Nature Climate Change, Published online: 26 September 2025; doi:10.1038/s41558-025-02442-7

Home to roughly a quarter of the world’s population, South Asia is a hotspot for global warming impacts. In this Viewpoint, nine researchers from South Asia discuss the progress made in understanding and responding to climate change in the region.

In 2024, an association of female senior citizens in Switzerland won a case at the European Court of Human Rights. Their country, the women contended, needed to do more to protect them from climate change, since heat waves can make the elderly particularly vulnerable. The court ruled in favor of the group, saying that states belonging to the Council of Europe have a “positive obligation” to protect citizens from “serious adverse effects of climate change on lives, health, well-being, and quality of life.”

The exact policy implications of such rulings can be hard to assess. But there are still subtle civic implications related to the ruling that bear consideration.

For one thing, although the case was brought by a particular special-interest association, its impact could benefit everyone in society. Yet the people in the group had not always belonged to it and are not wholly defined by being part of it. In a sense, while the senior-citizen association brought the case as a minority group of sorts, being a senior citizen is not the sole identity marker of the people in it.

These kinds of situations underline the complexity of interest-group dynamics as they engage with legal and political systems. Much public discourse on particularistic groups focuses on them as seemingly fixed entities with clear definitions, but being a member of a minority group is not a static thing.

“What I want to insist on is that it’s not like an absolute property. It’s a dynamic,” says MIT Professor Bruno Perreau. “It is both a complex situation and a mobile situation. You can be a member of a minority group vis-à-vis one category and not another.”

Now Perreau explores these dynamics in a book, “Spheres of Injustice,” published this year by the MIT Press. Perreau is the Cynthia L. Reed Professor of French Studies and Language in MIT’s Literature program. The French-language edition of the book was published in 2023.

Around the world, Perreau observes, much of the political contestation over interest-group politics and policies to protect minorities arrives at a similar tension point: Policies or legal rulings are sometimes crafted to redress problems, but when political conditions shift, those same policies can be discarded with claims that they themselves are unfair. In many places, this dynamic has become familiar through the contestation of policies regarding ethnic identity, gender, sexual orientation, and more.

But this is not the only paradigm of minority group politics. One aim of Perreau’s book is to add breadth to the subject, grounded in the empirical realities people experience.

After all, when it comes to being regarded as a member of a minority group, “in a given situation, some people will claim this label for themselves, whereas others will reject it,” Perreau writes. “Some consider this piece of their identity to be fundamental; others regard it as secondary. … The work of defining it is the very locus of its power.”

“Spheres of Injustice” both lays out that complexity and seeks to find ways to rethink group-oriented politics as part of an expansion of rights generally. The book arises partly out of previous work Perreau has published, often concerning France. It also developed partly in response to Perreau thinking about how rights might evolve in a time of climate change. But it arrived at its exact form as a rethinking of “Spheres of Justice,” a prominent 1980s text by political philosopher Michael Walzer.

Instead of there being a single mechanism through which justice could be applied throughout society, Walzer contended, there are many spheres of life, and the meaning of justice depends on where it is being applied.

“Because of the complexities of social relations, inequalities are impossible to fully erase,” Perreau says. “Even in the act of trying to resist an injustice, we may create other forms of injustice. Inequality is unavoidable, but his [Walzer’s] goal is to reduce injustice to the minimum, in the form of little inequalities that do not matter that much.”

Walzer’s work, however, never grapples with the kinds of political dynamics in which minority groups try to establish rights. To be clear, Perreau notes, in some cases the categorization as a minority is foisted upon people, and in other cases, it is developed by the group itself. In either case, he thinks we should consider how complex the formation and activities of the group may be.

As another example, consider that while disability rights are a contested issue in some countries and ignored in others, they also involve fluidity in terms of who advocates and benefits from them. Imagine, Perreau says, you break a leg. Temporarily, he says, “you experience a little bit of what people with a permanent disability experience.” If you lobby, for, say, better school building access or better transit access, you could be helping kids, the elderly, families with kids, and more — including people and groups not styling themselves as part of a disability-rights movement.

“One goal of the book is to enhance awareness about the virtuous circle that can emerge from this kind of minority politics,” Perreau says. “It’s often regarded by many privileged people as a protection that removes something from them. But that’s not the case.”

