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      Improving predictions of sea level rise for the next century

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      When we think of climate change, one of the most dramatic images that comes to mind is the loss of glacial ice. As the Earth warms, these enormous rivers of ice become a casualty of the rising temperatures. But, as ice sheets retreat, they also become an important contributor to one the more dangerous outcomes of climate change: sea-level rise. At MIT, an interdisciplinary team of scientists is determined to improve sea level rise predictions for the next century, in part by taking a closer look at the physics of ice sheets.

      Last month, two research proposals on the topic, led by Brent Minchew, the Cecil and Ida Green Career Development Professor in the Department of Earth, Atmospheric and Planetary Sciences (EAPS), were announced as finalists in the MIT Climate Grand Challenges initiative. Launched in July 2020, Climate Grand Challenges fielded almost 100 project proposals from collaborators across the Institute who heeded the bold charge: to develop research and innovations that will deliver game-changing advances in the world’s efforts to address the climate challenge.

      As finalists, Minchew and his collaborators from the departments of Urban Studies and Planning, Economics, Civil and Environmental Engineering, the Haystack Observatory, and external partners, received $100,000 to develop their research plans. A subset of the 27 proposals tapped as finalists will be announced next month, making up a portfolio of multiyear “flagship” projects receiving additional funding and support.

      One goal of both Minchew proposals is to more fully understand the most fundamental processes that govern rapid changes in glacial ice, and to use that understanding to build next-generation models that are more predictive of ice sheet behavior as they respond to, and influence, climate change.

      “We need to develop more accurate and computationally efficient models that provide testable projections of sea-level rise over the coming decades. To do so quickly, we want to make better and more frequent observations and learn the physics of ice sheets from these data,” says Minchew. “For example, how much stress do you have to apply to ice before it breaks?”

      Currently, Minchew’s Glacier Dynamics and Remote Sensing group uses satellites to observe the ice sheets on Greenland and Antarctica primarily with interferometric synthetic aperture radar (InSAR). But the data are often collected over long intervals of time, which only gives them “before and after” snapshots of big events. By taking more frequent measurements on shorter time scales, such as hours or days, they can get a more detailed picture of what is happening in the ice.

      “Many of the key unknowns in our projections of what ice sheets are going to look like in the future, and how they’re going to evolve, involve the dynamics of glaciers, or our understanding of how the flow speed and the resistances to flow are related,” says Minchew.

      At the heart of the two proposals is the creation of SACOS, the Stratospheric Airborne Climate Observatory System. The group envisions developing solar-powered drones that can fly in the stratosphere for months at a time, taking more frequent measurements using a new lightweight, low-power radar and other high-resolution instrumentation. They also propose air-dropping sensors directly onto the ice, equipped with seismometers and GPS trackers to measure high-frequency vibrations in the ice and pinpoint the motions of its flow.

      How glaciers contribute to sea level rise

      Current climate models predict an increase in sea levels over the next century, but by just how much is still unclear. Estimates are anywhere from 20 centimeters to two meters, which is a large difference when it comes to enacting policy or mitigation. Minchew points out that response measures will be different, depending on which end of the scale it falls toward. If it’s closer to 20 centimeters, coastal barriers can be built to protect low-level areas. But with higher surges, such measures become too expensive and inefficient to be viable, as entire portions of cities and millions of people would have to be relocated.

      “If we’re looking at a future where we could get more than a meter of sea level rise by the end of the century, then we need to know about that sooner rather than later so that we can start to plan and to do our best to prepare for that scenario,” he says.

      There are two ways glaciers and ice sheets contribute to rising sea levels: direct melting of the ice and accelerated transport of ice to the oceans. In Antarctica, warming waters melt the margins of the ice sheets, which tends to reduce the resistive stresses and allow ice to flow more quickly to the ocean. This thinning can also cause the ice shelves to be more prone to fracture, facilitating the calving of icebergs — events which sometimes cause even further acceleration of ice flow.

      Using data collected by SACOS, Minchew and his group can better understand what material properties in the ice allow for fracturing and calving of icebergs, and build a more complete picture of how ice sheets respond to climate forces. 

      “What I want is to reduce and quantify the uncertainties in projections of sea level rise out to the year 2100,” he says.

