Some democracies are struggling to ensure safe drinking water

About 2 billion people — just under a quarter of the world’s population — lack regular access to clean drinking water. And roughly 800,000 people annually die from illnesses associated with unsanitary water.

Drinking water access is a fundamental problem for human and economic development. The U.N., for instance, highlighted the issue in its Sustainable Development Goals of 2015, an ambitious 17-point agenda that specified safe drinking water as a basic global aim.

Past research shows that democracies, in comparison to other forms of government, tend to be more successful at delivering this kind of public good, which benefits a large portion of the population. This is likely due to accountability measures that include elections, greater transparency, and more freedom in civil society.

But now a study led by an MIT professor shows that across nearly 100 countries with developing economies, that dynamic has become more complex in the 21st century. While democracies are slightly ahead of non-democracies when it comes to providing at least some water, they have been falling behind when it comes to ensuring that there is safe water on tap. 

“Among low- and middle-income countries, which have not done as well economically, we found there wasn’t really a big difference between democracies and non-democracies in the provision of what is called basic drinking water,” says MIT political scientist Evan Lieberman, co-author of a new paper detailing the results. “But for safe drinking water, we found, surprisingly, that democratic countries were becoming less good at extending access.” 

While the study does not pinpoint the precise reasons for this, it suggests a lens for viewing the problem. Democracies tend to be better at delivering visible public goods, the kinds of things citizens can literally see — like infrastructure that delivers water. But the difference between safe and unsafe water is not necessarily visible and obvious, so public officials may not be as responsive.

“This is likely a big part of the equation, that the invisibility of safe water makes it a less compelling public good for politicians,” says Lieberman, the Total Professor of Political Science and Contemporary Africa, and director of MIT’s Center for International Studies.

The paper, “Beyond the tap: The limited value of democracy for delivering universal safe water access in low- and middle-income countries,” is published in the journal World Development. The authors are Lieberman, and Naomi Tilles, a doctoral student in political science at Stanford University.

Seeing is believing

To conduct the study, the scholars analyzed drinking water data recorded by the World Health Organization/UNICEF Joint Monitoring Programme. That provides information for basic availability to water, defined as access to an improved water source with no more than 30 minutes of collection time; and access to safe drinking water, defined as an improved water source that is available on premises, available when needed, and free from potentially disease-producing contaminants, which range from fecal matter to harmful chemicals.

Examining 96 low- and middle-income countries, the researchers looked at a variety of measures pertaining to its democratic or non-democratic features, and ran 39,000 regressions to see how the form of government related to its provision of water. Overall, Lieberman and Tilles found that democratic governance is modestly associated with an increase in the basic availability of water, compared to non-democracies. However, the effect is not particularly robust.

The good news is that between 2000 and 2024, 81 of the 90 countries with data available in both years made gains in safe drinking water access. However, democratic countries have been less successful than their non-democratic counterparts in advancing the goal of achieving universal access. 

“Moreover, the gap between democracies and non-democracies seems to be getting a little bit larger over time,” Lieberman observes. 

Because the study is focused on establishing the overall empirical situation, the scholars do not claim to have determined why this trend has been emerging. Many newer democracies have struggled to establish high-functioning governance in some regions, which may influence their overall results. 

More broadly, Lieberman suggests, visibility matters. Past scholarship has shown that democracies perform relatively well in delivering visible public goods, especially in countries with little information in the public sphere. Delivering water generates attention for politicians in a way that keeping water safe does not. 

“Politicians may figure out they should do things citizens like, to stay in office, such as bringing water to an area,” Lieberman says. “You can have a ribbon-cutting ceremony, and people feel it really happened. But water quality is often invisible.

It’s a more difficult challenge to ensure safe water: You have to do testing, prevent people from polluting, and you may need to treat the water.”

In any case, Lieberman notes, “Given what we find, what is clear is that the incentives are not aligned under the current systems for advancing safe-water access within all democracies. That provides opportunities for human agency to create incentives for citizens, nongovernment agencies, and governments to do what is needed.” 

Development for all

Lieberman comes to the topic of water access as an expert on African politics. His most recent book, “Until We Have Won Our Liberty” (Princeton University Press, 2022), examines the vicissitudes of South African democracy. In the book and in general, he suggests that democracy is the most viable path toward development with “dignity,” meaning economic growth accompanied by liberties and equal treatment under the law. 

“I think democracy provides dignified development, by granting people recognition and participation, and that’s an extremely valuable thing,” Lieberman says.

