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Scientists just found a faster, cleaner way to extract lithium for EV batteries
A breakthrough lithium-extraction method could help solve one of clean energy’s dirtiest problems. Researchers at Columbia Engineering have developed a fast new technique that pulls lithium directly from salty underground brines using a temperature-sensitive solvent, avoiding the giant evaporation ponds that can take years and drain precious water supplies. Even better, the method works on low-quality lithium sources that current technologies struggle to use.
Scientists uncover cancer-causing chemicals hidden in everyday foods
Scientists have identified potentially cancer-causing chemicals hiding in many everyday foods, especially those exposed to high heat cooking methods like grilling, roasting, smoking, and frying. The compounds, known as PAHs, can form during cooking or enter foods through contamination, raising concerns about long-term health risks.
AI & Science: What Is the Future of Discovery?
Continued progress in artificial intelligence, its expanding usefulness in science, and its contributions to landmark advances suggest that we may have entered a new era of AI for science.The breakthroughs so far—such as predicting the structure of practically every known protein, with profound implications for our understanding of biology, health, and the treatment of disease—are notable not only for what was achieved but also how it was achieved and what that suggests for scientific progress. This special double issue of Dædalus poses the question: What is the future of scientific discovery in this new age of AI? Thirty-three scientists responded. Bringing perspectives from life sciences and medicine, cognitive science and neuroscience, the physical and earth sciences, chemistry and materials science, computer science, mathematics and the social sciences—they draw on their work at the frontier of AI and science. The authors write with an eye to the future, not just the present. They explore what is being achieved and what possibilities lie ahead; examine AI’s limitations and efforts to move forward; and investigate the larger implications of AI-assisted science—on how science is done, the role of the scientist, and the scientific method, as well as the challenges and complexities involved.The authors together exemplify a long-standing bidirectional relationship: AI advancing science, while science advances AI. Where that relationship will take us—a golden age of discovery? New scientist-machine collaborations? Autonomous labs? Discoveries without human understanding?—is a future we are only beginning to imagine, and one we must also shape if the beneficial possibilities are to be realized.Image: UMAP sculptures visualizing three Dædalus volumes on artificial intelligence. © 2026 by Refik Anadol.
Google Reveals Gemini For Science, An AI Research Tool And Science Skills Platform
Google introduced Gemini for Science, a new collection of AI-powered scientific tools and experiments designed to accelerate discovery and support researchers across disciplines. The initiative combines multiple Google DeepMind technologies, including Co-Scientist, AlphaEvolve, Empirical Research Assistance (ERA), and NotebookLM, to streamline key parts of the scientific method.
US National Science Foundation pledges $1.5bn for innovative labs
Independent NSF X-Labs will tackle scientific challenges using non-traditional methods
Who reports wildlife the most? 300,000 citizen science records uncover participation bias
In recent years, citizen science methodology has gained significant momentum and is becoming increasingly important in large-scale ecological and conservation research. By involving volunteers, it enables ...
Observational Comparative Research in Cardiovascular and Brain Health and Disease
Resources for observational comparative research have rapidly expanded to include large, granular healthcare datasets and advanced analytical methods.
Scientists use light to create tiny molecules that could transform medicine
Researchers have developed a light-driven method for creating tiny, high-energy “housane” molecules that are valuable for drug development and materials science. These compact ring-shaped structures are difficult to produce because of the intense internal strain they contain. By using photocatalysis and carefully tuning the starting molecules, the team managed to guide the reaction into a clean and efficient pathway.
A strange ripple in spacetime could be the first fingerprint of dark matter
Black holes crashing together may be revealing clues about dark matter hidden across the universe. Physicists created a new model predicting how dark matter could subtly distort gravitational waves produced during black hole mergers. When they tested the method on real LIGO data, one signal stood out as potentially carrying a dark matter imprint.
New MatterChat Model Helps AI to ‘See’ the Language of Science
From writing emails to generating computer code, much of the artificial intelligence prevalent in our daily lives has succeeded by mastering one domain: text. However, this leaves a major blind spot in the physical sciences, where models depend on the high-resolution, three-dimensional data of the physical world, like the intricate lattice of atoms in a crystal. Delivering on the promise of using AI for science requires teaching these data-driven text models to seamlessly “talk to” physics-based models. Now, a new AI framework from Lawrence Berkeley National Laboratory (Berkeley Lab), called MatterChat, solves this problem by creating a specialized “bridge.” It connects the conversational power of a Large Language Model (LLM) with a physics-based AI that models “interatomic potentials”: the complex physical forces between atoms. The resulting system already significantly outperforms general-purpose AI tools like GPT-4 at predicting material properties, and the team hopes it can accelerate scientific discovery by serving as a robust research partner that provides grounded insights and generates step-by-step instructions for synthesizing novel materials. A paper describing this work was recently published in Nature Machine Intelligence. “Traditional simulations can provide the physical rigor required for materials science, yet their computational cost remains prohibitive for high-throughput screening. Conversely, while LLMs excel at rapid knowledge synthesis, they inherently lack the ‘structural vision’ to interpret materials directly from their underlying atomic coordinates,” said Yingheng Tang, a postdoctoral researcher in Berkeley Lab’s Applied Math and Computational Research Division (AMCR) and lead author on the paper. “MatterChat was built to solve this dilemma, empowering LLMs with a structural ‘vision’ that allows researchers to leverage their full potential for solving complex, real-world materials challenges.” Empowering language models to solve complex challenges in materials science To build MatterChat, the Berkeley Lab team drew inspiration from technologies like Vision Question Answering (VQA) and Text-to-Image (T2I) generation. In these tasks, AI must translate high-level text concepts into visual images, or vice versa. Doing so requires developers to build tools that “bridge” two fundamentally different forms of data. 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.
Best Sports Business Cities: Methodology
Explore the Best Sports Business Cities methodology, powered by 540,000+ data points and 120+ executive insights, detailing rankings, event weighting, market definitions, and indexing factors.
Samsung Plans To Transform Your Smartphone & Tablet Into On-Device AI Powerhouses With High-Bandwidth Memory Chips Using Complex Packaging
A unique and complex method of developing HBM for smartphones and tablets is being developed by Samsung to boost their AI capabilities
A grad student’s wild idea sparks a major aging breakthrough
A casual conversation between graduate students helped spark a breakthrough in aging research at Mayo Clinic. Researchers discovered that tiny synthetic DNA molecules called aptamers can selectively attach to senescent “zombie cells,” which are linked to aging, cancer, and neurodegenerative disease. The method could eventually help scientists identify and target these cells in living tissue with far greater precision.
Scientists Create Laser “Whirlpools” That Spin Tiny Cells Without Touching Them
Researchers have developed a new laser-based method that can rotate microscopic samples in all three spatial directions without touching them.
Hed: Crunch time? Take health advice from these 23 leaders
From creatine to pickleball, each leader figured out methods to preserve health during stressful periods.
UNC System Turns Attention to Math Instruction After ‘Science of Reading’ Battle
It took years for UNC system’s educator programs to adopt the method of teaching literacy. Now they’re looking at math “proactively.”
DeepAFM: A deep learning method to decode protein motion
Proteins change shape as they function, and these changes are essential for processes such as drug interactions and cellular activity. Researchers from the Tokyo University of Science developed an AI-based method called DeepAFM that is trained on millions of simulated images representing different protein states, accurately identifying transitions between closed and open states in a protein called SecA. This approach highlights the growing potential of AI to solve complex challenges in biology and medicine.
New method sharpens the search for alien biology
New UCR research shows that the search for life beyond Earth could benefit from a statistical approach that prioritizes patterns rather than searching for individual chemical or molecular traces.
New chemical kills 95% of termites without harming humans
Scientists may have found a smarter, safer way to wipe out termites hiding inside homes. A chemical called bistrifluron prevents drywood termites from forming new exoskeletons during molting, killing entire colonies from within. In tests, it eliminated about 95% of termites while avoiding the toxic side effects of traditional fumigation. Researchers say the method could provide longer-lasting protection as termites spread into new areas.
Scientists accidentally discover DNA that breaks the rules of life
A routine experiment with a new single-cell DNA sequencing method turned into a surprising scientific twist when researchers stumbled upon a bizarre genetic code in a microscopic pond organism. Instead of following the near-universal “rules” of life, this newly identified protist rewrites how genes signal their end. This unexpected discovery challenges long-held assumptions about how genetic translation works and hints that nature may be far more flexible—and mysterious—than scientists realized.
