective response against stress,” said study author Eran Meshorer in a press release.

The Problem With PolyQ

It’s long been believed that protein clumps in the brain gradually erode cognition. Whether they’re the main driver of neurodegenerative disorders is still debated, but their presence accelerates brain cell injury, causing neurons to wither away.

Alzheimer’s disease, for example, is associated with two sets of protein clumps. One lives inside neurons (tau) and another gunks up the space between cells (amyloid). Decades of research aimed at removing amyloid clumps have met with minimal success, earning these doomed efforts the notorious nickname “graveyard of dreams.” Despite their struggles, the FDA recently approved two major drugs that remove amyloid clumps and modestly slow cognitive decline, though the approval has been controversial due to doubts about safety.

Other untreatable neurodegenerative disorders also fall into this category. Clumps formed in Parkinson’s disease erode the brain’s ability to control movement, emotion, and even the perception of time. Lou Gehrig’s disease, or ALS, produces inclusion bodies inside motor neurons, leading to muscle weakness and trouble swallowing. The disease eventually robs people of speech and motion.

These diseases often have multiple genetic and environmental triggers. Huntington’s, in contrast, is entirely genetic. The condition stems from the genome over-copying parts of the huntingtin gene (HTT), which normally makes a key protein also called huntingtin.

Normally, cells use the protein’s large, stackable structure to build highways that transport all sorts of biological cargo, from molecules to organelles. The protein also plays an essential role during early brain development and neural wiring in adulthood.

But a mutant form of the HTT gene can wreak havoc. A common mutation, called polyQ expansion, produces unwieldy, misfolded proteins. Nearly 30 years ago, researchers found that these errant proteins aggregate inside parts of the cell. The clumps, or inclusion bodies, were widely thought to be detrimental. Some act like sticky tape that captures healthy proteins, such as those involved in gene expression, and torpedoes cellular health.

But telltale signs in cultured rat brain cells suggest a more nuanced story: Inclusion bodies could also be protective, sequestering mutant proteins as an early form of protection.

A Tale of Two

The common factor in diseases featuring polyQ mutation is repetition. Mutated genes have long, duplicated sequences of the DNA letters cytosine, adenosine, and guanine (CAG). More CAG repeats in the genome translates into earlier disease onset.

We all have this DNA triplet in our HTT gene. But more than 39 repeats results in longer, toxic huntingtin proteins. Severe cases of Huntington’s can feature over 100 CAG repeats, transforming the usually free-floating protein workers into sticky, dysfunctional layabouts.

In the new study, the researchers first established a baseline. They used the gene editing tool CRISPR-Cas9 to reduce CAG repeats in cells derived from Huntington’s patients—which carried over 180 copies—to near normal levels.

They then tagged the cells with a fluorescent marker that causes huntingtin proteins to glow bright green under the microscope. This let the team track protein aggregation in real time. Though they shared the same genetics, some cells formed inclusion bodies; others didn’t.

The team next challenged them with a chemical known to cause cellular stress. Those that formed clumps survived far more regularly than those that didn’t. It was a “striking difference,” the authors wrote. “Once a mutant PolyQ protein is expressed, the formation of IBs [inclusion bodies] protect[s] the cells rather than inflict[s] harm, at least short-term.”

Inflammation seems to be key. Although grown side-by-side, a genetic screen revealed cells with inclusion bodies were especially abundant in a gene called ATF3, which is known to regulate inflammation. Getting rid of the gene wiped out the neurons’ ability to form inclusion bodies, making them more vulnerable.

“Our results reveal a previously unknown role for ATF3 in orchestrating the formation of inclusion bodies in human neurons,” said Meshorer.

These are very early results. An immune molecule bridges ATF3 and inflammation and is associated with Huntington’s disease. Its levels are higher in patients with the condition. Increasing ATF3 activity could amp up the number of protective inclusion bodies and give neurons a fighting chance.

The findings suggest inclusion bodies gather free-floating mutant proteins into clumps to protect neurons and reduce brain damage—at least at the beginning of the disease. However, lab experiments rarely translate to treatments. How fast inclusion bodies form and when they begin to stress cells remains to be seen. Meanwhile, a gene therapy for Huntington’s is underway, and promising results in a small trial suggest an alternative path for treatment.

Still, the study challenges the idea that protein clumps are always detrimental. If replicated in other neurodegenerative diseases such as Alzheimer’s or ALS and if we can learn how long protection lasts, the results could pave the way for better-timed treatment that works with the body’s protection, not against it.

The post Toxic Clumps in Huntington’s Disease May Protect the Brain Too appeared first on SingularityHub.

The gap between what AI can do in biology and what governance systems are prepared to handle is growing.

Artificial intelligence is rapidly learning to autonomously design and run biological experiments, but the systems intended to govern those capabilities are struggling to keep pace.