Indeed, the politics Perreau envisions in “Spheres of Injustice” have an alternate framework, in which developing rights for some better protects others, to the point where minority rights translate into universal rights. That is not, again, meant to minimize the experience of core members of a group that has been discriminated against, but to encourage thinking about how solidifying rights for a particular group overlaps with the greater expansion of rights generally.

“I’m walking a fine line between different perspectives on what it means to belong,” Perreau says. “But this is indispensable today.”

Indeed, due to the senior citizens in Switzerland, he notes, “There will be better rights in Europe. Politics is not just a matter of diplomacy and majority decision-making. Sharing a complex world means drawing on the minority parts of our lives because it is these parts that most fundamentally connect us to others, intentionally or unintentionally. Thinking in these terms today is an essential civic virtue.”

Graduate school can feel like a race to the finish line, but it becomes much easier with a team to cheer you on — especially if that team is literally next to you, shouting encouragement from a decorated van.

From the morning of Sept. 12 into the early afternoon on Sept. 13, two teams made up of MIT Department of Aeronautics and Astronautics (AeroAstro) graduate students, alumni, and friends ran the 2025 Ragnar Road Reach the Beach relay in two friendly yet competitive teams of 12, aptly named Team Aero and Team Astro. Ragnar races are long-distance, team-based relay events that run overnight through some of the country’s most scenic routes. The Reach the Beach course began in Lancaster, New Hampshire, and sent teams on a 204-mile trek through the White Mountains, finishing at Hampton Beach State Park.

“This all began on the Graduate Association of Aeronautics and Astronautics North End Pastry Tour in 2024. While discussing our mutual love for running, and stuffing our faces with cannoli, Maya Harris jokingly mentioned the concept of doing a Ragnar,” says Nathanael Jenkins, the eventual Team Aero captain. The idea took hold, inspiring enough interest to form a team for the first AeroAstro Ragnar relay in April 2025. From there enthusiasm continued to grow, resulting in the two current teams. 

“I was surprised at the number of people, even people who don’t run very frequently, who wanted to do another race after finishing the first Ragnar,” says Patrick Riley, captain of Team Astro. “All of the new faces are awesome because they bring new energy and excitement to the team. I love the community, I love the sport, and I think the best way to get to know someone is to be crammed into a van with them for six hours at a time.”

Resource management and real-time support

The two teams organized four vans, adorned with words of encouragement and team magnets — a Ragnar tradition — to shepherd the teams through the race, serving as rolling rest stops for runners at each exchange point. Each runner completed three to four sections out of 36 total legs, running between 1.7 to 11.6 miles at a time. Runners could swap out there for a power nap or a protein bar. To keep morale high, teams played games and handed out awards of their own to teammates. “Noah (McAllister) got the prize for ‘Most bees removed from the car;’ Madison (Bronniman) won for ‘Eating the most tinned fish;’ I got the prize for ‘Most violent slamming of doors’ — which I hadn’t realized was in my skill set,” says Jenkins.

“This race is really unique because it bonds the team together in ways that many other races simply don’t,” says Riley, an avid runner prior to the event. “Marathons are strenuous on your body, but a Ragnar is about long-term resource management — eating, hydrating, sleep management, staying positive. Then communicating those logistics effectively and proceeding with the plan.”

Pulling off a logistics-heavy race across both teams required “magical spreadsheeting” that used distance, start time, elevation changes, and average pace to estimate finish time for each leg of the race. “Noah made it for the first race. Then a bunch of engineers saw a spreadsheet and zeroed in,” says Riley.

Engineering success

The careful planning paid off with a win for Team Astro, with a finishing time of 31:01:13. Team Aero was close behind, finishing at 31:19:43. Yet in the end, the competition mattered less than the camaraderie, when all runners celebrated together at the finish line.

“I think the big connection that we talk about is putting the teamwork skills we use in engineering into practice,” says Jenkins. “Engineers all like achieving. Runners like achieving. Many of our runners don’t run for enjoyment in the moment, but the feeling of crossing the finish line makes up for the, well, pain. In engineering, the feeling of finishing a difficult problem makes up for the pain of doing it.”

Call them gluttons for punishment or high achievers, the group is already making plans for the next race. “Everybody is immediately throwing links in the group chat for more Ragnars in the future,” says Riley. “MIT has so many people who want to explore and engage with the world around them, and they’re willing to take a chance and do crazy stuff. And we have the follow-through to make it happen.”