New AI method tackles one of science’s hardest math problems
Penn researchers have developed a smarter AI method for solving notoriously difficult inverse equations, which help scientists uncover hidden causes behind observable effects. By introducing “mollifier layers” that smooth noisy data, they’ve made these calculations more stable and far less computationally demanding. This could transform fields like genetics, where understanding how DNA behaves is key to disease research.
FSU researchers develop method to expand winter weather forecasting capabilities from weeks to months
Florida State University researchers have discovered how to accurately predict winter weather forecasts months in advance, affording sectors such as
Superconductors by design: Argonne scientists rewrite the rules of discovery
Scientists at Argonne and Northwestern developed a new method to design and predict entire families of materials with the same chemical formula but different structures, enabling more efficient, targeted materials innovation and research.
Global Health & WASH: May 2026 Funding Opportunities (14 new opportunities!)
The May update for Global Health & WASH brings new calls that cluster around three distinct shifts: LMIC research leadership moving from funder encouragement into a hard eligibility constraint, pharmaceutical and corporate-linked funders running structurally independent education and use-inspired research portfolios at substantial scale, and AI adoption shifting decisively from speculative tool to operational capability across health and WASH systems alike. In LMIC Research Leadership as Eligibility, Not Encouragement, Wellcome anchors the dominant signal of the month with three separate major calls that all require or strongly center LMIC leadership in their basic eligibility architecture. Wellcome’s ESIC Hubs (£1.5-£1.9M per hub over 3-5 years) explicitly requires an LMIC-based lead applicant, treating Global South leadership as a design constraint rather than a participation goal. Wellcome’s Infectious Disease Clinical Trial Development Award funds transdisciplinary teams whose lead must be a mid-career or established researcher based at an organization in Africa, South Asia, or Southeast Asia. Wellcome’s larger Infectious Disease Clinical Trial Award (£1M-£8M for optimizing licensed interventions) requires the administering organization to be in eligible LMIC regions and at least 50% of applicants based there. Around Wellcome, the same pattern shows up at smaller scale and across funders: CHINNOVA channels $1M into West and Central African research institutions for climate-health work; IBRO’s Neuroscience Training Grants explicitly tier ceilings by region of residence with Africa receiving the highest amount ($5,000); IHME’s GBD Emerging Researcher Award reserves at least one of its two annual awards for an LMIC researcher; Sidaction’s HIV Cure call funds research teams across France, the Netherlands, and eligible African countries with a minimum two-country collaboration requirement. Taken together, these calls signal a meaningful shift in how research equity is being operationalized — not as encouragement language at the bottom of a call, but as a structural feature of who can lead, where the work must be administered, and what proportion of the team must be regionally rooted. In Pharma-Funded Independent Medical Education and Use-Inspired Research, the lineup this month is unusually deep. Pfizer alone runs four parallel calls under independent education and quality improvement frameworks: a Migraine and Women’s Health RFP (200K x 2-year, $500K pool), Migraine IME ($200K, $1M pool), JAK Inhibitor evidence-based education ($100K), and Maternal Vaccination HCP Education in Saudi Arabia ($75K), plus a separate Pediatric Pneumococcal Surveillance research call in Saudi Arabia ($400K per project). Novo Nordisk Foundation runs four parallel calls of its own at substantial scale: Infectious Diseases Catalyst Grants (DKK 60M pool, up to DKK 7M collaborative), Non-Diabetic Endocrinology Collaborative Grants (DKK 53M pool, DKK 5-10M per project), Pioneer Innovator Grant Health (DKK 1.1M), and Distinguished Innovator Grant Health (DKK 6.8M). LEO Foundation deploys DKK 2-4M per project for dermatology research excellence. Pfizer’s framing across calls deliberately separates the funding from product promotion, emphasizing measurable practice-relevant change and structural distance between scientific outputs and commercial activity. Novo Nordisk’s “use-inspired” hard filter pushes researchers toward a credible translation pathway from mechanism to deployable tool. The pattern: industry-aligned funders are running structurally independent portfolios at meaningful $ that fund implementation evidence, not promotion. In AI as Operational Capability, Not Speculation, several calls this month explicitly treat AI adoption as a near-term operational question with real evidence and governance requirements, rather than a speculative trend to be watched. Wellcome’s ESIC Hubs frames AI as “the accelerator” for evidence synthesis, asking hubs to translate technical innovation into adoption-ready workflows that can match real-world policy timelines. UNICEF Venture Fund’s new Climate Tech for Children’s Health call funds for-profit startups deploying AI, machine learning, and blockchain in low-resource environments, with a strict requirement that solutions must already have a working prototype with promising pilot results. The Water Research Foundation has launched two simultaneous AI calls treating reproducibility, cybersecurity guardrails, and human-in-the-loop deployments as core design constraints rather than nice-to-haves: one on GenAI and Agentic AI in water utilities ($200K), one on alternatives to water shutoffs that explicitly screens for implementation economics evidence. Nordic Innovation’s quantum technology call (NOK 4.5M) extends the same logic into life science and healthcare, demanding consortium-stage projects mature enough to test with real users. TEF-Health offers €300K in subsidized testing infrastructure for European healthcare AI and robotics SMEs. The pattern: funders are converting AI pilots into playbooks, requiring transfer evidence and risk controls before AI can move from experimentation to sector-wide operational practice. For LMIC-based researchers in infectious disease or evidence synthesis, the three Wellcome calls together represent the deepest concentration of LMIC-led research capital in recent memory — pair the Infectious Disease Clinical Trial Development Award (£200K, deadline May 19) as a strategic pipeline feeder into the larger Infectious Disease Clinical Trial Award (£1-£8M, deadline June 2), since the development award functions explicitly as a track into the larger trial scheme. For Nordic researchers, the four Novo Nordisk Foundation calls plus LEO Foundation and Nordic Innovation Quantum represent ~$30M in regional research capital this cycle, all with deadlines clustered in May-August. For WASH practitioners, the three Water Research Foundation calls together (each up to $200K) are a cluster worth reading across — one on AI deployment, one on shutoff alternatives, one rewarding applied innovation — and the strongest applicants will articulate how their work bridges utility operations to policy-relevant evidence. For US-based clinical researchers and educators, the four Pfizer calls plus the APF Portfolio’s 80+ programs form an unusually accessible recurring pipeline; APF’s prohibition on indirect costs is a meaningful structural feature for early-career applicants whose institutions might otherwise absorb significant overhead. Snapshot of New Opportunities Total Estimated Funding Pool: $100 Million+ USD The grants are organized into three categories: Open Calls: Current grant and opportunities with a deadline. Grants are listed by closing date. 39 open opportunities- 14 new! Rolling Applications: current grant and opportunities with rolling applications (but it’s still best to submit as early as possible). 14 rolling opportunities- 1 new opportunity! Long term planning: Grants that have closed their current rounds, but are expected to open new windows. 4 long term opportunities! A quick tip for returning readers: if you want to jump straight to the newest additions, use CTRL F to search for “New!” and navigate quickly to the latest funding opportunities This post is for paid subscribers. This helps support the time and effort it takes to curate and organize these opportunities. Subscribe To keep this accessible to everyone who needs it, we’re happy to offer pay what you can rates. You can find more details here. Open Calls: Migraine Competitive Grant Program: Migraine and Women’s Health (Quality Improvement and/or Research RFP), Pfizer.*Closing soon!* Pfizer, in collaboration with the American Headache Society, is seeking independent quality improvement and research projects that close persistent gaps in migraine care for women across the United States. The funding logic centers on measurable, practice-relevant change: proposals should generate actionable evidence, tools, or system improvements that can be shared and used widely, not just within a single site. Priority areas span menstrual migraine and screening in women’s health settings, the role of hormonal fluctuations across the lifespan, sex-specific risk factors, comorbidities and quality-of-life burden, and disparities in outcomes by race, ethnicity, and socioeconomic status. The call is structured as a two-step competitive process with an initial Letter of Intent, with the strongest concepts expected to translate scientific insight into implementable improvements in diagnosis, management, and equity. Geographies: United States. Who can apply: U.S.-based organizations (not individuals), including professional schools, healthcare institutions, professional organizations, and other entities focused on healthcare improvement. Funding amount: Up to USD $200,000 total over 2 years; total pool USD $500,000. Targeted Sectors / SDGs: Health; Focus areas: migraine, women’s health, quality improvement, clinical research, menstrual migraine, health disparities. Deadline: LOI: May 5, 2026; Full proposal (by invitation): August 28, 2026. Learn more and apply here. This RFP rewards “shareable impact”: the most competitive submissions will pair rigorous methods with a clear plan for system uptake and dissemination beyond the originating institution. ReSSARC: Strengthening the Health Sector and Food Security in the Central African Republic (AID 013381/01/0) Call for Proposals, Italian Agency for Development Cooperation (AICS). *Closing soon!