For energy systems that power a reliable grid, the future is all about location

Will a warming climate and changing weather patterns lead to more grid blackouts and other energy disruptions? Answering that question requires studying both regional climate forecasts and local energy systems, including emerging renewable generation, storage, transmission lines, and demand forecasts. The lack of such studies is one reason why energy developers and grid operators rarely consider climate change when deciding where to build their next project.

Now MIT researchers have created a way to make more climate-informed energy siting choices, and shown how it can be used to make energy systems more resilient and reduce blackouts. The researchers’ framework, described today in Nature Energy, combines fine-scale meteorology with detailed simulations of energy infrastructure. It shows how the location of new energy projects will play a significant role in meeting future demand in a changing climate.

The researchers applied their framework to decarbonized energy systems in New England and Texas, finding that energy systems designed for historic climate conditions could face up to a fivefold increase in energy shortfalls, potentially leading to blackouts, by 2050. Taking climate change into account when designing the system, conversely, improved the resilience of both regions’ energy systems at no or very little additional costs.

“As we mitigate climate change with renewables, we can also adapt to climate change by using future weather projections in our power system planning, and the extra costs of that adaptation are, at least in this study, not much,” says senior author Michael Howland, MIT’s Jeffrey Cheah Career Development Professor. “It’s different from other climate adaptation studies, where building a big seawall or other mitigation efforts are really expensive. In this case, if we’re smart when we design our power system decarbonization plans, it could cost almost nothing extra to simultaneously adapt to climate change.”

Joining Howland on the paper are first author Liying Qiu, a former MIT postdoc; Rahman Khorramfar and Shen Wang, current postdocs at MIT; and Saurabh Amin, MIT’s Edmund K. Turner Professor in Civil Engineering.

A better way to think about energy projects

The world’s energy systems are in a period of change. On the demand side, that change is driven by trends like the rising demand for artificial intelligence and the electrification of industries including transportation. On the supply side, that change is driven by the plummeting costs of renewable systems like solar and wind energy.

“That drop in costs has enabled the widespread deployment of renewables, because they’re the cheapest electricity-generation solution in many locations,” Howland explains. “At the same time, for the first time in more than a decade, electricity demand is starting to increase in the U.S.”

As low-cost variable renewable energy supplies increase, matching supply and demand throughout the day can become a harder problem for energy system operators. Adding to that complexity is the fact that renewables and energy demand are both influenced by weather and climate in different ways in different regions.

In the past, researchers have generally studied the impacts of climate change on individual technologies, for instance studying how it might change global wind and solar patterns. Other studies have considered the impact of climate change on states or other large areas, overlooking the specifics of regional energy systems. More recently, region-specific studies have been done but typically relied on low-resolution, global climate models.

“That’s what climate models are good at: giving you the global picture at coarse resolution,” Howland explains. “That limits insights for regional system planning and risk assessments.”

For their paper, the MIT researchers chose to study Texas and New England because they provided two different climate types and energy systems. The team used fine-scale meteorology models and considered the influence of climate change on weather-related energy failures.

“This study explores the joint, simultaneous impacts on multiple components of the energy system, similar to compound events studied in climate science,” Howland explains. “An extreme weather event can impact wind and solar generation and electricity demand all at the same time. Our hypothesis is that’s likely to be the biggest impact we’ll see from climate change on energy systems.”

The researchers also considered the impact of using climate change models to help site energy projects, looking out to 2050 because that’s the typical lifetime of wind and solar plants being built today. They found that locations that are best suited to provide the renewable wind and solar energy that the grid needs were meaningfully different in future climate conditions than in the historic climate.

The researchers found that climate change could increase energy failures by as much as 500 percent by 2050 if the siting did not consider future climate conditions. Such failures were driven primarily by the interaction between multiday renewable shortfalls and energy system design decisions like where to build solar farms and transmission lines.

“We are telling people where you put your wind and solar matters a lot for your ability to deliver energy when you need it,” Qiu explains. “We need to think more about the when and where of adding renewables rather than only focusing on adding overall capacity.”

In New England’s power system, the researchers found that energy supply disruptions caused by climate-related weather changes necessitate investment in solar capacity and transmission lines close to energy demand centers like cities. In Texas, energy disruption risks were primarily driven by transmission constraints.

The researchers found that climate-informed designs would prioritize adding wind farms in West Texas to better align with future demand patterns. The study assumes both regions will continue adding renewable capacity, thus the researchers concluded that Texas could improve the resilience of its grid at near-zero additional cost.

“We are showing that increasing energy resilience requires more than just spending more money,” Qiu says. “It primarily requires better and smarter planning.”

