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The Literacy Crisis Reaches Dog-Bear Science
“New analysis of more than 300 bear encounters suggests our canine companions often increase odds of conflict and rarely serve as early warning systems many recreationists assume,” reads a subhead in Mountain Journal. The thing is, that study reaches no such conclusion. But you have to read beyond the abstract to understand that. Mountain Journal is not alone. Let’s look at a brief list of takeaways published elsewhere just to establish that none of them seem to know how to read science. Wes Siler’s Newsletter is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe Here’s CBC: “A new peer-reviewed study, The Canine Conundrum: Is a Dog a Help or Hindrance in Bear Country?, dug into conflicts similar to what the video depicts, by analyzing 326 reported human-bear conflicts from 1901 to 2023 involving dogs.” “The study found 54 per cent of conflicts were started by dogs. In the encounters researchers included in the study, five people and 56 dogs were killed, and 170 people were injured.” “Lana Ciarniello, an independent researcher who co-authored the study, said researchers found when a person goes hiking with at least one other person, the odds of a negative bear encounter decrease by 50 per cent.” “However, when a dog is involved, the chances of a negative bear encounter increase because ‘a dog really adds a chaotic factor into the mix of recreation,’ said Ciarniello.” Again, the study finds no data supporting that conclusion. “Hiking with your pet dog may make you feel safer in bear country—but a hiker’s recent encounter and a new study prove that the numbers don’t add up,” writes Backpacker, which is now part of Outside. The numbers don’t add up to support this take. There’s plenty more examples, but all are equally dumb, so I’ll stop there. How did so many people get this so wrong? Three reasons: 1) the study is published behind a paywall, with only the abstract freely available. 2) the methodology sets out to create work relevant to a hyper-specific audience, not the public. And 3) the dataset studied is almost absurdly limited and doesn’t bear (get it?!) comparison to wider information. Let’s work through all three. The Problem is Money I understand the irony, since I’m writing about this on a subscription-model newsletter, but information wants to be free. The study is published by The Wildlife Society and costs $16 to access. Reprinting it is specifically prohibited by the terms of use, so I can only share snippets and my conclusions with you here. Having worked as a journalist for 24 years, I can report with some authority that journalists are both lazy and cheap. I don’t think it’s a stretch to say that much of the material you’re seeing written about this study is drawn from the freely-available abstract (above) only, and not a deep dive into its methodology or data. And that’s a problem, because abstracts are where researchers are able to break from their numbers and editorialize their opinions about conclusions. So what’s getting reported isn’t based on numbers, it’s based on an opinion designed for a specific audience, not the general public. Hell yeah ladies, kick that bear’s ass. The Methodology is Designed to Support a Predetermined Conclusion One of the biggest issues with the science that everyone likes to cite about bear spray’s efficacy is that no one understands that study was designed for a specific audience, looking for a specific answer. And since that audience is not the public, and our safety was not the question being asked, its conclusion is rendered irrelevant to our needs. Yes, I’m talking about Efficacy of Bear Deterrent Spray in Alaska, which was created by Tom Smith and Stephen Herrero, two of the same scientists who put together this dog-bear conflict analysis. In the spray study, the purpose was to present relevant agencies with the information they needed to issue their field workers with bear spray. As such, incidents where people were unable to access the spray or similarly failed to get it deployed in time were excluded from the results, and only incidents they deemed successful were included. Could a ranger roll down their vehicle’s window and scare a bear away from a dumpster by spraying it? Turns out the answer was yes, and that was good enough for the purposes of that study. The sample size of actual bear attacks—the thing you and I would want bear spray to provide a solution to, was too small to draw conclusions from, and didn’t indicate success in that role. Is a Dog a Help or Hindrance in Bear Country? has similar limitations. “Insights provided here are intended to help wildlife managers create effective guidelines regarding the presence of domestic dogs in bear habitat,” is explained clearly by the study’s text. To conduct the study, Smith and Herrero went looking for publicly available reports of dog-bear conflicts: “All incidents included in our analysis involved at least one bear, one dog, and one person who was at immediate risk of injury (e.g., not in a protective structure such as a cabin or vehicle), injured, or killed,” explains the report’s methodology. Why is that a problem? “We recognize that data used in this study are biased in that people generally only report adverse interactions with bears, not benign ones. Consequently, we assume that for the most part, people and their dogs pass through bear country without incident.” As was the problem with the bear spray study, people are drawing conclusions here from a study that was never intended to be relevant to the general public. The Smith-Herrero database famously excludes Alaska’s Defense of Life and Property records, which are elsewhere considered the largest and most authoritative source on human-bear conflict. The Dataset is Absurdly Limited A 2009 study conducted by the Washington Department of Fish & Wildlife estimates that each year there are at least 43,000 documented complaints of human-bear conflict across North America. Is a Dog a Help or Hindrance in Bear Country? Examined those reports for years ranging from 1901 to 2023, and only managed to find 326 such incidents that met the criteria explained above. Let’s do some quick math, poorly. 43,000 incidents x 122 years = 5,246,000 total incidents of documented human-bear conflict. Smith and Herrero were only able to find 326 incidents that met their criteria for dog+human+bear+risk or occurrence of injury. That means there’s a 0.006 percent chance that the study’s conclusion—that dogs make bear encounters more dangerous—even gets a chance of occurring. And remember, that’s only in documented conflicts. There’s no way to know how many people across North America run into a bear in any given year without any conflict, but it’s probably safe to assume that the total number is well in excess of 43,000. And, dig deeper into the study and you can find further restrictions that skew its conclusions even more. “While the people involved in these incidents did not report the level of training their dogs had received, it is our assumption that most were untrained companion dogs as opposed to well-trained bear dogs, such as Karelians and livestock guardian breeds that have proven to be significant aids in bear country,” the study’s methodology explains. And that’s a problem, because an ongoing study here in Montana is finding that the simple presence of a Kangal like our youngest dog Teddy can reduce visits to private property from grizzly bears by 87.8 percent. In addition to being a badass, Teddy is also a companion animal. And in my admittedly limited sample size of the three dogs currently present in our living room, she’s no more effective than her mutt brothers. Plenty of people own a dog capable of deterring bears, even if they didn’t set out to own a bear dog. The real purpose of this study appears to be a recommendation that leashes are mandated and recreationists pursue training in their deterrent of choice. But that doesn’t make for sensational headlines. Are There Any Real Takeaways Here? Let’s throw some numbers around, just to try and get our hands around what the larger picture might look like for bears and dogs here. The American Veterinary Medical Association estimates that 42.6 percent of American households own a dog. The Canadian Animal Health Institute says roughly 35 percent of households in Canuckistan own a dog. In an attempt to create the worst possible result for dogs, let’s run with that 35 percent number. And given that outdoorsy and rural people are more likely to both own a dog, and have bear encounters, let’s just make a giant assumption and apply that number to those 43,000 conflicts documented annually. If 35 percent of 43,000 human-bear encounters involve a dog, then we can assume that at least 15,050 dog-bear conflicts are occurring annually in North America. 15,050 x 122 = 1,836,100. Smith and Herrero were only able to find 326 incidents that met their human, dog, chance or occurrence of injury criteria across 122 years. And only 170 documented human injuries. So, with all the obvious caveats about these numbers in no way describing reality beyond narrow study parameters, what they actually found is that your risk of injury if your dog tangles with a bear is only 0.009 percent. I’ll take those odds next time I’m hiking my dogs in bear country. Top photo: “Superintendent Horace Albright, eating pancakes with bears, at Lake Lodge, Yellowstone National Park.” NPS. A journalist with more than two decades of experience working around the world, Wes Siler is here to cut through the outrage and disinformation to bring you the factual, insightful, actionable reporting you need to understand what’s going on. Upgrading to a paid subscription supports this reporting, and buys personal access to Wes, who will help you save money on gear, plan outdoor adventures, and prepare for real life, and who promises he’s less salty in real life than he sometimes comes across as on the Internet. Wes Siler’s Newsletter is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe
Two Fathers, No Mother: Scientists Defy Nature and Shock the World - Futura-Sciences
A Bold Scientific Journey Two dads by heart—sure, that’s possible. Two biological fathers? Now that’s a whole different ball game. Nearly two years ago, a Japanese research team managed to bring into the world mice that didn’t have a mother, but rather two biological fathers. This was achieved by a...