Still, when it comes to the performance of many countries with regard to safe water, he says, “I think we just need to be clear-eyed about real problems.”

In some countries, he suggests, the time horizon of elected officials may also be relatively short-term, and they may be more oriented toward simpler problems than water safety. At the same time, other members of society need to find ways to make water safety a bigger issue in the eyes of the public. 

“There are important lessons for democracies to learn, and citizens in civil society who are aware of this challenge need to figure out ways to get people to care about it, to recognize the connection between illness and unsafe water, and to use political campaigns to advance their longer-term interests,” Lieberman says.

Overall, he adds, “There is something intrinsically important about democratic government. Then the question becomes how to make it work better to deliver really important outcomes like safe water.”

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Technology usually creates jobs for young, skilled workers. Will AI do the same?

At any given time, technology does two things to employment: It replaces traditional jobs, and it creates new lines of work. Machines replace farmers, but enable, say, aeronautical engineers to exist. So, if tech creates new jobs, who gets them? How well do they pay? How long do new jobs remain new, before they become just another common task any worker can do?

A new study of U.S. employment led by MIT labor economist David Autor sheds light on all these matters. In the postwar U.S., as Autor and his colleagues show in granular detail, new forms of work have tended to benefit college graduates under 30 more than anyone else. 

“We had never before seen exactly who is doing new work,” Autor says. “It’s done more by young and educated people, in urban settings.” 

The study also contains a powerful large-scale insight: A lot of innovation-based new work is driven by demand. Government-backed expansion of research and manufacturing in the 1940s, in response to World War II, accounted for a huge amount of new work, and new forms of expertise. 

“This says that wherever we make new investments, we end up getting new specializations,” Autor says. “If you create a large-scale activity, there’s always going to be an opportunity for new specialized knowledge that’s relevant for it. We thought that was exciting to see.” 

The paper, “What Makes New Work Different from More Work?” is forthcoming in the Annual Review of Economics. The authors are Autor; Caroline Chin, a doctoral student in MIT’s Department of Economics; Anna M. Salomons, a professor at Tilburg University’s Department of Economics and Utrecht University’s School of Economics; and Bryan Seegmiller PhD ’22, an assistant professor at Northwestern University’s Kellogg School of Management.

And yes, learning about new work, and the kinds of workers who obtain it, might be relevant to the spread of artificial intelligence — although, in Autor’s estimation, it is too soon to tell just how AI will affect the workplace.

“People are really worried that AI-based automation is going to erode specific tasks more rapidly,” Autor observes. “Eroding tasks is not the same thing as eroding jobs, since many jobs involve a lot of tasks. But we’re all saying: Where is the new work going to come from? It’s so important, and we know little about it. We don’t know what it will be, what it will look like, and who will be able to do it.”

“If everyone is an expert, then no one is an expert”

The four co-authors also collaborated on a previous major study of new work, published in 2024, which found that about six out of 10 jobs in the U.S. from 1940 to 2018 were in new specialties that had only developed broadly since 1940. The new study extends that line of research by looking more precisely at who fills the new lines of work. 

To do that, the researchers used U.S. Census Bureau data from 1940 through 1950, as well as the Census Bureau’s American Community Survey (ACS) data from 2011 to 2023. In the first case, because Census Bureau records become wholly public after about 70 years, the scholars could examine individual-level data about occupations, salaries, and more, and could track the same workers as they changed jobs between the 1940 and 1950 Census enumerations. 

Through a collaborative research arrangement with the U.S. Census Bureau, the authors also gained secure access to person-level ACS records. These data allowed them to analyze the earnings, education, and other demographic characteristics of workers in new occupational specialties — and to compare them with workers in longstanding ones.

New work, Autor observes, is always tied to new forms of expertise. At first, this expertise is scarce; over time, it may become more common. In any case, expertise is often linked to new forms of technology.

“It requires mastering some capability,” Autor says. “What makes labor valuable is not simply the ability to do stuff, but specialized knowledge. And that often differentiates high-paid work from low-paid work.” Moreover, he adds, “It has to be scarce. If everyone is an expert, then no one is an expert.”

By examining the census data, the scholars found that back in 1950, about 7 percent of employees had jobs in types of work that had emerged since 1930. More recently, about 18 percent of workers in the 2011-2023 period were in lines of work introduced since 1970. (That happens to be roughly the same portion of new jobs per decade, although Autor does not think this is a hard-and-fast trend.) 