AI company OpenAI and biotech company Ginkgo Bioworks announced in February 2026 that OpenAI’s flagship model GPT-5 had autonomously designed and run 36,000 biological experiments. It did this through a robotic cloud laboratory, a facility where automated equipment controlled remotely by computers carries out experiments. The AI model proposed study designs, and robots carried them out and fed the data back to the model for the next round. Humans set the goal, and the machines did much of the work in the lab, cutting the cost of producing a desired protein by 40 percent.

This is programmable biology: designing biological components on a computer and building them in the physical world, with AI closing the loop.

For decades, biology mostly moved from observation toward understanding. Scientists sequenced the genomes of organisms to catalog all of their DNA, learning how genes encode the proteins that carry out life’s functions. The invention of tools like CRISPR then allowed scientists to edit that DNA for specific purposes, such as disabling a gene linked to disease. AI is now accelerating a third phase, where computers can both design biological systems and rapidly test them.

The process looks less like traditional benchwork in a lab and more like engineering: design, build, test, learn, and repeat. Where a traditional experiment might test a single hypothesis, AI-driven programmable biology explores thousands of design variations in parallel, iterating the way an engineer refines a prototype.

As a data scientist who studies genomics and biosecurity, I research how AI is reshaping biological research and what safeguards that demands. Current safety measures and regulations have not kept pace with these capabilities, and the gap between what AI can do in biology and what governance systems are prepared to handle is growing.

What AI Makes Possible

The clearest example of how researchers are using AI to automate research is AI-accelerated protein design.

Proteins are the molecular machines that carry out most functions in living cells. Designing new ones has traditionally required years of trial and error because even small changes to a protein’s sequence can alter its shape and function in unpredictable ways.

Protein language models, which are AI systems trained on millions of natural protein sequences, can quickly predict how mutations will change a protein’s behavior or design new proteins. These AI models are designing potential new drugs and speeding vaccine development.

Paired with automated labs, these models create tight loops of experimentation and revision, testing thousands of variations in days rather than the months or years a human team would need.

Faster protein engineering could mean faster responses to emerging infections and cheaper drugs.

The Dual-Use Problem

Researchers have raised concerns that these same AI tools could be misused, a challenge known as the dual-use problem: Technologies developed for beneficial purposes can also be repurposed to cause harm.

For example, researchers have found that AI models integrated with automated labs can optimize how well a virus spreads, even without specialized training. Scientists have developed a risk-scoring tool to evaluate how AI could modify a virus’s capabilities, such as altering which species it infects or helping it evade the immune system.

Current AI models are able to walk users through the technical steps of recovering live viruses from synthetic DNA. Researchers have determined that AI could lower barriers at multiple stages in the process of developing a bioweapon, and that current oversight does not adequately address this risk.

Risk From Bio AI

Experienced scientists are already using AI to plan and design biological experiments. The question of whether AI can help people with limited biology training carry out dangerous lab work is the subject of active research.

Two recent studies have reached different conclusions.

A study by AI company Scale AI and biosecurity nonprofit SecureBio found that when people with limited biology experience were given access to large language models, which is the type of AI behind tools like ChatGPT, they were able to complete biosecurity-related tasks, such as troubleshooting complex virology lab protocols with four times greater accuracy. In some areas, these novices outperformed trained experts. Around 90 percent of these novices reported little difficulty getting the models to provide risky biological information, such as detailed instructions on working with dangerous pathogens, despite built-in safety filters meant to block such outputs.

In contrast, a study led by Active Site, a research nonprofit that studies the use of AI in synthetic biology, found that AI help did not lead to significant differences in the ability of novices to complete the complex workflow to produce a virus in a biosafety laboratory. However, the AI-assisted group succeeded more often on most tasks and finished some steps faster, most notably on growing cells in the lab.

Hands-on work in the lab has traditionally been a bottleneck to translating designs into results. Even a brilliant study plan still depends on skilled human hands to carry out. That may not last, as cloud laboratories and robotic automation become cheaper and more accessible, allowing researchers to send AI-generated experimental designs to remote facilities for execution.

Responding to AI-Driven Biological Risks

AI systems are now able to run experiments autonomously and at scale, but existing regulations were not designed for this. Rules governing biological research do not account for AI-driven automation, and rules governing AI do not specifically address its use in biology.

In the US, the Biden administration had issued a 2023 executive order on AI security that included biosecurity provisions, but the Trump administration revoked it. Screening the synthetic DNA that commercial providers make to ensure it cannot be misused to make pathogens or toxins remains mostly voluntary. A bipartisan bill introduced in 2026 to mandate DNA screening does not yet address AI-designed sequences that evade current detection methods.

The 1975 Biological Weapons Convention, an international treaty prohibiting the production and use of bioweapons, contains no provisions for AI. The UK AI Security Institute and the US National Security Commission on Emerging Biotechnology have both called for coordinated government action.