      From that more complete picture, the team — which also includes economists, engineers, and urban planning specialists — can work on developing predictive models and methods to help communities and governments estimate the costs associated with sea level rise, develop sound infrastructure strategies, and spur engineering innovation.

      Understanding glacier dynamics

      More frequent radar measurements and the collection of higher-resolution seismic and GPS data will allow Minchew and the team to develop a better understanding of the broad category of glacier dynamics — including calving, an important process in setting the rate of sea level rise which is currently not well understood.  

      “Some of what we’re doing is quite similar to what seismologists do,” he says. “They measure seismic waves following an earthquake, or a volcanic eruption, or things of this nature and use those observations to better understand the mechanisms that govern these phenomena.”

      Air-droppable sensors will help them collect information about ice sheet movement, but this method comes with drawbacks — like installation and maintenance, which is difficult to do out on a massive ice sheet that is moving and melting. Also, the instruments can each only take measurements at a single location. Minchew equates it to a bobber in water: All it can tell you is how the bobber moves as the waves disturb it.

      But by also taking continuous radar measurements from the air, Minchew’s team can collect observations both in space and in time. Instead of just watching the bobber in the water, they can effectively make a movie of the waves propagating out, as well as visualize processes like iceberg calving happening in multiple dimensions.

      Once the bobbers are in place and the movies recorded, the next step is developing machine learning algorithms to help analyze all the new data being collected. While this data-driven kind of discovery has been a hot topic in other fields, this is the first time it has been applied to glacier research.

      “We’ve developed this new methodology to ingest this huge amount of data,” he says, “and from that create an entirely new way of analyzing the system to answer these fundamental and critically important questions.”  More

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      An “oracle” for predicting the evolution of gene regulation

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      Despite the sheer number of genes that each human cell contains, these so-called “coding” DNA sequences comprise just 1 percent of our entire genome. The remaining 99 percent is made up of “non-coding” DNA — which, unlike coding DNA, does not carry the instructions to build proteins.

      One vital function of this non-coding DNA, also called “regulatory” DNA, is to help turn genes on and off, controlling how much (if any) of a protein is made. Over time, as cells replicate their DNA to grow and divide, mutations often crop up in these non-coding regions — sometimes tweaking their function and changing the way they control gene expression. Many of these mutations are trivial, and some are even beneficial. Occasionally, though, they can be associated with increased risk of common diseases, such as Type 2 diabetes, or more life-threatening ones, including cancer.

      To better understand the repercussions of such mutations, researchers have been hard at work on mathematical maps that allow them to look at an organism’s genome, predict which genes will be expressed, and determine how that expression will affect the organism’s observable traits. These maps, called fitness landscapes, were conceptualized roughly a century ago to understand how genetic makeup influences one common measure of organismal fitness in particular: reproductive success. Early fitness landscapes were very simple, often focusing on a limited number of mutations. Much richer datasets are now available, but researchers still require additional tools to characterize and visualize such complex data. This ability would not only facilitate a better understanding of how individual genes have evolved over time, but would also help to predict what sequence and expression changes might occur in the future.

      In a new study published on March 9 in Nature, a team of scientists has developed a framework for studying the fitness landscapes of regulatory DNA. They created a neural network model that, when trained on hundreds of millions of experimental measurements, was capable of predicting how changes to these non-coding sequences in yeast affected gene expression. They also devised a unique way of representing the landscapes in two dimensions, making it easy to understand the past and forecast the future evolution of non-coding sequences in organisms beyond yeast — and even design custom gene expression patterns for gene therapies and industrial applications.

      “We now have an ‘oracle’ that can be queried to ask: What if we tried all possible mutations of this sequence? Or, what new sequence should we design to give us a desired expression?” says Aviv Regev, a professor of biology at MIT (on leave), core member of the Broad Institute of Harvard and MIT (on leave), head of Genentech Research and Early Development, and the study’s senior author. “Scientists can now use the model for their own evolutionary question or scenario, and for other problems like making sequences that control gene expression in desired ways. I am also excited about the possibilities for machine learning researchers interested in interpretability; they can ask their questions in reverse, to better understand the underlying biology.”