Runners

Team Aero: Claire Buffington, Alex Chipps, Nathanael Jenkins, Noah McAllister, Garrett Siemen, Nick Torres (Course 16, AeroAstro), Madison Bronniman, Ceci Perez Gago, Juju Wang (Course 16 alum), Katie Benoit, and Jason Wang.

Team Astro: Tim Cavesmith, Evrard Constant, Mary Foxen, Maya Harris, Jules Penot, Patrick Riley, Alex Rose, Samir Wadhwania (Course 16), Henry Price (Course 3, materials science and engineering), Katherine Hoekstra, and Ian Robertson (Woods Hole Oceanographic Institute).

Honorary teammates: Abigail Lee, Celvi Lissy, and Taylor Hampson.

We need your help to make a common-sense bill into California law. Despite the fact that California has one of the nation’s most comprehensive data privacy laws, it’s not always easy for people to exercise those privacy rights. A.B. 566 intends to make it easy by directing browsers to give all their users the option to tell companies they don’t want personal information that’s collected about them on the internet to be sold or shared. Now, we just need Governor Gavin Newsom to sign it into law by October 13, 2025, and this toolkit will help us put on the pressure. Tweet at Gov. Gavin Newsom and help us get A.B. 566 signed into law!

First, pick your platform of choice. Reach Gov. Newsom at any of his social media handles:

• X: @CAgovernor
• Bluesky: @governor.ca.gov
• TikTok: @cagovernor
• Facebook: @CAgovernor

Then, pick a message that resonates with you. Or, feel free to remix!

Sample Posts

• It should be easy for Californians to exercise our rights under the California Consumer Privacy Act, but major internet browser companies are making it difficult for us to do that. @CAgovernor, sign AB 566 and give power to the consumers to protect their privacy!
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• People seeking abortion care, immigrants, and LGBTQ+ people are at risk of bad actors using their online activity against them. @CAgovernor could sign AB 566 and protect the privacy of vulnerable communities and all Californians.
• AB 566 gives Californians a practical way to use their right to opt-out of websites selling or sharing their private info. @CAgovernor can sign it and give consumers power over their privacy choices under the California Consumer Privacy Act.
• Hey @CAgovernor! AB 566 makes it easy for Californians to tell companies what they want to happen with their own private information. Sign it and make the California Consumer Privacy Act more user-friendly!
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• Right now, if a Californian wants to tell companies not to sell or share their info, they must go through the processes set up by each company, ONE BY ONE, to opt out of data sharing. AB 566 can remove that burden. @CAgovernor, sign AB 566 to empower consumers!
• Industry groups who want to keep the scales tipped in favor of corporations who want to profit off the sale of our private info have lobbied heavily against AB 566, a bill that will make it easy for Californians to tell companies what they want to happen with their own info. @CAgovernor—sign it!

Gleevec, a cancer drug first approved for sale in 2001, has dramatically changed the lives of people with chronic myeloid leukemia. This form of cancer was once regarded as very difficult to combat, but survival rates of patients who respond to Gleevec now resemble that of the population at large.

Gleevec is also a medicine developed with the help of federally funded research. That support helped scientists better understand how to create drugs targeting the BCR-ABL oncoprotein, the cancer-causing protein behind chronic myeloid leukemia.

A new study co-authored by MIT researchers quantifies how many such examples of drug development exist. The current administration is proposing a nearly 40 percent budget reduction to the National Institutes of Health (NIH), which sponsors a significant portion of biomedical research. The study finds that over 50 percent of small-molecule drug patents this century cite at least one piece of NIH-backed research that would likely be vulnerable to that potential level of funding change.

“What we found was quite striking,” says MIT economist Danielle Li, co-author of a newly published paper outlining the study’s results. “More than half of the drugs approved by the FDA since 2000 are connected to NIH research that would likely have been cut under a 40 percent budget reduction.”

Or, as the researchers write in the paper: “We found extensive connections between medical advances and research that was funded by grants that would have been cut if the NIH budget was sharply reduced.”