Jays Lose 3-1 to Yankees
Cam Schlittler was always going to be a tough assignment. They managed six hits and three walks off him, which under most circumstances you’d have to say was a good day, but couldn’t push more than one across. The Jays pitching staff was in some sense even better, allowing just four total hits and five […]
Maple Leafs Captain Auston Matthews Ripped As Overpaid
The Toronto Maple Leafs and former general manager Brad Treliving managed to get captain Auston Matthews to agree to a four-year, $53 million contract to remain with the club through the end of the 2027-28 NHL season.Right now, Matthews counts $13.25 million against the salary cap as one of the highest-paid players in the NHL. […] The post Maple Leafs Captain Auston Matthews Ripped As Overpaid appeared first on HEAVY.
Five group homes. Two scandals. Health Department program under microscope
The Health Department revoked the licenses for group homes owned and managed by Sekou Dukuly, the executive director of the National Port Authority of Liberia, as well as those connected to Andrea Sampson, a former Anoka County Attorney who prosecutors say used some of her properties to traffic women before converting them into group homes and billing Medicaid.
Webinar to discuss how Georgia health system improved capacity monitoring efforts
The AHA’s Hospital Capacity Management Consortium will host a webinar Sept. 9 at 1 p.m. ET for capacity management and operations leaders, exploring how Georgia-based Emory Healthcare’s Capacity Command Center improved patient access, optimized throughput and proactively managed systemwide demand.
Apple Card Turns Seven With Big Change Ahead
Today marks the seventh anniversary of the Apple Card becoming widely available in the U.S., following a more limited preview period. Apple's credit card can be managed in the iPhone's Wallet app, with key benefits including color-coded spending summaries, no fees, and Daily Cash cash back paid out daily. Apple Card holders can also open a high-yield savings account. The anniversary comes ahead of a big change planned for the Apple Card.
Protecting the eye health of an ageing global population
Unmanaged vision loss costs the global economy over $411 billion annually in lost productivity. Governments and businesses should help protect eye health.
Linux 7.2 Released With Faster I/O, New AMD & Intel Driver Improvements
Linus Torvalds just released Linux 7.2 as stable! After a very busy kernel cycle due to the increased patch and reporting churn due to AI/LLM ages, Linux 7.2 managed to make it out today on-schedule for this feature-packed kernel
Ex-Browns QB Blasts Team Over Handling of Shedeur Sanders
Robert Griffin III criticized how the Cleveland Browns have managed their quarterback battle between Shedeur Sanders and Deshaun Watson. The post Ex-Browns QB Blasts Team Over Handling of Shedeur Sanders appeared first on HEAVY.
Assassin's Creed Black Flag Resynced's Denuvo Has Been Cracked
Assassin's Creed Black Flag Resynced is the latest victim of voices38, who has managed to crack its Denuvo anti-tamper protection.
One UI 9.5 shows up on a Galaxy phone before Samsung is even done with One UI 9
A tipster has shared screenshots revealing that they managed to install an early build of One UI 9.5 on their Galaxy device.
Someone is already running One UI 9.5 on a Galaxy S26
As Galaxy users around the world wait for Samsung to bring One UI 9 (Android 17) to their devices, someone has managed to get hold of a One UI 9.5 firmware for
AI needs science’s search history
“Actually, that’s where the gold is,” said Alasdair Russell, my graduate school friend who leads a pre-clinical genome editing group at Cambridge. He was talking about the winding road of science that is omitted from published papers. “When you’re discussing how to do the experiment, and why this way is better than another way, and what does the data really mean? I know what it shows, but what does it mean?” At RAAIS 2026, he described how his group has begun logging the twisting path of discovery as it happens, recording the verbal and written exchanges that normally disappear. Ideas become nodes in a living graph: they branch when a meeting produces two plausible experiments, merge when separate lines of evidence converge, and go dark when someone quietly stops pursuing them. In his implementation, one agent scores novelty, while another tries to learn “how scientists think and how they navigate through a complex world of data,” so that high-potential nodes can trigger deeper investigation. As frontier AI labs deploy agents toward scientific discovery, giving AI authentic scientific taste remains a trillion-dollar dilemma. The bottleneck is that the record we have kept for centuries of science might be insufficient to get us there. The experiments that never make the paper Peter Medawar, 1960 Nobel laureate and “father of transplantation,” asked in 1963 whether the scientific paper was a fraud, and answered yes: the form of the paper misrepresents the thinking that produced it. Sixty years on, the diagnosis is unchanged. A paper presents a clean progression from hypothesis to result to conclusion. Lost along the way are the unconventional theories, the abandoned or unaffordable methods, and the underwhelming and inconclusive data. It reads like a browser history with every dead end deleted: the ten open tabs, the four rephrased queries, the wrong turn down a subforum. All scrubbed, leaving one clean path from question to answer as the canonical path. What has changed since then is that the discarded material is now worth something. No high-impact journal wants these artifacts, but raw trial and error is the most likely source of the training data required to develop scientific intuition, or what researchers call taste. Earlier attempts to capture the discards were motivated by scientific integrity. The Journal of Negative Results in Biomedicine launched in 2002 to publish rigorous studies that disputed established models or exposed ineffective treatments. Its archive preserved negative conclusions after they had become papers, rather than the live alternatives and arguments that produced them. BioMed Central closed it in 2017, saying the mission had been served now that other journals publish null results. A less generous reading is that in fifteen years it published around 200 papers because almost nobody wants to read a negative result, let alone write one up when it will not count toward academic tenure. AI models are different. Even an uninteresting negative result can be useful, provided it is labeled. Earlier this year, Anthropic put a version of this to the test. The company pulled 129 decision points from real Claude Code sessions between January and March 2026, showed models the work up to a human detour, and asked what should happen next. Claude Mythos Preview beat the human choice 64% of the time, while Opus 4.5 managed 51%. The comparison was tilted toward the models because Anthropic deliberately chose moments where the human decision had room for improvement. On 127 further scenarios where the human action was already strong, the models improved on it only about 20% of the time. The study was possible because Anthropic’s researchers work inside a tool that logs by default. The reasoning, detours and outcome are produced in the same working environment. Biology has no equivalent. Its reasoning happens in hallways, at benches, in Slack threads and on whiteboards, while the outcome arrives weeks later somewhere else. The record has to emerge from the work itself. Ask scientists to reconstruct it afterwards and we’ll create another polished account. Subscribe A log is not a label Before any of this becomes training data, a negative result has to say what failed. The first paper published in the Journal of Negative Results in Biomedicine examined 234 negative studies across five leading medical journals. Only 30% discussed statistical power, and half clearly defined a primary outcome. Tell a model an experiment failed, without telling it whether the assay was underpowered, a reagent had degraded, or the hypothesis was simply wrong, and it will learn noise with confidence. Decision histories have a second missing label too. When a lab considers five experiments and runs one, only the chosen branch returns an outcome. The other four are experimental counterfactuals. Researchers call this the selective labels problem: the data reveal results only for the actions someone chose to take. Such a record can teach a model to imitate a lab’s taste, but it cannot establish that the taste was good. It also smuggles local constraints into general lessons. A lab that never ran cryo-EM because it did not own a microscope teaches a model that cryo-EM is rarely the right call. Alasdair’s nodes preserve the candidate set, which is the necessary first step. To be useful, each decision record needs six fields: The evidence available at that moment, and nothing that arrived later. The candidates considered, including the ones dismissed in a sentence. For each candidate: the expected result, the confidence attached to it, and the cost in time and money. The route chosen, and the reason. What happened next. How the evidence changed the scientist’s view. These must be timestamped before the outcome, because hindsight turns uncertainty into inevitability. Disagreement also has to survive. Averaging three scientists into one clean rationale reproduces exactly the information loss we are trying to fix. But there is an obvious failure mode here. Once decisions are logged and scored, people log for the record post-facto. Anyone who has watched an electronic lab notebook fill up with retrospective tidying knows how quickly a research tool becomes a compliance exercise. A record that costs a scientist ten minutes of honest reflection per decision is worth far more than one that costs an hour of performance. A publication initiative called Registered Reports offers a useful starting point. Here, researchers submit their rationale, methods and analysis plan for peer review before the data exist, and the journal commits in principle to publish if they follow the approved plan. Nature has now expanded the format across every field it covers. This approach to paper writing timestamps intent before the outcome, but it freezes one plan. A useful search history must also preserve how the plan changed, which alternatives were rejected, when, and on what evidence. Subscribe Run the runner-up Autonomous labs show what happens when decisions and outcomes are connected in a loop. Liverpool’s mobile robotic chemist ran 688 experiments over eight days in a ten-variable formulation space. A batched Bayesian search used each result to choose the next experiments and found photocatalyst mixtures six times more active than the starting formulations. Every completed experiment changed what the system did next. The robot optimized within a goal and search space that humans had already chosen. It did not decide which scientific question mattered. So what this work demonstrates is narrower, but still useful: a search history pays off when the options return standardized outcomes quickly. Open-ended biology is harder because branches can take weeks and the discarded alternatives may never be run. Some exploration therefore has to be bought. Where two branches are plausible and the stakes justify the cost, a lab should sometimes run the runner-up. Otherwise the record captures what today’s scientists usually chose and stays silent on what they systematically overlooked. A funder could ring-fence a small fraction of a grant for the branch not taken, provided that the decision record and the outcome are both deposited. That costs money, but so does having every lab rediscover the same abandoned path. Test whether taste transfers When it comes time to evaluate if our new AI system exhibits taste, the comparison should run across laboratories and fields. At selected decision points in a live research campaign, we should freeze the six fields: the evidence, the candidates, expected results, confidence, costs and the choice made. Experts then rank the options before the outcomes are known, and those outcomes are checked independently later. We could give models one of two diets - papers alone, or papers plus decision histories - and ask them to rank the next experiment on unfamiliar projects. Then, we score information gained per dollar and week, calibration, and how quickly weak branches are abandoned. If histories improve choices only inside the lab that produced them, they amount to useful organizational memory. If they improve choices on unfamiliar problems elsewhere, Alasdair’s group will have captured something science has never managed to write down: a transferable record of taste. Until that happens, a scientific search history is a promising record, not yet a training set.