      Prior to this study, many researchers had simply trained their models on known mutations (or slight variations thereof) that exist in nature. However, Regev’s team wanted to go a step further by creating their own unbiased models capable of predicting an organism’s fitness and gene expression based on any possible DNA sequence — even sequences they’d never seen before. This would also enable researchers to use such models to engineer cells for pharmaceutical purposes, including new treatments for cancer and autoimmune disorders.

      To accomplish this goal, Eeshit Dhaval Vaishnav, a graduate student at MIT and co-first author; Carl de Boer, now an assistant professor at the University of British Columbia; and their colleagues created a neural network model to predict gene expression. They trained it on a dataset generated by inserting millions of totally random non-coding DNA sequences into yeast, and observing how each random sequence affected gene expression. They focused on a particular subset of non-coding DNA sequences called promoters, which serve as binding sites for proteins that can switch nearby genes on or off.

      “This work highlights what possibilities open up when we design new kinds of experiments to generate the right data to train models,” Regev says. “In the broader sense, I believe these kinds of approaches will be important for many problems — like understanding genetic variants in regulatory regions that confer disease risk in the human genome, but also for predicting the impact of combinations of mutations, or designing new molecules.”

      Regev, Vaishnav, de Boer, and their coauthors went on to test their model’s predictive abilities in a variety of ways, in order to show how it could help demystify the evolutionary past — and possible future — of certain promoters. “Creating an accurate model was certainly an accomplishment, but, to me, it was really just a starting point,” Vaishnav explains.

      First, to determine whether their model could help with synthetic biology applications like producing antibiotics, enzymes, and food, the researchers practiced using it to design promoters that could generate desired expression levels for any gene of interest. They then scoured other scientific papers to identify fundamental evolutionary questions, in order to see if their model could help answer them. The team even went so far as to feed their model a real-world population dataset from one existing study, which contained genetic information from yeast strains around the world. In doing so, they were able to delineate thousands of years of past selection pressures that sculpted the genomes of today’s yeast.

      But, in order to create a powerful tool that could probe any genome, the researchers knew they’d need to find a way to forecast the evolution of non-coding sequences even without such a comprehensive population dataset. To address this goal, Vaishnav and his colleagues devised a computational technique that allowed them to plot the predictions from their framework onto a two-dimensional graph. This helped them show, in a remarkably simple manner, how any non-coding DNA sequence would affect gene expression and fitness, without needing to conduct any time-consuming experiments at the lab bench.

      “One of the unsolved problems in fitness landscapes was that we didn’t have an approach for visualizing them in a way that meaningfully captured the evolutionary properties of sequences,” Vaishnav explains. “I really wanted to find a way to fill that gap, and contribute to the long-standing vision of creating a complete fitness landscape.”

      Martin Taylor, a professor of genetics at the University of Edinburgh’s Medical Research Council Human Genetics Unit who was not involved in the research, says the study shows that artificial intelligence can not only predict the effect of regulatory DNA changes, but also reveal the underlying principles that govern millions of years of evolution.

      Despite the fact that the model was trained on just a fraction of yeast regulatory DNA in a few growth conditions, he’s impressed that it’s capable of making such useful predictions about the evolution of gene regulation in mammals.

      “There are obvious near-term applications, such as the custom design of regulatory DNA for yeast in brewing, baking, and biotechnology,” he explains. “But extensions of this work could also help identify disease mutations in human regulatory DNA that are currently difficult to find and largely overlooked in the clinic. This work suggests there is a bright future for AI models of gene regulation trained on richer, more complex, and more diverse datasets.”

      Even before the study was formally published, Vaishnav began receiving queries from other researchers hoping to use the model to devise non-coding DNA sequences for use in gene therapies.

      “People have been studying regulatory evolution and fitness landscapes for decades now,” Vaishnav says. “I think our framework will go a long way in answering fundamental, open questions about the evolution and evolvability of gene regulatory DNA — and even help us design biological sequences for exciting new applications.” More

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      Can machine-learning models overcome biased datasets?

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      Artificial intelligence systems may be able to complete tasks quickly, but that doesn’t mean they always do so fairly. If the datasets used to train machine-learning models contain biased data, it is likely the system could exhibit that same bias when it makes decisions in practice.

