Usha Lee McFarling named director of the Knight Science Journalism Program

The Knight Science Journalism Program (KSJ) at MIT has announced that Usha Lee McFarling, national science correspondent for STAT and former KSJ Fellow, will be joining the team in August as their next director.

As director, McFarling will play a central role in helping to manage KSJ — an elite mid-career fellowship program that brings prominent science journalists from around the world for 10 months of study and intellectual exploration at MIT, Harvard University, and other institutions in the Boston area.

“I’m eager to take the helm during this critical time for science journalism, a time when journalism is under attack both politically and economically and misinformation — especially in areas of science and health — is rife,” says McFarling. “My goal is for the program to find even more ways to support our field and its practitioners as they carry on their important work.”

McFarling is a veteran science writer, most recently working for STAT News. She previously reported for the Los Angeles Times, The Boston Globe, Knight Ridder Washington Bureau, and the San Antonio Light, and was a Knight Science Journalism Fellow in 1992-93. McFarling graduated from Brown University with a degree in biology in 1988 and later earned a master’s degree in biological psychology from the University of California at Berkeley.

Her work on the diseased state of the world’s oceans earned the 2007 Pulitzer Prize for explanatory journalism and a Polk Award, among others. Her coverage of health disparities at STAT has earned an Edward R. Murrow award, and awards from the Association of Health Care Journalists, and the Asian American Journalists Association. In 2024, she was awarded the Victor Cohn prize for excellence in medical science reporting and the Bernard Lo, MD award in bioethics.

McFarling will succeed director Deborah Blum, who served as director for 10 years. Blum, also a Pulitzer-prize winning journalist and the bestselling author of six books, is retiring to return to a full-time writing career. She will join the board of Undark, a magazine she helped found while at KSJ, and continue as a board member of the Council for the Advancement of Science Writing and the Burroughs Wellcome Fund, among others.

“It’s been an honor to serve as director of the Knight Science Journalism program for the past 10 years and a pleasure to be able to support the important work that science journalists do,” Blum says. “And I know that under the direction of Usha McFarling — who brings such talent and intelligence to the job — that KSJ will continue to grow and thrive in all the best ways.”

de MIT News https://ift.tt/BqTXG7E

With AI, researchers predict the location of virtually any protein within a human cell

A protein located in the wrong part of a cell can contribute to several diseases, such as Alzheimer’s, cystic fibrosis, and cancer. But there are about 70,000 different proteins and protein variants in a single human cell, and since scientists can typically only test for a handful in one experiment, it is extremely costly and time-consuming to identify proteins’ locations manually.

A new generation of computational techniques seeks to streamline the process using machine-learning models that often leverage datasets containing thousands of proteins and their locations, measured across multiple cell lines. One of the largest such datasets is the Human Protein Atlas, which catalogs the subcellular behavior of over 13,000 proteins in more than 40 cell lines. But as enormous as it is, the Human Protein Atlas has only explored about 0.25 percent of all possible pairings of all proteins and cell lines within the database.

Now, researchers from MIT, Harvard University, and the Broad Institute of MIT and Harvard have developed a new computational approach that can efficiently explore the remaining uncharted space. Their method can predict the location of any protein in any human cell line, even when both protein and cell have never been tested before.

Their technique goes one step further than many AI-based methods by localizing a protein at the single-cell level, rather than as an averaged estimate across all the cells of a specific type. This single-cell localization could pinpoint a protein’s location in a specific cancer cell after treatment, for instance.

The researchers combined a protein language model with a special type of computer vision model to capture rich details about a protein and cell. In the end, the user receives an image of a cell with a highlighted portion indicating the model’s prediction of where the protein is located. Since a protein’s localization is indicative of its functional status, this technique could help researchers and clinicians more efficiently diagnose diseases or identify drug targets, while also enabling biologists to better understand how complex biological processes are related to protein localization.

“You could do these protein-localization experiments on a computer without having to touch any lab bench, hopefully saving yourself months of effort. While you would still need to verify the prediction, this technique could act like an initial screening of what to test for experimentally,” says Yitong Tseo, a graduate student in MIT’s Computational and Systems Biology program and co-lead author of a paper on this research.

Tseo is joined on the paper by co-lead author Xinyi Zhang, a graduate student in the Department of Electrical Engineering and Computer Science (EECS) and the Eric and Wendy Schmidt Center at the Broad Institute; Yunhao Bai of the Broad Institute; and senior authors Fei Chen, an assistant professor at Harvard and a member of the Broad Institute, and Caroline Uhler, the Andrew and Erna Viterbi Professor of Engineering in EECS and the MIT Institute for Data, Systems, and Society (IDSS), who is also director of the Eric and Wendy Schmidt Center and a researcher at MIT’s Laboratory for Information and Decision Systems (LIDS). The research appears today in Nature Methods.

Collaborating models

Many existing protein prediction models can only make predictions based on the protein and cell data on which they were trained or are unable to pinpoint a protein’s location within a single cell.

To overcome these limitations, the researchers created a two-part method for prediction of unseen proteins’ subcellular location, called PUPS.

The first part utilizes a protein sequence model to capture the localization-determining properties of a protein and its 3D structure based on the chain of  amino acids that forms it.

The second part incorporates an image inpainting model, which is designed to fill in missing parts of an image. This computer vision model looks at three stained images of a cell to gather information about the state of that cell, such as its type, individual features, and whether it is under stress.

PUPS joins the representations created by each model to predict where the protein is located within a single cell, using an image decoder to output a highlighted image that shows the predicted location.

“Different cells within a cell line exhibit different characteristics, and our model is able to understand that nuance,” Tseo says.

A user inputs the sequence of amino acids that form the protein and three cell stain images — one for the nucleus, one for the microtubules, and one for the endoplasmic reticulum. Then PUPS does the rest.

A deeper understanding

The researchers employed a few tricks during the training process to teach PUPS how to combine information from each model in such a way that it can make an educated guess on the protein’s location, even if it hasn’t seen that protein before.

For instance, they assign the model a secondary task during training: to explicitly name the compartment of localization, like the cell nucleus. This is done alongside the primary inpainting task to help the model learn more effectively.

A good analogy might be a teacher who asks their students to draw all the parts of a flower in addition to writing their names. This extra step was found to help the model improve its general understanding of the possible cell compartments.

In addition, the fact that PUPS is trained on proteins and cell lines at the same time helps it develop a deeper understanding of where in a cell image proteins tend to localize.

PUPS can even understand, on its own, how different parts of a protein’s sequence contribute separately to its overall localization.

“Most other methods usually require you to have a stain of the protein first, so you’ve already seen it in your training data. Our approach is unique in that it can generalize across proteins and cell lines at the same time,” Zhang says.

Because PUPS can generalize to unseen proteins, it can capture changes in localization driven by unique protein mutations that aren’t included in the Human Protein Atlas.

The researchers verified that PUPS could predict the subcellular location of new proteins in unseen cell lines by conducting lab experiments and comparing the results. In addition, when compared to a baseline AI method, PUPS exhibited on average less prediction error across the proteins they tested.

In the future, the researchers want to enhance PUPS so the model can understand protein-protein interactions and make localization predictions for multiple proteins within a cell. In the longer term, they want to enable PUPS to make predictions in terms of living human tissue, rather than cultured cells.

This research is funded by the Eric and Wendy Schmidt Center at the Broad Institute, the National Institutes of Health, the National Science Foundation, the Burroughs Welcome Fund, the Searle Scholars Foundation, the Harvard Stem Cell Institute, the Merkin Institute, the Office of Naval Research, and the Department of Energy.

de MIT News https://ift.tt/zsC5Pr6

Particles carrying multiple vaccine doses could reduce the need for follow-up shots

Around the world, 20 percent of children are not fully immunized, leading to 1.5 million child deaths each year from diseases that are preventable by vaccination. About half of those underimmunized children received at least one vaccine dose but did not complete the vaccination series, while the rest received no vaccines at all.

To make it easier for children to receive all of their vaccines, MIT researchers are working to develop microparticles that can release their payload weeks or months after being injected. This could lead to vaccines that can be given just once, with several doses that would be released at different time points.

In a study appearing today in the journal Advanced Materials, the researchers showed that they could use these particles to deliver two doses of diphtheria vaccine — one released immediately, and the second two weeks later. Mice that received this vaccine generated as many antibodies as mice that received two separate doses two weeks apart.

The researchers now hope to extend those intervals, which could make the particles useful for delivering childhood vaccines that are given as several doses over a few months, such as the polio vaccine.

“The long-term goal of this work is to develop vaccines that make immunization more accessible — especially for children living in areas where it’s difficult to reach health care facilities. This includes rural regions of the United States as well as parts of the developing world where infrastructure and medical clinics are limited,” says Ana Jaklenec, a principal investigator at MIT’s Koch Institute for Integrative Cancer Research.

Jaklenec and Robert Langer, the David H. Koch Institute Professor at MIT, are the senior authors of the study. Linzixuan (Rhoda) Zhang, an MIT graduate student who recently completed her PhD in chemical engineering, is the paper’s lead author.