In these time periods, new work has emerged more often in urban areas, with people under 30 benefitting more than any other age category. Getting a job in a line of new work seems to have a lasting effect: People employed in new work in 1940 were 2.5 times as likely to be in new work in 1950, compared to the general population. College graduates were 2.9 percentage points more likely than high school graduates to be engaged in new work. 

New work also has a wage premium, that is, better salaries on aggregate than in already-existing forms of work. Yet as the study shows, that wage premium also fades over time, as the particular expertise in many forms of new work becomes much more widely grasped. 

“The scarcity value erodes,” Autor says. “It becomes common knowledge. It itself gets automated. New work gets old.”

After all, Autor points out, driving a car was once a scarce form of expertise. For that matter, so was being able to use word-processing programs such as WordPerfect or Microsoft Word, well into the 1990s. After a while, though, being able to handle word-processing tools became the most elementary part of using a computer.

Back to AI for a minute

Studying who gets new jobs led the scholars to striking conclusions about how new work is created. Examining county-level data from the World War II era, when the federal government was backing new manufacturing in public-private partnerships throughout the U.S., the study shows that counties with new factories had more new work, and that 85 to 90 percent of new work from 1940 to 1950 was technology-driven. 

In this sense there was a great deal of demand-driven innovation at the time. Today, public discourse about innovation often focuses on the supply side, namely, the innovators and entrepreneurs trying to create new products. But the study shows that the demand side can significantly influence innovative activity. 

“Technology is not like, ‘Eureka!’ where it just happens,” Autor says. “Innovation is a purposive activity. And innovation is cumulative. If you get far enough, it will have its own momentum. But if you don’t, it’ll never get there.”

Which brings us back to AI, the topic so many people are focused on in 2026. Will AI create good new jobs, or will it take work away? Well, it likely depends how we implement it, Autor thinks. Consider the massive health care sector, where there could be a lot of types of tech-driven new work, if people are interested in creating jobs.

“There are different ways we could use AI in health care,” Autor says. “One is just to automate people’s jobs away. The other is to allow people with different levels of expertise to do different tasks. I would say the latter is more socially beneficial. But it’s not clear that is where the market will go.” 

On the other hand, maybe with government-driven demand in various forms, AI could get applied in ways that end up boosting health care-sector productivity, creating new jobs as a result. 

“More than half the dollars in health care in the U.S. are public dollars,” Autor observes. “We have a lot of leverage there, we can push things in that direction. There are different ways to use this.” 

This research was supported, in part, by the Hewlett Foundation, the Google Technology and Society Visiting Fellows Program, the NOMIS Foundation, the Schmidt Sciences AI2050 Fellowship, the Smith Richardson Foundation, the James M. and Cathleen D. Stone Foundation, and Instituut Gak.

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Q&A: The path to a PhD in computational science and engineering at MIT

In 2023, the Center for Computational Science and Engineering (CCSE), an academic unit in the MIT Schwarzman College of Computing, introduced a new standalone PhD degree program. This interdisciplinary PhD program blends both coursework and a thesis, enabling students to pursue research in cross-cutting methodological aspects of computational science and engineering.

PhD candidate Emily Williams is poised to be the first graduate of the program. With a technical background in aerospace engineering and applied mathematics, her research interests include stochastic and generative modeling for multiscale chaotic systems. She earned a BS in aerospace engineering from the University of Illinois Urbana-Champaign and an MS in aeronautics and astronautics from MIT. She was awarded the Department of Energy Computational Science Graduate Fellowship, which funded her doctoral research. Here, she discusses her experience with the program and its impact on her career trajectory.

Q: What has been a highlight of the CCSE degree program?

A: I found the program curriculum to be extremely thoughtful and intentional. In particular, the program of study was constructed to cover many important areas of computational science and engineering research and education, from engineering and mathematical modeling to scientific and parallel computing. I found a lot of overlap with the DoE CSGF program of study, so I was given a lot of freedom to pursue very interesting technical electives that fit within CSE that I might not have been able to explore if I had been in a discipline-centric program.

Q: Why is this program so impactful, especially in the context of having a stand-alone PhD program?

A: I think a stand-alone PhD program helps to further establish the MIT CCSE as a leader in CSE research and education. The joint programs give graduate researchers more opportunity to learn and apply leading CSE methodologies to their disciplinary areas and primarily stay within their home department. For me, I’ve found that I’ve had more opportunities for collaboration, in potentially applying my methods to a wide range of different exciting applications. I think this theme of collaboration will continue to foster through those advancing through the standalone program in particular.