A new approach to adaptation

Howland says taking a broader view of climate change’s impact on energy systems helped his team get a clearer picture of blackout risks and other potential supply problems.

“On the individual power plant level, it’s not necessarily that climate change is a dominant uncertainty, so it really comes down to how all these energy system components and energy demand relate to each other,” Howland says. “That’s where we see the biggest impact of climate change, rather than on the level of individual wind or solar plants.”

Because the researchers used expensive, high-resolution models, Howland says their new model wouldn’t be practical for grid operators to use in their daily work today, but they hope to soon develop faster models that grid operators could use more easily.

“This study shows the opportunity and the need,” Howland says. “There are risks to not adapting our system, but if we do adapt our system, there could be big opportunities that are not costly. Now the key challenge is that we have to address the massive data and translation gap we have between meteorology and energy system planning and management. Right now, there’s too big of a divide between climate and weather modelers and power system practitioners. We want to continue to break that barrier down through interdisciplinary research.”

This work was supported by the MIT Climate Grand Challenges, the MIT Climate and Sustainability Consortium, and the MIT Energy Initiative Future Energy Systems Center.

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A better way to turn 2D designs into 3D models for rapid prototyping

Engineers often use vision-language models to produce new designs, such as for airplane or automobile components. To simulate how those components will perform in realistic situations, they’ll use tried-and-true computer-aided design (CAD) software to generate 3D models of those designs, which they can put through virtual crash or durability tests. 

Researchers from MIT and elsewhere have now developed a system that can teach a vision-language model to automatically convert 2D designs into CAD programs that are much more accurate and functional compared to other approaches, while using only a fraction of the computation.

By improving the performance and efficiency of AI-driven CAD generation, this technique could streamline the rapid prototyping process and reduce costs. It could also help engineers identify beneficial design choices they might otherwise overlook. 

The system generates new data based on the model’s abilities as it attempts to convert a 2D image into a CAD program. The framework corrects the model’s failures and incorporates them into a dataset with its successful solutions. 

It uses these data to teach the model how to fix specific mistakes and tackle tricky problems it would struggle with on its own.

“We want engineers to be able to point our framework at an underperforming CAD model, set a compute budget, and let the system take over — turning the model’s own mistakes into better training data,” says lead author Giorgio Giannone, a research affiliate in the Design Computation and Digital Engineering (DeCoDE) Lab at MIT and a principal research scientist on the AI Innovation Team at Red Hat.

He is joined on the paper by Anna Claire Doris, a mechanical engineering graduate student at MIT; Amin Heyrani Nobari, an MIT postdoc; Kai Xu of RedHat; and co-senior authors Akash Srivastava, director of Core AI at IBM and a principal investigator at the MIT-IBM Computing Research Lab; and Faez Ahmed, associate professor of mechanical engineering at MIT, leader of the DeCoDE Lab, and a principal investigator at the MIT-IBM Computing Research Lab. The research was recently presented at the International Conference on Machine Learning.

“Nearly every physical product around us, from airplanes to appliances, begins its life as a CAD model. Industry teams are eager for AI that can help speed-up the creation of these designs, but today's models often produce simple shapes inadequate for practice. What excites me about this work is that it gives many image-to-CAD-code models a way to improve themselves, learning from their own errors rather than waiting for more human-made data — and that brings trustworthy AI design tools much closer to everyday engineering,” says Ahmed.

Model-aware data

The researchers are working toward building vision-language models (VLMs) for CAD generation. These VLMs take a 2D image and some descriptive text, and output Python code that can be executed in a CAD software program to generate a 3D model of a physical object.

They studied the challenges of deploying existing VLMs for this task and determined the main bottleneck that limits their capabilities is the lack of diverse, high-quality CAD datasets to train them. 

To remedy this, they sought to create new data to teach a model how to perform CAD generation, using a process known as data augmentation.

In data augmentation, scientists typically create new data by randomly tweaking existing data to generate more samples, often by adjusting the color, size, and shape of objects in images. 

Instead, the MIT researchers built a data augmentation system called GIFT (which stands for Geometric Inference Feedback Tuning) that generates data designed to improve the performance of one VLM for a specific task.

GIFT develops an understanding of the model’s strengths and weaknesses by testing it. Then it uses this knowledge to generate data that could improve the model’s performance on the CAD generation problems it struggles to solve.

“We want to obtain data augmentation that is informed by the model itself,” Giannone says. 

Learning from mistakes

To do this, GIFT asks the model to generate code that solves a CAD generation problem multiple times in parallel. It checks the correctness of these guesses to understand how well the model can solve this problem.