The paper, “What if NIH funding had been 40% smaller?” is published today as a Policy Article in the journal Science. The authors are Pierre Azoulay, the China Program Professor of International Management at the MIT Sloan School of Management; Matthew Clancy, an economist with the group Open Philanthropy; Li, the David Sarnoff Professor of Management of Technology at MIT Sloan; and Bhaven N. Sampat, an economist at Johns Hopkins University. (Biomedical researchers at both MIT and Johns Hopkins could be affected by adjustments to NIH funding.)

To conduct the study, the researchers leveraged the fact that the NIH uses priority lists to determine which projects get funded. That makes it possible to discern which projects were in the lower 40 percent of NIH-backed projects, priority-wise, for a given time period. The researchers call these “at-risk” pieces of research. Applying these data from 1980 through 2007, the scholars examined the patents of the new molecular entities — drugs with a new active ingredient — approved by the U.S. Food and Drug Administration since 2000. There is typically a time interval between academic research and subsequent related drug development.

The study focuses on small-molecule drugs — compact organic compounds, often taken orally as medicine — whereas NIH funding supports a wider range of advancements in medicine generally. Based on how many of these FDA-approved small-molecule medicines were linked to at-risk research from the prior period, the researchers estimated what kinds of consequences a 40 percent cut in funding would have generated going forward.

The study distinguishes between two types of links new drugs have to NIH funding. Some drug patents have what the researchers call “direct” links to new NIH-backed projects that generated new findings relevant to development of those particular drugs. Other patents have “indirect “ links to the NIH, when they cite prior NIH-funded studies that contributed to the overall body of knowledge used in drug development.

The analysis finds that 40 of the FDA-approved medications have direct links to new NIH-supported studies cited in the patents — or 7.1 percent. Of these, 14 patents cite at-risk pieces of NIH research.

When it comes to indirect links, of the 557 drugs approved by the FDA from 2000 to 2023, the study found that 59.4 percent have a patent citing at least one NIH-supported research publication. And, 51.4 percent cite at least one NIH-funded study from the at-risk category of projects. 

“The indirect connection is where we see the real breadth of NIH's impact,” Li says. “What the NIH does is fund research that forms the scientific foundation upon which companies and other drug developers build.”

As the researchers emphasize in the paper, there are many nuances involved in the study. A single citation of an NIH-funded study could appear in a patent for a variety of reasons, and does not necessarily mean “that the drug in question could never have been developed in its absence,” as they write in the paper. To reckon with this, the study also analyzes how many patents had at least 25 percent of their citations fall in the category of at-risk NIH-backed research. By this metric, they found that 65 of the 557 FDA-approved drugs, or 11.7 percent, met the threshold.

On the other hand, as the researchers state in the paper, it is possible the study “understates the extent to which medical advances are connected to NIH research.” For one thing, as the study’s endpoint for examining NIH data is 2007, there could have been more recent pieces of research informing medications that have already received FDA approval. The study does not quantify “second-order connections,” in which NIH-supported findings may have led to additional research that directly led to drug development. Again, NIH funding also supports a broad range of studies beyond the type examined in the current paper.

It is also likely, the scholars suggest, that NIH cuts would curtail the careers of many promising scientists, and in so doing slowdown medical progress. For a variety of these reasons, in addition to the core data itself, the scholars say the study indicates how broadly NIH-backed research has helped advance medicine.

“The worry is that these kinds of deep cuts to the NIH risk that foundation and therefore endanger the development of medicines that might be used to treat us, or our kids and grandkids, 20 years from now,” Li says.

Azoulay and Sampat have received past NIH funding. They also serve on an NIH working group about the empirical analysis of the scientific enterprise.

Machine-learning models can speed up the discovery of new materials by making predictions and suggesting experiments. But most models today only consider a few specific types of data or variables. Compare that with human scientists, who work in a collaborative environment and consider experimental results, the broader scientific literature, imaging and structural analysis, personal experience or intuition, and input from colleagues and peer reviewers.

Now, MIT researchers have developed a method for optimizing materials recipes and planning experiments that incorporates information from diverse sources like insights from the literature, chemical compositions, microstructural images, and more. The approach is part of a new platform, named Copilot for Real-world Experimental Scientists (CRESt), that also uses robotic equipment for high-throughput materials testing, the results of which are fed back into large multimodal models to further optimize materials recipes.

Human researchers can converse with the system in natural language, with no coding required, and the system makes its own observations and hypotheses along the way. Cameras and visual language models also allow the system to monitor experiments, detect issues, and suggest corrections.