Q: What advice would you give to students considering this program?

A: I think my advice would be to keep an open mind. My interest in CSE was shaped by common threads in my education and research interests over the years that I didn’t think were connected at all. Through my fellowship and the standalone program, I felt like I was able to create my own path to my degree and take courses that excited me and fit within the CSE themes of our program of study.

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Steel developed at MIT is key to Formula One, Baja 1000, and MIT Motorsports

A high-performance steel with MIT origins has come full circle. 

After proving its worth in Formula One and Baja 1000 race cars, the computationally designed material has now been incorporated into the 2026 electric race car built by the student-run MIT Motorsports team.

The MIT car is scheduled to race against cars from other universities in the Formula SAE Electric competition in June.

Designing materials

Gregory B. Olson, professor of the practice in the MIT Department of Materials Science and Engineering, founded the MIT Steel Research Group (SRG) in 1985 with the goal of using computers to accelerate the hunt for new materials by plumbing databases of those materials’ fundamental properties. It was the beginning of a new field — computational materials design — that would eventually lead to the Materials Genome Initiative, a national program announced by President Barack Obama in 2011.

In 1985, however, “nobody knew whether we could really do this,” says Olson. Olson and colleagues eventually showed that the approach worked, and around 1990 the Army Research Office funded an SRG project aimed at developing high-performance steels for the gears in helicopters. That work came to the attention of producers at “Infinite Voyage,” a science documentary that ran on the Public Broadcasting System.

“When “Infinite Voyage” came to see me about the helicopter gear steels,” Olson remembers, “we got into a discussion about my interest in race cars” and whether the steels might have an application there.

The answer was yes, and Olson found himself connecting with the Newman/Haas racing team that Michael and Mario Andretti were driving for. Newman/Haas was also featured in the “Infinite Voyage” program, so “my first discussion with their chief engineer was on live television,” says Olson, who is also affiliated with the MIT Materials Research Laboratory.

He and colleagues went on to design a novel gear steel that could withstand the extreme conditions associated with a race car. They did the work over a weekend. “The surface hardness was the same as for a conventional gear steel, but we gave it the core properties of an armor steel,” Olson says.

Introducing Ferrium C61

That steel, which became known as Ferrium C61, was commercialized through QuesTek Innovations, the materials-design company Olson co-founded. It became the company’s first product.

Although it was never used in Newman/Haas cars, QuesTek pitched it to Baja 1000 off-road racers.

“We particularly focused on the 1600 class of those racing dune buggies. They would go flying over a sand dune with the wheels spinning in the air. And when they land, there would be a tremendous jolt to the drive gears,” Olson says. The result: The racers’ gears made with conventional steel regularly failed.

“The average life for conventional drive gears was point-six race,” says Olson (meaning on average they lasted for only 60 percent of a race). “With Ferrium C61, we changed it from point-six to six races.” The gears could now complete an average six races before failing.

QuesTek brought that data to meetings with different Formula One teams “to try to get C61 into other racing classes,” Olson says. 

Enter Red Bull, the British-licensed Formula One team. “The leading mechanical failure in Formula One racing is gearbox failures,” Olsen says. The gearbox houses the gearset, or collection of gears, in a car’s engine. “Once Red Bull adopted our steel for the gearset, they never had any gearbox failures, and they were world champions four times in the last decade.”

MIT Motorsports heard of this history and within the past year approached Olson about getting a sample of C61. “QuesTek had some stock available, and sold it at a high discount to the MIT team with, of course, instructions on how to heat-treat it,” Olson says. 

Because, of course, the students, who are mostly undergraduates, made the gears — and the car — themselves.

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Building AI models that understand chemical principles

Among all of the possible chemical compounds, it’s estimated that between 10²⁰ and 10⁶⁰ may hold potential as small-molecule drugs.

Evaluating each of those compounds experimentally would be far too time-consuming for chemists. So, in recent years, researchers have begun using artificial intelligence to help identify compounds that could make good drug candidates. 

One of those researchers is MIT Associate Professor Connor Coley PhD ’19, the Class of 1957 Career Development Associate Professor with shared appointments in the departments of Chemical Engineering and Electrical Engineering and Computer Science and the MIT Schwarzman College of Computing. His research straddles the line between chemical engineering and computer science, as he develops and deploys computational models to analyze vast numbers of possible chemical compounds, design new compounds, and predict reaction pathways that could generate those compounds. 