“For a model, generating CAD query code that is almost correct is not that hard, but generating code that is perfectly correct and can be executed is much more challenging for a standard VLM,” Giannone says.

For guesses that are nearly correct, GIFT adjusts them to become successful solutions. It saves these “near-misses” and successful solutions in a new dataset that can teach the model how to overcome problems that would usually trip it up.

“If we sample the model 10 times and it generates 10 correct answers to the same problem, then there is not much for it to learn. We care about the in-between cases, where the model might only solve the problem 50 percent of the time,” he says.

Using these in-between cases allows GIFT to generate data augmentations that are both model-aware and task-aware. In addition, by incorporating multiple correct solutions to the same problem, the new data expand the model’s general knowledge of CAD code generation.

This automatic system does not require human intervention to correct the model’s mistakes.

GIFT creates data augmentations from a pre-trained VLM using a process known as inference-time scaling. This process allows a static model, which has already been trained, to generate better outputs without the high computational costs of retraining the entire model. 

Using inference-time scaling, the user can determine how much computation they want to use for GIFT, tailoring it to their time and budget constraints. 

GIFT outperformed several competing techniques, generating CAD programs that were more accurate while using only about 20 percent as much computation. The CAD models generated by VLMs using GIFT were better aligned with the shapes of ground-truth models.

“With GIFT, we started with geometry because with engineering problems, if the geometry of a 3D shape is not correct, nothing else will be correct, but there are many other aspects to consider,” Giannone says.

In the future, the researchers want to expand GIFT so the framework can teach models to generate CAD programs that improve the performance and manufacturability of 3D models. They also want to apply the system to larger models and more diverse CAD generation tasks.

This research was funded, in part, by the MIT-IBM Computing Research Lab. 

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MIT Professor Susumu Tonegawa, renowned molecular biologist and Nobel laureate, dies at 86

Susumu Tonegawa, the Picower Professor of Biology and Neuroscience at MIT and a Nobel laureate, died July 11 at the age of 86.

Tonegawa was a renowned molecular biologist who wielded his keen insight in a variety of fields, including immunology and neuroscience. In the early 1980s, Tonegawa discovered how the immune system generates its incredible diversity of antibodies — a breakthrough that earned him the Nobel Prize in Physiology or Medicine in 1987.

Following that landmark achievement, he turned his attention to neuroscience, where his work has helped to reveal how the brain stores memories as traces called “engrams.”

An MIT faculty member for more than 40 years, Tonegawa also served as the founding director of MIT’s Picower Institute for Learning and Memory and director of the RIKEN Brain Science Institute of Japan, and was a Howard Hughes Medical Institute Investigator.

“Few scientists have reshaped our understanding of biology as profoundly as Susumu Tonegawa,” says Myriam Heiman, director of the Picower Institute. “His intellectual fearlessness, extraordinary creativity, and relentless pursuit of fundamental questions opened entirely new frontiers in both immunology and neuroscience. His influence on science and on the people who had the privilege of working alongside him is immeasurable.” 

Drawn to molecular biology

Born in Nagoya, Japan, Tonegawa spent his early years moving between rural towns, due to his father’s job as an engineer for a textile company. When it was time for him to go to high school, his parents sent him to a school in Tokyo, where he became interested in chemistry.

He was admitted to the University of Kyoto to study chemistry, and while there, he was drawn to the nascent field of molecular biology. He began his graduate studies at the Institute for Virus Research at the University of Kyoto, but after only a couple of months, his advisor, Professor Itaru Watanabe, suggested that he apply to a school in the United States, which had more advanced molecular biology programs.

Tonegawa took that advice and was accepted at the University of California at San Diego, where he studied how a virus called phage lambda controls gene transcription. After earning his PhD in 1968, he went on to a postdoc in a lab at the Salk Institute.

In that lab, Tonegawa began studying gene expression of a virus known as SV40. However, his U.S. visa was set to expire at the end of 1970, so he soon headed for a position at the newly established Basel Institute for Immunology in Switzerland. 

At the time, Tonegawa had little background in immunology, but he soon became fascinated by the 100-year-old question of “antibody diversity” — how the body’s immune system is able to generate hundreds of millions of antibodies from a relatively small set of genes. (The entire human genome contains about 20,000 genes.) That antibody diversity is what allows the immune system to recognize so many pathogens, including those it has never seen before.

With colleagues in Basel, Tonegawa discovered that each antibody protein is not encoded by its own gene — instead, genes for different components of the antibody can be randomly recombined to generate limitless combinations.