“In the field of AI for science, the key is designing new experiments,” says Ju Li, School of Engineering Carl Richard Soderberg Professor of Power Engineering. “We use multimodal feedback — for example information from previous literature on how palladium behaved in fuel cells at this temperature, and human feedback — to complement experimental data and design new experiments. We also use robots to synthesize and characterize the material’s structure and to test performance.”

The system is described in a paper published in Nature. The researchers used CRESt to explore more than 900 chemistries and conduct 3,500 electrochemical tests, leading to the discovery of a catalyst material that delivered record power density in a fuel cell that runs on formate salt to produce electricity.

Joining Li on the paper as first authors are PhD student Zhen Zhang, Zhichu Ren PhD ’24, PhD student Chia-Wei Hsu, and postdoc Weibin Chen. Their coauthors are MIT Assistant Professor Iwnetim Abate; Associate Professor Pulkit Agrawal; JR East Professor of Engineering Yang Shao-Horn; MIT.nano researcher Aubrey Penn; Zhang-Wei Hong PhD ’25, Hongbin Xu PhD ’25; Daniel Zheng PhD ’25; MIT graduate students Shuhan Miao and Hugh Smith; MIT postdocs Yimeng Huang, Weiyin Chen, Yungsheng Tian, Yifan Gao, and Yaoshen Niu; former MIT postdoc Sipei Li; and collaborators including Chi-Feng Lee, Yu-Cheng Shao, Hsiao-Tsu Wang, and Ying-Rui Lu.

A smarter system

Materials science experiments can be time-consuming and expensive. They require researchers to carefully design workflows, make new material, and run a series of tests and analysis to understand what happened. Those results are then used to decide how to improve the material.

To improve the process, some researchers have turned to a machine-learning strategy known as active learning to make efficient use of previous experimental data points and explore or exploit those data. When paired with a statistical technique known as Bayesian optimization (BO), active learning has helped researchers identify new materials for things like batteries and advanced semiconductors.

“Bayesian optimization is like Netflix recommending the next movie to watch based on your viewing history, except instead it recommends the next experiment to do,” Li explains. “But basic Bayesian optimization is too simplistic. It uses a boxed-in design space, so if I say I’m going to use platinum, palladium, and iron, it only changes the ratio of those elements in this small space. But real materials have a lot more dependencies, and BO often gets lost.”

Most active learning approaches also rely on single data streams that don’t capture everything that goes on in an experiment. To equip computational systems with more human-like knowledge, while still taking advantage of the speed and control of automated systems, Li and his collaborators built CRESt.

CRESt’s robotic equipment includes a liquid-handling robot, a carbothermal shock system to rapidly synthesize materials, an automated electrochemical workstation for testing, characterization equipment including automated electron microscopy and optical microscopy, and auxiliary devices such as pumps and gas valves, which can also be remotely controlled.  Many processing parameters can also be tuned.

With the user interface, researchers can chat with CRESt and tell it to use active learning to find promising materials recipes for different projects. CRESt can include up to 20 precursor molecules and substrates into its recipe. To guide material designs, CRESt’s models search through scientific papers for descriptions of elements or precursor molecules that might be useful. When human researchers tell CRESt to pursue new recipes, it kicks off a robotic symphony of sample preparation, characterization, and testing. The researcher can also ask CRESt to perform image analysis from scanning electron microscopy imaging, X-ray diffraction, and other sources.

Information from those processes is used to train the active learning models, which use both literature knowledge and current experimental results to suggest further experiments and accelerate materials discovery.

“For each recipe we use previous literature text or databases, and it creates these huge representations of every recipe based on the previous knowledge base before even doing the experiment,” says Li. “We perform principal component analysis in this knowledge embedding space to get a reduced search space that captures most of the performance variability. Then we use Bayesian optimization in this reduced space to design the new experiment. After the new experiment, we feed newly acquired multimodal experimental data and human feedback into a large language model to augment the knowledgebase and redefine the reduced search space, which gives us a big boost in active learning efficiency.”

Materials science experiments can also face reproducibility challenges. To address the problem, CRESt monitors its experiments with cameras, looking for potential problems and suggesting solutions via text and voice to human researchers.