“It’s a very general approach that could be applied to any application of organic molecules, but the primary application that we think about is small-molecule drug discovery,” he says.

The intersection of AI and science

Coley’s interest in science runs in the family. In fact, he says, his family includes more scientists than non-scientists, including his father, a radiologist; his mother, who earned a degree in molecular biophysics and biochemistry before going to the MIT Sloan School of Management; and his grandmother, a math professor.

As a high school student in Dublin, Ohio, Coley participated in Science Olympiad competitions and graduated from high school at the age of 16. He then headed to Caltech, where he chose chemical engineering as a major because it offered a way to combine his interests in science and math.

During his undergraduate years, he also pursued an interest in computer science, working in a structural biology lab using the Fortran programming language to help solve the crystal structure of proteins. After graduating from Caltech, he decided to keep going in chemical engineering and came to MIT in 2014 to start a PhD.

Advised by professors Klavs Jensen and William Green, Coley worked on ways to optimize automated chemical reactions. His work focused on combining machine learning and cheminformatics — the application of computation methods to analyze chemical data — to plan reaction pathways that could make new drug molecules. He also worked on designing hardware that could be used to perform those reactions automatically. 

Part of that work was done through a DARPA-funded program called Make-It, which was focused on using machine learning and data science to improve the synthesis of medicines and other useful compounds from simple building blocks.

“That was my real entry point into thinking about cheminformatics, thinking about machine learning, and thinking about how we can use models to understand how different chemicals can be made and what reactions are possible,” Coley says.

Coley began applying for faculty jobs while still a graduate student, and accepted an offer from MIT at age 25. He received a mix of advice for and against taking a job at the same school where he went to graduate school, and eventually decided that a position at MIT was too enticing to turn down.

“MIT is a very special place in terms of the resources and the fluidity across departments. MIT seemed to be doing a really good job supporting the intersection of AI and science, and it was a vibrant ecosystem to stay in,” he says. “The caliber of students, the enthusiasm of the students, and just the incredible strength of collaborations definitely outweighed any potential concerns of staying in the same place.”

Chemistry intuition

Coley deferred the faculty position for one year to do a postdoc at the Broad Institute, where he sought more experience in chemical biology and drug discovery. There, he worked on ways to identify small molecules, from billions of candidates in DNA-encoded libraries, that might have binding interactions with mutated proteins associated with diseases.

After returning to MIT in 2020, he built his lab group with the mission of deploying AI not only to synthesize existing compounds with therapeutic potential, but also to design new molecules with desirable properties and new ways to make them. Over the past few years, his lab has developed a variety of computational approaches to tackle those goals. 

“We try to think about how to best pair a challenge in chemistry with a potential computational solution. And often that pairing motivates the development of new methods,” Coley says. One model his lab has developed, known as ShEPhERD, was trained to evaluate potential new drug molecules based on how they will interact with target proteins, based on the drug molecules’ three-dimensional shapes. This model is now being used by pharmaceutical companies to help them discover new drugs.

“We’re trying to give more of a medicinal chemistry intuition to the generative model, so the model is aware of the right criteria and considerations,” Coley says.

In another project, Coley’s lab developed a generative AI model called FlowER, which can be used to predict the reaction products that will result from combining different chemical inputs. 

In designing that model, the researchers built in an understanding of fundamental physical principles, such as the law of conservation of mass. They also compelled the model to consider the feasibility of the intermediate steps that need to take place on the pathway from reactants to products. These constraints, the researchers found, improved the accuracy of the model’s predictions.

“Thinking about those intermediate steps, the mechanisms involved, and how the reaction evolves is something that chemists do very naturally. It’s how chemistry is taught, but it’s not something that models inherently think about,” Coley says. “We’ve spent a lot of time thinking about how to make sure that our machine-learning models are grounded in an understanding of reaction mechanisms, in the same way an expert chemist would be.”

Students in his lab also work on many different areas related to the optimization of chemical reactions, including computer-aided structure elucidation, laboratory automation, and optimal experimental design.

“Through these many different research threads, we hope to advance the frontier of AI in chemistry,” Coley says.