Self-boosting vaccines

In recent years, Jaklenec, Langer, and their colleagues have been working on vaccine delivery particles made from a polymer called PLGA. In 2018, they showed they could use these types of particles to deliver two doses of the polio vaccine, which were released about 25 days apart.

One drawback to PLGA is that as the particles slowly break down in the body, the immediate environment can become acidic, which may damage the vaccine contained within the particles.

The MIT team is now working on ways to overcome that issue in PLGA particles and is also exploring alternative materials that would create a less acidic environment. In the new study, led by Zhang, the researchers decided to focus on another type of polymer, known as polyanhydride.

“The goal of this work was to advance the field by exploring new strategies to address key challenges, particularly those related to pH sensitivity and antigen degradation,” Jaklenec says.

Polyanhydrides, biodegradable polymers that Langer developed for drug delivery more than 40 years ago, are very hydrophobic. This means that as the polymers gradually erode inside the body, the breakdown products hardly dissolve in water and generate a much less acidic environment.

Polyanhydrides usually consist of chains of two different monomers that can be assembled in a huge number of possible combinations. For this study, the researchers created a library of 23 polymers, which differed from each other based on the chemical structures of the monomer building blocks and the ratio of the two monomers that went into the final product.

The researchers evaluated these polymers based on their ability to withstand temperatures of at least 104 degrees Fahrenheit (40 degrees Celsius, or slightly above body temperature) and whether they could remain stable throughout the process required to form them into microparticles.

To make the particles, the researchers developed a process called stamped assembly of polymer layers, or SEAL. First, they use silicon molds to form cup-shaped particles that can be filled with the vaccine antigen. Then, a cap made from the same polymer is applied and sealed using heat. Polymers that proved too brittle or didn’t seal completely were eliminated from the pool, leaving six top candidates.

The researchers used those polymers to design particles that would deliver diphtheria vaccine two weeks after injection, and gave them to mice along with vaccine that was released immediately. Four weeks after the initial injection, those mice showed comparable levels of antibodies to mice that received two doses two weeks apart.

Extended release

As part of their study, the researchers also developed a machine-learning model to help them explore the factors that determine how long it takes the particles to degrade once in the body. These factors include the type of monomers that go into the material, the ratio of the monomers, the molecular weight of the polymer, and the loading capacity or how much vaccine can go into the particle.

Using this model, the researchers were able to rapidly evaluate nearly 500 possible particles and predict their release time. They tested several of these particles in controlled buffers and showed that the model’s predictions were accurate.

In future work, this model could also help researchers to develop materials that would release their payload after longer intervals — months or even years. This could make them useful for delivering many childhood vaccines, which require multiple doses over several years.

“If we want to extend this to longer time points, let’s say over a month or even further, we definitely have some ways to do this, such as increasing the molecular weight or the hydrophobicity of the polymer. We can also potentially do some cross-linking. Those are further changes to the chemistry of the polymer to slow down the release kinetics or to extend the retention time of the particle,” Zhang says.

The researchers now hope to explore using these delivery particles for other types of vaccines. The particles could also prove useful for delivering other types of drugs that are sensitive to acidity and need to be given in multiple doses, they say.

“This technology has broad potential for single-injection vaccines, but it could also be adapted to deliver small molecules or other biologics that require durability or multiple doses. Additionally, it can accommodate drugs with pH sensitivities,” Jaklenec says.

The research was funded, in part, by the Koch Institute Support (core) Grant from the National Cancer Institute.

de MIT News https://ift.tt/MCxNnKs

Class pairs students with military officers to build mission-critical solutions

On a recent Friday afternoon, Marine Corps General and U.S. Congressman Jake Auchincloss stood in the front of a crowded MIT classroom in Building 1 and made his case for modernizing America’s military to counter the threat from China. Part of his case involved shifting resources away from the U.S. Army to bolster the Marines, Navy, and Air Force.

When it was time for questions, several hands shot up. One person took exception to Auchincloss’ plans for the Army, although he admitted his views were influenced by the fact that he was an active member of the Army’s Special Forces. Another person had a question about the future of wartime technology. Again, the questioner had some personal experience: He sits on the board of a Ukrainian drone-manufacturing company. Next up was an MIT student with a question about artificial intelligence.

Course 15.362/6.9160 (Engineering Innovation: Global Security Systems) is not your typical MIT class. It teaches students about the most pressing problems in global security and challenges them to build functioning prototypes over the course of one whirlwind semester. Along the way, students hear from high-ranking members of the military, MIT professors, government officials, startup founders, and others to learn about the realities of combat and how best to create innovative solutions.

“As far as I know, this is the only class in the world that works in this way,” says Gene Keselman MBA ’17, a lecturer in the MIT Sloan School of Management and a colonel in the U.S. Air Force Reserves who helped start the class. “There are other classes trying to do something similar, but they use intermediaries. In this course, the Navy SEALs are in the classroom working directly with the students. By teaching students in this way, we’re giving them exposure to something they’d never otherwise be exposed to.”

In the beginning of the semester, students split into interdisciplinary groups that feature both undergraduate and graduate students. Each group is assigned mentors with deep military experience. From there, students learn to take a problem, map out a set of possible solutions, and pitch their prototypes to the active members of the armed services they’re trying to help.

They get feedback on their ideas and iterate as they go through a series of presentation milestones throughout the semester.

“The outcomes are twofold,” says A.J. Perez ’13, MEng ’14, PhD ’23, a lecturer in the MIT School of Engineering and a research scientist with the Office of Innovation, who built the course’s engineering design curriculum. “There are the prototypes, which could have real impact on war fighters, and then there are the learnings students get by going through the process of defining a problem and building a prototype. The prototype is important, but the process of the class leads to skills that are transferable to a multitude of other domains.”

The class’s organizers say although the course is only in its second year, it aligns with MIT’s long legacy of working alongside the military.

“MIT has these incredibly fruitful relationships with the Department of Defense going back to World War II,” says Keselman. “We developed advanced radar systems that helped win the war and launched the military-industrial complex, including organizations like MIT Lincoln Laboratory and MITRE. It’s in our ethos, it’s in our culture, and this is another extension of that. This is another way for MIT to lead in tough tech and work on the world’s hardest problems. We couldn’t do this class in another university in this country.”

Tapping into student interest

Like many things at MIT, the class was inspired by a hackathon. For several years, college students in the U.S. Armed Forces’ Reserve Officers’ Training Corps (ROTC) program came to MIT from across the country for a weekend hackathon focused on solving specific military problems.

Last year, Keselman, Perez, and others decided to create the class to give MIT’s ROTC cadets more time to work on the projects and give them the opportunity to earn course credit. But when Keselman and Perez announced a class geared toward solving problems in the armed forces, many non-ROTC MIT students enrolled.

“We realized there was a lot of interest in national security at MIT beyond the ROTC cadets,” Keselman explains. “National security is obviously important to a lot of people, but it also offers super interesting problems you can’t find anywhere else. I think that attracted students from all over MIT.”

About 25 students enrolled the first year to work on a problem that prevented U.S. Navy SEALs from bringing lithium-ion batteries onto submarines. This year, the organizers who include senior faculty members Fiona Murray, Sertac Karaman, and Vladimir Bulovic, couldn’t fit everyone who showed up, so they expanded to room 1-190, a lecture hall. They also added graduate-level credits and were more prepared for student interest.

More than 70 students registered this year from 15 different MIT departments, Harvard College, the Harvard Business School, and the Harvard Kennedy School. Student groups contain undergraduates, graduates, engineers, and business students. Many have military experience, and each group has access to mentors from places including the Navy, Air Force, Special Operations, and the Massachusetts State Police.

“Last year a student said, ‘This class is weird, and that’s exactly why it needs to stick around,’” Keselman says. “It is weird. It’s not normal for this many disciplines to come together, to have a Congressman showing up, Navy Seals, and members of the Army’s Delta Force all sitting in a room. Some are active-duty students, some are mentors, but it’s an incredible melting pot. I think it’s exactly what MIT embodies.”

From projects to military programs

This year’s class project challenges students to develop countermeasures for autonomous drone systems, which either travel by air or sea. Over the course of the semester, teams have built solutions that achieve early drone detection, categorization, and countermeasures. The solutions also must integrate AI and consider domestic manufacturing capabilities and supply chains.

One group is using sensors to detect the auditory signature of drones in the air. In the class, they gave a live demo that would only signal a threat when it detected a certain steady pitch associated with the electric motor of an air drone.

“Nothing motivates MIT students like a problem in the real world that they know really matters,” Perez says. “At the core of this year’s problem is how we protect a human from a drone attack. They take the process seriously.”

Last year, the military funded a $2 million program to further develop one student’s project for the U.S. Special Operations Command (USSOCOM).

“Students gain important skills to become product design engineers,” Perez says. “The hard results are inventions, prototypes, academic papers, and proposals to continue developing the technology.”

Organizers have also talked about extending the class into a year-long program that would allow the teams to build their projects into real products in partnership with groups at places like Lincoln Laboratory.