The safety evaluations that AI labs run before releasing new models are often opaque and unsuited to capture real-world risk. Researchers have estimated that even modest improvements in an AI model’s ability to help plan pathogen-related experiments could translate to thousands of additional deaths from bioterrorism per year. Timelines for when these capabilities cross critical thresholds remain unclear.

The Nuclear Threat Initiative has proposed a managed access framework for biological AI tools, matching who can use a given tool to the risk level of the model rather than blanket restrictions. The RAND Center on AI, Security and Technology outlined a set of actions researchers could take to improve biosecurity, including improved DNA synthesis screening and model evaluations before release. Researchers have also argued that biological data itself needs governance, especially genomic data that could train models with dangerous capabilities.

Some AI companies have started voluntarily imposing their own safety measures. Anthropic activated its highest safety tier when it released its most advanced model in mid-2025. At the same moment, OpenAI updated its Preparedness Framework, revising the thresholds for how much biological risk a model can pose before additional safeguards are required. But these are voluntary, company-specific steps. Anthropic’s CEO, Dario Amodei, wrote that the pace of AI development may soon outrun any single company’s ability to assess the risk of a given model.

When used in a well-controlled setting, AI can help scientists quickly reach their research goals. What happens when the same capabilities operate outside those controls is a question that policy has not yet answered. Overreact, and talent and investment may move elsewhere while the technology continues advancing anyway. Underreact, and the risks of that technology could be exploited to cause real harm.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The post AI Can Now Design and Run Thousands of Experiments Without Human Hands. We Aren’t Ready for the Risk to Biosecurity. appeared first on SingularityHub.

A new process for mining lithium-rich rock could slash costs and pollution—and decentralize global lithium production.

Lithium mining is like a modern gold rush. The element is the main ingredient in batteries powering smartphones, electric cars, and even AI. Global demand is surging. Increased production could guide the world toward a more sustainable energy future.

But ironically, current extraction methods offset some of those gains. Lithium mining involves separating the element from brines using toxic chemicals, a process that also pumps out carbon dioxide. This, alongside enormous water and energy costs—due to high temperature requirements—has confined mining to a handful of countries.

To address these drawbacks, scientists at the Massachusetts Institute of Technology have now developed a low-cost, low-temperature, greener process relying on an abundant resource: Hard rock. Although rocks containing lithium cover large parts of the US, Europe, and Africa, extracting it from them is challenging.

While renovating his bathroom, study author Yet-Ming Chiang realized a chemical in glass etching cream—which makes glass translucent—could eat away at lithium-rich rocks. His team then designed a recyclable process to extract lithium as well as two ingredients used to make greener cement and other materials.

“You’ve heard of nose-to-tail eating?” said Chiang in a press release. “We refer to this as nose-to-tail mining.”

Unlike previous methods, the process runs at temperatures below the boiling point of water. All liquid chemicals are almost recyclable and can be reused in multiple rounds of extraction.

“This could establish a low-carbon alternative to hard rock refining, addressing both the surging demand for lithium and the carbon footprint that undermines the sustainability of the energy transition that lithium is meant to enable,” wrote Gang San Lee and Karthish Manthiram at the California Institute of Technology, who were not involved in the study.

A Rock and a Hard Place

The Earth’s crust teems with lithium. Getting it out is the hard part.

Currently, many mining operations rely on brine that naturally leaches lithium over millennia. Later steps purify the lithium into a battery-ready product. The process relies on large evaporation pools and is limited to a few countries, making the resource scarce.

Lithium could, alternatively, be harvested from solid rocks. One ore, spodumene, is packed with lithium, roughly 1.5 percent by weight. But liberating it has been a tough nut to crack.

Traditionally, miners crush rocks and remove chunks that don’t contain lithium. The rocks are then blasted at temperatures as high as 1,100 degrees Celsius (2,012 degrees Fahrenheit) and showered in a cocktail of dangerous chemicals. The process spews liquid waste into the environment and releases 20 tons of carbon for each ton of lithium.

Researchers are working on more temperate methods.

One of these is called ball milling. Ore is rotated in a container filled with hard balls that mechanically grind the stone into a fine power. It’s like using a mortar and pestle instead of a blender. But the process takes longer, and lithium is lost along the way, resulting in lower yields. Another method, called electrochemical leaching, refines the ore at room temperature. But researchers have had mixed success with the process, and it’s tough to scale up. It also produces in a lot of waste rock that could, in theory, be harvested for other uses instead being discarded.

Triple Threat

The new method popped into Chiang’s mind as he was brainstorming ways to break apart spodumene, a lithium-rich ore with high amounts of silica—the main ingredient in glass.