The researchers used CRESt to develop an electrode material for an advanced type of high-density fuel cell known as a direct formate fuel cell. After exploring more than 900 chemistries over three months, CRESt discovered a catalyst material made from eight elements that achieved a 9.3-fold improvement in power density per dollar over pure palladium, an expensive precious metal. In further tests, CRESTs material was used to deliver a record power density to a working direct formate fuel cell even though the cell contained just one-fourth of the precious metals of previous devices.

The results show the potential for CRESt to find solutions to real-world energy problems that have plagued the materials science and engineering community for decades.

“A significant challenge for fuel-cell catalysts is the use of precious metal,” says Zhang. “For fuel cells, researchers have used various precious metals like palladium and platinum. We used a multielement catalyst that also incorporates many other cheap elements to create the optimal coordination environment for catalytic activity and resistance to poisoning species such as carbon monoxide and adsorbed hydrogen atom. People have been searching low-cost options for many years. This system greatly accelerated our search for these catalysts.”

A helpful assistant

Early on, poor reproducibility emerged as a major problem that limited the researchers’ ability to perform their new active learning technique on experimental datasets. Material properties can be influenced by the way the precursors are mixed and processed, and any number of problems can subtly alter experimental conditions, requiring careful inspection to correct.

To partially automate the process, the researchers coupled computer vision and vision language models with domain knowledge from the scientific literature, which allowed the system to hypothesize sources of irreproducibility and propose solutions. For example, the models can notice when there’s a millimeter-sized deviation in a sample’s shape or when a pipette moves something out of place. The researchers incorporated some of the model’s suggestions, leading to improved consistency, suggesting the models already make good experimental assistants.

The researchers noted that humans still performed most of the debugging in their experiments.

“CREST is an assistant, not a replacement, for human researchers,” Li says. “Human researchers are still indispensable. In fact, we use natural language so the system can explain what it is doing and present observations and hypotheses. But this is a step toward more flexible, self-driving labs.”

This site turns your URL into something sketchy-looking.

For example, www.schneier.com becomes
https://cheap-bitcoin.online/firewall-snatcher/cipher-injector/phishing_sniffer_tool.html?form=inject&host=spoof&id=bb1bc121&parameter=inject&payload=%28function%28%29%7B+return+%27+hi+%27.trim%28%29%3B+%7D%29%28%29%3B&port=spoof.

Found on Boing Boing.

Clean energy is the "trend of our time," Chinese President Xi Jinping said, announcing targets for trimming his country's carbon pollution.

Fossil fuel and auto groups are eager to see the EPA climate finding repealed, but they worry it could lead to lawsuits over their emissions.

Legislatures have blasted the effort as an attempt to revoke state laws and exceed "the established bounds of federal authority."

Despite heavy criticism from climate scientists, Energy Secretary Chris Wright said DOE's report contained just one factual disagreement.

Supporters already have won regulatory changes in Utah for so-called balcony solar systems, which provide a small boost of electrical power.

The voluntary initiative comes ahead of the United Nations climate talks scheduled to take place in Brazil in November.

Small versus large, young against long-established — farm policy reform is setting up a clash over scarce funding.

The EU and China have expressed interest, which may make it one of the most significant outcomes of the November summit.

The "King of Storms" led to nearly 1.9 million people being evacuated in China's southern Guangdong province.

Nature Climate Change, Published online: 25 September 2025; doi:10.1038/s41558-025-02421-y

Reducing the wildfire risk of electric grids requires assessing and comparing various adaptation measures. A study shows that a grid technology innovation cuts the risk more cost-effectively than conventional approaches such as burying power lines.

Nature Climate Change, Published online: 25 September 2025; doi:10.1038/s41558-025-02436-5

Extreme events are increasingly becoming severe risks to the electric grid, yet there is limited understanding of the cost-effectiveness of adaptation investments. This research demonstrates that dynamic grid management could reduce large capital spending and limit wildfire risks in the USA.

Mucus is more than just a sticky substance: It contains a wealth of powerful molecules called mucins that help to tame microbes and prevent infection. In a new study, MIT researchers have identified mucins that defend against Salmonella and other bacteria that cause diarrhea.

The researchers now hope to mimic this defense system to create synthetic mucins that could help prevent or treat illness in soldiers or other people at risk of exposure to Salmonella. It could also help prevent “traveler’s diarrhea,” a gastrointestinal infection caused by consuming contaminated food or water.