Moderate Dem scores upset in Wisconsin governor's race; Darline Graham in runoff
Milwaukee County Executive David Crowley managed a razor-thin victory over his democratic socialist challenger, state Rep. Francesca Hong
City Vault: The Keegan era begins in winning style
On this day 25 years ago, Kevin Keegan took charge of his first competitive game for Manchester City.Keegan was in the Maine Road dugout for a clash with Watford, managed by another new boss in Gianlu...
Battlefield 6 Player Successfully Refunds Game After 470 Hours of Game Time Due to Game Modes Being Removed by Devs
One Battlefield 6 player managed to successfully issue a refund for the game, and this is despite pouring in 470 hours of playtime.
Scientists Release Biggest 2D Map of the Universe
Key Takeaways The DESI Legacy Imaging Surveys combined more than 263,000 telescope exposures to make the largest 2D map of the universe in visible and near-infrared light. Astronomers can pair the Legacy Surveys map with their own observations to explore our universe and search for rare phenomena. The 2D map serves as the foundation for the Dark Energy Spectroscopic Instrument survey to measure the universe in 3D and investigate dark energy. Hold on to your telescopes: the DESI Legacy Imaging Surveys team has released the largest-ever 2D color map of the universe. The 5.6-trillion-pixel map contains nearly 4 billion celestial objects, primarily stars and galaxies. The data is available for all to use and publicly viewable through the Legacy Survey Sky Viewer. Astronomers and citizen scientists can explore the map or combine it with their own observations to better understand our universe. Researchers can search for rare phenomena like gravitational lenses, observe fleeting events like supernovae, and investigate two of physics’ biggest mysteries: dark matter, the invisible substance that accounts for most of the mass in our universe, and dark energy, the force driving our universe’s accelerating expansion. The new map builds on earlier versions from the DESI Legacy Imaging Surveys that have already proved invaluable. To date, more than 1,800 science papers that reference the Legacy Surveys data have been published. “It’s part of the fabric of astronomy research now,” said David Schlegel, a co-lead of the Legacy Surveys and scientist at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab). “When you’re working with astronomical objects today, you often start by pulling up the Legacy Imaging Viewer to see what you’re looking at.” Covering roughly 75% of the sky in visible and near-infrared light, the updated map provides a deep view of the extragalactic universe not blocked by the dust and stars of our own Milky Way. Researchers expect it will remain the most comprehensive 2D map of our universe for years to come. More than 160 scientists contributed to data collection for the project, and a team of 20 produced the final dataset released today. It was built by combining 263,407 telescope exposures from three ground-based sky surveys: the Dark Energy Camera Legacy Survey (DECaLS) at NSF Cerro Tololo Inter-American Observatory, the Mayall z-band Legacy Survey (MzLS) at NSF Kitt Peak National Observatory, and the Beijing-Arizona Sky Survey (BASS) at the University of Arizona’s Steward Observatory. That was supplemented by years of data from NASA’s Wide-field Infrared Survey Explorer (WISE) satellite mission and additional public data. “For our team, these data are fundamental to our investigation of the expansion history of the universe and the formation of our galaxy,” said Arjun Dey, co-lead of the Legacy Surveys and an astronomer at NSF NOIRLab. “But the skies belong to everyone, and this survey gives everyone the chance to explore the sky and marvel at its wonders.” Here be galaxies The DESI Legacy Imaging Surveys were originally conducted to prepare for the Dark Energy Spectroscopic Instrument (DESI) survey. The Legacy Surveys’ 2D map is essentially a deep photograph of the sky; it records where galaxies and stars appear and how bright they appear. This crucial step enables DESI to select objects and measure their light in different wavelengths to determine their distances, building the largest high-resolution 3D map ever made. Scientists study the way galaxies have clustered at different ages of the universe to track dark energy over time. In April 2026, DESI completed its original five-year survey ahead of schedule and with vastly more objects than expected. The early results have shown surprising hints that dark energy’s impact may be weakening over time — a paradigm shift that could potentially shape the predicted fate of our universe. DESI expects to publish improved results using their first five years of data in 2027 and is continuing observations into 2028. DESI was so efficient at observing galaxies, the Legacy Surveys map needed to expand. Early on, “it became clear we might run out of galaxies to look at and run out of sky, and we better start doing something about that,” said Schlegel, who also works on DESI. The new Legacy Surveys map has been used to select DESI targets since June 2026 and will guide the telescope’s operations over the coming years. Computing the cosmos Merging hundreds of thousands of images taken on 2,285 nights, each with unique atmospheric and telescope conditions, was a massive computational effort. It took about a year to develop the computer code and eight weeks to process all the images at the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC) at Berkeley Lab. Beyond supporting DESI, the Legacy Surveys will be a foundational reference for the next generation of telescopes. As new observatories like the NSF-DOE Vera C. Rubin Observatory (jointly funded by NSF and DOE’s Office of Science) and NASA’s Nancy Grace Roman Space Telescope come online, researchers can compare their observations with one of the deepest and most comprehensive views of the sky ever assembled. The Legacy Surveys data will also help scientists train artificial intelligence tools to analyze petabytes of astronomical data and accelerate new discoveries. It will be among the datasets used in an astrophysics pilot project within the American Science Cloud, part of the DOE’s Genesis Mission. The DESI Legacy Imaging Surveys are supported by the U.S. Department of Energy’s Office of High Energy Physics; the National Energy Research Scientific Computing Center, a DOE Office of Science user facility; the U.S. National Science Foundation, Division of Astronomical Sciences; and the partner institutions. DESI is supported by the DOE Office of Science and NERSC. Additional support for DESI is provided by the NSF; the Science and Technology Facilities Council of the United Kingdom; the Gordon and Betty Moore Foundation; the Heising-Simons Foundation; the French Alternative Energies and Atomic Energy Commission (CEA); the Secretariat of Science, Humanities, Technology and Innovation (SECIHTI) of Mexico; the Ministry of Science and Innovation of Spain; and by the DESI member institutions. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
The rise of managed intelligence: AI is rewriting the cybersecurity playbook
AI adoption is accelerating. Learn why governance, cybersecurity and executive oversight are essential to reduce risk and scale AI safely.
Barbara Eden says Lucille Ball ‘appreciated’ her avoiding Desi Arnaz on ‘I Love Lucy’ (exclusive)
Barbara Eden tells EW about her memorable guest appearance on ‘I Love Lucy,’ and how she managed to get along with Lucille Ball.