* AICS is seeking a limited set of high-capacity humanitarian partners to deliver an integrated package that stabilizes essential health services while reducing acute malnutrition and strengthening food security resilience in priority areas of the Central African Republic. The funder’s logic is explicitly nexus-based: proposals should meet urgent, life-saving needs while reinforcing local systems, coordination mechanisms, and community-level capacity so results persist beyond the emergency window. AICS emphasizes inclusion and protection outcomes, expecting gender-sensitive design and explicit measures for groups facing compounded vulnerability, including women, children under five, internally displaced people, and people with disabilities. The call also signals a localization pathway, encouraging stronger collaboration with national NGOs and balanced partnerships, alongside clear coordination with existing cluster and humanitarian actors to minimize duplication and improve coverage. Geographies: Central African Republic (Bangui, Ombella-Mpoko, Ouham, Ouham-Pende, Lim-Pende, Lobaye). Who can apply: Non-profit organizations registered with AICS, plus eligible non-profits without an office in Italy that have a pre-existing collaboration agreement with an AICS-listed organization. Funding amount: Total pool EUR €1,800,000; max EUR €800,000 (single applicant) or EUR €1,000,000 (ATS). Targeted Sectors / SDGs: Health; Focus areas: malnutrition prevention and treatment, maternal and child health, community health services, food security and resilience, localization partnerships Deadline: May 5, 2026. Learn more and apply here. This call is structured to reward partners who can link frontline service delivery to system durability and localization, not just short-term coverage gains. Contracts for Innovation in drug and alcohol addiction healthcare, Innovate UK. *Closing soon!* Innovate UK, on behalf of the Office for Life Sciences Addiction Healthcare Goals program, is procuring R&D to accelerate innovations that can improve treatment outcomes, strengthen recovery, and reduce harm and deaths linked to drug and alcohol addiction. The competition is positioned as a market-facing readiness push: selected projects are expected to advance solutions toward later-stage validation, generate evidence of user acceptability and UK market fit, and map credible routes through regulatory and certification requirements. Innovate UK signals a preference for innovations that can be field-tested in relevant UK settings and progressed to TRL 6 or 7, with practical plans for commercialization after contract completion. This structure favors applicants that can execute most work in-house, co-develop with service providers and people with lived experience, and translate technical progress into deployable tools for addiction healthcare delivery. Geographies: United Kingdom. Who can apply: Single organizations of any size (including EU, EEA, or international) leading delivery, with most work and key deliverables carried out in the UK; subcontractors for specialist skills only. Funding amount: GBP £200,000–1.5 million per project (inclusive of VAT); Total pool: GBP £20 million (across two strands). Targeted Sectors / SDGs: Health; Focus areas: Mental Health, Health Systems Strengthening, Research & Development, Technology Access. Deadline: May 6, 2026 (11:00am). Learn more and apply here. This call uses procurement to pull near-market addiction innovations toward operational proof and adoption pathways in UK services.
Appeals court blocks FDA rule that allows women to obtain abortion drugs by mail
A federal appeals court temporarily reinstated a nationwide requirement that abortion pills be obtained in person, undermining access to the method of abortion that has only grown more widespread since the US Supreme Court overturned Roe v. Wade.
AI method tackles one of science's hardest math problems
Penn Engineers have developed a new way to use AI to solve inverse partial differential equations (PDEs), a particularly challenging class of mathematical problems with broad implications for understanding the natural world. The advance, which the researchers call “Mollifier Layers,” could benefit fields as varied as genetics and weather forecasting, because inverse PDEs help scientists work backward from observable patterns to infer the hidden dynamics that produced them.
Press gala shooter / Political violence / Iran war price tag
Saturday’s attack at the White House Correspondents' Association Dinner was the third assassination attempt on President Trump.Host Mary Louise Kelly speaks with NPR National Security Correspondent Greg Myre and NPR Justice Correspondent Ryan Lucas about what the attack says about the state of political violence in the U.S. and the debate over whether the Secret Service was ready to protect Trump and those around him. Email the show at sourcesandmethods@npr.orgNPR+ supporters hear every episode without sponsor messages and unlock access to our complete archive. Sign up at plus.npr.org.
Ypsilanti Lincoln vs Jackson | HS Boys Lacrosse Highlights
Airdate: April 29th, 2026 Ypsilanti Lincoln vs Jackson | HS Boys Lacrosse Highlights | JTV SportsApril 30, 2026Tim & Torri Meunier, Jackson First United Methodist ChurchApril 30, 2026Katie Osman & Megan Stevens, VCL Schools Yes CommitteeApril 30, 2026Today’s News – Thursday April 30th – The Morning ShowApril 30, 2026Girls Soccer Ypsilanti Lincoln at Jackson, 04-29-2026…
The pastor of the nation’s largest Methodist church is running for the US Senate in Kansas
The pastor of the largest United Methodist Church in the U.S. has launched a campaign for the Democratic nomination for a U.S. Senate seat in Kansas.
Fine chandeliers are a specialty for New Orleans business | Home/Garden
Michael Melesurgo and Ricardo “Rico” Mencia purchased Classic Chandelier from the business’s founder, Peter Sather, in 2023, when he decided to retire. The business partners rebranded the company as Vintage Chandelier and relocated it to 455 Philip St. in the Irish Channel in December 2025.The company offers a variety of services including cleaning, moving, restoration, installation, pick-up, delivery and general maintenance of light fixtures, with a specialty in fine antique chandeliers.Additional specialties include the installation and restoration of mirrors and artwork, with pick-up and delivery included. googletag.cmd.push(function() { googletag.display('fixed-breakout-mobile-1'); }); googletag.cmd.push(function() { googletag.display('fixed-breakout-tablet-1'); }); “We mainly focus on older fixtures, but we have also worked on newer ones, especially with installation,” Melesurgo said. “We aim to be able to work on almost anything within reason. But chandeliers are the meat and potatoes of the business.” googletag.cmd.push(function() { googletag.display('fixed-sticky-rail-1'); }); googletag.cmd.push(function() { googletag.display('fixed-breakout-desktop-1'); }); The team at Vintage Chandelier has worked on some of the best-known chandeliers in New Orleans, at hotels including the Ritz-Carlton and the Hyatt Regency, the Milton H. Latter Memorial branch of the New Orleans Public Library and several prominent homes.Inside Out sat down with the duo to discuss the identification and care of fine chandeliers. googletag.cmd.push(function() { googletag.display('fixed-breakout-mobile-2'); }); googletag.cmd.push(function() { googletag.display('fixed-breakout-tablet-2'); }); What are some of the hallmarks of a fine chandelier?Melesurgo: Some of the finer points of elegant chandeliers are pristine crystal, clean craftsmanship, original pieces and parts and stamped pieces to ensure authenticity. Everything should be clean and working.How should you clean a fine chandelier?Melesurgo: The material in question determines the methodology for cleaning. Crystal chandeliers should be cleaned by hand, crystal by crystal, to ensure the best outcome, and to avoid deterioration of metal wiring typically used in connecting pieces together. Spraying cleaners can, over time, sit on the metal and end up rusting connections that compromise the connections. googletag.cmd.push(function() { googletag.display('fixed-breakout-mobile-3'); }); googletag.cmd.push(function() { googletag.display('fixed-breakout-tablet-3'); }); googletag.cmd.push(function() { googletag.display('fixed-sticky-rail-2'); }); googletag.cmd.push(function() { googletag.display('fixed-breakout-desktop-2'); }); What in your backgrounds led you to this business?Melesurgo: I have an electrical background. I started as an electrical apprentice in the local union (IBEW 130) in 2005. Then Katrina hit. I ended up going back to school and getting a degree in Hotel, Restaurant and Tourism and graduating in 2011. I started working for Peter (Sather) at Classic Chandelier & Mirror in 2012, which was supposed to only be temporary, but I ended up staying for a lot longer. I have always enjoyed working with my hands and being creative. This allows me to be creative and keep a lot of beautiful history alive. googletag.cmd.push(function() { googletag.display('fixed-breakout-mobile-4'); }); googletag.cmd.push(function() { googletag.display('fixed-breakout-tablet-4'); }); Rico, you worked as a draftsman alongside an engineer at YKM Consulting. googletag.cmd.push(function() { googletag.display('fixed-sticky-rail-3'); }); googletag.cmd.push(function() { googletag.display('fixed-breakout-desktop-3'); }); Mencia: The job was interesting, especially working on the Ochsner expansion, but staring at two monitors all day became unbearable. I went to work for Peter (Sather), which opened a new world to antiques and chandeliers that I find fascinating. Vintage Chandelier, 455 Philip St., (504) 309-8983, @vintagechandelier.504 on Instagram googletag.cmd.push(function() { googletag.display('fixed-sticky-rail-4'); });
Jazz Fest part entertainment, part education
Many rooms across Hastings were classrooms as much as they were improvised concert spaces during the 22nd Annual Thornapple Arts Council (TAC) Jazz Festival. Kerry Moffit sat in the back of the bottom floor of the Green Street United Methodist Church in Hastings Friday morning, with his trumpet at his side, taking notes as the