      For instance, if a dataset contains mostly images of white men, then a facial-recognition model trained with these data may be less accurate for women or people with different skin tones.

      A group of researchers at MIT, in collaboration with researchers at Harvard University and Fujitsu Ltd., sought to understand when and how a machine-learning model is capable of overcoming this kind of dataset bias. They used an approach from neuroscience to study how training data affects whether an artificial neural network can learn to recognize objects it has not seen before. A neural network is a machine-learning model that mimics the human brain in the way it contains layers of interconnected nodes, or “neurons,” that process data.

      The new results show that diversity in training data has a major influence on whether a neural network is able to overcome bias, but at the same time dataset diversity can degrade the network’s performance. They also show that how a neural network is trained, and the specific types of neurons that emerge during the training process, can play a major role in whether it is able to overcome a biased dataset.

      “A neural network can overcome dataset bias, which is encouraging. But the main takeaway here is that we need to take into account data diversity. We need to stop thinking that if you just collect a ton of raw data, that is going to get you somewhere. We need to be very careful about how we design datasets in the first place,” says Xavier Boix, a research scientist in the Department of Brain and Cognitive Sciences (BCS) and the Center for Brains, Minds, and Machines (CBMM), and senior author of the paper.  

      Co-authors include former MIT graduate students Timothy Henry, Jamell Dozier, Helen Ho, Nishchal Bhandari, and Spandan Madan, a corresponding author who is currently pursuing a PhD at Harvard; Tomotake Sasaki, a former visiting scientist now a senior researcher at Fujitsu Research; Frédo Durand, a professor of electrical engineering and computer science at MIT and a member of the Computer Science and Artificial Intelligence Laboratory; and Hanspeter Pfister, the An Wang Professor of Computer Science at the Harvard School of Enginering and Applied Sciences. The research appears today in Nature Machine Intelligence.

      Thinking like a neuroscientist

      Boix and his colleagues approached the problem of dataset bias by thinking like neuroscientists. In neuroscience, Boix explains, it is common to use controlled datasets in experiments, meaning a dataset in which the researchers know as much as possible about the information it contains.

      The team built datasets that contained images of different objects in varied poses, and carefully controlled the combinations so some datasets had more diversity than others. In this case, a dataset had less diversity if it contains more images that show objects from only one viewpoint. A more diverse dataset had more images showing objects from multiple viewpoints. Each dataset contained the same number of images.

      The researchers used these carefully constructed datasets to train a neural network for image classification, and then studied how well it was able to identify objects from viewpoints the network did not see during training (known as an out-of-distribution combination). 

      For example, if researchers are training a model to classify cars in images, they want the model to learn what different cars look like. But if every Ford Thunderbird in the training dataset is shown from the front, when the trained model is given an image of a Ford Thunderbird shot from the side, it may misclassify it, even if it was trained on millions of car photos.

      The researchers found that if the dataset is more diverse — if more images show objects from different viewpoints — the network is better able to generalize to new images or viewpoints. Data diversity is key to overcoming bias, Boix says.

      “But it is not like more data diversity is always better; there is a tension here. When the neural network gets better at recognizing new things it hasn’t seen, then it will become harder for it to recognize things it has already seen,” he says.

      Testing training methods

      The researchers also studied methods for training the neural network.

      In machine learning, it is common to train a network to perform multiple tasks at the same time. The idea is that if a relationship exists between the tasks, the network will learn to perform each one better if it learns them together.

      But the researchers found the opposite to be true — a model trained separately for each task was able to overcome bias far better than a model trained for both tasks together.

      “The results were really striking. In fact, the first time we did this experiment, we thought it was a bug. It took us several weeks to realize it was a real result because it was so unexpected,” he says.

      They dove deeper inside the neural networks to understand why this occurs.

      They found that neuron specialization seems to play a major role. When the neural network is trained to recognize objects in images, it appears that two types of neurons emerge — one that specializes in recognizing the object category and another that specializes in recognizing the viewpoint.