In 1987, Tonegawa was a solo recipient of the Nobel Prize for discovering that process, known as V(D)J gene rearrangement. In announcing the award, the Nobel committee noted that Tonegawa’s discoveries “explain the genetic background allowing the enormous richness of variation amongst antibodies. Beyond deeper knowledge of the basic structure of the immune system these discoveries will have importance in improving immunological therapy of different kinds, such as for instance the enforcement of vaccinations and inhibition of reactions during transplantation.”

From antibodies to engrams

In the early 1980s, after his groundbreaking antibody discoveries, Tonegawa began to feel the urge to turn to new research directions. He also wanted to return to the United States, so in 1981, he accepted the offer of a professorship at MIT’s Center for Cancer Research (today known as the Koch Institute for Integrative Cancer Research). There, he began working on T cells and contributed to scientists’ understanding of how T cells are able to generate a large diversity of T-cell receptors.

While at the CCR, he also began to study questions in neuroscience. As he told an interviewer from the Picower Institute in 2022, he was always in search of new scientific endeavors to keep him interested in his work.

“When I decided to become a scientist, my criteria of what to do was whether the scientific problem I got to solve was interesting or not. Whether I’m curious our not. I didn’t think about other things like, Could it be too risky? Can I really develop my career by venturing into the field I am not familiar with? That never occurred to me. I just followed my curiosity and instinct,” Tonegawa said in an interview published in the summer 2022 Picower Institute newsletter.

In 1994, he was chosen as the founding director for MIT’s Center for Learning and Memory, which became the Picower Institute for Learning and Memory in 2002. Tonegawa continued to serve as the center’s director until the end of 2006. 

Professor Li-Huei Tsai, who succeeded Tonegawa as the Picower Institute’s director, calls working alongside Tonegawa “one of the greatest honors of my career.”

“His passion, boundless energy, and unwavering pursuit of the fundamental mechanisms underlying memory were contagious, inspiring generations of neuroscientists to join and advance the field. Today, we lost a giant. His scientific legacy will continue to shape neuroscience for years to come, and he will be deeply missed by all of us,” she says.

Over the past two decades, Tonegawa’s lab has made significant discoveries in the field of memory research. In 2013, he and his colleagues reported that they had identified “engrams” in the brain’s hippocampus. These engrams consist of episodic memories — memories of experiences — that are stored in specific groups of hippocampal cells. Engrams encode elements including objects, space, and time, linked to a specific experience.

At that time, the researchers also found that it was possible to implant “false memories” in mice by using optogenetics to reactivate an existing engram while the animals formed a new memory. This prompted the mice to associate a new location with the memory of an event that had actually happened in a different location.

Later work from Tonegawa’s lab showed that engrams extend beyond the hippocampus and are stored across a widely distributed complex that spans many brain circuits. More recently, he had been working on engrams of “knowledge memory” to decipher the fundamental mechanism of abstract memory. His recent work also delved into how the emotional associations of memories are encoded, and how the brain maintains a timeline of chronological events.

In addition to the Nobel Prize, Tonegawa received many other awards, including the Albert and Mary Lasker Award for Basic Research in 1987, the Bristol-Myers Award for Distinguished Achievement in Cancer Research in 1986, and the David M. Bonner Lifetime Achievement Award from the University of California at San Diego in 2010. He was also known for training many scientists who are now leaders in the field of neuroscience.

Tonegawa was a longtime fan of the Boston Red Sox, and in May 2004, he had the opportunity to throw out the ceremonial first pitch at Fenway Park, as part of the team’s tribute to the Boston area’s scientific and medical communities.

He is survived by his wife, Mayumi Tonegawa ’92, two children, Hidde Tonegawa ’09 and Hanna Tonegawa, and two grandchildren. He was predeceased by a son, Satto Tonegawa. 

Following a private funeral, his ashes will be buried in Kyoto, Japan.

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3D-printed bridge points the way to greener construction

Concrete is the most widely used building material on Earth, and producing it is one of the largest single sources of carbon emissions. One promising way to reduce its environmental footprint is to 3D-print concrete, laying it down bead by bead like a giant icing-piping robot. This process eliminates the labor-intensive formwork of pouring it into molds, and places the material only where a structure needs it.

But many of the most efficient designs created by computers are impossible for today’s printers to build. Engineers use a technique called topology optimization to find the strongest structure that uses the least amount of material. But those mathematically ideal designs, with their intricate, spider-web shapes, don’t account for the physical limitations of large-scale concrete printers with their thick nozzles, limited turning, and need to print in one continuous motion.