Dissolving silica to get to lithium requires hydrofluoric acid, a highly toxic chemical. But glass etching cream also eats away at silica with ammonium fluoride. Tubes of the mild acid are available in home improvement stores, and it works at room temperature. Why not give it a try?

By mixing ammonium fluoride with water, the team showed they could completely dissolve spodumene at temperatures below 100 degrees Celsius without releasing toxic fumes. They only needed to continuously stir the ore in a simple plastic tank. The process yielded several types of lithium salt with 99 percent purity. In early experiments, extraction took several days, but the team has since cut the time to under 12 hours.

“Dissolving silica is the hard part in mining,” said study author Benjamin Mowbray. “The next question was how do we apply it to impactful mineral processing problems?”

Along with lithium, spodumene is jam-packed with two usually discarded ingredients: Alumina, which after smelting makes aluminum, and silica, which can be directly used as a sustainable ingredient in greener cement. The new process can separate out both materials, and the team vetted the resulting products, including strength testing cubes of fabricated cement.

“First our goal was to produce these products, then there were additional steps of characterizing their purity and properties and making sure our products met the specifications for target markets,” said Mowbray.

“If any product didn’t meet the target specs, you’d end up with a waste stream.”

With a few chemical tweaks, the team showed the acid could be regenerated and reused at least five times. The team successfully processed 17 spodumene ores sourced from around the world, suggesting the method could be broadly applicable.

They’ve also spun the work into a startup, Rock Zero, and aim to scale it. If the acid can be recycled with near-perfect efficiency, the team estimates the process would cut costs over 40 percent compared to conventional hard-rock extraction, making it competitive with brine operations.

Its simplicity could also reshape where lithium gets produced. In 2024, roughly 74 percent of global lithium output came from just three countries: China, Australia, and Chile. By eliminating the need for extreme heat and massive waste-treatment plants, the process could be easier to implement, especially in countries rich in spodumene but lacking the capital for infrastructure.

That opens the door to a network of smaller refineries built closer to the mines themselves, reducing transportation costs and supply-chain bottlenecks. Because the process is also far less energy intensive, it could be powered by solar and wind, further shrinking its environmental impact.

The technology could also be adapted to recover other valuable metals hidden inside mineral ores. One candidate is beryllium, a lightweight but extremely stiff and stable metal used in satellites and the James Webb Space Telescope’s mirrors. Current manufacturing processes often generate toxic dust and fumes linked to serious lung inflammation. A cleaner extraction route could make it safer and cheaper to produce.  

As for Rock Zero, going up against established lithium giants is like David and Goliath. They’ll also have to contend with global market volatility and increasing competitiveness of sodium-ion batteries and other alternative battery chemistries.

But the team is unfazed. “We believe this approach is the lowest-energy, lowest-cost way of getting lithium not only out of hard rock, but period,” said Chiang. “That’s what’s motivating us to scale this.”

The post Three Countries Own the Lithium Market. An MIT Startup Wants to Break Their Grip. appeared first on SingularityHub.

A huge analysis of gene expression across species revealed genetic hallmarks of aging and could accelerate anti-aging treatments.

There’s truth to the old adage, “Age is just a number.” People of the same age differ vastly in health and mental capabilities. One 80-year-old may be vibe coding with Claude, while another is gradually forgetting familiar faces and memories.

To better gauge this difference, scientists have been developing “clocks” that measure biological age. Rather than the number of candles on a birthday cake, these tools capture health at the cellular level and are remarkably accurate at estimating disease risk and even life expectancy. But how they work is hard to explain.

Now Harvard scientists and collaborators have released a powerful and more interpretable clock. Using the gene activity of thousands of individuals and animals, the clock predicts biological age in rodents, monkeys, and humans, including how many years they have left.

The analysis involved over 11,000 gene activity profiles across four species, highlighted shared mechanisms during aging, and responded to known anti-aging interventions—such as parabiosis, during which aging animals receive blood from a young donor.

Although the clock isn’t ready for clinical use, it is a boon to scientists working to slow or even reverse the unstoppable progression of time. It “could help researchers to pinpoint which processes are modulated by interventions or diseases,” wrote João Pedro de Magalhães at the University of Birmingham, who was not involved in the work.

Tick, Tock

Biological clocks come in a variety of flavors.

Most rely on AI to make sense of information held in large databases of people. One of these, for example, uses blood proteins related to brain aging to reflect cognition and its decline better than chronological age. Another type, metabolomic age clocks, sorts through protein and fatty acid building blocks to estimate biological age. These clocks correlate well with risk of inflammation, chronic disease, and frailty (where the body struggles to recover from a mild infection or minor fall). More recent multi-omics clocks combine blood measures, metabolism, gene activity, and clinical data for a comprehensive bird’s-eye view of biological age.

But epigenetic clocks remain the field’s defining breakthrough.