“This class is spreading the seeds of collaboration between academia and government,” Keselman says. “It’s a true partnership as opposed to just a funding program. Government officials come to MIT and sit in the classroom and see what’s actually happening here — and they rave about how impressive all the work is.”

de MIT News https://ift.tt/5VzCPu2

3 Questions: Making the most of limited data to boost pavement performance

Pavements form the backbone of our built environment. In the United States, almost 2.8 million lane-miles, or about 4.6 million lane-kilometers, are paved. They take us to work or school, take goods to their destinations, and much more.

To secure a more sustainable future, we must take a careful look at the long-term performance and environmental impacts of our pavements. Haoran Li, a postdoc at the MIT Concrete Sustainability Hub and the Department of Civil and Environmental Engineering, is deeply invested in studying how to give stakeholders the information and tools they need to make informed pavement decisions with the future in mind. Here, he discusses life-cycle assessments for pavements as well as research from MIT in addressing pavement sustainability.

Q: What is life-cycle assessment, and why does it matter for pavements?

A: Life-cycle assessment (LCA) is a method that helps us holistically assess the environmental impacts of products and systems throughout their life cycle — everything from the impacts of raw materials to construction, use, maintenance, and repair, and finally decommissioning. For pavements, up to 78 percent of the life-cycle impact comes from the use phase, with the majority stemming from vehicle fuel use impacted by pavement characteristics, such as stiffness and smoothness. This phase also includes the sunlight reflected by pavements: Lighter, more reflective pavement bounces heat back into the atmosphere instead of absorbing it, which can help keep nearby buildings and streets cooler, At the same time, there are positive use phase impacts like carbon uptake — the natural process by which cement-based products like concrete roads and infrastructure sequester CO₂ [carbon dioxide] from the atmosphere. Due to the sheer area of our pavements, they offer a great potential for the sustainability solution. Unlike many decarbonization solutions, pavements are managed by government agencies and influence the emissions from vehicles and surrounding buildings, allowing for a coordinated push toward sustainability through better materials, designs, and maintenance.

Q: What are the gaps in current pavement life-cycle assessment methods and tools and what has the MIT Concrete Sustainability Hub done to address them so far?

A: A key gap is the complexity of performing pavement LCA. Practitioners should assess both the long-term structural performance and environmental impacts of paving materials, considering the pavements’ interactions with the built environment. Another key gap is the great uncertainty associated with pavement LCA. Since pavements are designed to last for decades, it is necessary to handle the inherent uncertainty through their long-term performance evaluations.

To tackle these challenges, the MIT Concrete Sustainability Hub (CSHub) developed an innovative method and practical tools that address data intensity and uncertainty while offering context-specific and probabilistic LCA strategies. For instance, we demonstrated that it is possible to achieve meaningful results on the environmentally preferred pavement alternatives while reducing data collection efforts by focusing on the most influential and least variable parameters. By targeting key variables that significantly impact the pavement’s life cycle, we can streamline the process and still obtain robust conclusions. Overall, the efforts of the CSHub aim to enhance the accuracy and efficiency of pavement LCAs, making them better aligned with real-world conditions and more manageable in terms of data requirements.

Q: How does the MIT Concrete Sustainability Hub’s new streamlined pavement life-cycle assessment method improve on previous designs?

A: The CSHub recently developed a new framework to streamline both probabilistic and comparative LCAs for pavements. Probabilistic LCA accounts for randomness and variability in data, while comparative LCA allows the analysis of different options simultaneously to determine the most sustainable choice.

One key innovation is the use of a structured data underspecification approach, which prioritizes the data collection efforts. In pavement LCA, underspecifying can reduce the overall data collection burden by up to 85 percent, allowing for a reliable decision-making process with minimal data. By focusing on the most critical elements, we can still reach robust conclusions without the need for extensive data collection.

To make this framework practical and accessible, it is being integrated into an online LCA software tool. This tool facilitates use by practitioners, such as departments of transportation and metropolitan planning organizations. It helps them identify choices that lead to the highest-performing, longest-lasting, and most environmentally friendly pavements. Some of these solutions could include incorporating low-carbon concrete mixtures, prioritizing long-lasting treatment actions, and optimizing the design of pavement geometry to reduce life-cycle greenhouse gas emissions.

Overall, the CSHub’s new streamlined pavement LCA method significantly improves the efficiency and accessibility of conducting pavement LCAs, making it easier for stakeholders to make informed decisions that enhance pavement performance and sustainability.

de MIT News https://ift.tt/EgCJb0U

Steven Truong ’20 named 2025 Knight-Hennessy Scholar

MIT alumnus Steven Troung ’20 has been awarded a 2025 Knight-Hennessy Scholarship and will join the eighth cohort of the prestigious fellowship. Knight-Hennessy Scholars receive up to three years of financial support for graduate studies at Stanford University.

Knight-Hennessy Scholars are selected for their independence of thought, purposeful leadership, and civic mindset. Troung is dedicated to making scientific advances in metabolic disorders, specifically diabetes, a condition that has affected many of his family members.

Truong, the son of Vietnamese refugees, originally hails from Minneapolis and graduated from MIT in 2020 with bachelor’s degrees in biological engineering and creative writing. During his time at MIT, Truong conducted research on novel diabetes therapies with professors Daniel Anderson and Robert Langer at the Koch Institute for Integrative Cancer Research and with Professor Douglas Lauffenburger in the Department of Biological Engineering.

Troung also founded a diabetes research project in Vietnam and co-led Vietnam’s largest genome-wide association study with physicians at the University of Medicine and Pharmacy in Ho Chi Minh City, where the team investigated the genetic determinants of Type 2 diabetes.

In his senior year at MIT, Truong won a Marshall Scholarship for post-graduate studies in the U.K. As a Marshall Scholar, he completed an MPhil in computational biology at Cambridge University and an MA in creative writing at Royal Holloway, University of London. Troung is currently pursuing an MD and a PhD in biophysics at the Stanford School of Medicine.

In addition to winning a Knight-Hennessy Scholarship and the Marshall Scholarship, Truong was the recipient of a 2019-20 Goldwater Scholarship and a 2023 Paul and Daisy Soros Fellowship for New Americans.

Students interested in applying to the Knight-Hennessy Scholars program can contact Kim Benard, associate dean of distinguished fellowships in Career Advising and Professional Development. 

de MIT News https://ift.tt/MmvPDXk

Drug injection device wins MIT $100K Competition

The winner of this year’s MIT $100K Entrepreneurship Competition is helping advanced therapies reach more patients faster with a new kind of drug-injection device.

CoFlo Medical says its low-cost device can deliver biologic drugs more than 10 times faster than existing methods, accelerating the treatment of a range of conditions including cancers, autoimmune diseases, and infectious diseases.

“For patients battling these diseases, every hour matters,” said Simon Rufer SM ’22 in the winning pitch. “Biologic drugs are capable of treating some of the most challenging diseases, but their administration is unacceptably time-consuming, infringing on the freedom of the patient and effectively leaving them tethered to their hospital beds. The requirement of a hospital setting also makes biologics all but impossible in remote and low-access areas.”

Today, biologic drugs are mainly delivered through intravenous fusions, requiring patients to sit in hospital beds for hours during each delivery. That’s because many biologic drugs are too viscous to be pushed through a needle. CoFlo’s device enables quick injections of biologic drugs no matter how viscous. It works by surrounding the viscous drug with a second, lower-viscosity fluid.

“Imagine trying to force a liquid as viscous as honey through a needle: It’s simply not possible,” said Rufer, who is currently a PhD candidate in the Department of Mechanical Engineering. “Over the course of six years of research and development at MIT, we’ve overcome a myriad of fluidic instabilities that have otherwise made this technology impossible. We’ve also patented the fundamental inner workings of this device.”

Rufer made the winning pitch to a packed Kresge Auditorium that included a panel of judges on May 12. In a video, he showed someone injecting biologic drugs using CoFlo’s device using one hand.

Rufer says the second fluid in the device could be the buffer of the drug solution itself, which wouldn’t alter the drug formulation and could potentially expedite the device’s approval in clinical trials. The device can also easily be made using existing mass manufacturing processes, which will keep the cost low.

In laboratory experiments, CoFlo’s team has demonstrated injections that are up to 200 times faster.

“CoFlo is the only technology that is capable of administering viscous drugs while simultaneously optimizing the patient experience, minimizing the clinical burden, and reducing device cost,” Rufer said.

Celebrating entrepreneurship

The MIT $100K Competition started more than 30 years ago, when students, along with the late MIT Professor Ed Roberts, raised $10,000 to turn MIT’s “mens et manus” (“mind and hand”) motto into a startup challenge. Over time, with sponsor support, the event grew into the renown, highly anticipated startup competition it is today, highlighting some of the most promising new companies founded by MIT community members each year.

The Monday night event was the culmination of months of work and preparation by participating teams. The $100K program began with student pitches in December and was followed by mentorship, funding, and other support for select teams over the course of ensuing months.

This year more than 50 teams applied for the $100K’s final event. A network of external judges whittled that down to the eight finalists that made their pitches.

Other winners

In addition to the grand prize, finalists were also awarded a $50,000 second-place prize, a $5,000 third-place prize, and a $5,000 audience choice award, which was voted on during the judge’s deliberations.