Sony clarifies recent PlayStation DRM update
PlayStation fans and gaming preservations have been alarmed since late last week that a new 30-day timer attached attached to newly-purchased digital games for PlayStation 4 and 5 might render those games unplayable if the system they’re on is offline for longer than a month. But a rep from Sony told Game File today that new system will require only a single online check before it functions as a perpetual license for the digitally purchased game. That is consistent with a newer theory that emerged by gamers testing the 30-day timer over the last two days. “Players can continue to access and play their purchased games as usual,” a rep for PlayStation told me over email. “A one-time online check is required after purchase to confirm the game’s license, after which no further check-ins are needed.” The rep’s explanation squares with the current understanding among players about what’s happening. A multi-day mystery A post last Friday by YouTuber Lance MacDonald spread awareness over the 30-day timer. Quickly, players began stress-testing the system, removing a battery within their PlayStations and checking what would happen if their systems were offline past the timer window. They noticed that setting a PS4 or PS5 to be the “primary” console—a common Sony-authorized method to retain rights to a digital game—wasn’t overriding the 30-day limit. On Tuesday came the breakthrough: A gamer called Andshrew on the Resetera forums posted the results of some tasting. They found that digital games purchased on PS4/PS5 after some time in mid-April would get a 30-day timer that could then be removed and replaced with a perpetual license that would allow the game to be played offline. The transition to the perpetual license occurred some number of days after the digital game was purchased and the system was connected to the internet. They believed that a PlayStation’s online check-in would trigger the license change no sooner than 15 days after the user had purchased the game. This synced with the theory that Sony’s new system is related to the 14-day window that PlayStation users have to request a refund for digitally purchased games. The new timer/check-in system could be a way to defeat some exploit in that refund system, some users theorized. Game File is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe In a follow-up, I asked the Sony rep if the 15-day timeline is accurate or if the perpetual license can be activated sooner after purchase. If they share more, I’ll update this post. Sony has not said why they’ve introduced the timer and whether it is designed to stop people from exploiting the refund system. But their explanation suggests that this new DRM method should not result in long-term loss of access to digitally purchased games, just because a PlayStation is offline for a month or Sony’s servers are down. Xbox flashback For many some gamers, the initial reporting of the 30-day timer conjured memories of Microsoft’s disastrous May 2013 announcement of the Xbox One, which was going to require an internet-connected check-in once a day, lest your games become unplayable.1 After widespread backlash, Microsoft abandoned that plan, ahead of the Xbox One’s launch. 1 Fun fact: That 24-hour check-in detail was my scoop from an interview I had that day with then-Xbox exec Phil Harrison. I was EiC of Kotaku at the time and rushing from one Xbox One preview appointment on Microsoft’s campus to another. I phoned the news in to reporter Jason Schreier, who wrote it up for the site.
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Chevy Humphrey Is Running Experiments at the Griffin Science Museum in Chicago
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Shaky political science: using "simulated" elections over real-world elections
[Dear DSOS readers – this article is the latest in our “Shaky Political Science” series. In our 38 page report, “Shaky political ‘science’ misses mark on ranked choice voting,” released in early December, we examined over 40 studies focused on ranked choice voting. We found that many of these studies suffered from puzzling research methodologies, poorly constructed surveys, unrealistic simulated elections, cherry picked data, and faulty analyses that often were contradicted by results from real-world elections (a link to our full paper is here). Here are links to the first three articles in our series: the first one summarizing the overall results of our report, the second one analyzing a study co-authored by University of Minnesota academic Larry Jacobs, which was one of the most error-prone of all the research we reviewed in our report, and a third article showing an admirable example of two political scientists holding two other political scientists accountable for their flawed, sloppy research]. In this fourth article in our “Shaky Political Science” series, we shine a spotlight on seven studies that were web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group (the ERRG is an ad hoc committee headed by New America’s Political Reform Program and joined by the American Enterprise Institute, the Unite America Institute and Stanford University’s Center on Democracy, Development, and the Rule of Law). This research consortium has been led by Lee Drutman, senior fellow with New America’s Political Reform Program. These seven studies are simply not credible from any objective standard about how political “science” is supposed to illuminate our understanding of either ranked choice voting or politics in general. The feature that six of these seven studies have in common is that they all used surveys – artificial, simulated elections -- to attempt to say something cogent or interesting about ranked choice voting (RCV) elections. None of them used real world data from actual RCV elections – either the 1000+ elections in the US or the many thousands of elections from places like Australia, Ireland and elsewhere where RCV has been used for 100 years. While a few of these studies conclude with some mixed positives about RCV, our analysis here is content neutral. The idea that, with a political reform like RCV, you can ask respondents in a mock, artificial election to vote with that method, with only the barest of explanation about how RCV works or its likely impacts (i.e. no spoiler candidates, majority winners, voters liberated to pick their favorite candidates, coalition-building), and then ask them their thoughts/reactions about voting in that system, with fake candidates and no real campaigning, no real exposure to an actual election or actual candidates, rests on some very puzzling assumptions. The findings of these seven studies actually are contradicted by the findings from many other studies based on real-world RCV elections. These New America studies also failed to list the real-world RCV studies in their own citations, instead including those studies that reinforced their conclusions. The seven studies featured in this article were chosen because they form a cohesive cohort which precisely illustrates one of the main points of our research paper, namely that using surveys, i.e. artificial elections, or mathematical/theoretical models, instead of real-world data, is in fact “shaky political science.” Below are summaries of our analysis of five of the seven New America studies. You can read our complete analysis of all seven studies at our paper, “Shaky political ‘science’ misses mark on ranked choice voting.” Choosing to “Vote As Usual” by Andre Blais, Carolina Plescia, and Semra Sevi. New America. (Blais et al., 2021). This study, web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group, used two surveys in which respondents completed ballots for simulated elections using plurality, approval, RCV and point (Score) voting. In both cases the authors provided the respondents with only minimal explanation of each method. For example, the entire explanation for RCV was “For this vote, rank the candidates from your first to your last choice (you do not need to rank them all). This is called the ranked vote.” That’s it. The study did not provide any context or explanation to survey respondents about why she/he might want to rank multiple candidates. Participants were not even told that ballots would be tabulated using an “instant” runoff, not to mention any description of RCVs ability to eliminate the spoiler effect or liberate voters to rank their favorite candidates without fear of helping the “greater evil” candidate to win. That’s like taking an iPhone back in time to 1970 and asking someone if they like it better than their rotary phone without explaining how it works. Their first survey targeted states that held 2020 Super Tuesday Democratic primaries and used the real candidates’ names in the survey; the second survey used fictitious candidates. In other words, they surveyed respondents who had only ever participated in plurality elections, had no history or background with using RCV or any of the other methods, had no clue about its likely impacts, and did not communicate how the alternative methods would be counted, and then asked them: “How satisfied are you with using each system?” Predictably, the winner was plurality elections, the system used for president, Congress, gubernatorial and state legislative elections – in other words, the method they have been using, year after year, all their voting lives. With so little explanation of RCV and its impacts, this study is simply not credible in discovering anything useful about real-world RCV elections. It merely shows that a basic voter education campaign is needed when adopting a new election system. Additionally, the result contradicts many other post-election surveys following real RCV elections, asking opinions from voters who had just participated in a RCV election, that showed a great deal of satisfaction with RCV (see details on that in Studies 22-25 in our paper, as well as the four studies cited in the review for Study 28). Share Other studies on “satisfaction” and “understanding” make similar mistakes, in terms of basing their surveys on inadequate explanation to respondents and lack of education about context. How could any survey ever