Penguins’ Bill Zonnon Reveals Sidney Crosby’s True Character
Longtime Pittsburgh Penguins captain and future Hall of Fame franchise icon Sidney Crosby is under contract for at least one more season, though he’s openly spoken about how he intends to continue his career with year-to-year contracts.In the meantime, Crosby managed to help lead the Penguins back to the Stanley Cup Playoffs earlier this spring […] The post Penguins’ Bill Zonnon Reveals Sidney Crosby’s True Character appeared first on HEAVY.
Medicover to sell India hospital business to KKR for €1.2 billion
Swedish healthcare provider Medicover agreed on Thursday to sell its India hospital business to funds managed by global investment firm KKR for €1.2 billion ($1.39 billion).
Disney's strong quarter driven by 'Toy Story 5' and the draw of its US theme parks
Disney managed a solid showing in its third quarter, bolstered by movies like “Toy Story 5” and ongoing strength from its U.S. theme parks that made up for continued weakness from international tourism.
Disney's strong quarter driven by 'Toy Story 5' and the draw of its US theme parks
Disney managed a solid showing in its third quarter, bolstered by movies like “Toy Story 5” and ongoing strength from its U.S. theme parks that made up for continued weakness from international tourism.
If Not AOC, Then Who?
What do we do if Alexandria Ocasio-Cortez decides not to run for president in 2028? It makes sense that many leftists are currently focused on calling on her to take the plunge. But the absence of a Plan B, or even much discussion about one, is worrisome. The US Left can’t afford to sit out the 2028 Democratic primary. Doing so in 2024 was a disaster: our abstention self-marginalized anti-billionaire politics from the national conversation and thereby gave space for Harris to pivot to the “center” (i.e. the policy preferences of corporate donors, establishment hacks, and AIPAC). Abstaining again in 2028 would be especially counterproductive since democratic socialists now have so much wind in our sails after our big electoral wins in NYC and across the country. It’s time to deepen our momentum, organization, and profile — not to retreat to the margins during America’s single most important political battle. Even more urgently, keeping Trump’s heir out of the White House may depend on whether leftists and the party’s unprecedentedly disenchanted base can force the Democrats in 2028 to take meaningful steps towards a very angry electorate and away from the corporate-consultant blob that has helped get us into this mess. If AOC (or Abdul El-Sayed) decides to run, we should enthusiastically go all in — DSA, fighting unions, progressive organizations, anybody who wants to see a better country and world. But, as far as I can tell from the outside, there’s a real chance that she doesn’t run. And we can’t just wait and see where she lands before discussing alternatives, since AOC’s decision might not come for quite a while, giving us little time to scramble at the last minute for an alternative. So we should start working now on getting a Plan B into motion, and recruiting a good candidate for the role. Here’s one idea. A Worker Candidate — Healthcare Not Warfare 2028 Run a union worker Leftists don’t need to always run experienced, well-known socialist politicians for high-profile contests. Lula, the current president of Brazil, began his political career as a metalworker militant who ran for governor of São Paulo in 1982. Especially in an era of institutional distrust, it’s possible to lean hard into making a virtue out of a working-class comrade’s distance from traditional politics. Experience abroad shows this clearly. Rachel Kéké — an immigrant hotel housekeeper — ran as the candidate of the leftist La France Insoumise for the National Assembly in 2022, defeating a former minister in the Macron government. The Belgian Workers’ Party in 2024 won its first Flemish seat in the European Parliament by running Rudi Kennes, a three-decade veteran of the Opel Antwerp auto factory. And the South Korean left in 2024 elected auto factory militant Yoon Jong-oh to the National Assembly. If a tiny French Trotskyist organization could make such a big splash in 2002 and 2007 by running a well-spoken postal worker cadre for office — the LCR’s Olivier Besancenot received about 1.5 million first-round votes — it’s not far-fetched to expect that a much larger organization like DSA, together with a broader coalition of the willing, could run this playbook to much greater effect. This is especially the case now that almost everything DSA does immediately becomes a national news story. We don’t need someone famous. We just need a charismatic, well-vetted comrade who works in a relatable working-class occupation — e.g. a nurse, or a worker in auto or construction, or at Amazon. Ideally, they have a compelling personal story or have been part of a significant strike or organizing campaign, but even those criteria don’t necessarily have to be deal-breakers. The main lesson of the Graham Platner fiasco isn’t that we should never run outsiders for office (the momentum he generated proves the exact opposite), but that they need proper vetting and roots in democratic membership organizations like DSA or a union. In any case, there are plenty of ways to signal working-class authenticity without resorting to Platner’s or Fetterman’s performative macho white dude affect. Better to have someone of any gender or race who is actually working class, with firm socialist politics, who can speak from experience about the day-to-day realities of trying to make it in America. There are surely at least a dozen such worker leaders in our movement nationwide; we just need to sit down and convince one that their personal sacrifice for the greater good (i.e. running for president) will be worth it. If we don’t have a frontrunner with a clear path to victory like AOC (or El-Sayed, were he to win Michigan), it’s better to embrace someone whose decision to run in itself tells an important story about US politics: workers have been iced out of our political system and are now fighting hard to break back in. The novelty factor, in itself, would generate press. And it would help democratic socialists signal to the American public that our movement’s defining characteristic is that we represent workers vs. bosses, not that we’re “the furthest left” force in American politics — a framework that suggests we’re extremists and that helps our opponents isolate us from non-college-educated voters by highlighting extremely minoritarian (and misguided) stances included in DSA’s platform like prison abolition. We don’t get many opportunities to speak to tens of millions of Americans; we need to use these opportunities to broaden our appeal beyond those already in our orbit. Run to polarize the 2028 primary and general election around “Medicare for All” and “End All Aid to Israel” Why should democratic socialists use our scarce time and volunteer energy to run in a race we’re unlikely to win? Running in the 2028 primary will not only recruit us scores of members (imagine having a DSA member intervening in eleven nationally televised primary debates!), but — more importantly — it has the potential to be a decisive leap forward towards an America that finally starts meeting the needs of working people instead of HMO and AIPAC billionaire donors. Polarizing litmus tests are good, actually — as long as they’re fought for around popular demands in a thoughtful and strategic way. Democratic socialists have an opportunity and responsibility to make the 2028 primary about two of the most widely and deeply felt issues of our moment: domestically, America’s unaffordable, family-breaking private healthcare system; internationally, America’s bankrolling of the genocidal Israeli government. A candidacy relentlessly demanding Medicare for All and No Aid to Israel would pose a sharp question to the American public, the Democratic Party, and other 2028 contenders: Why can’t working-class Americans afford their healthcare bills at the same time that our government is spending at least $3.8 billion yearly to fund an Israeli regime eager to keep dragging us and the world into more forever wars? Our candidate can agitate around a clear message: I’m running to give voice to all those working-class Americans who are struggling to get by. No matter what state you live in, who you voted for in 2024, or your background, it’s just plain common sense that we should make healthcare more affordable instead of spending billions to enable the Israeli government’s crimes against humanity. Every American who agrees with me when I say ‘Medicare for All — Not a Dime for Israel’ should make their voice heard through our campaign. Racking up a high number of votes for this agenda is a crucial way to show the Democratic Party and the country as a whole that we can’t keep supporting a life-destroying status quo in our healthcare system and in the Middle East. If we want to defeat Trump’s handpicked crony this coming November, we need to speak to ordinary people’s frustrations with the status quo — we can’t keep running the same tired establishment playbook and expecting a different result. For those highly engaged voters (or skeptical media pundits) who raise concerns about wasting votes, the candidate can take the time to explain why this isn’t the case: It's true I'm a long shot. But Zohran showed anything can happen — and voting for me is a pragmatic decision, not a wasted vote. In a heavily divided primary with a large number of candidates and a very discredited establishment, my campaign and the democratic socialist movement I’m part of are positioned to be the balance of power if we can get a large enough number of voters to loudly demand Healthcare Not Warfare. Since there’s a high chance no candidate will get to 50% in the 2028 primaries, the Democrats could very well be heading towards a brokered convention, where a disciplined, principled current committed to breaking from corporate donors can play an outsized role in making sure whoever ends up with the nomination raises the banner of single-payer healthcare and cutting aid to Israel. How exactly could running this sort of primary campaign be translated into actual leverage? The candidate replies: I’m aiming to win the nomination, but if I don’t achieve that goal, my delegates and I will pledge our support in the primary only for a candidate who agrees in the general election to call for ending US aid to Israel and establishing Medicare for All at home. These are both winning planks for November against the Republicans, and the only reason I can see that other candidates haven’t yet raised these is that they’re still more scared of the donor class than they are of the people. That needs to change. If Democratic leaders want young people, workers, and those sick of the status quo to show up in big numbers this November, they need to meet us halfway. In this way, whether or not we win the nomination, democratic socialists could still play a decisive role in 2028. The approach outlined here is similar to what radical left parties periodically do in parliamentary democracies like Denmark or Sweden when they find themselves as a junior partner electorally: they dictate to center-left allies what their terms would be for enabling, from the outside, the formation of a new government. Running a Healthcare Not Warfare candidate will make it significantly more likely that the 2028 Democratic candidate stands with ordinary people instead of donors on these two pivotal planks — a shift that will, in turn, make it easier to achieve the existentially