The second-place prize went to Haven, an artificial intelligence-powered financial planning platform that helps families manage lifelong disability care. Haven’s pitch was delivered by Tej Mehta, a student in the MIT Sloan School of Management who explained the problem by sharing his own family’s experience managing his sister’s intellectual disability.

“As my family plans for the future, a number of questions are keeping us up at night,” Mehta told the audience. “How much money do we need to save? What public benefits is she eligible for? How do we structure our private assets so she doesn’t lose those public benefits? Finally, how do we manage the funds and compliance over time?”

Haven works by using family information and goals to build a personalized roadmap that can predict care needs and costs over more than 50 years.

“We recommend to families the exact next steps they need to take, what to apply for, and when,” Mehta explained.

The third-place prize went to Aorta Scope, which combines AI and ultrasound to provide augmented reality guidance during vascular surgery. Today, surgeons must rely on a 2-D X-ray image as they feed a large stent into patients’ body during a common surgery known as endovascular repair.

Aorta Scope has developed a platform for real-time, 3-D implant alignment. The solution combines intravascular ultrasound technology with fiber optic shape sensing. Tom Dillon built the system that combines data from those sources as part of his ongoing PhD in MIT’s Department of Mechanical Engineering.

Finally, the audience choice award went to Flood Dynamics, which provides real-time flood risk modeling to help cities, insurers, and developers adapt and protect urban communities from flooding.

Although most urban flood damages are driven by rain today, flood models don’t account for rainfall, making cities less prepared for flooding risks.

“Flooding, and especially rain-driven flooding, is the costliest natural hazard around the world today,” said Katerina Boukin SM ’20, PhD ’25, who developed the company’s technology at MIT. “The price of staying rain-blind is really steep. This is an issue that is costing the U.S. alone more than $30 billion a year.”

de MIT News https://ift.tt/TNwaiYE

Study shows vision-language models can’t handle queries with negation words

Imagine a radiologist examining a chest X-ray from a new patient. She notices the patient has swelling in the tissue but does not have an enlarged heart. Looking to speed up diagnosis, she might use a vision-language machine-learning model to search for reports from similar patients.

But if the model mistakenly identifies reports with both conditions, the most likely diagnosis could be quite different: If a patient has tissue swelling and an enlarged heart, the condition is very likely to be cardiac related, but with no enlarged heart there could be several underlying causes.

In a new study, MIT researchers have found that vision-language models are extremely likely to make such a mistake in real-world situations because they don’t understand negation — words like “no” and “doesn’t” that specify what is false or absent. 

“Those negation words can have a very significant impact, and if we are just using these models blindly, we may run into catastrophic consequences,” says Kumail Alhamoud, an MIT graduate student and lead author of this study.

The researchers tested the ability of vision-language models to identify negation in image captions. The models often performed as well as a random guess. Building on those findings, the team created a dataset of images with corresponding captions that include negation words describing missing objects.

They show that retraining a vision-language model with this dataset leads to performance improvements when a model is asked to retrieve images that do not contain certain objects. It also boosts accuracy on multiple choice question answering with negated captions.

But the researchers caution that more work is needed to address the root causes of this problem. They hope their research alerts potential users to a previously unnoticed shortcoming that could have serious implications in high-stakes settings where these models are currently being used, from determining which patients receive certain treatments to identifying product defects in manufacturing plants.

“This is a technical paper, but there are bigger issues to consider. If something as fundamental as negation is broken, we shouldn’t be using large vision/language models in many of the ways we are using them now — without intensive evaluation,” says senior author Marzyeh Ghassemi, an associate professor in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Institute of Medical Engineering Sciences and the Laboratory for Information and Decision Systems.

Ghassemi and Alhamoud are joined on the paper by Shaden Alshammari, an MIT graduate student; Yonglong Tian of OpenAI; Guohao Li, a former postdoc at Oxford University; Philip H.S. Torr, a professor at Oxford; and Yoon Kim, an assistant professor of EECS and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. The research will be presented at Conference on Computer Vision and Pattern Recognition.

Neglecting negation

Vision-language models (VLM) are trained using huge collections of images and corresponding captions, which they learn to encode as sets of numbers, called vector representations. The models use these vectors to distinguish between different images.

A VLM utilizes two separate encoders, one for text and one for images, and the encoders learn to output similar vectors for an image and its corresponding text caption.

“The captions express what is in the images — they are a positive label. And that is actually the whole problem. No one looks at an image of a dog jumping over a fence and captions it by saying ‘a dog jumping over a fence, with no helicopters,’” Ghassemi says.

Because the image-caption datasets don’t contain examples of negation, VLMs never learn to identify it.

To dig deeper into this problem, the researchers designed two benchmark tasks that test the ability of VLMs to understand negation.

For the first, they used a large language model (LLM) to re-caption images in an existing dataset by asking the LLM to think about related objects not in an image and write them into the caption. Then they tested models by prompting them with negation words to retrieve images that contain certain objects, but not others.

For the second task, they designed multiple choice questions that ask a VLM to select the most appropriate caption from a list of closely related options. These captions differ only by adding a reference to an object that doesn’t appear in the image or negating an object that does appear in the image.

The models often failed at both tasks, with image retrieval performance dropping by nearly 25 percent with negated captions. When it came to answering multiple choice questions, the best models only achieved about 39 percent accuracy, with several models performing at or even below random chance.

One reason for this failure is a shortcut the researchers call affirmation bias — VLMs ignore negation words and focus on objects in the images instead.

“This does not just happen for words like ‘no’ and ‘not.’ Regardless of how you express negation or exclusion, the models will simply ignore it,” Alhamoud says.

This was consistent across every VLM they tested.

“A solvable problem”

Since VLMs aren’t typically trained on image captions with negation, the researchers developed datasets with negation words as a first step toward solving the problem.

Using a dataset with 10 million image-text caption pairs, they prompted an LLM to propose related captions that specify what is excluded from the images, yielding new captions with negation words.

They had to be especially careful that these synthetic captions still read naturally, or it could cause a VLM to fail in the real world when faced with more complex captions written by humans.

They found that finetuning VLMs with their dataset led to performance gains across the board. It improved models’ image retrieval abilities by about 10 percent, while also boosting performance in the multiple-choice question answering task by about 30 percent.

“But our solution is not perfect. We are just recaptioning datasets, a form of data augmentation. We haven’t even touched how these models work, but we hope this is a signal that this is a solvable problem and others can take our solution and improve it,” Alhamoud says.

At the same time, he hopes their work encourages more users to think about the problem they want to use a VLM to solve and design some examples to test it before deployment.

In the future, the researchers could expand upon this work by teaching VLMs to process text and images separately, which may improve their ability to understand negation. In addition, they could develop additional datasets that include image-caption pairs for specific applications, such as health care.

de MIT News https://ift.tt/Z9FgVWN

MIT Department of Economics to launch James M. and Cathleen D. Stone Center on Inequality and Shaping the Future of Work

Starting in July, MIT’s Shaping the Future of Work Initiative in the Department of Economics will usher in a significant new era of research, policy, and education of the next generation of scholars, made possible by a gift from the James M. and Cathleen D. Stone Foundation. In recognition of the gift and the expansion of priorities it supports, on July 1 the initiative will become part of the new James M. and Cathleen D. Stone Center on Inequality and Shaping the Future of Work. This center will be officially launched at a public event in fall 2025.

The Stone Center will be led by Daron Acemoglu, Institute Professor, and co-directors David Autor, the Daniel (1972) and Gail Rubinfeld Professor in Economics, and Simon Johnson, the Ronald A. Kurtz (1954) Professor of Entrepreneurship. It will join a global network of 11 other wealth inequality centers funded by the Stone Foundation as part of an effort to advance research on the causes and consequences of the growing accumulation at the top of the wealth distribution.

“This generous gift from the Stone Foundation advances our pioneering economics research on inequality, technology, and the future of the workforce. This work will create a pipeline of scholars in this critical area of study, and it will help to inform the public and policymakers,” says Provost Cynthia Barnhart.

Originally established as part of MIT Blueprint Labs with a foundational gift from the William and Flora Hewlett Foundation, the Shaping the Future of Work Initiative is a nonpartisan research organization that applies economics research to identify innovative ways to move the labor market onto a more equitable trajectory, with a central focus on revitalizing labor market opportunities for workers without a college education. Building on frontier micro- and macro-economics, economic sociology, political economy, and other disciplines, the initiative seeks to answer key questions about the decline in labor market opportunities for non-college workers in recent decades. These labor market changes have been a major driver of growing wealth inequality, a phenomenon that has, in turn, broadly reshaped our economy, democracy, and society.

Support from the Stone Foundation will allow the new Stone Center to build on the Shaping the Future of Work Initiative’s ongoing research agenda and extend its focus to include a growing emphasis on the interplay between technologies and inequality, as well as the technology sector’s role in defining future inequality.

Core objectives of the James M. and Cathleen D. Stone Center on Inequality and Shaping the Future of Work will include fostering connections between scholars doing pathbreaking research on automation, AI, the intersection of work and technology, and wealth inequality across disciplines, including within the Department of Economics, the MIT Sloan School of Management, and the MIT Stephen A. Schwarzman College of Computing; strengthening the pipeline of emerging scholars focused on these issues; and using research to inform and engage a wider audience including the public, undergraduate and graduate students, and policymakers.     