Mucins are bottlebrush-shaped polymers made of complex sugar molecules known as glycans, which are tethered to a peptide backbone. In this study, the researchers discovered that a mucin called MUC2 turns off genes that Salmonella uses to enter and infect host cells.

“By using and reformatting this motif from the natural innate immune system, we hope to develop strategies to preventing diarrhea before it even starts. This approach could provide a low-cost solution to a major global health challenge that costs billions annually in lost productivity, health care expenses, and human suffering,” says Katharina Ribbeck, the Andrew and Erna Viterbi Professor of Biological Engineering at MIT and the senior author of the study.

MIT Research Scientist Kelsey Wheeler PhD ’21 and Michaela Gold PhD ’22 are the lead authors of the paper, which appeared Tuesday in the journal Cell Reports.

Blocking infection

Mucus lines much of the body, providing a physical barrier to infection, but that’s not all it does. Over the past decade, Ribbeck has identified mucins that can help to disarm Vibrio cholerae, as well as Pseudomonas aeruginosa, which can infect the lungs and other organs, and the yeast Candida albicans.

In the new study, the researchers wanted to explore how mucins from the digestive tract might interact with Salmonella enterica, a foodborne pathogen that can cause illness after consuming raw or undercooked food, or contaminated water.

To infect host cells, Salmonella must produce proteins that are part of the type 3 secretion system (T3SS), which helps bacteria form needle-like complexes that transfer bacterial proteins directly into host cells. These proteins are all encoded on a segment of DNA called Salmonella pathogenicity island 1 (SPI-1).

The researchers found that when they exposed Salmonella to a mucin called MUC2, which is found in the intestines, the bacteria stopped producing the proteins encoded by SPI-1, and they were no longer able to infect cells.

Further studies revealed that MUC2 achieves this by turning off a regulatory bacterial protein known as HilD. When this protein is blocked by mucins, it can no longer activate the T3SS genes.

Using computational simulations, the researchers showed that certain monosaccharides found in glycans, including GlcNAc and GalNAc, can attach to a specific binding site of the HilD protein. However, their studies showed that these monosaccharides can’t turn off HilD on their own — the shutoff only occurs when the glycans are tethered to the peptide backbone of the mucin.

The researchers also discovered that a similar mucin called MUC5AC, which is found in the stomach, can block HilD. And, both MUC2 and MUC5AC can turn off virulence genes in other foodborne pathogens that also use HilD as a gene regulator.

Mucins as medicine

Ribbeck and her students now plan to explore ways to use synthetic versions of these mucins to help boost the body’s natural defenses and protect the GI tract from Salmonella and other infections.

Studies from other labs have shown that in mice, Salmonella tends to infect portions of the GI tract that have a thin mucus barrier, or no barrier at all.

“Part of Salmonella’s evasion strategy for this host defense is to find locations where mucus is absent and then infect there. So, one could imagine a strategy where we try to bolster mucus barriers to protect those areas with limited mucin,” Wheeler says.

One way to deploy synthetic mucins could be to add them to oral rehydration salts — mixtures of electrolytes that are dissolved in water and used to treat dehydration caused by diarrhea and other gastrointestinal illnesses.

Another potential application for synthetic mucins would be to incorporate them into a chewable tablet that could be consumed before traveling to areas where Salmonella and other diarrheal illnesses are common. This kind of “pre-exposure prophylaxis” could help prevent a great deal of suffering and lost productivity due to illness, the researchers say.

“Mucin mimics would particularly shine as preventatives, because that’s how the body evolved mucus — as part of this innate immune system to prevent infection,” Wheeler says.

The research was funded by the U.S. Army Research Office, the U.S. Army Institute for Collaborative Biotechnologies, the U.S. National Science Foundation, the U.S. National Institute of Health and Environmental Sciences, the U.S. National Institutes of Health, and the German Research Foundation.

Annotating regions of interest in medical images, a process known as segmentation, is often one of the first steps clinical researchers take when running a new study involving biomedical images.

For instance, to determine how the size of the brain’s hippocampus changes as patients age, the scientist first outlines each hippocampus in a series of brain scans. For many structures and image types, this is often a manual process that can be extremely time-consuming, especially if the regions being studied are challenging to delineate.