important task of defeating MAGA’s heir in November. Look at how much popular traction the Uncommitted movement got with a similar strategy in 2024 with so little on-the-ground organization — surely we can go much further with a working-class candidate, a much bigger DSA, and a Democratic Party base that has turned against Israel, billionaires, and the hacks in the party who prop them up. Objections and Responses There are quite a few objections to this proposal, many of which are very reasonable. Let me briefly address each here. Socialists shouldn’t waste their scarce time and resources on a candidate unlikely to win. This is a good rule in general, but there are important exceptions, and presidential elections are the main one. There’s no other political contest that gets anything like this level of attention and engagement in the US, so if you want to be a serious force in nationwide politics, you basically can’t afford not to have a candidate. Far from being an overall drain on our resources, running a dynamic 2028 campaign would likely result in a major net increase in members and capacity. In that spirit, the very pragmatic Milwaukee sewer socialists — who mostly ran to win at home in Wisconsin — supported Eugene Debs’s propagandistic presidential campaigns, which they correctly saw as a crucial means of recruiting to the Socialist Party. And now that the (post-1972) Democratic Party gives more weight to voters than party operatives in determining its presidential nomination, it’s become feasible to run as a socialist in primaries without risking a split in the vote against the Republicans in November. We should just support Ro Khanna, who also supports M4A and ending aid to Israel. I like Ro Khanna and would enthusiastically vote and canvass for him for president were he to get the nomination. But there’s a real chance that Khanna’s bid for office doesn’t catch on; he lacks the grassroots organizational apparatus of DSA, and, because he’s not really of the Left or labor (despite his current very good positions), he’s unlikely to generate the type of buy-in and enthusiasm that a DSA candidate would. Moreover, Khanna would be running as an individual, not as someone emerging from and building an organized movement — his campaign would not likely focus on building sustained power from below, nor would it likely polarize the 2028 primary around M4A and Israel aid. Also, and not inconsequentially, the poor electoral results for Tom Steyer and Saikat Chakrabarti this spring suggest that there’s perhaps not much of a popular appetite in the Democrats’ base to support multi-millionaire class traitors. Much better, then, to run a democratic socialist worker. If by Super Tuesday it’s clear that our campaign has captured the main left lane in the primary, Khanna should drop out and endorse us; conversely, if he’s managed to capture this lane, DSA could at that point transform its Healthcare Not Warfare campaign into an independent campaign to elect Ro Khanna focused on winning (and holding him accountable to) those two critical planks. An openness to consolidating an anti-corporate candidate by Super Tuesday is a necessary component of the strategy I’m proposing, because otherwise we risk splitting the primary votes (per DNC rules, you need at least 15% to get any delegates to convention). We should convince Shawn Fain or Sara Nelson to run for president instead. I agree it’d be great if either ran for president, but my impression is that there’s basically no chance that will happen in 2028, because both are firmly focused on their union leadership duties (and, in the case of Fain, beating back a federal witch-hunt). This plan could have DSA end up endorsing a non-socialist, non-DSAer for president. That’s against our principles. It’s a myth, and bad politics, that socialists can’t ever support candidates who aren’t members of their organization and/or who don’t call themselves socialists. Bernie, after all, was not a DSAer. And socialists abroad regularly make tactical decisions about if and when supporting a non-socialist candidate makes sense from the standpoint of promoting working-class interests, defending democracy, and building a socialist current in the process. If your “principles” prevent you and DSA from helping achieve the momentous stride forward of having a Democratic candidate in 2028 run on Medicare for All and ending aid to Israel, those “principles” are not very good. In fact, they actively undermine the causes they’re aiming to support. We shouldn’t confuse principles (working-class independence) with tactics (the best way to advance this in a given context). In my recent article in Catalyst on Milwaukee sewer socialism (I’ve uploaded the full text here), I have a section showing how similarly rigid rules about endorsements had to be eventually discarded in the old Socialist Party nationwide after WWI because they tended to lead socialists towards a sectarian abstention from broader anti-corporate electoral insurgencies beyond their party’s control. For what it’s worth, Lenin’s current under Tsarism frequently backed bourgeois candidates in the second round of elections and Lenin himself advocated that British Communists support Labour Party candidates; for his part, Kautsky in 1912 defended the German Social Democracy’s electoral pact with the Progressives in the runoffs. If our predecessors could be that tactically flexible in an era before the limits of their underlying strategic assumptions about intransigent class politics in capitalist democracies were made clear, then there’s no need to insist on being more tactically rigid today, after a century’s worth of accumulated experience. As long as you maintain your own political profile and organization while engaging in broader coalitional efforts, there’s zero inherent contradiction with the principle of political independence. More specifically: DSA and allied organizations should enthusiastically back Michigan’s Abdul El-Sayed if AOC declines to run and he throws his hat in the presidential ring. (I have no idea whether he’s even considering this path, but there’ll surely be chatter about it if he wins his Senate race this November.) Though he doesn’t call himself a socialist, El-Sayed is a principled Berniecrat, a consistent fighter for Palestine, and would have a clear path to victory. Using the “s” word is far less important than whether a campaign fights for the interests of working people at home and abroad — and whether actively participating in it can grow the organized working class and the Left. And the same holds true if a less anti-corporate candidate were, under pressure, to adopt our two Healthcare Not Warfare planks. Why focus only on M4A and ending US aid to Israel? There are so many other important issues to fight for! Obviously there are many pivotal issues beyond healthcare and Israel, and our candidate — and especially our organizations involved in the campaign — should be prepared to speak on all of these. But one of the crucial lessons of the Zohran campaign is that you have to relentlessly stay on message if you want to cut through the noise. CNN and Fox News will want to talk about DSA’s views on Venezuela and abolishing the police; we need to polarize the debate on the widely and deeply felt issues that are most favorable for growing the Left, isolating our opponents, and winning urgently needed changes. One recent poll found 63% of Americans support M4A, even after respondents were told it would raise taxes and eliminate most private insurance. Another found that 74% of Democrats opposed “providing additional economic and military support to Israel.” And focusing on two demands sets the campaign up to be able to leverage its mandate to make clear demands upon other candidates (which we can’t do if we run with a laundry list of demands). Talking about a Plan B is a distraction from the more urgent work of building a groundswell of calls on AOC to run. It’s good for DSAers, fightback unionists, and progressive organizations to try to convince AOC to run. This proposal is meant as an addendum to pushes to get AOC to run and to line DSA up for that eventuality, not as a substitute for those efforts. But I’m worried that, like in 2024, we might eventually find ourselves with no time to find a viable alternative unless we start now. DSA, with its abundant deliberative processes, moves slowly. The approach you’re suggesting seems risky. If a DSA candidate runs but doesn’t receive a lot of votes, it’ll undercut our current national momentum. This is a reasonable worry. All big initiatives always carry risks, and the path I’m advocating is certainly not free of these. That said, if a Healthcare Not Warfare campaign doesn’t catch on like we envisioned, it’s a pretty simple pivot in March 2028 to explain that in the future we really need our standard-bearers like AOC, or our growing bench of national electeds, to step up in presidential primaries. Moreover, it’s worth noting that the risk of my Plan B proposal is on the whole significantly less than the risk entailed by an AOC run. We should all back her if she runs, since she’d fire up the base, provide huge openings for organizing, have a real shot at defeating a Republican (especially given MAGA’s unpopularity), be a great standard-bearer for our issues, particularly M4A and ending aid to Israel, and be less willing than mainstream candidates to capitulate to any anti-democratic machinations from Trump and co. But it’s also true that AOC could lose, which would be a devastating setback to the country and the Left. Or she could win and find herself unable to pass much, if any, of her (and our) program, a demoralizing prospect. So if we’re willing to take these risks with AOC (and we should), it follows that we should be able to take lower-level risks if she doesn’t throw her hat in the ring. What’s Your Plan B? The US Left is facing its biggest opening for growth and influence since the 1930s. Initiatives that would have been impossible a few months ago are now potentially feasible. A President AOC is now easier to imagine, but if she ultimately assesses that the risks aren’t worth the potential reward in 2028, we can’t be left flat-footed. So even if you’re not convinced by the specifics of my Plan B, I hope you can at least agree that it’d be a disaster for us not to have one — and not to start getting a Plan B into motion. Hopefully other organizers and political currents will put forward their responses and alternative ideas ASAP. The stakes are too high to just wait and see. More As always, please share this article widely. This newsletter depends on all you lovely people to get the word out. Thank you! I uploaded the full text of my Catalyst article on Milwaukee sewer socialism here (it’s updated and 3x as long as the version published earlier on this substack). If you haven’t yet, please make sure to subscribe to Catalyst here; I read every issue front to back, highly recommend. 🌹🎵 Ivan Lins (arranged by Arthur Verocai) — “General da Banda” 🌹🎵
OHSU study: A handful of corporations now shape how kids in Medicaid get care
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Bruins Could Lose Forward Who Hit Career Highs Last Season
The Boston Bruins managed to return to the Stanley Cup Playoffs earlier this spring, marking a return to the annual tournament for the first time since 2024 after having failed to qualify last spring.However, their stay in the postseason was short, as they were eliminated in the Eastern Conference Quarterfinal by the divisional rival Buffalo […] The post Bruins Could Lose Forward Who Hit Career Highs Last Season appeared first on HEAVY.