hope to measure accurately, in essence simulate a real-world election, without actual campaigning candidates, the media writing about those candidates, organizations endorsing candidates and voters being inundated with crucial information? More than anything, what these “studies” based on surveys show is that if you ask voters about something they don’t know very much about, and fail to adequately educate them, their responses will unsurprisingly reflect their confusion. Equally puzzling is that New America’s Electoral Reform Research Group, headed by political scientist Lee Drutman, found this to be a credible enough study that they not only published it but highlighted it in Drutman’s own discussion of research related to ranked choice voting. This pattern was repeated in study after study featured by New America’s Electoral Reform Research Group. For example in Ranked-Choice Voting and Political Expression: Voter Guides Narrow the Gap between Informed and Uninformed Citizens by Cheryl Boudreau, Jonathan Colner, and Scott MacKenzie, the researchers based their analysis on a survey which found that respondents who were asked to vote in a simulated RCV election, with minimal explanation of RCV or how it works, displayed less evidence of being informed about how to use RCV effectively, with the least informed voters ranking fewer candidates. As pointed out under the study above, in a real election voters are informed by a wide variety of methods, including public education by the election administration, by interested political groups, the media, and the candidates themselves who benefit from voters being aware of how to vote in the new system. In Support for Ranked-Choice Voting across Race and Partisanship by Joseph Anthony, David C. Kimball, Jack Santucci and Jamil Scott, the researchers used respondents from different parties and races voting in simulated plurality and RCV elections, who indicated their satisfaction with each voting method. Instead of using real world election results, the authors fell back on the easy, armchair and less reliable academic research methodology of making up mock elections. As with the other studies, little explanation was given of how RCV works or what its implications might be (electing majority winners or eliminating spoilers, for example). Unsurprisingly, this study found that respondents were not very satisfied with RCV. However, when the authors added an explanation that RCV has a tendency to elect more women and people of color, they found that this greater explanation increased the preference for RCV. The more voters were familiar with it, and the more they understood potential positive impacts – surprise! -- the more they were open to switching away from the status quo. Of note, the results from this study are thoroughly contradicted by a number of other studies whose data was obtained from real RCV elections with real voters. Those studies show a great deal of satisfaction with RCV. More “fake election” research An interesting wrinkle on this “research by fake elections” approach, which only served to illustrate how unreliable such “research” can become, was taken in Ranked-Choice Voting, Runoff, and Democracy: Insights from Maine and Other U.S. States by Joseph Cerrone and Cynthia McClintock_,_ also web-hosted and funded by New America’s ERRG. It gets high marks for creativity, but an eye-roll for thinking this could possibly add anything meaningful to our understanding of real world RCV elections. This study tried to determine whether RCV, plurality or two-round runoff elections provided greater voter satisfaction and acceptance of election results. To determine that, the authors conducted a survey asking if voters were satisfied with a fictional newspaper report of an election, with three different versions given to respondents – plurality, runoff and RCV, with variations of Republican/Democrat winners and variations of “come-from-behind” winners,” i.e. candidates not initially in first place but who win the “runoff.” Again, a minimal explanation of RCV or how it works was given, and the survey showed that respondents had the greatest unfamiliarity with RCV than the other methods. The unsurprising result? Respondents were much more satisfied with “come-from-behind” victories in a traditional two-round runoff election than in RCV, even though RCV is also a runoff system but with a single round of counting. Interestingly, the dissatisfaction with RCV disappeared for those who were more familiar with RCV. In fact, among those respondents who were “very familiar” with RCV, they were equally satisfied with RCV, runoff and plurality. Surprise, surprise. Again, what this study revealed, more than anything, is not only the inadequacy of the explanation for RCV given by the authors of this study, but the failure of the research format itself, thinking that such an artificial mock election could somehow simulate what actually happens in the real world in a real RCV election. Those four studies reviewed above are fitting examples of inadequate and shaky political “science.” Other studies reviewed in our paper from the New America/Electoral Reform Research Group cohort which also used mock elections included: Ranked-Choice Voting is an Acquired Taste by Joseph Anthony and David C. Kimball. New America. (Anthony et al., 2021). Ranked-Choice Voting is No Refuge for Extreme Candidates by Melissa Baker. New America. (Baker, 2021). Also included in this New America cohort is Does Ranked-Choice Voting Reduce Racial Polarization? by Yuki Atsusaka and Theodore Landsman, which actually did use real-world election results data but applied an odd and unconventional methodology to the data which relied on shaky assumptions and non-real-world scenarios that were too divorced from reality. To determine levels of “racially polarized voting,” the researchers imagined “ethnic parties,” by which they mean political parties for each ethnic group, which was especially odd since the election data they were using came from nonpartisan RCV races. But then they made an even odder assumption, namely that since race and ethnic characteristics of studied voters were not available, the authors tried to infer that information based on who voters selected and the racial make-up of voters in each district. This amounted to little more than vague and ambiguous guesswork dressed up with a bunch of mathematical equations used to give it a veneer of scientific appearance. Indeed, it seemed to assume racial polarization in order to prove racial polarization. Based on this questionable methodology, the authors concluded that RCV doesn’t reduce racial polarization. But the premise of this study missed an obvious reality: since RCV incentivizes more candidates to run and can result in more diverse fields of candidates, one would expect more racially-polarized voting in voters’ top rankings, not less, because more voters would have someone on that ballot who represents their racial or ethnic identity and more freedom to vote for their real first choice. By encouraging more diverse candidates and eliminating the spoiler effect, RCV facilitates voting for a favorite candidate of your own race and then selecting a second choice with the best likelihood of winning. The real measure is what voters do with their additional rankings – do they consider other candidates and vote “for the best of the rest”? This study didn’t tell us anything useful about that. But readily available data shows this is common in real RCV elections, and candidates act on this knowledge by engaging with voters outside their racial group. So the authors of this study failed to consider or account for what actually happens in real-world elections, and their conclusions are tainted by this failure. New America has contributed a great deal over the past 2.5 decades to our understanding of many policy areas, including politics and political reform (full disclosure: Steven Hill, one of the authors of this article and of the “Shaky Political Science” research paper and series, is a former senior fellow and former director of New America’s political reform program, 2005-2010). So it was especially disappointing that New America funded and published such a large batch of dubious studies about ranked choice voting. The stated praiseworthy goal of its Electoral Reform Research Group was to inspire new academic research about ranked choice voting that would shed light and help the public better understand the impacts of RCV. Instead, the ERRG research did just the opposite. It produced dubious research that created more confusion and misinformation. In so doing, New America’s political reform program contributed to “shaky political science.” Steven Hill and Paul Haughey Share Upgrade to $5 subscription Thanks for reading DemocracySOS, a reader-supported digital portal for the pro-democracy movement. Subscribe for only $5 per month to receive full benefits and to support our work. Subscribe
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Advancing epidemic and pandemic preparedness through research readiness: SEAR Unity Studies Network Sites chart the way forward in 2026