The Stone Foundation’s support will allow the center to strengthen and expand its commitments to produce new research, convene additional events to share research findings, promote connection and collaboration between scholars working on related topics, provide new resources for the center’s research affiliates, and expand public outreach to raise awareness of this important emerging challenge. “Cathy and I are thrilled to welcome MIT to the growing family of Stone Centers dedicated to studying the urgent challenges of accelerating wealth inequality,” James M. Stone says.

Agustín Rayo, dean of the School of Humanities, Arts, and Social Sciences, says, “I am thrilled to celebrate the creation of the James M. and Cathleen D. Stone Center in the MIT economics department. Not only will it enhance the cutting-edge work of MIT’s social scientists, but it will support cross-disciplinary interactions that will enable new insights and solutions to complex social challenges.”

Jonathan Gruber, chair of the Department of Economics, adds, “I couldn’t be more excited about the Stone Foundation’s support for the Shaping the Future of Work Initiative. The initiative’s leaders have been far ahead of the curve in anticipating the rapid changes that technological forces are bringing to the labor market, and their influential studies have helped us understand the potential effects of AI and other technologies on U.S. workers. The generosity of the Stone Foundation will allow them to continue this incredible work, while expanding their priorities to include other critical issues around inequality. This is a great moment for the paradigm-shifting research that Acemoglu, Autor, and Johnson are leading here at MIT.”

“We are grateful to the James M. and Cathleen D. Stone Foundation for their generous support enabling us to study two defining challenges of our age: inequality and the future of work,” says Acemoglu, who was awarded the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel in 2024 (with co-laureates Simon Johnson and James A. Robinson). “We hope to go beyond exploring the causes of inequality and the determinants of the availability of good jobs in the present and in the future, but also develop ideas about how society can shape both the work of the future and inequality by its choices of institutions and technological trajectories.”

“We are incredibly fortunate to be joining the family of Stone Centers around the world. Jim and Cathleen Stone are far-sighted and generous donors, and we are delighted that they are willing to back us and MIT in this way,” says Johnson. “We look forward to working with all our colleagues, at MIT and around the world, to advance understanding and practical approaches to inequality and the future of work.”

Autor adds, “This support will enable us — and many others — to focus our scholarship, teaching and public outreach towards shaping a labor market that offers opportunity, mobility, and economic security to a far broader set of people.” 

de MIT News https://ift.tt/DMSsdk4

Daily mindfulness practice reduces anxiety for autistic adults

Just 10 to 15 minutes of mindfulness practice a day led to reduced stress and anxiety for autistic adults who participated in a study led by scientists at MIT’s McGovern Institute for Brain Research. Participants in the study used a free smartphone app to guide their practice, giving them the flexibility to practice when and where they chose.

Mindfulness is a state in which the mind is focused only on the present moment. It is a way of thinking that can be cultivated with practice, often through meditation or breathing exercises — and evidence is accumulating that practicing mindfulness has positive effects on mental health. The new open-access study, reported April 8 in the journal Mindfulness, adds to that evidence, demonstrating clear benefits for autistic adults.

“Everything you want from this on behalf of somebody you care about happened: reduced reports of anxiety, reduced reports of stress, reduced reports of negative emotions, and increased reports of positive emotions,” says McGovern investigator and MIT Professor John Gabrieli, who led the research with Liron Rozenkrantz, an investigator at the Azrieli Faculty of Medicine at Bar-Ilan University in Israel and a research affiliate in Gabrieli’s lab. “Every measure that we had of well-being moved in significantly in a positive direction,” adds Gabrieli, who is also the Grover Hermann Professor of Health Sciences and Technology and a professor of brain and cognitive sciences at MIT.

One of the reported benefits of practicing mindfulness is that it can reduce the symptoms of anxiety disorders. This prompted Gabrieli and his colleagues to wonder whether it might benefit adults with autism, who tend to report above average levels of anxiety and stress, which can interfere with daily living and quality of life. As many as 65 percent of autistic adults may also have an anxiety disorder.

Gabrieli adds that the opportunity for autistic adults to practice mindfulness with an app, rather than needing to meet with a teacher or class, seemed particularly promising. “The capacity to do it at your own pace in your own home, or any environment you like, might be good for anybody,” he says. “But maybe especially for people for whom social interactions can sometimes be challenging.”

The research team, including Cindy Li, the autism recruitment and outreach coordinator in Gabrieli’s lab, recruited 89 autistic adults to participate in their study. Those individuals were split into two groups: one would try the mindfulness practice for six weeks, while the others would wait and try the intervention later.

Participants were asked to practice daily using an app called Healthy Minds, which guides participants through seated or active meditations, each lasting 10 to 15 minutes. Participants reported that they found the app easy to use and had little trouble making time for the daily practice.

After six weeks, participants reported significant reductions in anxiety and perceived stress. These changes were not experienced by the wait-list group, which served as a control. However, after their own six weeks of practice, people in the wait-list group reported similar benefits. “We replicated the result almost perfectly. Every positive finding we found with the first sample we found with the second sample,” Gabrieli says.

The researchers followed up with study participants after another six weeks. Almost everyone had discontinued their mindfulness practice — but remarkably, their gains in well-being had persisted. Based on this finding, the team is eager to further explore the long-term effects of mindfulness practice in future studies. “There’s a hypothesis that a benefit of gaining mindfulness skills or habits is they stick with you over time — that they become incorporated in your daily life,” Gabrieli says. “If people are using the approach to being in the present and not dwelling on the past or worrying about the future, that’s what you want most of all. It’s a habit of thought that’s powerful and helpful.”

Even as they plan future studies, the researchers say they are already convinced that mindfulness practice can have clear benefits for autistic adults. “It’s possible mindfulness would be helpful at all kinds of ages,” Gabrieli says. But he points out the need is particularly great for autistic adults, who usually have fewer resources and support than autistic children have access to through their schools. Gabrieli is eager for more people with autism to try the Healthy Minds app. “Having scientifically proven resources for adults who are no longer in school systems might be a valuable thing,” he says.

This research was funded, in part, by The Hock E. Tan and K. Lisa Yang Center for Autism Research at MIT and the Yang Tan Collective.

de MIT News https://ift.tt/2THyeXn

How we think about protecting data

How should personal data be protected? What are the best uses of it? In our networked world, questions about data privacy are ubiquitous and matter for companies, policymakers, and the public.

A new study by MIT researchers adds depth to the subject by suggesting that people’s views about privacy are not firmly fixed and can shift significantly, based on different circumstances and different uses of data.

“There is no absolute value in privacy,” says Fabio Duarte, principal research scientist in MIT’s Senseable City Lab and co-author of a new paper outlining the results. “Depending on the application, people might feel use of their data is more or less invasive.”

The study is based on an experiment the researchers conducted in multiple countries using a newly developed game that elicits public valuations of data privacy relating to different topics and domains of life.

“We show that values attributed to data are combinatorial, situational, transactional, and contextual,” the researchers write.

The open-access paper, “Data Slots: tradeoffs between privacy concerns and benefits of data-driven solutions,” is published today in Nature: Humanities and Social Sciences Communications. The authors are Martina Mazzarello, a postdoc in the Senseable City Lab; Duarte; Simone Mora, a research scientist at Senseable City Lab; Cate Heine PhD ’24 of University College London; and Carlo Ratti, director of the Senseable City Lab.

The study is based around a card game with poker-type chips the researchers created to study the issue, called Data Slots. In it, players hold hands of cards with 12 types of data — such as a personal profile, health data, vehicle location information, and more — that relate to three types of domains where data are collected: home life, work, and public spaces. After exchanging cards, the players generate ideas for data uses, then assess and invest in some of those concepts. The game has been played in-person in 18 different countries, with people from another 74 countries playing it online; over 2,000 individual player-rounds were included in the study.

The point behind the game is to examine the valuations that members of the public themselves generate about data privacy. Some research on the subject involves surveys with pre-set options that respondents choose from. But in Data Slots, the players themselves generate valuations for a wide range of data-use scenarios, allowing the researchers to estimate the relative weight people place on privacy in different situations. 

 

The idea is “to let people themselves come up with their own ideas and assess the benefits and privacy concerns of their peers’ ideas, in a participatory way,” Ratti explains.

The game strongly suggests that people’s ideas about data privacy are malleable, although the results do indicate some tendencies. The data privacy card whose use players most highly valued was for personal mobility; given the opportunity in the game to keep it or exchange it, players retained it in their hands 43 percent of the time, an indicator of its value. That was followed in order by personal health data, and utility use. (With apologies to pet owners, the type of data privacy card players held on to the least, about 10 percent of the time, involved animal health.)

However, the game distinctly suggests that the value of privacy is highly contingent on specific use-cases. The game shows that people care about health data to a substantial extent but also value the use of environmental data in the workplace, for instance. And the players of Data Slots also seem less concerned about data privacy when use of data is combined with clear benefits. In combination, that suggests a deal to be cut: Using health data can help people understand the effects of the workplace on wellness.