Why Congressman Vince Fong is urging the Trump administration to reject a health tax in California
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Killing slime: an AI-first biotech solution | Investors Lab
We love public science lessons, like the algae debacle that turned Washington, D.C.'s Lincoln Memorial Reflecting Pool a vibrant green this summer after a renovation. It's not exactly a new development. Algal blooms have intermittently plagued the site for generations, including during the last big overhaul in 2012. But the latest episode lingers despite treatments with hydrogen peroxide and an ozone nanobubble system. As Bethany Brookshire makes clear for Science News, there's another option that might have managed the crisis far more effectively, had it been used from the start.
Andy Burnham and the rise of ‘aura farming’ in politics
Is “aura farming” the new political superpower? There are some days in TV news when you find yourself standing in the middle of a farm, surrounded by baby goats, trying to explain internet slang. Subscribe This was one of those days. There I was, in wellies, trying to unpack aura farming - a Gen Z term for carefully cultivating your image online so that you look effortlessly cool, authentic and charismatic. The trick, apparently, is that it can’t look like you’re trying too hard. Which, I’ll admit, felt slightly ironic as we filmed an entire report on a farm purely because the word farming is in the title. ‘Shaped by algorithms’ Whether you like it or not, politics increasingly feels like it’s being shaped by algorithms as much as manifestos. Take Andy Burnham. Since arriving at Number 10, he’s been impossible to miss online. One minute he’s out for a casual jog with Mo Farah. Next he’s announcing policy alongside TikTok high priestess Bus Aunty. His social media paints a very deliberate picture: less polished Westminster operator, more bloke from the North who just happens to be running the country. It’s not accidental. Burnham’s “King of the North”, New Order-loving persona has become part of a carefully constructed political brand. Authenticity is now something politicians work incredibly hard to make look effortless. But Burnham isn’t inventing this playbook. Like so many of us doomscrolling on an evening, he’s been influenced online. The digital strategy of New York Mayor Zohran Mamdani, pictured above right, has become a case study in viral political communication, and some of the people behind that campaign have recently been advising the Labour Party in Britain. Their message is simple: if you want to reach younger voters, you need to show up where they already spend their time. Embracing content creators And increasingly, that doesn’t mean traditional news interviews (awkward). It means speaking to content creators like Max from The Career Ladder, whose channels reach around 20 million followers worldwide. His interview with Burnham, pictured below, was posted almost immediately after he became Prime Minister. What struck me wasn’t just the audience size, but something Max told me afterwards: many people in the comments said they’d discovered Britain had a new Prime Minister through his interview. Think about that for a moment. Not through rolling news. Not through newspapers. Through a creator they were already following while scrolling. Share That’s a profound shift in how political information reaches people, one that’s been gathering momentum for some time. It also explains why politicians are embracing content creators and spending millions on digital marketing. Of course, this isn’t unique to one party. ‘Personality before policy’ In many ways, the right were quicker off the mark in building their aura online. Donald Trump built much of his political identity on social media. Nigel Farage remains one of Britain’s strongest political performers on TikTok. Politicians across the spectrum now understand that personality often reaches people before policy does. During a six-week UK general election campaign alone, overall digital ad spending routinely tops £11m. Share Channel 4 News Former Number 10 social media adviser Andrew Wong put it well when he told me that “vibes” can help someone get noticed, but they only work if they’re rooted in a genuine personality. Force it, and audiences spot it instantly. The bigger question is whether internet charisma can backfire on you - especially when you’re Prime Minister. Because behind Burnham’s carefully curated videos is a veteran politician with decades of Westminster experience. The spontaneity is produced. While the authenticity may be genuine, it’s managed. That’s not necessarily a criticism. It’s simply the reality of modern political communication. Aura farming alone is not enough to harvest change. But in the age of algorithms, winning attention may be the first battle. Subscribe Share Channel 4 News Leave a comment
When It Comes to Fusion, Materials Matter
Fusion at high temperatures powers the sun and, if harnessed, could provide a potential source of energy here on Earth. But controlling fusion reactions has other benefits. The process also generates subatomic particles called neutrons that are used in a range of applications spanning medicine, research, and national security. Now, scientists at the University of California, Davis (UC Davis), and the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have found that the materials surrounding a fusion reaction can dramatically increase how often it occurs, particularly at low energies where fusion is rare. The study was published July 18 in Nature Communications. Their approach sets up a way to study and engineer nuclear reactions within solid materials, opening a new field of “materials-driven fusion.” Instead of designing materials just to survive the harsh conditions of fusion, researchers might be able to design materials that boost the reaction in specific conditions, similar to the way catalysts speed up chemical processes. “It gives you a new knob to turn that you didn’t have before,” said Arun Persaud, head of the Fusion Science & Ion Beam Technology group in Berkeley Lab’s Accelerator Technology & Applied Physics (ATAP) Division. “If we understand this effect better, it opens the door to engineering new materials that would affect the fusion rate under certain conditions. Someday future progress might enable more compact and efficient neutron generators, which have all kinds of applications, like cargo screening, planetary science, and medical therapy and imaging.” In the experiment, researchers used two different methods to pack deuterium, a heavy form of hydrogen atoms that is often used in fusion, into thin foils of palladium and titanium. They then fired a beam of deuterium ions at the foils at different energies and measured how often fusion happened. Then they compared the rates from the different materials and methods with the “bare” fusion reaction (not in a material). The team found that fusion rates depended on how the deuterium was loaded into the metal foils. The biggest effect was at the lowest energies, below 2.5 kiloelectronvolts (keV), where theory predicts fusion rates drop off sharply. Instead, researchers found a surprising plateau: Some samples showed elevated fusion rates roughly a quintillion times higher than bare fusion reactions. (A quintillion is a 1 followed by 18 zeroes.) Researchers are not exactly sure why that’s happening, though they have some ideas and ways to test them. The electrons and defects within the material might partially shield repulsive electrostatic forces between deuterium nuclei, making it easier for them to get close together and fuse. Tuning the electronic structure, defects, and composition of materials could help make nuclear reactions happen more readily. “It comes down to better understanding the mechanism so that we can try to enhance it,” said corresponding author Jeremy Munday, a professor at UC Davis. “We’ve seen that we can increase fusion rates, but what is the limit? Can we bring it to lower temperatures or energies? There’s obviously a lot of interest and excitement about fusion, so if there’s something we can learn about the physics at these lower energy scales, maybe there’s something we can translate to other areas of nuclear science.” The team plans to explore a wider range of materials and continue probing the unexpected fusion plateau at lower energies. Their work establishes a reproducible experimental platform to study how solid materials influence nuclear reactions, creating a new area of research that links fusion science with materials science and chemistry. “The work shows conclusively that the material environment where fusion occurs at low temperatures is an active participant rather than a passive container,” said Cameron Geddes, director of ATAP. “That adds a new dimension to fusion research.” This work was funded by the U.S. Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E). ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
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Making Our Own Luck