Now a team of MIT researchers has developed a way to close that gap. Their framework, described in a new article in Additive Manufacturing, bakes a printer’s real fabrication limits directly into the optimization, so the design that comes out is one a machine can build and print with little or no manual redesign. They demonstrated it by designing, printing, and load-testing a 2.3-meter concrete bridge and found that today’s printing hardware, not the concrete itself, limits how light a structure can be.

“We were finding a lot of cracks you can fall through when it comes to translating these super-optimal designs into manufacturable designs,” says co-first author Hajin Kim-Tackowiak PhD ’26, a postdoc in MIT’s Department of Civil and Environmental Engineering (CEE). “Those cracks were like chasms.”

Designing for what can be built

To pin down the constraints, the team worked with the people who run the large-scale printing machines at Autodesk’s facility in Boston.

“They pointed at some of our sharp angles, and they went, 'I don't feel safe printing something like that,'” Kim-Tackowiak recalls.

Those conversations surfaced three key limitations: how thick each printed bead must be, how sharply the nozzle can turn, and the need to print in a single continuous line. The researchers translated each constraint directly into the mathematical rules of their framework.

Existing 3D-printed structures are typically produced with older methods that optimize the shape first, and then require “a massive amount of post-processing,” taking days to run, Kim-Tackowiak explains. By contrast, the team’s framework generated fully printable designs in about two minutes on a laptop. When the team needed to slightly reduce the bridge’s size on the day of printing, they simply reran the optimization and had an updated design five to 10 minutes later. 

“Reaching that speed at all is recent,” says co-first author Zane Schemmer, a PhD student in CEE. The math the method relies on, mixed-integer optimization, was long considered too hard to use. “You go back five, 10 years ago, the solver we used, even three years ago, could not solve these problems,” he says. “This field has been avoided, because everyone thinks that’s not an avenue we can go down. But with new algorithms and resources, it’s becoming a way we can start to frame problems.”

A bridge reveals the real limitation

To validate the framework, the researchers went back to Autodesk’s facility to print a 2.3-meter-long concrete bridge.

“The bridge took about 30 minutes to make and was built from off-the-shelf mortar,” says senior author Josephine Carstensen, the Gilbert W. Winslow (1937) Career Development Professor in Civil Engineering.

In testing, the roughly 900-pound structure held more than 2,000 pounds spread across it with virtually no measurable bending, closely matching the team’s simulations.

But the test also revealed the study’s biggest surprise. “What we found was our result was super over-engineered,” Kim-Tackowiak says. “From zero to 200,000 pounds, your design is entirely driven by these 'can I build it or not' constraints. And then, after 200,000 pounds, you can start to think about the physics.” In other words, the limits of current printing technology, not the strength of concrete, were dictating how efficient the structure could be.

A roadmap for better printers

Because the framework finds the mathematically best possible design, the researchers could measure exactly how much each hardware limitation costs in material.

“With mixed-integer optimization, we can find the global optimum, the best solution there is, as opposed to just a good solution,” Carstensen says. “Because we know we’re finding the best solution out there, we can also quantify: If we had a machine that could do other things, what would that mean for how much material we’re using?”

The single biggest lever was the width of the printed bead. The bridge used a 4 centimeter bead. The analysis showed a machine that was able to lay a 1cm bead could cut material use by as much as 76 percent while staying “well within safety margins,” Carstensen says. The result surprised her. “I thought the continuous path would be the problem, the one that had the highest effect,” she says. “But it wasn’t. It was the bead width.”

The result is a roadmap for printer-makers showing that modest hardware improvements could unlock large gains in efficiency and cut concrete’s carbon footprint.

Part of what made the bridge possible is that every piece is in compression. “With concrete, it’s really good when you push on it, really bad when you pull on it,” Schemmer says. “We're able to guarantee that every piece of concrete that you see is in compression, there’s no part that’s being pulled on.”

The savings come not only from using less material, but from skipping molds entirely, an advantage that grows for one-off shapes. Carstensen sees early promise in disaster relief, “You can quickly put up new infrastructure without needing to make formwork.”

The bridge’s compression-only nature showed itself dramatically after testing. It had held more than 2,000 pounds without budging, but when a worker lifted one corner a few inches to sweep beneath it, it broke. The failure wasn’t a design flaw so much as a demonstration of the principle behind it: Concrete is weak when pulled, and the lift put parts of the bridge in tension they were never meant to carry. “It’s optimal in one way, but it’s definitely not optimal in every way,” Kim-Tackowiak says.

That points to the team’s next step of reinforced concrete. “We know a pure concrete structure is not necessarily going to be the most optimal thing, so we’re moving it more into the world we live in today, which is reinforced concrete,” Kim-Tackowiak says, “though working out how to feed rebar into a printed concrete structure,” she adds, “is proving its own challenge.”