“Even in terms of health data in work spaces, if they are used in an aggregated way to improve the workspace, for some people it’s worth combining personal health data with environmental data,” Mora says.

Mazzarello adds: “Now perhaps the company can make some interventions to improve overall health. It might be invasive, but you might get some benefits back.”

In the bigger picture, the researchers suggest, taking a more flexible, user-driven approach to understanding what people think about data privacy can help inform better data policy. Cities — the core focus on the Senseable City Lab — often face such scenarios. City governments can collect a lot of aggregate traffic data, for instance, but public input can help determine how anonymized such data should be. Understanding public opinion along with the benefits of data use can produce viable policies for local officials to pursue.

“The bottom line is that if cities disclose what they plan to do with data, and if they involve resident stakeholders to come up with their own ideas about what they could do, that would be beneficial to us,” Duarte says. “And in those scenarios, people’s privacy concerns start to decrease a lot.” 

de MIT News https://ift.tt/n7AQTHK

Eldercare robot helps people sit and stand, and catches them if they fall

The United States population is older than it has ever been. Today, the country’s median age is 38.9, which is nearly a decade older than it was in 1980. And the number of adults older than 65 is expected to balloon from 58 million to 82 million by 2050. The challenge of caring for the elderly, amid shortages in care workers, rising health care costs, and evolving family structures, is an increasingly urgent societal issue.

To help address the eldercare challenge, a team of MIT engineers is looking to robotics. They have built and tested the Elderly Bodily Assistance Robot, or E-BAR, a mobile robot designed to physically support the elderly and prevent them from falling as they move around their homes.

E-BAR acts as a set of robotic handlebars that follows a person from behind. A user can walk independently or lean on the robot’s arms for support. The robot can support the person’s full weight, lifting them from sitting to standing and vice versa along a natural trajectory. And the arms of the robot can them by rapidly inflating side airbags if they begin to fall.

With their design, the researchers hope to prevent falls, which today are the leading cause of injury in adults who are 65 and older. 

“Many older adults underestimate the risk of fall and refuse to use physical aids, which are cumbersome, while others overestimate the risk and may not to exercise, leading to declining mobility,” says Harry Asada, the Ford Professor of Engineering at MIT. “Our design concept is to provide older adults having balance impairment with robotic handlebars for stabilizing their body. The handlebars go anywhere and provide support anytime, whenever they need.”

In its current version, the robot is operated via remote control. In future iterations, the team plans to automate much of the bot’s functionality, enabling it to autonomously follow and physically assist a user. The researchers are also working on streamlining the device to make it slimmer and more maneuverable in small spaces.

“I think eldercare is the next great challenge,” says E-BAR designer Roberto Bolli, a graduate student in the MIT Department of Mechanical Engineering. “All the demographic trends point to a shortage of caregivers, a surplus of elderly persons, and a strong desire for elderly persons to age in place. We see it as an unexplored frontier in America, but also an intrinsically interesting challenge for robotics.”

Bolli and Asada will present a paper detailing the design of E-BAR at the IEEE Conference on Robotics and Automation (ICRA) later this month.

Asada’s group at MIT develops a variety of technologies and robotic aides to assist the elderly. In recent years, others have developed fall prediction algorithms, designed robots and automated devices including robotic walkers, wearable, self-inflating airbags, and robotic frames that secure a person with a harness and move with them as they walk.

In designing E-BAR, Asada and Bolli aimed for a robot that essentially does three tasks: providing physical support, preventing falls, and safely and unobtrusively moving with a person. What’s more, they looked to do away with any harness, to give a user more independence and mobility.

“Elderly people overwhelmingly do not like to wear harnesses or assistive devices,” Bolli says. “The idea behind the E-BAR structure is, it provides body weight support, active assistance with gait, and fall catching while also being completely unobstructed in the front. You can just get out anytime.”

The team looked to design a robot specifically for aging in place at home or helping in care facilities. Based on their interviews with older adults and their caregivers, they came up with several design requirements, including that the robot must fit through home doors, allow the user to take a full stride, and support their full weight to help with balance, posture, and transitions from sitting to standing.

The robot consists of a heavy, 220-pound base whose dimensions and structure were optimized to support the weight of an average human without tipping or slipping. Underneath the base is a set of omnidirectional wheels that allows the robot to move in any direction without pivoting, if needed. (Imagine a car’s wheels shifting to slide into a space between two other cars, without parallel parking.)

Extending out from the robot’s base is an articulated body made from 18 interconnected bars, or linkages, that can reconfigure like a foldable crane to lift a person from a sitting to standing position, and vice versa. Two arms with handlebars stretch out from the robot in a U-shape, which a person can stand between and lean against if they need additional support. Finally, each arm of the robot is embedded with airbags made from a soft yet grippable material that can inflate instantly to catch a person if they fall, without causing bruising on impact. The researchers believe that E-BAR is the first robot able to catch a falling person without wearable devices or use of a harness.

They tested the robot in the lab with an older adult who volunteered to use the robot in various household scenarios. The team found that E-BAR could actively support the person as they bent down to pick something up from the ground and stretched up to reach an object off a shelf — tasks that can be challenging to do while maintaining balance. The robot also was able to lift the person up and over the lip of a tub, simulating the task of getting out of a bathtub.

Bolli envisions a design like E-BAR would be ideal for use in the home by elderly people who still have a moderate degree of muscle strength but require assistive devices for activities of daily living.

“Seeing the technology used in real-life scenarios is really exciting,” says Bolli.

In their current paper, the researchers did not incorporate any fall-prediction capabilities in E-BAR’s airbag system. But another project in Asada’s lab, led by graduate student Emily Kamienski, has focused on developing algorithms with machine learning to control a new robot in response to the user’s real-time fall risk level.

Alongside E-BAR, Asada sees different technologies in his lab as providing different levels of assistance for people at certain phases of life or mobility.

“Eldercare conditions can change every few weeks or months,” Asada says. “We’d like to provide continuous and seamless support as a person’s disability or mobility changes with age.”

This work was supported, in part, by the National Robotics Initiative and the National Science Foundation.

de MIT News https://ift.tt/njoUQmg

In Down syndrome mice, 40Hz light and sound improve cognition, neurogenesis, connectivity

Studies by a growing number of labs have identified neurological health benefits from exposing human volunteers or animal models to light, sound, and/or tactile stimulation at the brain’s “gamma” frequency rhythm of 40Hz. In the latest such research at The Picower Institute for Learning and Memory and Alana Down Syndrome Center at MIT, scientists found that 40Hz sensory stimulation improved cognition and circuit connectivity and encouraged the growth of new neurons in mice genetically engineered to model Down syndrome.

Li-Huei Tsai, Picower Professor at MIT and senior author of the new study in PLOS ONE, says that the results are encouraging, but also cautions that much more work is needed to test whether the method, called GENUS (for gamma entrainment using sensory stimulation), could provide clinical benefits for people with Down syndrome. Her lab has begun a small study with human volunteers at MIT.

“While this work, for the first time, shows beneficial effects of GENUS on Down syndrome using an imperfect mouse model, we need to be cautious, as there is not yet data showing whether this also works in humans,” says Tsai, who directs The Picower Institute and The Alana Center, and is a member of MIT’s Department of Brain and Cognitive Sciences faculty.

Still, she says, the newly published article adds evidence that GENUS can promote a broad-based, restorative, “homeostatic” health response in the brain amid a wide variety of pathologies. Most GENUS studies have addressed Alzheimer’s disease in humans or mice, but others have found benefits from the stimulation for conditions such as “chemo brain” and stroke.

Down syndrome benefits

In the study, the research team led by postdoc Md Rezaul Islam and Brennan Jackson PhD ’23 worked with the commonly used “Ts65Dn” Down syndrome mouse model. The model recapitulates key aspects of the disorder, although it does not exactly mirror the human condition, which is caused by carrying an extra copy of chromosome 21.

In the first set of experiments in the paper, the team shows that an hour a day of 40Hz light and sound exposure for three weeks was associated with significant improvements on three standard short-term memory tests — two involving distinguishing novelty from familiarity and one involving spatial navigation. Because these kinds of memory tasks involve a brain region called the hippocampus, the researchers looked at neural activity there and measured a significant increase in activity indicators among mice that received the GENUS stimulation versus those that did not.

To better understand how stimulated mice could show improved cognition, the researchers examined whether cells in the hippocampus changed how they express their genes. To do this, the team used a technique called single cell RNA sequencing, which provided a readout of how nearly 16,000 individual neurons and other cells transcribed their DNA into RNA, a key step in gene expression. Many of the genes whose expression varied most prominently in neurons between the mice that received stimulation and those that did not were directly related to forming and organizing neural circuit connections called synapses.

To confirm the significance of that finding, the researchers directly examined the hippocampus in stimulated and control mice. They found that in a critical subregion, the dentate gyrus, stimulated mice had significantly more synapses.

Diving deeper

The team not only examined gene expression across individual cells, but also analyzed those data to assess whether there were patterns of coordination across multiple genes. Indeed, they found several such “modules” of co-expression. Some of this evidence further substantiated the idea that 40Hz-stimulated mice made important improvements in synaptic connectivity, but another key finding highlighted a role for TCF4, a key regulator of gene transcription needed for generating new neurons, or “neurogenesis.”  