We are cross-posting a piece that former NIH official Kris Willis wrote for Macroscience. Kris is now the President and Founder of the Woodley Park Institute, for which I’m a board member. Picking winners or hedging bets? Federal science agencies have long wrestled with the question of how to ensure their billions of dollars of grants and contracts result in maximum benefits for Americans. On the surface, it might seem that a goal-oriented, interventionist style of management would be the best policy: prioritize our most pressing problems and distribute funding accordingly.This top-down strategy creates a unique hazard, though: bad choices can send resources in the wrong direction, resulting in slow progress that impedes the delivery of tangible benefits to citizens. Moreover, if decision makers pursue an applied advance when the underlying scientific principles remain poorly understood, they risk costly failures and a loss of support for the research enterprise at large. Examples include Nixon’s 1971 War on Cancer, undertaken on the premise that a cure could be achieved in less than a decade, the drive to build an operational commercial fusion plant by the mid-1990s, and the National Plan to Address Alzheimer’s Disease, which failed to produce the effective treatments and prevention strategies promised by 2025. The contrasting laissez-faire approach is to hedge your bets. If the path to an advance is impossible to predict, then the wisest choice might be to distribute funding to as widely as possible. The main risk of this strategy is that spreading funding thinly may limit the resources available to the most promising research, resulting in the same negative outcomes as the interventionist approach: limited support for the most promising topics, slow progress, and delayed returns. In reality, at science agencies like the National Science Foundation and National Institutes of Health, program managers navigate between these two extremes, setting some funds aside to target high-priority areas and using the rest to cover the widest possible range of meritorious proposals. This middle path seems like the most rational way to proceed, but it still requires making choices. How much should be set aside for high priority topics? What should those topics be? How should we define merit? Expert opinion can provide a helpful guide, but leading experts can have remarkably different opinions about what matters and what should be prioritized. Moreover, experts and administrators alike can be risk averse, giving an edge to high-profile, well-established concepts. What’s really needed is a data-driven framework to help guide decision-making, one that doesn’t simply repackage prestige or incumbency. Recognizing breakthroughs years in advance Many breakthroughs fail to attract funding or recognition at the earliest stages of development. Katalin Karikó, for example, spent years struggling to win grants for work on mRNA that would eventually win her and Drew Weissman a Nobel Prize. Similar stories can be told about Jim Allison’s groundbreaking studies leading to the development of cancer immunotherapy, Stan Pruisner’s demonstration that prions can self-replicate without DNA, or Robin Warren and Barry Marshall’s proof that Helicobacter pylori, not stress, causes ulcers. Yet at some point, the scientific community recognizes and responds to transformative breakthroughs like these. How does that change happen? And what if science funders could identify the areas that are on the cusp of a breakthrough? Those questions led my collaborators and me to study how scientists responded to past breakthroughs. Based on patterns in that data, we developed a means of predicting future breakthroughs, using biomedicine as a proof of concept. It’s worth taking a moment to describe how our approach works. Briefly, our prediction process begins by grouping the 18 million or so peer-reviewed papers in PubMed, the authoritative database of biomedical research, into roughly 50,000 unique topics. A small subset of these topics are so widely acclaimed as breakthroughs that they have been recognized with a major prize like a Nobel or a Lasker Award. We theorized that studying the development of these outliers over time, especially before they gained acclaim, was the key to identifying future instances of them. After defining topics, we chose 21 representative breakthroughs and asked what they looked like before they won prestigious prizes. To answer that question, we re-ran our topic-mapping algorithm repeatedly, winding back the clock one year each time, stringing together the years to follow the progress of each field as it grew and advanced. We found that in each case, the years leading up to a discovery followed a pattern: a burst of papers on a new, rapidly evolving topic, many of which quickly became influential. These characteristics—the percent of papers that are new, the percentage of papers that are brought in from other topics, and the influence of each individual paper—can all be measured separately for any given topic. Together, they act as a predictive signal that is detectable an average of five years before a transformative discovery is made, and up to thirty years before the discovery receives a major prize. Scanning the current research landscape for topics that display this signal allows us to predict what work will likely produce a future breakthrough. Each year we analyzed includes four or five signals, a number that appears to have remained constant across a twenty year time frame. In their classic work on the sociology of science, Bruno Latour and Steve Woolgar established that the desire to participate in a major discovery is an important motivator for scientists as they consider whether they should take up (or abandon) a research problem. Although further work is needed, the simplest explanation for our results is that this behavior is widespread enough to detect at scale. A foundational advance, or something like it, draws the attention of scientists and causes them to change the direction of their research. They vote with their feet, staking their own reputation and careers by publishing on and citing the new idea. That upswell of interest is the foundation of our breakthrough signal. What kind of research does our model identify as a breakthrough? After building a model based on the patterns in our 21 preselected breakthroughs, we tested it by identifying all early signals of discovery from 1994 through 1997. Of the 18 signals we found in that time period, 17 can be traced forward in time to a breakthrough. Examples include super-resolution fluorescence microscopy (see the image above), the directed evolution of proteins and enzymes (Chemistry Nobel, 2018), and sequencing the human genome (National Medal of Science, 2008). The discovery of the role of leptin signaling in obesity is a particularly interesting case study. Around 1950, scientists noticed the existence of a type of lab mouse that suffered from a unique inherited form of obesity. When given the freedom to choose their own diet, these mice ate more than normal and preferred food that was high in fat. Researchers had few clues as to why the animals overate; early experiments pointed to the existence of a soluble ob factor found circulating in the bloodstream that regulated appetite, but its identity was unknown. In 1994, Jeffrey Friedman and colleagues identified and sequenced the ob gene, noting that the protein it encoded appeared to fit the profile of a circulating factor. A year later, a trio of high-profile publications demonstrated that ob was a hormone that regulated body weight and fat deposition by regulating appetite. One of these was co-authored by Friedman, who christened it leptin from the ancient Greek word for thin. Using our model, the data from 1996 make it clear this advance would become a breakthrough. Scientists had flocked to study the biology of the new hormone, generating the flurry of new papers and citations required to produce a signal. One of these demonstrated that serum leptin concentrations reflected the amount of adipose tissue in the human body, providing clinical validation of the earlier mouse studies. Before the identification of leptin, scientists were unable to point to a specific molecule that controlled appetite and adiposity, so that it was impossible to rule out non-physiological causes of obesity; its characterization fundamentally changed our concept of weight gain. In 2010, Friedman and fellow pioneer Douglas Coleman were awarded a Lasker for the discovery. Our work also identifies breakthroughs in clinical practice and behavioral and social sciences, even though these areas are less likely to draw the attention of major prize committees. One example that falls into this category is the introduction of endovascular aneurysm repair, a minimally invasive surgical procedure for the treatment of abdominal aortic aneurysm, a leading cause of death among Americans over the age of 65. Relative to the previous standard of care, it reduces in-hospital mortality by almost four-fold. In 2017, Timothy Chuter received the American College of Surgeons’ prestigious Jacobsen Innovation award for his multiple refinements to the technique. Another under-recognized breakthrough identified by our model is the development of standardized survey instruments to assess the quality of life for HIV+ patients. The antiretroviral cocktails introduced in the mid-1990s reduced AIDS mortality, but treatment came with serious side effects. The new evaluations showed that patients whose disease progressed had worse physical functioning than Americans with other chronic diseases, while those who were asymptomatic maintained a health-related quality of life on par with the overall US population. This evidence made it possible for patients and activists to argue that even in the absence of a total cure, and in spite of the side effects, alleviation of symptoms was a worthwhile priority for clinicians. My colleagues and I have failed to identify any significant recognition for the physicians and health policy experts who pioneered this advance, although in 2016, researchers called for the World Health Organization to set targets for good health-related quality of life as part of its framework to end the AIDS pandemic. From forecasting to funding How should science funders use our predictions? Any serious discussion of what we should do with this new capability needs to begin with an understanding of its limitations. We can’t (and shouldn’t) invest all of our resources in the small number of topics predicted to produce breakthroughs. Innovative new fields are born from existing ones; they rarely arise de novo. If funders don’t maintain a diverse portfolio, the breakthroughs of tomorrow will have no antecedents. Think of this as eating the seed corn, or killing the goose that lays the golden egg. There are also multiple goals of funding beyond basic scientific discovery, including translating discoveries into clinical practice, developing new technologies, supporting economic development, and training new scientists. The investments needed to accomplish these goals are likely different from those required to support emerging breakthroughs. Finally, we must be aware that we may not capture every instance of a breakthrough. The concept is fuzzy, and not every