As we age, chemical tags accumulate on DNA, switching genes on or off. The pattern of these tags shifts over time and is shaped by everyday life—diet, exercise, stress, sleep quality. Studies have found that the age gaps between biological and lived years measured by the well-known Horvath epigenetic clock, which relies on DNA methylation, were associated with the risk of various types of diseases. Later versions of the Horvath clock could predict maximum lifespan. And other groups have developed “pan-mammalian” epigenetic clocks that work across species.

“One drawback of epigenetic clocks, however, is their limited interpretability,” wrote Magalhães. “The mechanisms that underpin age-related methylation changes are still debated.”

Clocking In

In the new study, the team measured aging by looking at gene activity, or transcriptomics. Transcriptome profiles capture which genes are switched on at any given moment.

Previous studies have linked the aging transcriptome to chronic inflammation, faltering mitochondria, and the gradual breakdown of the extracellular matrix, the molecular scaffolding that supports tissues and organs. With age, these systems go awry.

“Because the signatures reflect changes in the activity of specific genes, transcriptomic biomarkers are more interpretable than are epigenetic ones,” wrote Magalhães. The tradeoff is that gene activity is far more dynamic than DNA methylation, the epigenetic signature used in the Horvath clock. A transcriptome can shift in response to stress, illness, exercise, or even the time of day, making it a less reliable measure of aging.

To make the new clock, the team assembled over 11,000 transcriptomes, heavily relying on data from the Interventions Testing Program, a giant effort to study longevity treatments in mice. The dataset included mice exposed to genetic tweaks, drugs, and dietary therapies known to affect aging and lifespan. The team also added more than 2,600 samples from monkeys, several hundred from rats, and over 4,000 from humans to deliver a cross-species view of aging.

They then built multiple transcriptome clocks that estimated age and mortality risk. To validate the clocks, they turned to an independent dataset that included rodent models of accelerated aging, Alzheimer’s diseases, chronic kidney disease, and other age-related conditions. When applied to individual cells, the clocks yielded older transcriptomic ages in more than 90 percent of the samples, suggesting that aging is deeply rooted at the cellular level.

In humans, the clocks accurately predicted the lifespans of participants enrolled in a large heart health study. They were also sensitive to environmental factors that affect aging, ticking forward after exposure to radiation or chronic diseases and rewinding after treatments such as young-blood transfusion, a strategy shown to rejuvenate elderly rodents.

An analysis of the genes driving the clocks highlighted many of the usual molecular suspects. Aging turned on genes involved in inflammation, cellular energy disfunction, and senescence—where failing cells leak toxic molecules. Many of these signatures appeared across organs and species, suggesting that core aspects of aging have been conserved in mammals.

These findings are especially valuable for longevity researchers, who often work with rodent models. Despite living a fraction of a human lifespan, aging rodents undergo transcriptomic shifts similar to those found in us. The new clock could easily test their biological age after potential anti-aging treatments, capture the immediate effects, and predict lifespan, long before they die. It could, in theory, speed up aging research and the quest for treatments.

But to be clear, like other aging clocks, it isn’t a crystal ball. Scientists don’t know if the transcriptome changes drive aging or merely reflect its aftermath. The signatures could be capturing overall health and resilience, rather than molecular changes associated with aging per se.

That distinction matters. As we grow older, cells activate a variety of protective genes to counter rising stress, inflammation, and damage. Not every age-related transcriptomic change is harmful. Some changes reflect the body’s attempt to fight back. Because transcriptomes capture only a snapshot in time, scientists still need to differentiate genes that contribute to aging from those that help defend against it and learn how those patterns shift over time.

There’s a broader challenge too. Researchers are building more and more biological clocks using different criteria, and they don’t always agree. One may say you’re far older than another. This highlights “the need for any aging biomarker to be validated carefully,” wrote Magalhães.

The post How Fast Are You Aging? New Genetic Clock May Have the Answer appeared first on SingularityHub.

Artificial Intelligence

In This Manhattan Lab, AI Designs Materials From ScratchAdele Peters | Fast Company

“The lab uses standard materials science equipment, but it’s almost all automated and run by AI; if it has a new idea at 4am, it starts running again. It can run as many as 50 experiments in a day, and the team is aiming to increase that to 100 experiments a day by the end of the summer. A human materials scientist, Krause says, might do 50 experiments in a year.”

The post This Week’s Awesome Tech Stories From Around the Web (Through May 30) appeared first on SingularityHub.

Sodium-ion batteries are rapidly gaining on lithium in consistency and fast charging.

As demand for electric vehicles and grid storage surges, battery makers are searching for alternatives to lithium that are cheaper and easier to source. New research suggests sodium-ion batteries, which have long been heralded as a promising alternative, may be maturing faster than expected.