On 25 March 2026, WHO Regional Office for the South‑East Asia Region (SEARO) convened the first meeting of the SEAR Chapter of the Unity Studies Network Meeting in 2026 to take stock of progress and plan next steps for strengthening epidemic and pandemic preparedness across the Region. The meeting focused on reviewing the ongoing adaptation of selected, standard Unity Study methodologies, sharing field implementation experiences from 2025, and identifying key priorities to guide collaborative work in 2026.Held virtually, the meeting brought together representatives from six Unity Studies network sites across five countries (Bangladesh, India, Nepal, Sri Lanka, and Thailand) as well as participants from all three levels of WHO.The meeting took place against the backdrop of ongoing regional efforts in South‑East Asia to strengthen early investigations and studies for epidemic and pandemic preparedness, with a focus on pandemic influenza and other emerging respiratory pathogens with epidemic and pandemic potential. In this context, and as part of preparedness for the rapid generation of evidence, the WHO Health Emergencies Programme in SEAR (WHE/SEAR) encourages Unity Studies sites to adapt prioritized, standardized Unity Study protocols to their national contexts. This approach supports the generation of timely and reliable evidence to inform public health decision‑making during epidemics and pandemics.These efforts are supported through a combination of experience sharing, participation in the global Unity Studies network webinars, capacity‑building of the national public health workforce, and regional collaboration that promotes inter‑country, inter‑agency, and inter‑ministerial action. In parallel, sites are encouraged to explore diversified funding sources to help sustain research preparedness over the long term.Discussions highlighted the importance of WHO’s catalytic financial support through the Pandemic Influenza Preparedness (PIP) Partnership Contributions (PIP‑PC). When combined with national and institutional resources, this support enables countries and sites to maintain continuity of activities at a core level and remain prepared to respond rapidly and effectively to future epidemics and pandemics.Regional and Global Updates: Opening the session, Dr Pushpa Ranjan Wijesinghe, Programme Area Manager, Pandemic and Epidemic Management (PEM) Unit, WHO Health Emergencies Programme, highlighted the progress made in operationalizing the Unity Studies Network across the Region and acknowledged the collaborative efforts of WHO at all three levels, together with country unity studies sites. He emphasized the availability of limited but catalytic funding through the Pandemic Influenza Preparedness (PIP) Partnership Contribution (PIP‑PC) workplan at SEARO to sustain network activities in 2026, while encouraging sites to leverage additional funding sources to expand implementation.Representing SEARO, Dr Ashok Basnet, consultant, Pandemic Influenza Preparedness reported that, at the regional level, the Unity Studies Network currently includes six sites across five countries: Bangladesh, India, Nepal, Sri Lanka, and Thailand. He noted that in 2025, catalytic funding supported protocol adaptation, training, and pilot implementation. In addition, a regional Unity Studies symposium held during the Global Public Health Summit in Sri Lanka strengthened and advocated collaboration between the Unity Studies site, national decision‑making forum, relevant technical units of the ministry of health, WHO and other partners.The symposium also highlighted the role of early investigations and studies as a key component of a multi‑source surveillance system needed at country level to generate a broad range of evidence. It underscored the critical role of country Unity Studies sites and similar research entities in generating evidence to support public health decision‑making during epidemics and pandemics.Participants of the WHO GISRS Unity Studies Sites Network- SEAR Chapter Meeting 2026 (Photo credit- PEM/WHE/SEARO). SEARO also updated participants on ongoing efforts to develop a regional Unity Studies webpage and to promote participation in global knowledge‑sharing platforms, including webinars and the WHO Community of Practice. Priorities for 2026 include supporting the implementation of Unity protocols, strengthening collaboration and experience sharing across sites, documenting and publishing findings, integrating Unity Studies activities as a component of multi-source national surveillance systems, and building workforce capacity.Dr Nickle Boddington of the Global Influenza Programme (GIP) at WHO Headquarters, which coordinates the Unity Studies Network globally, provided updates on global Unity Studies activities, noting that the network has expanded to 16 sites worldwide. New and updated protocols, including rapid clinical severity assessment tools and standardized data platforms, are being developed to support implementation. The global Community of Practice has also been launched to facilitate technical exchange, and continued support will be provided to sites for protocol adaptation, ethical approvals, and readiness for early investigation during outbreaks. Updates from Unity Studies sites in SEAR in 2025Unity Studies sites shared progress and experiences in implementing protocols across diverse country contexts. Sites initiated activities in 2025 using catalytic funding from SEARO, complemented by the mobilization of internal national resources. The Institute of Epidemiology, Disease Control and Research (IEDCR), Bangladesh, reported the adoption and piloting of investigation protocols on non‑seasonal influenza and emerging respiratory diseases during recent avian influenza outbreaks, demonstrating a multi‑source funding approach involving government, WHO and One Health partners.The Hamdard Institute of Medical Sciences and Research (HIMSR), New Delhi, India, highlighted the piloting of household transmission investigation studies, the expansion of the national Unity Studies network, and the contribution of study findings to global evidence through published outputs. The All-India Institute of Medical Sciences (AIIMS), New Delhi, India, shared experiences from large‑scale seroprevalence studies conducted during the COVID‑19 pandemic, recent efforts to strengthen national Unity network sites through training activities, and plans to initiate multi‑centre studies on respiratory viruses.Similarly, Prince of Songkla University, Thailand, highlighted its experience in developing integrated surveillance and diagnostic protocols for multiple respiratory pathogens, enhancing readiness for targeted investigations of emerging threats. The University of Sri Jayewardenepura, Sri Lanka, reported ongoing engagement with the Ministry of Health and exploration of additional funding sources to initiate studies. Meanwhile, the National Public Health Laboratory, Nepal, provided updates on continued coordination with national stakeholders to initiate implementation, despite delays related to the country context.Across sites, shared experiences underscored the importance of protocol adaptation, multisectoral collaboration, and sustainable funding to operationalize Unity Studies and generate evidence for public health action.Discussions and Way ForwardThe meeting reinforced key priorities for 2026, including strengthening unity studies protocol implementation, enhancing collaboration, knowledge sharing, integrating research as a component of national multi-source surveillance systems, and ensuring that generated evidence informs public health decision‑making. Participants also emphasized the importance of documentation, visibility, and leveraging multiple funding sources, and agreed on the following action points.Unity Studies Sites and WHO Country Offices:Advance adoption and implementation of at least one Unity protocol, continuing activities initiated in 2025 with catalytic funding, supplemented with other sources of funding where feasible.Document site-specific experiences, outcomes, and lessons learned; participate in regional and global knowledge-sharing platforms including WHO webinars and the Community of Practice.Work closely with ministries of health to establish unified national platforms for translating generated evidence into national public health decision-making. WHO Country Offices may facilitate technical support and complementary, synergistic funding opportunities where feasible. WHO Regional OfficeCoordinate with WHO Country Offices to support implementation of core unity studies activities by network sites through catalytic funding and provide technical guidance and virtual engagement opportunities throughout 2026.Facilitate the participation of all Unity Studies sites in the regional meeting on Unity Studies implementation in October 2026, to be held back‑to‑back with the Annual SEAR Regional PIP‑PC Meeting; develop and host a SEAR Unity Studies website; and mobilize support from academic institutions within the SEAR Unity Studies Network to support the development of regional manuscripts that showcase Unity Studies activities to scientific audiences and promote knowledge‑sharing.Explore complementary funding opportunities and partnerships to sustain and expand network activities across the Region.WHO HeadquartersProvide global technical guidance, updated protocols, and toolkits to support the implementation of Unity Studies in SEAR.Facilitate engagement of SEAR sites in global webinars, the Community of Practice, and opportunities to showcase their work.Support joint publications, inclusion of SEAR experiences in global outputs, and identification of additional funding and collaborative opportunities to strengthen Unity Studies activities.
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“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
Acting CDC director delayed release of study showing benefit of Covid vaccines
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Quantum computers keep losing data. This breakthrough finally tracks it
Quantum computers struggle with a major flaw: their information vanishes unpredictably. Scientists have now created a new method that can measure this loss over 100 times faster than before. By tracking changes in near real time, researchers can finally see what’s going wrong inside these systems. This could be a big step toward making quantum computers stable and practical.
Scientists map the brain’s hidden wiring using RNA barcodes in major breakthrough
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Scientists discover reversible male birth control that stops sperm production
Scientists at Cornell University may be closing in on the long-sought “holy grail” of male contraception: a safe, reversible, nonhormonal method that completely halts sperm production. In a breakthrough mouse study, researchers used a compound called JQ1 to temporarily shut down meiosis—the critical process that produces sperm—without causing lasting harm. After treatment stopped, sperm production bounced back, fertility returned, and the animals produced healthy offspring.
She tried using her own online-dating method— it worked
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Scientists Are Recycling Centuries-Old Bullets for a Badly Needed Toxic Compound
Researchers want to recycle more lead waste for high-tech solar panels via a new method.
Scientists Are Recycling Centuries-Old Bullets for a Badly Needed Toxic Compound
Researchers want to recycle more lead waste for high-tech solar panels via a new method.