The team’s analysis of genetic data suggested that TCF4 is underexpressed in Down syndrome mice, but the researchers saw improved TCF4 expression in GENUS-stimulated mice. When the researchers went to the lab bench to determine whether the mice also exhibited a difference in neurogenesis, they found direct evidence that stimulated mice exhibited more than unstimulated mice in the dentate gyrus. These increases in TCF4 expression and neurogenesis are only correlational, the researchers noted, but they hypothesize that the increase in new neurons likely helps explain at least some of the increase in new synapses and improved short-term memory function.

“The increased putative functional synapses in the dentate gyrus is likely related to the increased adult neurogenesis observed in the Down syndrome mice following GENUS treatment,” Islam says.

This study is the first to document that GENUS is associated with increased neurogenesis.

The analysis of gene expression modules also yielded other key insights. One is that a cluster of genes whose expression typically declines with normal aging, and in Alzheimer’s disease, remained at higher expression levels among mice who received 40Hz sensory stimulation.

And the researchers also found evidence that mice that received stimulation retained more cells in the hippocampus that express Reelin. Reelin-expressing neurons are especially vulnerable in Alzheimer’s disease, but expression of the protein is associated with cognitive resilience amid Alzheimer’s disease pathology, which Ts65Dn mice develop. About 90 percent of people with Down syndrome develop Alzheimer’s disease, typically after the age of 40.

“In this study, we found that GENUS enhances the percentage of Reln+ neurons in hippocampus of a mouse model of Down syndrome, suggesting that GENUS may promote cognitive resilience,” Islam says.

Taken together with other studies, Tsai and Islam say, the new results add evidence that GENUS helps to stimulate the brain at the cellular and molecular level to mount a homeostatic response to aberrations caused by disease pathology, be it neurodegeneration in Alzheimer’s, demyelination in chemo brain, or deficits of neurogenesis in Down syndrome.

But the authors also cautioned that the study had limits. Not only is the Ts65Dn model an imperfect reflection of human Down syndrome, but also the mice used were all male. Moreover, the cognitive tests in the study only measured short-term memory. And finally, while the study was novel for extensively examining gene expression in the hippocampus amid GENUS stimulation, it did not look at changes in other cognitively critical brain regions, such as the prefrontal cortex.

In addition to Jackson, Islam, and Tsai, the paper’s other authors are Maeesha Tasnim Naomi, Brooke Schatz, Noah Tan, Mitchell Murdock, Dong Shin Park, Daniela Rodrigues Amorim, Fred Jiang, S. Sebastian Pineda, Chinnakkaruppan Adaikkan, Vanesa Fernandez, Ute Geigenmuller, Rosalind Mott Firenze, Manolis Kellis, and Ed Boyden.

Funding for the study came from the Alana Down Syndrome Center at MIT and the Alana USA Foundation, the U.S. National Science Foundation, the La Caixa Banking Foundation, a European Molecular Biology Organization long-term postdoctoral fellowship, Barbara J. Weedon, Henry E. Singleton, and the Hubolow family.

de MIT News https://ift.tt/sWmxUMo

Student spotlight: Aria Eppinger ’24

This interview is part of a series of short interviews from the MIT Department of Electrical Engineering and Computer Science, called Student Spotlights. Each spotlight features a student answering their choice of questions about themselves and life at MIT. Today’s interviewee, Aria Eppinger ’24, graduated with her undergraduate degree in Course 6-7 (Computer Science and Molecular Biology) last spring. This spring, she will complete her MEng in 6-7. Her thesis, supervised by Ford Professor of Engineering Doug Lauffenburger in the Department of Biological Engineering, investigates the biological underpinnings of adverse pregnancy outcomes, including preterm birth and preeclampsia, by applying polytope-fitting algorithms.

Q: Tell us about one teacher from your past who had an influence on the person you’ve become. 

A: There are many teachers who had a large impact on my trajectory. I would first like to thank my elementary and middle school teachers for imbuing in me a love of learning. I would also like to thank my high school teachers for not only teaching me the foundations of writing strong arguments, programming, and designing experiments, but also instilling in me the importance of being a balanced person. It can be tempting to be ruled by studies or work, especially when learning and working are so fun. My high school teachers encouraged me to pursue my hobbies, make memories with friends, and spend time with family. As life continues to be hectic, I’m so grateful for this lesson (even if I’m still working on mastering it).

Q: Describe one conversation that changed the trajectory of your life.

A: A number of years ago, I had the opportunity to chat with Warren Buffett. I was nervous at first, but soon put to ease by his descriptions of his favorite foods — hamburgers, French fries, and ice cream — and his hitchhiking stories. His kindness impressed and inspired me, which is something I carry with me and aim to emulate all these years later.

Q: Do you have any pets?

A: I have one dog who lives at home with my parents. Dodger, named after “Artful Dodger” in Oliver Twist, is as mischievous as beagles tend to be. We adopted him from a rescue shelter when I was in elementary school. 

Q: Are you a re-reader or a re-watcher — and if so, what are your comfort books, shows, or movies?

A: I don’t re-read many books or re-watch many movies, but I never tire of Jane Austen’s “Pride and Prejudice.” I bought myself an ornately bound copy when I was interning in New York City last summer. Austen’s other novels, especially “Sense and Sensibility,” “Persuasion,” and “Emma,” are also favorites, and I’ve seen a fair number of their movie and miniseries adaptations. My favorite adaptation is the 1995 BBC production of “Pride and Prejudice” because of the cohesion with the original book and the casting of the leads, as well as the touches and plot derivations added by the producer and director to bring the work to modern audiences. The adaptation is quite long, but I have fond memories of re-watching it with some fellow Austinites at MIT.

Q: If you had to teach a really in-depth class about one niche topic, what would you pick?

A: There are two types of people in the world: those who eat to live, and those who live to eat. As one of the latter, I would have to teach some sort of in-depth class on food. Perhaps I would teach the science behind baking chocolate cake, or churning the perfect ice cream. Or maybe I would teach the biochemistry of digesting. In any case, I would have to have lots of hands-on demos and reserve plenty for taste-testing!

Q: What was the last thing you changed your mind about?

A: Brisket! I never was a big fan of brisket until I went to a Texas BBQ restaurant near campus, The Smoke Shop BBQ. Growing up, I had never had true BBQ, so I was quite skeptical. However, I enjoyed not only the brisket but also the other dishes. The Brussels sprouts with caramelized onions is probably my favorite dish, but it feels like a crime to say that about a BBQ place!

Q: What are you looking forward to about life after graduation? What do you think you’ll miss about MIT? 

A: I’m looking forward to new adventures after graduation, including working in New York City and traveling to new places. I cross-registered to take Intensive Italian at Harvard this semester and am planning a trip to Italy to practice my Italian, see the historic sites, visit the Vatican, and taste the food. Non vedo l’ora di viaggiare all’Italia! [I can't wait to travel to Italy!]

While I’m excited for what lies ahead, I will miss MIT. What a joy it is to spend most of the day learning information from a fire hose, taking a class on a foreign topic because the course catalog description looked fun, talking to people whose viewpoint is very similar or very different from my own, and making friends that will last a lifetime.

de MIT News https://ift.tt/XVMK5lq

School of Engineering faculty and staff receive awards for winter 2025

MIT faculty and researchers receive many external awards throughout the year. The MIT School of Engineering periodically highlights the honors, prizes, and medals won by community members working in academic departments, labs, and centers. Winter 2025 honorees include the following:

• Faez Ahmed, the American Bureau of Shipping Career Development Professor in Naval Engineering and Utilization and an assistant professor in the Department of Mechanical Engineering (MechE), received a 2024 National Science Foundation (NSF) CAREER Award. The CAREER program is one of NSF’s most prestigious awards that supports early-career faculty who display outstanding research, excellent education, and the integration of education and research.
   
• Martin Zdenek Bazant, the E.G. Roos (1944) Professor in the Department of Chemical Engineering (ChemE), was elected to the National Academy of Engineering (NAE). Membership in the NAE is awarded to individuals who have made outstanding contributions to “engineering research, practice, or education.”
   
• Angela Belcher, the James Mason Crafts Professor in the Department of Biological Engineering and the Department of Materials Science and Engineering (DMSE), received the National Medal of Science. The award is the nation’s highest honor for scientists and innovators.
   
• Moshe E. Ben-Akiva, the Edmund K. Turner Professor in Civil Engineering, was elected to the National Academy of Engineering. Membership in the NAE is given to individuals who have made outstanding contributions to “engineering research, practice, or education.”
   
• Emery Brown, the Edward Hood Taplin Professor of Medical Engineering, received the National Medal of Science. The award is the nation’s highest honor for scientists and innovators.
   
• Charles L. Cooney, professor emeritus of the Department of ChemE, was elected to the National Academy of Engineering. Membership in the NAE is given to individuals who have made outstanding contributions to “engineering research, practice, or education.”
   