Lithium-ion batteries dominate the market thanks to their excellent energy density and well-developed supply chains. But lithium prices have been swinging wildly in recent years, and there are concerns about lithium market concentration—the vast majority of extraction happens in a handful of countries, like Australia and Chile, and China dominates lithium processing.

This has driven interest in novel chemistries. Sodium is a leading contender due its low price and abundant deposits all over the globe, but performance concerns have held back adoption.

Chinese companies, however, have begun to take sodium batteries seriously. And in a new analysis in Cell Reports Physical Science, German scientists found that cells made by the Chinese manufacturer HiNa compare favorably to the lithium-ion batteries Tesla uses in its cars.

“The combination of good uniformity, high power capability, and strong low‑temperature performance makes these cells attractive for stationary storage, grid services, and shorter‑range or commercial vehicles where potential lower cost and resource availability matter more than maximum driving range,” Moritz Schütte, a battery researcher at RWTH Aachen University who co-led the study, said in a press release.

A good battery needs uniform cells. If some cells are weaker than others, it can degrade the entire battery over multiple charge and discharge cycles, and it also makes it harder to control and optimize power flow in and out of the pack. It’s also a key indicator of a mature production process.

To see how the HiNa batteries stacked up, the researchers tested 120 individual sodium-ion cells using a non-destructive technique called impedance spectroscopy. Here, they applied a current across various frequencies to probe the internal physical chemical properties of the device.

The team then tested the cells at varying currents and temperatures from -4 to 113 degrees Fahrenheit to get a picture of their power performance under a wide range of conditions. They also used X-rays to probe the batteries’ internal structure, before opening them up to analyze the size and composition of various components in more detail.

Across the 120 cells, resistance varied by just 5.3 percent—a level of consistency the researchers say is comparable to well-established lithium-ion production lines. And while fast charging can rapidly degrade performance, the cells maintained full capacity at charge rates high enough to fill the battery in just 15 minutes.

Low temperature also reduces capacity by slowing down a battery’s chemical reactions. But the researchers found the HiNa device discharged over 80 percent of its usable energy at -4 degrees Fahrenheit after charging at roughly room temperature. That figure fell to 56 percent, however, when it was also charged at -4 degrees Fahrenheit (as opposed to room temperature).

The batteries didn’t get a universally glowing report. The team found energy density still lags the best lithium-ion cells, and as noted, charging at low temperatures remains a problem. “The high‑power performance was better than one might expect from an early commercial sodium‑ion product,” said Schütte. “However, for applications that require frequent charging at low ambient temperatures, appropriate thermal management or operating strategies will be important.”

But given the technology’s other attractive characteristics, the battery industry appears to be forging ahead. Chinese automaker Changan Automobile recently began selling the Nevo A06, which is fitted with a sodium-ion battery made by CATL, the world’s dominant battery manufacturer.

According to Bloomberg, CATL’s chief technology officer recently told a media event that the company will begin mass-producing sodium-ion cells in the fourth quarter of this year, declaring “the era of sodium and lithium shining together has arrived.”

A typical SUV powered by a sodium-ion battery would only have a range of around 215 miles, compared to the 250 to 370 miles for a lithium-ion powered vehicle, according to calculations from the International Energy Agency. But that’s nothing to turn your nose up at, particularly considering the fast-charging capabilities discovered by the RWTH researchers.

Whether the technology establishes a commercial foothold may well depend more on the vagaries of geopolitics than its inherent qualities. But cheaper, easier to source batteries can only be a win for the planet.

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AI can rifle through enormous libraries of information to connect far-flung ideas—conceptual leaps remain a purely human skill.

Last week, OpenAI shocked the mathematical community by revealing that one of its internal artificial intelligence models had found a counterexample to a famous conjecture made by legendary Hungarian mathematician Paul Erdős in 1946.

The planar unit distance problem, or Erdős problem 90, has intrigued mathematicians for decades. The new result is no mere curiosity. Canadian mathematician Daniel Litt described it as “the first result produced autonomously by an AI that I find interesting in itself.”

The breakthrough, produced with a general-purpose AI model rather than one specialized for mathematics, also highlights how AI is changing mathematical research itself. Days after OpenAI’s paper, US mathematician Will Sawin followed the same line of reasoning to an improved result. Also last week, a team from Google DeepMind used one of their own models to resolve nine lesser open problems left by Erdős.

At the same time, results like this show us what kind of mathematics current AI models are good at—and where their capabilities are still uncertain.

Dots and Lines

Paul Erdős was one of the most prolific mathematicians of the twentieth century. He was famous for asking deceptively simple questions whose solutions often resisted decades of effort.

At first glance, the underlying problem seems relatively straightforward. Suppose you have some number of points—call the number n—drawn on an infinitely large piece of paper. Given you can arrange the points any way you like, how many pairs of points can be positioned exactly one unit of distance away from each other?