The work was funded by the National Science Foundation and supported by the MIT Center for Advanced Production Technologies. Joining Kim-Tackowiak, Schemmer, and Carstensen on the paper are co-authors Pittipat Wongsittikan, a PhD student in the MIT Building Technology Architecture program, and Jackson Jewett MEng ’18, PhD ’25, a former MIT postdoc.

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Electric fields help guide neural activity, even from moment to moment

It’s a fact of life that the electrical activity of neurons will vary during repetitions of the same task, even when the ultimate outcome is the same. A new study shows that a lot of ongoing fluctuations in the brain’s activity could be explained by the influence local electric fields exerted on the neurons, a phenomenon called “ephaptic coupling.” The finding, published in Cerebral Cortex, adds to evidence that the brain’s electric fields act as important control signals for underlying brain function.

“The brain is a rollicking sea of electrical influences,” says study co-author Earl K. Miller, Picower Professor of Neuroscience in The Picower Institute for Learning and Memory and MIT’s Department of Brain and Cognitive Sciences. “But the traditional view of brain function focuses only on the spiking and synaptic connections among individual neurons. Now, there is growing evidence for electric field effects. For instance, in this study we show that neural variability is explained by how ephaptic effects are influencing neural activity.”

In 2022 and 2023, Miller and fellow author Dimitris Pinotsis, associate professor at City St George’s, University of London, published several studies showing that local electric fields in the brain’s cortex not only reflected the information neurons were processing better than any individual neuron did, but also that the fields actively helped to organize the underlying neural spiking that executes that processing. Like an orchestra conductor, the electric waves can conduct crowds of neurons so that they are “playing the same tune.” They further theorize that fields physically exert influence on the structure of the brain via cytoelectric coupling, in which the fields alter the cytoskeleton of neurons, optimizing them to oscillate in synchrony.

Because electric fields can be manipulated, Miller and Pinotsis argue in the new study that understanding how they influence momentary brain function could open the door to therapeutic interventions designed to improve it when it is faltering in disease. It would be difficult to adjust every neural connection, but ephaptic coupling suggests that intervening at the level of electric fields could accomplish that therapeutic end, the researchers say.

“Properly devised electric field manipulations could help patients rewire faulty circuits,” Pinotsis and Miller wrote.

In the duo’s prior studies, they analyzed signals averaged over time, documenting that in general, even though local (or “mesoscale”) electric fields in the cortex arise from the electrical activity of individual neurons, the field ultimately represents and coordinates their function. Think of it this way: Neurons are like individual citizens, and the electric fields are their government. Once the citizens establish a government with their individual votes, they are then subject to and unified by the laws the government creates and enforces. 

In the new study, the team asked whether mesoscale electric fields not only provide this ephaptic influence overall during working memory tasks, but also trial by trial. After all, that’s closer to the timescale of actual brain operations that matter both for healthy function and in disease. 

So the scientists looked anew at the data they recorded as animals played a simple video game. The animals were shown a dot in one of six positions around a screen. After the dot disappeared, the animals had to hold its former position in memory because to succeed in the game and earn a reward, they had to glance when cued to indicate the direction where the dot had appeared. Meanwhile, the scientists recorded neural electrical spiking and more collective local field potentials. Using that information, they calculated the local prevailing electric field at each moment.

In their statistical analysis of the data, they made several findings. One, as expected, was that neural activity varied sometimes quite widely trial by trial during the task. Another, using a mathematical technique called Granger Causality, showed that the direction of influence between the electric field and the neural activity was strongly in favor of the field. In other words, in the coupling between the two, the fields were dominant.

“We found that electric fields that emerge from neural activity, captured with LFPs [local field potentials], turn around and influence this activity in a top-down fashion (ephaptic coupling),” the researchers wrote.

Moreover, the team’s modeling and calculations showed that the strength of the ephaptic coupling between the field and the neural activity was proportional to the variations in the LFP power — another sign that the fields influenced the neural activity.

“The larger the variability, the more evident the top-down organizing effects,” the researchers wrote. “The emerging picture is that electric fields serve as control parameters.”

The U.K. Medical Council, the U.S. Army Research Office, the U.S. Office of Naval Research, the Freedom Together Foundation, and the Picower Institute funded the study.

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Ketogenic diets may increase cancer risk in the small intestine

A high-fat, low-carbohydrate diet, also called a ketogenic diet, can help some people lose weight by forcing their bodies to burn fat for fuel instead of sugar. 