significant discovery is recognized with a major award. Further, breakthroughs are rare, meaning that our dataset is, by necessity, small. This makes formal estimates of accuracy challenging. Although our overall success rate appears to be high, any one signal may turn out to be a false positive. We found one apparent example: the development of the third generation COX-2 inhibitors, Vioxx and Celebrex, which were much celebrated in the early 2000s as effective but non-addictive pain relievers, meets all the algorithmic criteria of a breakthrough. Five years after it received FDA approval, researchers demonstrated that Vioxx was associated with serious cardiovascular complications, and the manufacturer voluntarily pulled the drug from the market. Celebrex remains available, although like other NSAIDs, it carries an FDA warning for increased cardiovascular risk. Decision makers who were overly focused on the breakthrough potential of this research might have wasted substantial resources nurturing an area that ultimately proved to be a failure—but so would those who relied on expert opinion at the time. That said, the potential benefits of funding the next mRNA vaccine or cancer immunotherapy years earlier than we might have are large. When we can’t assume the outcome of an action, it’s worth conducting an experiment. Can targeted support for areas that are poised to produce a breakthrough increase the return on scientific investment without cannibalizing future advances? With the necessary administrative infrastructure in place, conducting this experiment would be straightforward. First, identify all the breakthrough signals between 2018 and 2023, select half at random, and commit to a decade of investment. For biomedicine, a six year window should yield about 30 topics with the potential to produce a breakthrough. Providing strong support to half of these might be accomplished for $300 million a year. That’s roughly half of the recent annual budget of the NIH Common Fund or around 20% of the fiscal year 2025 budget for ARPA-H. After five years, then again at ten, compare to see which group — the one that was actively managed, or the control — produced more breakthroughs, and on what timescale. Although we used biomedicine as an example, the same experiment could just as well be run for physics, materials science, or any other discipline. Any attempt to undertake such an experiment should keep three points top of mind. First, we can predict the topic of future breakthroughs, but the people and vision for how to move the work forward are still important. In practice, this means success requires active program managers with the expertise to develop the science and the authority to do so, including by recruiting investigators. Second, we would be wise to learn from the failures of past attempts to fund transformative research. High among these are funding modestly repackaged work, confusing the genuinely novel with the merely unfamiliar, and relying too much on incumbent investigators. All of these risks can be mitigated by thoroughly and intelligently integrating high quality data into portfolio management. Finally, we need to have patience. Even with strong funding support, producing a breakthrough takes time. If supporting breakthroughs is the best way to nurture the birth of new fields, we also need to accurately describe and properly manage the other two stages, so that we invest wisely throughout the productive lifetime of ideas and divest once they reach the point of diminishing returns. We would benefit from more study of all of these phases, especially the transition from one to another. More research could answer questions like: When does a breakthrough become an established field? How do the availability of funding, the size and characteristics of the available workforce, and the accumulation of evidence that disagrees with prevailing models affect a field’s decline? Exactly how do old ideas give birth to new ones, and can we speed up the process? The more we understand about the dynamics of scientific progress, the better we can maximize the odds that our curiosity leads to results. I’d call that finding a way to make our own luck.
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The researchers adapted this concept to the physical sciences. With MatterChat, they created a “bridge model” that successfully connects an LLM’s general knowledge with the deep understanding of the atomic-scale world encoded in scientific interatomic potentials. Until now, researchers using LLMs to solve materials problems usually had to feed them raw data files as if they were just strings of text. It’s like asking an AI to understand a complex 3D engine based only on a parts list: the LLM can read the names, but it can’t “see” how the atoms fit together in space. MatterChat solves this by training a specialized AI bridge model, pre-trained on millions of crystal structures and an LLM, to align the LLM’s representation of the world with the interatomic potential’s representation of the world. The bridge model can translate physical insights into a format the LLM can actually understand. By giving the LLM these “scientific eyes”— a scientific “inductive bias” in the terminology of AI — the Berkeley Lab team has transformed it into a robust research tool capable of providing grounded scientific insights into complex materials challenges, such as predicting thermal stability or analyzing electronic band gaps. “We think of atoms as living in a physical space, but from a machine learning perspective, they are just vectors living in some very non-trivially structured manifold in a high-dimensional Euclidean space; and, of course, the same is true for the sentences and paragraphs that we use to express our ideas about those atoms,” said co-author Michael Mahoney, Berkeley Lab’s Scientific Data Division (SDD) AI Initiative Research Lead. “The bridge model basically gets those two structures to ‘talk with’ each other.” As a proof-of-concept of this general approach, the team trained their bridge model on a dataset curated by pairing nearly 143,000 stable atomic structures from the Materials Project with their corresponding physical properties. This training data was automatically assembled using the Materials Project’s API and deliberately enriched with properties fundamental to microelectronics design — like formation energy and bandgap — allowing MatterChat to learn the complex patterns connecting a material’s atomic blueprint to its functional performance. To validate their model, the researchers benchmarked MatterChat against a suite of other AI systems, from general-purpose LLMs to other specialized scientific AI methods. The results show that MatterChat consistently outperformed its competitors across a range of tasks. The model was more accurate in classifying material types and demonstrated superior precision in predicting numerical properties. For example, it excelled at predicting a material’s bandgap, a property critical for designing new electronics from high-capacity energy storage to next-generation computer chips. “Our design is significantly more efficient because we don’t have to build a massive AI model from the ground up,” said co-author Zhi (Jackie) Yao, a research scientist in Berkeley Lab’s AMCR. “Instead, we take two powerful, pre-trained models — a structural encoder for materials physics and an open-source LLM — and use them off-the-shelf. The only component we actually train is the lightweight ‘bridge model’ that translates between them. It’s the difference between building an entire car factory and simply designing a smart adapter that connects a world-class engine to a world-class navigation system. This approach is not only computationally efficient, but also makes the system modular, so we can easily upgrade components or adapt the bridge for other scientific domains in the future.” Crucially, this modular design highlights exactly how institutions like Berkeley Lab and the Department of Energy are carving out a highly valuable niche in the booming AI landscape. Rather than competing with Silicon Valley tech giants to build ever-larger language models from scratch, the lab is focusing on the specialized connective tissue that makes commercial AI useful for hardcore science. Because the bridge model approach underlying MatterChat is forward-compatible, it is perfectly positioned to leverage these parallel tracks of innovation. As Mahoney pointed out, “We expect that industry will continue to develop improved LLMs, and we expect domain scientists and facilities will continue to generate new data. An important part of scientific machine learning is not simply to solve problems on today’s data, but instead to develop general methods that will be forward-compatible with orders of magnitude more data, whether from scientific domains or from LLMs.” According to Yao, the MatterChat project, which was initially developed and enhanced with funding from a Berkeley Lab Laboratory Directed Research & Development (LDRD) Program, will now expand its capabilities. In a collaboration with Fermilab, MatterChat is already contributing to a U.S. Department of Energy Genesis Mission project — called Accelerating eXtreme Environment Specs-to-Silicon (AXESS) — that aims to speed up the development of next-generation, high-speed, radiation-hardened detectors for challenging particle physics experiments by using advanced 3D integrated circuits (chiplets) and AI-driven data analysis. In addition to the LDRD support, the team also credits supercomputing resources at the National Energy Research Scientific Computing Center (NERSC), located at Berkeley Lab, with MatterChat’s success. “We are incredibly grateful to NERSC; this research simply would not have happened without access to the Perlmutter supercomputer through their AI for Science program,” said Tang. Wenbin Xu, a NERSC postdoctoral fellow at the time, was also a major co-author of the work, as was Benjamin Erichson, a research scientist in Berkeley Lab’s SDD, highlighting the benefits of AMCR-SDD collaboration on AI for science. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
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