      When the network is trained to perform tasks separately, those specialized neurons are more prominent, Boix explains. But if a network is trained to do both tasks simultaneously, some neurons become diluted and don’t specialize for one task. These unspecialized neurons are more likely to get confused, he says.

      “But the next question now is, how did these neurons get there? You train the neural network and they emerge from the learning process. No one told the network to include these types of neurons in its architecture. That is the fascinating thing,” he says.

      That is one area the researchers hope to explore with future work. They want to see if they can force a neural network to develop neurons with this specialization. They also want to apply their approach to more complex tasks, such as objects with complicated textures or varied illuminations.

      Boix is encouraged that a neural network can learn to overcome bias, and he is hopeful their work can inspire others to be more thoughtful about the datasets they are using in AI applications.

      This work was supported, in part, by the National Science Foundation, a Google Faculty Research Award, the Toyota Research Institute, the Center for Brains, Minds, and Machines, Fujitsu Research, and the MIT-Sensetime Alliance on Artificial Intelligence. More

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      Professor Emery Brown has big plans for anesthesiology

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      Emery N. Brown — the Edward Hood Taplin Professor of Medical Engineering and of Computational Neuroscience at MIT, an MIT professor of health sciences and technology, an investigator with The Picower Institute for Learning and Memory at MIT, and the Warren M. Zapol Professor of Anaesthesia at Harvard Medical School and Massachusetts General Hospital (MGH) — clearly excels at many roles. Renowned internationally for his anesthesia and neuroscience research, he embodies a unique blend of anesthesiologist, statistician, neuroscientist, educator, and mentor to both students and colleagues. Notably, Brown is one of the most decorated clinician-scientists in the country; he is one of only 25 people — and the first African-American, statistician, and anesthesiologist — to be elected to all three National Academies (Science, Engineering, and Medicine).

      Now, he is handing off one of his many key roles and responsibilities. After almost 10 years, Brown is stepping down as co-director of the Harvard-MIT Program in Health Sciences and Technology (HST). He will turn his energies toward working to develop a new joint center between MIT and MGH that uses the study of anesthesia to design novel approaches to controlling brain states. While a goal of the new center will be to improve anesthesia and intensive care unit management, according to Brown, it will also study related problems such as treating depression, insomnia, and epilepsy, as well as enhancing coma recovery.

      Founded in 1970, HST is one of the oldest interdisciplinary educational programs focused on training the next generation of clinician-scientists and engineers, who learn to translate science, engineering, and medical research into clinical practice, with the aim of improving human health. The MIT Institute for Medical Engineering and Science (IMES), where Brown is associate director, is HST’s home at MIT. Brown was the first HST co-director after the establishment of IMES in 2012; Wolfram Goessling is the Harvard University co-director of HST.

      “Emery has been an exemplary leader for HST during his tenure, and has helped it become a hub for the training of world-class scientists, engineers, and clinicians,” says Anantha Chandrakasan, dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “I am deeply grateful for his many years of service and wish him well as he moves on to new endeavors.”

      Elazer R. Edelman, director of IMES, calls Brown “a phenom who has been dedicated to our programs for years.”

      “With his thoughtful leadership and understated style, Emery made many contributions to the HST community,” Edelman continues. “On a personal note, this is bittersweet for me, as Emery has been a partner and mentor in my role as IMES director. And while I know that he will always be there for me, as he has been for all of us at IMES and HST, I will miss our late-night calls and midday conferences on matters of import for MIT, IMES, and HST.”

      Brown says “it was an honor and a privilege to co-direct HST with Wolfram.”

      “The students, staff, and faculty are simply amazing,” Brown continues. “Although, now more than 50 years old, HST remains at the vanguard for training PhD and MD students to work at the intersection between engineering, science, and medicine.”

      Goessling also thanks Brown for his leadership: “I truly valued Emery’s partnership and friendship, working together to deepen ties between the MIT and Harvard sides of HST. I am particularly grateful for working with Emery on our combined diversity efforts, leading to the HST Diversity Ambassadors initiative that made HST a better and stronger program.”