In recent years, scientists have been exploring how this type of diet might affect other aspects of health and disease, including cancer. While some research has shown that the diet may protect against the development of colon cancer, a new study by MIT researchers suggests that in the small intestine, a ketogenic diet may increase the risk of cancer.

“Ketogenic diets have distinct effects on different tissues even within the gastrointestinal tract. I think the message here is that we need to be very careful in generalizing the effects that these diets can have, because what might be beneficial for one tissue may be detrimental for another tissue,” says Omer Yilmaz, director of the MIT Stem Cell Initiative, an associate professor of biology at MIT, and a member of MIT’s Koch Institute for Integrative Cancer Research.

Yilmaz is the senior author of the study, which appears today in Nature. MIT postdocs Jessica Shay and Fangtao Chi are the lead authors of the paper. Researchers from the labs of Alex K. Shalek, director of MIT’s Institute for Medical Engineering and Science, and Matthew Vander Heiden, director of the Koch Institute, also contributed to the study.

Diet and cancer

Ketogenic diets, originally developed in the 1920s as a way to treat epilepsy, have been adapted in the past few decades as a strategy to lose weight or increase lifespan. The diet comprises a high percentage of fat, low percentage of carbohydrates, and normal or reduced amounts of protein.

This type of diet forces the body to burn fatty acids for energy in place of carbohydrates such as glucose. Burning these lipids produces ketone bodies — primarily β-hydroxybutyrate (BHB) and acetoacetate — as byproducts of fatty acid metabolism. These ketone bodies are also generated when people fast or follow very low-calorie diets, which force the body to burn its own fatty stores.

A 2022 Nature study suggested that ketogenic diets have a protective effect against colon cancer and that BHB — the most abundant ketone body — is responsible for this effect. In the new Nature study, the MIT team wanted to explore whether ketogenic diets might have a similar protective effect in the small intestine.

The researchers fed mice who were genetically predisposed to developing intestinal cancer either a ketogenic diet, a control diet, or a high fat/high calorie diet. They found that mice on a ketogenic diet were more likely to develop tumors of the small intestine than those on a control diet. While they did not become obese, mice on the ketogenic diet developed tumors at rates similar to or even higher than those of mice on an obesogenic high fat/high calorie diet. 

Additional studies revealed that ketone bodies did not play a role in tumor development. Instead, tumor growth was driven by how intestinal cells burn dietary fat for energy — a metabolic pathway called fatty acid oxidation. This pathway activates a family of proteins called PPARs, which signal stem cells to multiply more rapidly, increasing the chance that some become cancerous. 

This stem cell proliferation can be beneficial in certain situations, such as when the intestinal lining needs to be repaired after illness or injury. However, too much proliferation can tip cells toward becoming cancerous. 

“Having more stem cells means that when you injure the small intestine, it can repair itself better, but the downside is that having more active stem cells can lead to tumor formation,” Yilmaz says.

Opposite effects

Surprisingly, the same ketogenic diet that promoted tumors in the small intestine had the opposite effect in the colon. The researchers found, similar to the earlier Nature study back in 2022, that a ketogenic diet suppressed the development of colon tumors. However, the new findings suggest that ketone bodies are not responsible for this protective effect.

“Given how much attention has been paid to ketone bodies like BHB, both as a commercial health trend and in recent high-profile studies suggesting BHB suppresses colon cancer, we fully expected them to be the direct drivers. Instead, our experiments in genetically engineered mice revealed that these molecules are essentially metabolic bystanders. The real surprise is that tumor acceleration is driven entirely by how stem cells process and burn the heavy influx of dietary fat itself,” Yilmaz says.

The researchers now hope to further study why ketogenic diets have such different effects in the colon and the small intestine. As ketogenic diets continue to gain popularity, understanding these tissue-specific effects will be critical for guiding their use, the researchers say.

“The deeper question is why the same diet has opposite consequences in two adjacent parts of the gut. That is what we are working to understand next,” Chi says.

The findings carry practical implications. Because the diet’s effects — both the tumor acceleration in the small intestine and the protection in the colon — are driven entirely by fat metabolism rather than the ketones themselves, commercial ketone supplements or drinks would not be expected to mimic either the risks or the benefits discovered in this study. This may be especially relevant given that small intestinal tumors have been rising in incidence in recent decades, with the greatest impact on patients with inherited conditions that predispose them to intestinal cancer, such as familial adenomatous polyposis.