If you try this problem yourself (on a presumably finite piece of paper), you may quickly gravitate towards a square grid as a promising candidate for the best arrangement. The spacing of the grid naturally creates many pairs at a regular distance apart.

This intuition influenced much of the early thinking about the problem. As the number of points grows, grid-like arrangements continue to appear to be remarkably effective.

For decades it was widely believed these highly regular structures were about as good as it gets. Erdős himself conjectured that no construction could improve substantially on these intuitive arrangements, even for an extremely large number of points. (The new best result, by Sawin, reportedly only starts to yield improvements for around 10^2000000 points—that’s a one followed by two million zeroes.)

Over the past 80 years, mathematicians have tried to prove Erdős either right or wrong. Their efforts have linked the problem to other areas of mathematics called incidence geometry, graph theory, and extremal combinatorics. While a full proof remained elusive, there was a general feeling that Erdős’ conjecture was probably true.

However, OpenAI’s recent breakthrough proved Erdős’ intuition wrong. The new result uses tools from an area of mathematics called algebraic number theory to show there are patterns of dots that involve many more unit-distance pairs than the square grid, for infinitely many values of n.

No Hesitation

In an article OpenAI published alongside the new paper, several leading mathematicians remarked on the result.

Fields Medalist Timothy Gowers wrote that if a human researcher had submitted the paper with this result to the prestigious journal Annals of Mathematics, he would have recommended publication “without any hesitation.” He also added that no previous AI-generated proof had come close to this level of sophistication.

This breakthrough also represents the first major mathematical open problem solved with AI with minimal human intervention beyond the initial prompt. The accompanying paper shows the prompt given to the model, as well as a recount of the “chain of thought” conducted by the model.

This has renewed broader questions about the capabilities of AI to aid in, and perform, mathematical research.

Three Keys to Mathematical Research

Research mathematicians have been using computers for a long time, but their work is rarely driven by computation alone. Most major breakthroughs emerge from a delicate combination of three things: expertise developed over years, sustained effort to apply that expertise creatively to explore ideas (many of which turn out to be dead ends), and occasional conceptual leaps that suddenly reorganize how a problem is understood.

The first two are domains where AI models excel: as noted by Gowers, large language models such as ChatGPT have an “encyclopedic knowledge of mathematics.” Moreover, they can follow huge numbers of speculative lines of inquiry, even those unlikely to lead anywhere, without human time constraints.

The latter seems to be what provided the key to success here. In hindsight, it seems an expert given a small number of hints would be likely to be able to reach the same proof. As Gowers notes:

“Many of the ideas needed for the proof were present in the literature already, and for such ideas either no hint is needed, since the expert is aware of that piece of literature, or a highly generic ‘look it up’ hint would be enough.”

Lightbulb Moments

The harder question is how much AI can contribute to genuine conceptual leaps. These acute moments of insight, where a lightbulb moment reframes a problem in an entirely new way, are often seen as the most human part of mathematics.

These leaps are hard to formalize and even harder to predict. It remains unclear whether AI models can replicate them, even with recent advances.

What is clear is that AI models are causing a seismic shift in the way mathematics is discovered.

For centuries, progress in mathematics depended almost entirely on human creativity and persistence. Now, for the first time, researchers are working alongside systems capable of autonomously exploring enormous spaces of ideas and contributing to problems once thought accessible only to human insight.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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The drops, tested on mice, healed eye damage using light-sensitive particles—sourced from ordinary spinach.

The unassuming vial of eye drops could easily belong on a pharmacy shelf. But swirling inside are microscopic bits of photosynthetic machinery made from plants. Within minutes of giving the drops to mice, their eyes gain an extraordinary ability beyond that of any mammal. Like a leaf, they can now harness the power of sunlight.

Photosynthetic eyes sound like they’re straight out of science fiction, but there’s a practical use researchers are after. Chemical reactions during photosynthesis generate powerful antioxidants that ward off inflammation and could potentially treat a range of health conditions.

Called LEAF, the technology is creative, effective, and simple. Its main ingredient can be found in grocery store spinach. In a paper detailing the work, researchers at the National University of Singapore and collaborators say they developed a gentle chemical cocktail to extract some of the core mechanisms used in photosynthesis.

Introduced to mammalian cells—including those that make up the cornea and immune cells—the floating photosynthetic particles made themselves at home and restarted work as usual when exposed to light. In mice with dry eye disease, LEAF continuously pumped out protective antioxidants, healed corneal scarring, and kept their eyes hydrated for days.

The animals scurried around as usual, without any inkling their eyes were now part plant.

“This is an exciting finding as we have, for the first time, demonstrated that plant photosynthetic machinery can be transplanted into mammalian tissue to generate biologically useful molecules, powered entirely by the same light that enables our vision,” study author  Kuoran Xing at the National University of Singapore said in a press release. “We, too, can have limited photosynthetic abilities.”