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The Haystack 37m Telescope: A new era of astrophysical research

The Haystack 37m Telescope has been a landmark in radio astronomy and radar studies of the solar system since its first light in 1964. Over the following four decades, it supported NASA's Apollo landings on the moon, made planetary radar maps of the surface of Venus, contributed to experimental tests of Einstein's general relativity, supported the development of VLBI, and conducted foundational studies of quasars and star-forming regions. 

Recently, the Haystack 37m Telescope — a 37-meter radio and millimeter-wavelength antenna at MIT Haystack Observatory in Westford, Massachusetts — made its return to front-line astronomical research following an extended period of system upgrades. These observations reconnect this instrument with its long tradition of scientific discovery and open a new chapter.

On Dec. 8, 2025, Haystack scientists observed the supermassive black hole system at the center of the galaxy Messier 87 (M87) using a technique called very long baseline interferometry (VLBI) that links telescopes across continents to achieve extraordinary resolution. These observations mark the return of one of America's most storied radio telescopes to its historical scientific and educational mission.

The observations targeted the powerful jet of energy and matter launched from M87’s central black hole, M87*. This jet, driven by a black hole six-and-a-half billion times the mass of our sun, extends thousands of light years into intergalactic space and is one of the most energetic phenomena in the known universe. 

Previous international campaigns, namely those led by the Event Horizon Telescope, have imaged the black hole's immediate “shadow.” The Haystack 37m Telescope observations, performed in collaboration with the telescopes of the Very Long Baseline Array (VLBA) and the Greenland Telescope (GLT), help to probe the larger-scale structure of the jet, investigating how energy is transported far beyond the black hole's vicinity. Understanding this process is central to explaining how supermassive black holes shape the galaxies that surround them.

“The Haystack 37m Telescope’s exceptional sensitivity enables the intercontinental telescope array to detect faint emission from around the distant M87* black hole,” says Paul Tiede, principal investigator of the M87 study. “In tandem with the GLT and the VLBA, Haystack is helping create the first multifrequency movies of M87*’s faint jet, greatly improving our understanding of black hole physics.”

The upgraded Haystack 37m Telescope opens multiple new lines of research. At MIT, Saverio Cambioni and Richard Teague of the Department of Earth, Atmospheric and Planetary Sciences (EAPS) plan to use the instrument within MIT’s Planetary Defense Project to measure asteroid sizes and shapes, characterizing objects that could pose a hazard to Earth and deepening our understanding of the solar system’s formation. Associate Professor Brett McGuire of the Department of Chemistry plans to search for complex organic molecules in space, work that speaks to the question of how the chemical precursors to life arise.

“We are thrilled to provide the research community with a powerful telescope at a time where few such instruments are available,” says Jens Kauffmann, principal investigator of the Haystack 37m Telescope Astronomy Program, who uses the telescope to study the formation of stars and their planets. “Even more exciting are the prospects this generates for the next generation of astronomers. Hands-on training opportunities on world-class research telescopes have become exceptionally rare worldwide, and now we can offer this singular advanced workforce development program right here in Massachusetts.”

Student involvement with the Haystack 37m Telescope has already resumed: Undergraduate interns at Haystack Observatory played an active role in developing the telescope’s control systems and data analysis algorithms. This work exemplifies Haystack’s role as a hands-on research and training environment where students contribute directly and gain practical experience with a frontline research instrument.

The return to research-focused observations is the result of more than 10 years of careful, sustained work. From 2010 to 2014, the Haystack 37m Telescope underwent a major upgrade and refurbishment that enhanced its ability to observe at millimeter wavelengths. This work was primarily done to improve the antenna’s capability as a space radar. The dish now primarily serves U.S. government agencies in that capability, and astronomy was temporarily a secondary activity. 

But work to restore the telescope's science capability never stopped. Initial support from the National Science Foundation (NSF) in 2015 modernized systems for data analysis and radio signal processing. The first successful engineering-oriented VLBI experiments with the new dish were conducted at the same time. Additional NSF funding in 2019, provided in the context of the Next Generation Event Horizon Telescope (ngEHT) program, enabled a more general and sustained effort to upgrade receiver equipment and computing systems. Support from private donors to Haystack also aided in this longer-term effort.

Several recent developments, particularly in 2025, proved significant. With support from MIT's Jarve Seed Fund for Science Innovation, scientists and engineers removed lingering technical limitations with astronomy systems and expanded the telescope's scientific reach. Other funding for projects led by the Smithsonian Astrophysical Observatory enabled the M87 campaign and commissioning of the next-generation digital back end, a highly advanced signal-processing system developed for the ngEHT. Together, these advances made the December 2025 observations possible. MIT Haystack Observatory is now pursuing support from both private and federal sources for further improvements under the Haystack 37m Telescope Astronomy Program.