Institute of Industrial Science, The University of Tokyo and George Mason University scientists mine past air temperature forecasts, lower cost with greater subseasonal prediction accuracy
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Faster Detection of Forest Loss
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Study of 1,700 languages reveals surprising hidden patterns
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Braiding knowledge: how Indigenous expertise and western science are converging
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Scientists Have Made French Fry Breakthrough
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Gemma 4 and what makes an open model succeed
Having written a lot of model release blog posts, there’s something much harder about reviewing open models when they drop relative to closed models, especially in 2026. In recent years, there were so few open models, so when Llama 3 was released most people were still doing research on Llama 2 and super happy to get an update. When Qwen 3 was released, the Llama 4 fiasco had just gone down, and a whole research community was emerging to study RL on Qwen 2.5 — it was a no brainer to upgrade. Today, when an open model releases, it’s competing with Qwen 3.5, Kimi K2.5, GLM 5, MiniMax M2.5, GPT-OSS, Arcee Large, Nemotron 3, Olmo 3, and others. The space is populated, but still feels full of hidden opportunity. The potential of open models feels like a dark matter, a potential we know is huge, but few clear recipes and examples for how to unlock it are out there. Agentic AI, OpenClaw, and everything brewing in that space is going to spur mass experimentation in open models to complement the likes of Claude and Codex, not replace them. Especially with open models, the benchmarks at release are an extremely incomplete story. In some ways this is exciting, as new open models have a much higher variance and ability to surprise, but it also points at some structural reasons that make building businesses and great AI experiences around open models harder than the closed alternatives. When a new Claude Opus or GPT drops, spending a few hours with them in my agentic workflows is genuinely a good vibe test. For open models, putting them through this test is a category error. Something else to be said about open models in the era of agents is that they get out of the debate of integration, harnesses, and tools and let us see close to the ground on what exactly is the ability of just a model. Of course, we can’t test some things like search abilities without some tool, but being able to measure exactly the pace of progress of the model alone is a welcome simplification to a systematically opaque AI space. Share The list of factors I’d use to assess a new open-weight model I’m considering investing in includes: Model performance (and size) — how this model performs on benchmarks I care about and how it compares to other models of a similar size. Country of origin — some businesses care deeply about provenance, and if a model was built in China or not. Model license — if a model needs legal approval for use, uptake will be slower at mid-sized and large companies. Tooling at release — many models release with half-broken, or at least substantially slower, implementations in popular software like vLLM, Transformers, SGLANG, etc due to pushing the envelope of architectures or tools. Model fine-tunability — how easy or hard it is to modify the given model to your use-case when you actually try and use it. The core problem is that some of these are immediately available at release, e.g. general performance, license, origin, etc. but others such as tooling take day(s) to week(s) to stabilize, and others are open research questions — with no group systematically monitoring fine-tunability. In the early era of open models, the days of Llama 2 or 3 and Qwen pre v3.5, the architectures were fairly simple and the models tended to work out of the box. Some of this was due to the extremely hard work of the Llama, Qwen, Mistral, etc. developer teams. Some is due to the new models being genuinely harder to work with. When it comes to something like Qwen 3.5 or Nemotron 3, with hybrid models (either gated delta net or mamba layers), the tooling is very rough at release. Things you would expect to “just work” often don’t. I’ve been following this area closely since we released Olmo Hybrid with a similar architecture, and Qwen 3.5 is just starting to work well in the various open-source tools that need to all play nice together for RL research. That’s 1.5 months after the release date! This is just to start really investing more into understanding the behavior of the models. Of course, others started working on these models sooner by investing more engineering resources or relying on partially closed software. The fully open and distributed ecosystem takes a long time to get going on some new models. All of this is lead-in for the most important question for open models — how easy is it to adapt to specific use-cases? This is a different problem for different model sizes. Large MoE open-weight models may be used by entities like Cursor who need complex capabilities in their domain, e.g. Composer 2 trained on Kimi K2.5. Other applications can be built on much smaller models, such as Chroma’s Context-1 model for agentic search, built on GPT-OSS 20B. The question of “which models are fine-tunable” is largely background knowledge known by engineers across the industry. There should be a thriving research area here to support the open ecosystem model. The first step is to understand characteristics of different base and post-trained models to understand what they look like. The second step is to tune pretraining recipes for open models so they’re more flexible. Interconnects AI is a reader-supported publication. Consider becoming a subscriber. Subscribe For The ATOM Project and other Interconnects endeavors, we’ve put in substantial effort to measuring adoption trends in the open ecosystem. Everything takes a long time to unfold after a model is first publicly available — and adaptability is why. What we know for sure now, when Qwen has been going from strength to strength with its releases, is that technical staff across the industry has gotten comfortable working with Qwen models. Countless research methods and datasets were made to work with Qwen. It’ll take patience for any other model family to get to this point — a patience I’m not sure many open model builders have. This takes us to Gemma 4, Google’s latest open models. Gemma 3 was released more than a year ago, in March of 2025, and is a bit underrated. Gemma 4 comes in 4 sizes for now, with a bigger, MoE model of over 100B total parameters rumored but not released yet. The models we have today come in sizes of ~5B dense, 8B dense, 26B total 4B active MoE, and 31B dense. I’m most excited that they’re finally adopting a standard Apache 2.0 open source license. This’ll massively boost adoption. The standard of better licenses for strong open-weight LLMs was set by mostly Chinese open model labs in the last 1-2 years, and now U.S. companies are following suit. I will personally be so happy if the horrible Llama licenses and Gemma terms of service were an ~18-month transient dynamic of the industry being nervous about releasing strong open models. The Gemma 4 scores look very solid, the small models have incredible benchmark scores (especially in general domains like LMArena) and the 31B model rivals the recent Qwen 3.5 27B, which is the leading member of that class. The ~30B size range is an important one, as it’s accessible both to researchers and to enterprises looking to deploy the model in real use-cases. Where the 7B model scale is the default for tinkering and research, a 30B model is the default for seeing if an open model can unlock substantial value in your specific workflow — a good mix of intelligence, low price, tractability for downstream training, etc. This takes us back to the above adoption criteria I mentioned for open models and the bigger question — do I think Gemma 4 will be an overwhelming success? Previous Gemma models have been plagued by tooling issues and poorer performance when being finetuned. Gemma 4’s success is going to be entirely determined by ease of use, to a point where a 5-10% swing on benchmarks wouldn’t matter at all. It’s strong enough, small enough, with the right license, and from the U.S., so many companies are going to slot it in. I’m cautiously optimistic that Gemma 4 is going to work better here. Winds are shifting for open models built in America. We saw GPT-OSS go through a bumpy launch to become an overwhelming success. There’s a collective energy around the likes of Reflection, Arcee, Nemotron, Gemma, Olmo, and peers that show substantial demand for building new stacks around open models. There’s capital to be spent on AI stacks across the economy by those who want more ownership of everything, including the model. After launching The ATOM Project 240 days ago, the conversation is shifting into the next stage. Summer of 2025 was a crisis moment where the U.S. AI scene realized it can’t wait and figure out open models after building AGI. The two markets will capture different areas and proceed in parallel. Now that more companies in the U.S. are releasing strong models, we need to improve the ecosystem so that these models are easy to use, understand, and build value around. It’s the hard work to build another inflection point in these adoption plots I’ve been updating consistently, but that’s the work to be done. Join me in it. More data coming soon! Here’s a sneak peek:
Computer Science faculty has article published
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Health officials have new advice on what fish are safe to eat in Minnesota
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Sharing her passion for science
By Danielle Rush Communications Specialist, Indiana University Kokomo Hisako Masuda, Indiana University Kokomo professor of biochemistry, was honored by the Indiana Academy of Science (IAS) as its 2026 Distinguished Scholar. The award is given to an individual who is recognized by the IAS as having an exceptional record of scholarship. Recipients are selected from nominations submitted to its awards committee. “I was surprised,” said Masuda, who joined IU Kokomo’s faculty in 2013. “I feel there are many good scientists in the Academy, so I wasn’t expecting it at all.” Patrick Motl, dean of the School of Sciences, said the Academy recognizes up to one member per year, and that Masuda is the second or third IU Kokomo faculty member to receive the commendation. “Clearly this speaks to the strength of Dr. Masuda’s accomplishments in research and scholarship and that her work stands out amongst her peers in Indiana,” said Motl, who received the award in 2019. “Many of the institutions that participate in the IAS are undergraduate institutions or primarily undergraduate institutions, but even having that caveat, Dr. Masuda has built and maintains a very active research program in her lab, working exclusively with undergraduates. She is passionate about science and can share that passion with her students. To carry on a quality research program with so many students involved in so many projects is truly remarkable and I am glad that the IAS acknowledged that for Dr. Masuda.” The IAS specifically commended her for involving more than 50 undergraduates in her research in bacterial metabolism, genomics, and their phenotypic plasticity at IU Kokomo, and publishing 23 articles with many student co-authors. Masuda’s current work primarily focuses on biodegradation and chemical repurposing of nylon. Using soil bacteria, her lab studies bacterial biodegradation of nylon and other environmental pollutants. She applies principles of green chemistry while developing a method to convert nylon waste into other nontoxic polymers. In collaborative work with health scientists, her lab investigates gut microbiomes of Indiana native birds as well as human subjects in a clinical study. She became interested in converting plastic waste while earning her Ph.D. at Rutgers University, working on methods to remove environmental waste not using chemicals or other intrusive methods. She learned about Nylon 11, which is used in pipes for transferring oil, interior components for cars, catheters, and in other industrial hard plastics. Nylon 11 is nontoxic, she added, so it is safe to include students in the research. “Those are considered to be nonbiodegradable, and have to go to a landfill,” she said. “I thought if we could come up with a way to biodegrade it, that would make some kind of impact.” Eventually, she wants to develop a product that would convert nylon waste to something useful, and to reduce waste and microplastics in the environment. “That would be my goal at some point, that we can make that change, and also make people more aware of their use of plastics,” she said. “I hope students stop to think about what we can do as scientists to address issues currently impacting us.” She appreciates the IAS for its support of scientific research at Indiana institutions, and IU Kokomo and the School of Sciences for providing funding for students to attend the Academy’s annual meeting. She added that IU Kokomo has also supported her research with funding from the School of Sciences and Academic Affairs. “Without that, I couldn’t do my research,” she said. Masuda has also earned a Senior Research Grant from the IAS, IU Kokomo’s Distinguished Research Award, and the IU Kokomo Junior Faculty Annual Research Award. She is a Fellow of the Indiana Academy of Science. Education is KEY at Indiana University Kokomo.