• Yoel Fink, the Danae and Vasilis (1961) Salapatas Professor in the DMSE, was elected to the National Academy of Engineering. Membership in the NAE is given to individuals who have made outstanding contributions to “engineering research, practice, or education.”
   
• James Fujimoto, the Elihu Thomson Professor in the Department of Electrical Engineering and Computer Science (EECS), is a 2025 inductee into the National Inventors Hall of Fame. Inductees are patent-holding inventors whose work has made all our lives easier, safer, healthier, and more fulfilling.
   
• Mohsen Ghaffari, an associate professor in the Department of EECS, received a 2025 Sloan Research Fellowship. The fellowship honors exceptional researchers at U.S. and Canadian educational institutions, whose creativity, innovation, and research accomplishments make them stand out as the next generation of leaders.
   
• Marzyeh Ghassemi, the Germeshausen Career Development Professor and associate professor in the Department of EECS and the Institute for Medical Engineering and Science, received a 2025 Sloan Research Fellowship. The fellowships honor exceptional researchers at US and Canadian educational institutions, whose creativity, innovation, and research accomplishments make them stand out as the next generation of leaders.
   
• Linda Griffith, the School of Engineering Professor of Teaching Innovation in the Department of Biological Engineering, received the 2025 BMES Robert A. Pritzker Distinguished Lectureship Award. The award is given to individuals who have demonstrated impactful leadership and accomplishments in biomedical engineering science and practice.
   
• Paula Hammond, MIT’s vice provost for faculty and an Institute Professor in the Department of ChemE, received the National Medal of Technology and Innovation. The award is the nation’s highest honor for scientists and innovators.
   
• Kuikui Liu, the Elting Morison Career Development Professor and an assistant professor in the Department of EECS, received the 2025 Michael and Sheila Held Prize. The award is presented annually to honor outstanding, innovative, creative, and influential research in combinatorial and discrete optimization or related parts of computer science, such as the design and analysis of algorithms and complexity theory.
   
• Farnaz Niroui, an associate professor in the Department of EECS, received a DARPA Innovation Fellowship. The highly selective program chooses fellows to develop and manage a portfolio of high-impact, exploratory research efforts to help identify breakthrough technologies for the U.S. Department of Defense.
   
• Tomás Lozano-Pérez, the School of Engineering Professor of Teaching Excellence in the Department of EECS, was elected to the National Academy of Engineering. Membership in the NAE is given to individuals who have made outstanding contributions to “engineering research, practice, or education.”
   
• Kristala L. Prather, the Arthur Dehon Little Professor and head of the Department of ChemE, was elected to the National Academy of Engineering. Membership in the NAE is given to individuals who have made outstanding contributions to “engineering research, practice, or education.”
   
• Frances Ross, the TDK Professor in DMSE, received the Joseph F. Keithley Award for Advances in Measurement Science. The award recognizes physicists who have been instrumental in developing measurement techniques or equipment that have impacted the physics community by providing better measurements.
   
• Henry “Hank” Smith, the Joseph F. and Nancy P. Keithley Professor of Electrical Engineering Emeritus in the Department of EECS, received the SPIE Frits Zernike Award for Microlithography. The award is presented for outstanding accomplishments in microlithographic technology, especially those furthering the development of semiconductor lithographic imaging and patterning solutions.
   
• Eric Swanson, research affiliate at the Research Laboratory of Electronics, was elected to the National Academy of Engineering. Membership in the NAE is given to individuals who have made outstanding contributions to “engineering research, practice, or education.”
   
• Evelyn N. Wang, MIT's vice president for energy and climate and Ford Professor of Engineering in the Department of MechE, was elected to the National Academy of Engineering. Membership in the NAE is given to individuals who have made outstanding contributions to “engineering research, practice, or education.”
   
• Bilge Yildiz, the Breene M. Kerr (1951) Professor in the Department of Nuclear Science and Engineering and the DMSE, received the Faraday Medal. The award is given to individuals for notable scientific or industrial achievement in engineering or for conspicuous service rendered to the advancement of science, engineering, and technology.
   
• Feng Zhang, the James and Patricia Poitras Professor of Neuroscience and professor of brain and cognitive sciences and biological engineering, received the National Medal of Technology and Innovation. The award is the nation’s highest honor for scientists and innovators.

de MIT News https://ift.tt/K4bsv2E

Biologists identify targets for new pancreatic cancer treatments

Researchers from MIT and Dana-Farber Cancer Institute have discovered that a class of peptides expressed in pancreatic cancer cells could be a promising target for T-cell therapies and other approaches that attack pancreatic tumors.

Known as cryptic peptides, these molecules are produced from sequences in the genome that were not thought to encode proteins. Such peptides can also be found in some healthy cells, but in this study, the researchers identified about 500 that appear to be found only in pancreatic tumors.

The researchers also showed they could generate T cells targeting those peptides. Those T cells were able to attack pancreatic tumor organoids derived from patient cells, and they significantly slowed down tumor growth in a study of mice.

“Pancreas cancer is one of the most challenging cancers to treat. This study identifies an unexpected vulnerability in pancreas cancer cells that we may be able to exploit therapeutically,” says Tyler Jacks, the David H. Koch Professor of Biology at MIT and a member of the Koch Institute for Integrative Cancer Research.

Jacks and William Freed-Pastor, a physician-scientist in the Hale Family Center for Pancreatic Cancer Research at Dana-Farber Cancer Institute and an assistant professor at Harvard Medical School, are the senior authors of the study, which appears today in Science. Zackery Ely PhD ’22 and Zachary Kulstad, a former research technician at Dana-Farber Cancer Institute and the Koch Institute, are the lead authors of the paper.

Cryptic peptides

Pancreatic cancer has one of the lowest survival rates of any cancer — about 10 percent of patients survive for five years after their diagnosis.

Most pancreatic cancer patients receive a combination of surgery, radiation treatment, and chemotherapy. Immunotherapy treatments such as checkpoint blockade inhibitors, which are designed to help stimulate the body’s own T cells to attack tumor cells, are usually not effective against pancreatic tumors. However, therapies that deploy T cells engineered to attack tumors have shown promise in clinical trials.

These therapies involve programming the T-cell receptor (TCR) of T cells to recognize a specific peptide, or antigen, found on tumor cells. There are many efforts underway to identify the most effective targets, and researchers have found some promising antigens that consist of mutated proteins that often show up when pancreatic cancer genomes are sequenced.

In the new study, the MIT and Dana-Farber team wanted to extend that search into tissue samples from patients with pancreatic cancer, using immunopeptidomics — a strategy that involves extracting the peptides presented on a cell surface and then identifying the peptides using mass spectrometry.

Using tumor samples from about a dozen patients, the researchers created organoids — three-dimensional growths that partially replicate the structure of the pancreas. The immunopeptidomics analysis, which was led by Jennifer Abelin and Steven Carr at the Broad Institute, found that the majority of novel antigens found in the tumor organoids were cryptic antigens. Cryptic peptides have been seen in other types of tumors, but this is the first time they have been found in pancreatic tumors.

Each tumor expressed an average of about 250 cryptic peptides, and in total, the researchers identified about 1,700 cryptic peptides.

“Once we started getting the data back, it just became clear that this was by far the most abundant novel class of antigens, and so that’s what we wound up focusing on,” Ely says.

The researchers then performed an analysis of healthy tissues to see if any of these cryptic peptides were found in normal cells. They found that about two-thirds of them were also found in at least one type of healthy tissue, leaving about 500 that appeared to be restricted to pancreatic cancer cells.

“Those are the ones that we think could be very good targets for future immunotherapies,” Freed-Pastor says.

Programmed T cells

To test whether these antigens might hold potential as targets for T-cell-based treatments, the researchers exposed about 30 of the cancer-specific antigens to immature T cells and found that 12 of them could generate large populations of T cells targeting those antigens.

The researchers then engineered a new population of T cells to express those T-cell receptors. These engineered T cells were able to destroy organoids grown from patient-derived pancreatic tumor cells. Additionally, when the researchers implanted the organoids into mice and then treated them with the engineered T cells, tumor growth was significantly slowed.

This is the first time that anyone has demonstrated the use of T cells targeting cryptic peptides to kill pancreatic tumor cells. Even though the tumors were not completely eradicated, the results are promising, and it is possible that the T-cells’ killing power could be strengthened in future work, the researchers say.

Freed-Pastor’s lab is also beginning to work on a vaccine targeting some of the cryptic antigens, which could help stimulate patients’ T cells to attack tumors expressing those antigens. Such a vaccine could include a collection of the antigens identified in this study, including those frequently found in multiple patients.

This study could also help researchers in designing other types of therapy, such as T cell engagers — antibodies that bind an antigen on one side and T cells on the other, which allows them to redirect any T cell to kill tumor cells.

Any potential vaccine or T cell therapy is likely a few years away from being tested in patients, the researchers say.

The research was funded in part by the Hale Family Center for Pancreatic Cancer Research, the Lustgarten Foundation, Stand Up To Cancer, the Pancreatic Cancer Action Network, the Burroughs Wellcome Fund, a Conquer Cancer Young Investigator Award, the National Institutes of Health, and the National Cancer Institute.

de MIT News https://ift.tt/inIf9G5