“The upgraded Haystack 37m Telescope empowers MIT students and researchers to pursue fundamental questions relating to our origins and our solar system,” says Richard Teague, professor at MIT EAPS. “With privileged access to such a powerful facility, we can undertake ambitious observational programs previously impossible to schedule. This is the beginning of what we expect will be an exciting era of new discoveries with the Haystack 37m Telescope.”

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Single-molecule tracker illuminates workings of cancer-related proteins

Using a powerful single-molecule imaging method they developed, a research team from the Broad Institute of MIT and Harvard has unveiled a dynamic view of how some cancer-related proteins interact in living cells. 

The technique relies on highly stable nanoparticle probes that brightly illuminate individual molecules for long periods of time. The researchers used their method to observe, for the first time, individual receptors as they move around the cell membrane, attaching to and then letting go of other receptors to alter signaling within the cell.

Described in the journal Cell, the work demonstrates the method’s potential for investigating other receptors and molecules, and for improved drug screening to better understand the effects of therapeutics on living cells.

“With our photostable probes, we can map out the entire lifespan of these molecules in their native environment and see things that have never been observable before,” says study leader Sam Peng, a Broad Institute core institute member and assistant professor of chemistry at MIT.

Molecular movies

Peng’s method solves a problem with existing contrast agents used in single-molecule tracking, such as dyes. Under the laser light that’s used to excite these dyes, they burn out after a few seconds in a phenomenon known as photobleaching, which means that scientists could only use them to take a few snapshots of cell receptors, and not follow them over the entirety of the signaling process.

For a longer and richer view, Peng’s lab developed long-lasting probes, known as upconverting nanoparticles, which emit signals that remain stable under laser excitation. The nanoparticles contain rare-earth ions that continue to luminescence for minutes, hours, and potentially years. In addition, by altering the type and doses of the ions, scientists can engineer probes emitting in many different colors, enabling tracking of many targets in a single experiment.

In the current study, the researchers aimed to uncover new biology by focusing on the EGFR family of cell receptors, which have been linked to several kinds of cancer. They collaborated with EGFR experts Matthew Meyerson and Heidi Greulich of the Broad’s Cancer Program. They knew that EGFR receptors need to pair up, or “dimerize,” in order to initiate signaling within the cell, but they wanted to learn more about the dynamics of these pairings — what the receptors partner with, how long they stay together, and how they find new partners.

For a better and more sustained look at the receptors, the research team customized their upconverting nanoparticles to tag EGFR and related receptors HER2 and HER3, which are linked to cancer, and used them to track the molecules in living human cells.

A new view of protein pairings

In this study, Peng and his team observed that, when activated with a stimulating molecule, EGFR receptors can pair up and stay dimerized for several minutes, something not observable using traditional dyes. Excessive and prolonged dimerization can lead to too much cell growth and cancer.

A microscopy video shows upconverting nanoparticles tagged to EGFR receptors (labeled pink and green), which track individual receptors as they dimerize. Image courtesy of the researchers.

When the EGFR molecules carried cancer-related mutations, the dimers became more stable, with the more stabilizing mutations linked to more potent cancers in people. In addition, the mutated receptors could form stable dimers even without an external stimulus prompting them to dimerize. The finding helps explain how EGFR mutations can lead to uncontrolled cell growth and cancer, and could inform efforts to target this process therapeutically.

The team discovered several other new and surprising details about how HER2 and HER3 form stable pairings with themselves, which helps illuminate the role of these molecules in related cancers.

When the research team tagged all three receptor types in one experiment, they observed a vibrant scene with receptors navigating the cell surface, finding partners, unpairing, and then finding new partners, over and over again.

Beyond shedding light on EGFR biology, the scientists hope that collaborators in other fields will apply their method to ask new scientific questions about other proteins of interest. “We think this technique could be transformative for studying molecular biology, because it enables dynamic biological processes to be observed with high spatiotemporal resolution over unprecedented timescales,” says Peng.

They are also planning to explore the method’s use in studying the mechanism of drug action, to reveal how potential therapeutics alter individual molecules over time. In addition, they will continue to improve their methods, such as making the probes smaller, brighter, and able to emit more colors.

de MIT News https://ift.tt/izZL2V5