Super Mario Bros. Speedrunner Lays Out Claims Of Sabotage
Speedrun.com suddenly banned his preferred input method after voting to allow it just four months prior
Eureka! Scientists develop new way to detect breakthroughs in science
The history of science and technology is marked by major breakthroughs — the theory of evolution, the splitting of the atom, the development of antibiotics — and a research team including faculty at Binghamton University, State University of New York, has developed a method to help pinpoint discoveries that reshaped the course of science. A study publishing in Science Advances on April 1 maps the landscape of innovation to identify disruptive studies and patents that challenge existing paradigms and inspire waves of follow-up research. The measure was developed by a team including Sadamori Kojaku, assistant professor of systems science and industrial engineering at Binghamton University, along with his colleagues Munjung Kim and Yong-Yeol Ahn at the University of Virginia.
Scientists found a baby dinosaur hidden in rock and it is surprisingly cute
Scientists uncovered a rare baby dinosaur in South Korea and named it Doolysaurus after a famous cartoon character. Using cutting-edge CT scans, they discovered hidden bones—including a skull—inside rock much faster than traditional methods. The young dinosaur, possibly fluffy and lamb-like, even had stomach stones that reveal it ate a mix of plants and small animals. The discovery suggests many more dinosaurs may still be hidden in Korea’s rocks.
Swope telescope: five decades of observing and reinvention
Astronomer Nidia Morrell looks back on more than 20 years of observing at the Las Campanas Observatory—an era marked by the transition from manual methods to digital systems—and reflects on the enduring relevance of a telescope that remains central to modern astronomy.
The revolution in dinosaur science started 50 years ago – here’s what we have learned
Scientific methods validated from modern life have brought dinosaurs to life in a testable way.
Scientists Stunned: Colossal Eggs Discovered 3,500 Meters Beneath an Active Volcano
Beneath an active seafloor volcano off Vancouver Island, researchers have documented a congregation of giant eggs at roughly 3,500 meters, a discovery that reframes how life can flourish under extreme conditions. The eggs belong to the Pacific white skate, an elusive deep‑sea species adapted to cold, crushing pressure and perpetual darkness. What seemed an unlikely nursery is actually a geothermally warmed refuge, where subtle heat helps embryos survive a marathon incubation. The finding links volcanic energy to a quiet engine of biodiversity, turning a hostile landscape into an incubator. A living volcano, a hidden nursery The seamount long assumed dormant now exhales mineral‑rich, shimmering plumes, proof of hydrothermal life at depth. Expedition cameras swept across black basalt and pale bacterial mats, revealing vents that pulse with warmth. That warmth, though modest by human standards, alters the physics of development for cold‑adapted embryos. In a realm where metabolism creeps, even a few degrees can mean faster growth and higher survival. The volcano is not only active; it is ecologically formative, drafting a blueprint for a thriving oasis. Giant eggs and a skate built for the abyss The Pacific white skate, Bathyraja spinosissima, is built for the abyss: broad wings, armored spines, and a life history that trades speed for endurance. Females lay outsized, rectangular “mermaid’s purses,” often 46–50 centimeters long, packed with rich yolk to fuel years of growth. In the frigid deep, embryos can require around four years to hatch, a timeline that magnifies every energetic advantage. The scale is striking—large eggs, large adults, and a textbook case of deep‑sea gigantism. The volcano’s gentle heat may literally shave months or years from the wait. Why heat matters at the bottom of the sea Hydrothermal circulation delivers low‑level warmth without the turbulence that would scatter fragile egg‑cases. Slightly elevated temperatures accelerate enzyme kinetics, nudging development along while keeping embryos within a safe range. Mineral‑laden fluids likely shape local microbiomes, which, in turn, can influence biofilms on egg surfaces. The environment acts like a slow, steady incubator, countering the deep sea’s tyrannies of cold and scarcity. For a species with long maturation and few offspring, every incremental boost compounds across generations. An ecosystem sculpted by fire and water Seafloor volcanoes create steep environmental gradients, stitching together hot vents, cool eddies, and mineral gardens into mosaic habitats. Microbes harvest chemical energy, seeding food webs that support crustaceans, fishes, and skates. Cameras documented egg‑cases nestled among rocky ledges, near communities nourished by vent‑driven chemistry. These islands of productivity behave like deep‑sea oases, sustaining life far beyond the vent orifice’s flickering light. The nursery’s success underscores how geological forces engineer biological opportunity. “Finding a cradle of life tucked beneath a breathing volcano reminds us that the planet’s harshest places can be profoundly generous.” What the discovery could change The nursery reframes conservation priorities in regions where volcanism quietly underwrites reproduction. Protecting hydrothermal corridors could safeguard entire lifecycles, not just charismatic adults spotted during occasional surveys. It also sharpens questions about how climate‑driven shifts in ocean circulation might influence vent activity, temperature fields, and embryo survival. With methodical mapping, time‑series cameras, and environmental DNA, scientists can track whether nurseries remain stable or drift with geologic rhythms. Key implications include: Enhanced understanding of deep‑sea reproduction at hydrothermal sites. New conservation targets centered on geothermal nurseries. Comparative studies with other volcanic regions, from the Galápagos to rift zones. Biotechnological insights from organisms adapted to long, cold development. Better models of how seafloor heat shapes regional biodiversity. Methods behind the revelation Remotely operated vehicles threaded through chimney fields, gathering video, temperatures, and mineral signatures. High‑definition imagery captured the distinctive, horned egg cases, their edges dusted with fine volcanic silt. Geochemical profiles mapped how heat and chemicals disperse, outlining the “thermal lanes” where eggs most cluster. Combined, these datasets reveal a nursery organized by geothermal topography, with egg‑cases concentrated in pockets of gentle, sustained warmth. A fragile boon in a changing ocean Deep‑sea environments feel remote, yet they intersect with global pressures: mining proposals, cable routes, and shifting oxygen levels. The nursery’s resilience hinges on stable heat flow, intact substrates, and minimal disturbance from noise or sediment. Safeguarding such places is not just about protecting novelty; it preserves a slow, ancient strategy that lets large, long‑lived animals endure in the coldest reaches of the Pacific. Where geology and biology meet, the planet’s deep time still writes living stories. This volcano‑warmed nursery adds a vital chapter to that story, revealing how subtle thermal gifts can tip the scales toward life in the abyss. With each dive, researchers trace the contours of an unseen world, where fire feeds water, and water—against all odds—feeds the future.
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