Planting an Idea

Dry eye disease is one the most common eye problems, affecting roughly 1.5 billion people worldwide. Symptoms are hardly trivial. Irritation and chronic pain make daily life miserable. Overtime, the disease causes scarring of the cornea, blurred vision, and sensitivity to light. The condition has been linked to depression, anxiety, and other health struggles.

Current treatments address the underlying inflammation, but they’re expensive, have limited availability, and long-term use can provoke uncomfortable side effects throughout the body.

At the heart of the disease is a vicious, runaway cycle of cellular dysfunction. When our cells generate energy, they also produce byproducts called reactive oxygen species. Like tiny bullets, these wreak havoc if left unchecked. Some tunnel through delicate protective membranes and disrupt protein function. Others damage DNA, and in severe cases, cause cell death.

Our bodies constantly mop them up with a molecule called NADPH. But during inflammation the defenses are overwhelmed. Reactive oxygen species destroy the cells’ ability to make NADPH. Left unchecked, the cell enters a death spiral: It tries to maintain its supply of energy, but this ironically, generates more bullets and these activate immune cells. Trying to boost NADPH under these conditions is a losing battle.

That’s why spinach caught the team’s attention. Plants make NADPH during photosynthesis. Powered by sunlight, they churn out energy and the antioxidant in completely different ways than our cells. Theoretically, adding plant-based machinery into our cells could bypass existing cellular mayhem and provide a new source of NADPH.

A plant-animal crossover sounds preposterous, but it already occurs in nature. The sacoglossan sea slug eats microalgae high in chloroplasts—the photosynthetic organelle in plant cells—and stores them intact in its guts. When it can’t find food, the slug can survive on photosynthesis.

In previous studies inspired by the slug, scientists have tried transplanting core bits of photosynthetic machinery called thylakoids into animal cells. They look like stacks of coins, but their interior structure is far more complex—any misalignment results in catastrophic failure.

Researchers had already tried transplanting bits of this machinery into mouse knee cells but found it required high levels of an additional chemical to keep it in working order. In another study, a team targeted rheumatoid arthritis, an inflammatory disease of the joints. But getting light into the tissues was a struggle, and the system needed lengthy exposure.

Eyes, however, are a natural window to visible light.

Eyes on the Prize

In the new study, the team’s main invention was figuring out how to keep thylakoids intact while stripping away other parts of the chloroplast that destroy NADPH.

They eventually learned how to extract thylakoid particles from spinach in such a way as to maximize NADPH production. Measuring roughly 400 nanometers across—the size of a very small bacteria—the particles produce NADPH when exposed to ambient light.

The team tested them on two types of cells responsible for dry eye disease: Large immune cells called macrophages and corneal cells. In petri dishes, both cell types readily soaked up LEAF. Once released inside the cell, the plant thylakoids steadily pumped out NADPH.

Within 30 minutes of light exposure, the amount of reactive oxygen species tanked. Angry macrophages relaxed into a state that battles inflammation. In tears collected from patients with dry eye disease, LEAF boosted NADPH levels roughly 20-fold and slashed a damaging oxidative chemical over 95 percent. Tests examining the wider metabolic landscape showed cells reverted to a healthier state after being treated with LEAF.

This photosynthesized NADPH supply can “power antioxidant metabolism,” promote cell repair, restore balance, and break the vicious cycle, wrote the team.

In a final test, they treated a mouse model of dry eye disease with the drops twice daily for five days and pitted it against an approved chemical treatment. LEAF easily entered the animal’s eyes after 30 minutes. Under ambient light, the system doubled the amount of NADPH and reversed corneal damage, outperforming the therapeutic drug.

Surprisingly, although the treatment is made of plant matter, it didn’t trigger immune attacks in the eyes or other parts of the body, such as the liver or heart. But the team didn’t specifically test to see if the drops improved the animals’ eyesight or if adding the photosynthetic machinery changed their perception.

That said, LEAF is especially well-suited for clinical use. It’s easily manufactured and stored and was consistently effective across four independent batches made in Singapore and China, with each sourced from local spinach. The nanoparticles are stable for two weeks at room temperature and last up to a year at -80 degrees Celsius.

Because LEAF “is derived from spinach, delivered as a simple eye drop, [and it] requires no external device or power source…we believe it has a strong potential for clinical translation,” said study author David Tai Leong.

Beyond dry eye disease, LEAF could be made into a cream that harnesses sunlight to treat skin inflammation disorders. The team is also looking to generate photosynthetic molecules in deeper organs and boost the health of mitochondria, the cell’s energy factories.

“It is almost surreal when thinking of a possible future reality where human cells can have some limited but beneficial form of photosynthetic ability not only in the eye but elsewhere, too,” said Leong.

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