To streamline the process, MIT researchers developed an artificial intelligence-based system that enables a researcher to rapidly segment new biomedical imaging datasets by clicking, scribbling, and drawing boxes on the images. This new AI model uses these interactions to predict the segmentation.

As the user marks additional images, the number of interactions they need to perform decreases, eventually dropping to zero. The model can then segment each new image accurately without user input.

It can do this because the model’s architecture has been specially designed to use information from images it has already segmented to make new predictions.

Unlike other medical image segmentation models, this system allows the user to segment an entire dataset without repeating their work for each image.

In addition, the interactive tool does not require a presegmented image dataset for training, so users don’t need machine-learning expertise or extensive computational resources. They can use the system for a new segmentation task without retraining the model.

In the long run, this tool could accelerate studies of new treatment methods and reduce the cost of clinical trials and medical research. It could also be used by physicians to improve the efficiency of clinical applications, such as radiation treatment planning.

“Many scientists might only have time to segment a few images per day for their research because manual image segmentation is so time-consuming. Our hope is that this system will enable new science by allowing clinical researchers to conduct studies they were prohibited from doing before because of the lack of an efficient tool,” says Hallee Wong, an electrical engineering and computer science graduate student and lead author of a paper on this new tool.

She is joined on the paper by Jose Javier Gonzalez Ortiz PhD ’24; John Guttag, the Dugald C. Jackson Professor of Computer Science and Electrical Engineering; and senior author Adrian Dalca, an assistant professor at Harvard Medical School and MGH, and a research scientist in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). The research will be presented at the International Conference on Computer Vision.

Streamlining segmentation

There are primarily two methods researchers use to segment new sets of medical images. With interactive segmentation, they input an image into an AI system and use an interface to mark areas of interest. The model predicts the segmentation based on those interactions.

A tool previously developed by the MIT researchers, ScribblePrompt, allows users to do this, but they must repeat the process for each new image.

Another approach is to develop a task-specific AI model to automatically segment the images. This approach requires the user to manually segment hundreds of images to create a dataset, and then train a machine-learning model. That model predicts the segmentation for a new image. But the user must start the complex, machine-learning-based process from scratch for each new task, and there is no way to correct the model if it makes a mistake.

This new system, MultiverSeg, combines the best of each approach. It predicts a segmentation for a new image based on user interactions, like scribbles, but also keeps each segmented image in a context set that it refers to later.

When the user uploads a new image and marks areas of interest, the model draws on the examples in its context set to make a more accurate prediction, with less user input.

The researchers designed the model’s architecture to use a context set of any size, so the user doesn’t need to have a certain number of images. This gives MultiverSeg the flexibility to be used in a range of applications.

“At some point, for many tasks, you shouldn’t need to provide any interactions. If you have enough examples in the context set, the model can accurately predict the segmentation on its own,” Wong says.

The researchers carefully engineered and trained the model on a diverse collection of biomedical imaging data to ensure it had the ability to incrementally improve its predictions based on user input.

The user doesn’t need to retrain or customize the model for their data. To use MultiverSeg for a new task, one can upload a new medical image and start marking it.

When the researchers compared MultiverSeg to state-of-the-art tools for in-context and interactive image segmentation, it outperformed each baseline.

Fewer clicks, better results

Unlike these other tools, MultiverSeg requires less user input with each image. By the ninth new image, it needed only two clicks from the user to generate a segmentation more accurate than a model designed specifically for the task.

For some image types, like X-rays, the user might only need to segment one or two images manually before the model becomes accurate enough to make predictions on its own.

The tool’s interactivity also enables the user to make corrections to the model’s prediction, iterating until it reaches the desired level of accuracy. Compared to the researchers’ previous system, MultiverSeg reached 90 percent accuracy with roughly 2/3 the number of scribbles and 3/4 the number of clicks.

“With MultiverSeg, users can always provide more interactions to refine the AI predictions. This still dramatically accelerates the process because it is usually faster to correct something that exists than to start from scratch,” Wong says.

Moving forward, the researchers want to test this tool in real-world situations with clinical collaborators and improve it based on user feedback. They also want to enable MultiverSeg to segment 3D biomedical images.

This work is supported, in part, by Quanta Computer, Inc. and the National Institutes of Health, with hardware support from the Massachusetts Life Sciences Center.