The research was funded, in part, by the National Institutes of Health, a Pew-Stewart Trust scholar award, the Kathy and Curt Marble cancer research award, a Koch Institute-Dana Farber/Harvard Cancer Center Bridge project grant, the American Federation for Aging Research, the MIT Stem Cell Initiative, a Damon Runyon Postdoctoral Research Fellowship, and the Koch Institute Support (core) grant from the National Cancer Institute.

de MIT News https://ift.tt/6no8EYU

Three MIT Press journals lead their fields with Clarivate No. 1 rankings

In an increasingly crowded, for-profit landscape for scholarly research, the health of a publishing program is often measured by the influence of its publications. This year, three MIT Press journals demonstrated their stature by earning the highest impact factors in their disciplines.

Computational Linguistics ranked first in the Linguistics category, International Security led the International Relations category, and The Review of Economics and Statistics topped the Social Sciences, Mathematical Models category in Clarivate’s 2026 journal impact factor rankings.

For the MIT Press, this achievement highlights the distinctive strength of its journals program. Although relatively small compared to other commercial and university press publishers, MIT Press journals consistently publish widely cited scholarship across a broad range of disciplines, from social science and the humanities to neuroscience and artificial intelligence. 

Clarivate’s impact factors capture the previous year’s scholarly citation activity, but the influence of MIT Press journals often extends well beyond academia. In recent months, International Security articles have been cited by Foreign Policy, Foreign Affairs, The Conversation, CBC, and Brookings. The journal has also published research with significant real-world policy relevance, including a widely discussed article by MIT political scientist Caitlin Talmadge that anticipated how a limited strike on Iran could escalate into attempts to disrupt shipping through the Strait of Hormuz, triggering a broader military and economic crisis. 

“I am proud and humbled that International Security has had the number one impact factor in International Relations for two years running,” says Jacqueline Hazelton, editor of International Security. “Thanks are due to our generous reviewers, our brilliant authors, our talented editors who handle the often-thankless work of copy editing and production, and, of course, our readers. We plan to continue leading the field in IR/security studies with rigorous scholarship that challenges the conventional wisdom, identifies new threats and opportunities, engages with policy and theory, and illuminates history.”

The MIT Press journals team is small, with under 10 people working across production, sales, and marketing; but that small team collaborates with the editorial staff of 50 disparate journals to publish around 2,500 articles annually. “Some of the joy I take in editing International Security stems from working with the people at MIT Press,” Hazelton adds. “They are helpful and patient. They know what to do, and they do it.”

“The journals division at MIT Press has undergone significant change over the past decade — from business model upheaval and rapid technological advances to the ongoing challenge of competing with commercial publishers many times our size,” says Nick Lindsay, director of journals and institutional partnerships at the MIT Press. “Through it all, the journals group has adapted and evolved to meet those challenges and remains a home for experimentation and fair and equitable publishing.”

The MIT Press’ reputation for influential publishing has attracted many prestigious partners to its journals program, including Harvard University, the American Academy of Arts and Sciences, and the University of California at Berkeley. Amid this growth and development, the program continues to launch and support new journals in emerging and interdisciplinary fields while upholding the high editorial and publishing standards that have made it what it is today.

“Computational Linguistics has long stood for depth and rigor, and in a field that moves remarkably fast, our aspiration is for it to remain a home for work that lasts — scholarship the community can keep building on for years to come,” says Wei Lu, editor of Computational Linguistics. “We are very proud of this result, which reflects both the strength of the work our authors publish and the care our reviewers and editors bring to the journal. We are grateful to MIT Press for being such a steadfast partner.”

This strong performance extended well beyond the press’ three top-ranked publications. Transactions of the Association for Computational Linguistics was ranked 2nd in the Linguistics Category out of 312 journals; Global Environmental Politics was 2nd in the International Relations category out of 173 journals; and The Review of Economics and Statistics was 17th in the Economics category among 626 journals. Other highlights include Harvard Data Science Review ranking 7th in Statistics and Probability; European Societies ranking No. 13 in Sociology; and Neurobiology of Language ranking No. 13 in Psychology, Experimental.

Overall, 13 MIT Press journals earned impact factors that place them in the top quartile of their area of publishing, including: 

• Computational Linguistics 
• European Societies
• Evolutionary Computation 
• Global Environmental Politics
• Harvard Data Science Review
• International Security
• Journal of Cold War Studies
• The Journal of Interdisciplinary History
• Linguistic Inquiry
• Neurobiology of Language
• Quantitative Science Studies
• The Review of Economics and Statistics
• Transactions of the Association for Computational Linguistics

Together, these rankings point to the strong reputation that the MIT Press has built for its journals portfolio, a relatively small program that shapes conversations across the humanities, social sciences, and STEM fields.

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