      According to Edelman, Brown was instrumental in the transition to new paradigms and relationships with HMS in the context of IMES. In 2014, he led the establishment of clear criteria for HST faculty membership, thereby strengthening the community of faculty experts who train students and provide research opportunities. More recently, he provided guidance through the turmoil of the ongoing Covid-19 pandemic, including the transition to online instruction and the return to the classroom. And Brown has always been a strong supporter of student diversity efforts, serving as an advocate and advisor to HST students.

      Brown holds BA, MA, and PhD degrees from Harvard University, and an MD from Harvard Medical School. He has been recognized with many awards, including the 2020 Swartz Prize in Theoretical and Computational Neuroscience, the 2018 Dickson Prize in Science, and an NIH Director’s Pioneer Award. Brown also served on President Barack Obama’s BRAIN Initiative Working Group. Among his many accomplishments, he has been cited for developing neural signal processing algorithms to characterize how neural systems represent and transmit information, and for unlocking the neurophysiology of how anesthetics produce the states of general anesthesia.

      Edelman says the process is underway to name a successor to Brown as co-director of HST at MIT. More

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      Probing how proteins pair up inside cells

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      Despite its minute size, a single cell contains billions of molecules that bustle around and bind to one another, carrying out vital functions. The human genome encodes about 20,000 proteins, most of which interact with partner proteins to mediate upwards of 400,000 distinct interactions. These partners don’t just latch onto one another haphazardly; they only bind to very specific companions that they must recognize inside the crowded cell. If they create the wrong pairings — or even the right pairings at the wrong place or wrong time — cancer or other diseases can ensue. Scientists are hard at work investigating these protein-protein relationships, in order to understand how they work, and potentially create drugs that disrupt or mimic them to treat disease.

      The average human protein is composed of approximately 400 building blocks called amino acids, which are strung together and folded into a complex 3D structure. Within this long string of building blocks, some proteins contain stretches of four to six amino acids called short linear motifs (SLiMs), which mediate protein-protein interactions. Despite their simplicity and small size, SLiMs and their binding partners facilitate key cellular processes. However, it’s been historically difficult to devise experiments to probe how SLiMs recognize their specific binding partners.

      To address this problem, a group led by Theresa Hwang PhD ’21 designed a screening method to understand how SLiMs selectively bind to certain proteins, and even distinguish between those with similar structures. Using the detailed information they gleaned from studying these interactions, the researchers created their own synthetic molecule capable of binding extremely tightly to a protein called ENAH, which is implicated in cancer metastasis. The team shared their findings in a pair of eLife studies, one published on Dec. 2, 2021, and the other published Jan. 25.

      “The ability to test hundreds of thousands of potential SLiMs for binding provides a powerful tool to explore why proteins prefer specific SLiM partners over others,” says Amy Keating, professor of biology and biological engineering and the senior author on both studies. “As we gain an understanding of the tricks that a protein uses to select its partners, we can apply these in protein design to make our own binders to modulate protein function for research or therapeutic purposes.”

      Most existing screens for SLiMs simply select for short, tight binders, while neglecting SLiMs that don’t grip their partner proteins quite as strongly. To survey SLiMs with a wide range of binding affinities, Keating, Hwang, and their colleagues developed their own screen called MassTitr.

      The researchers also suspected that the amino acids on either side of the SLiM’s core four-to-six amino acid sequence might play an underappreciated role in binding. To test their theory, they used MassTitr to screen the human proteome in longer chunks comprised of 36 amino acids, in order to see which “extended” SLiMs would associate with the protein ENAH.

      ENAH, sometimes referred to as Mena, helps cells to move. This ability to migrate is critical for healthy cells, but cancer cells can co-opt it to spread. Scientists have found that reducing the amount of ENAH decreases the cancer cell’s ability to invade other tissues — suggesting that formulating drugs to disrupt this protein and its interactions could treat cancer.

      Thanks to MassTitr, the team identified 33 SLiM-containing proteins that bound to ENAH — 19 of which are potentially novel binding partners. They also discovered three distinct patterns of amino acids flanking core SLiM sequences that helped the SLiMs bind even tighter to ENAH. Of these extended SLiMs, one found in a protein called PCARE bound to ENAH with the highest known affinity of any SLiM to date.

      Next, the researchers combined a computer program called dTERMen with X-ray crystallography in order understand how and why PCARE binds to ENAH over ENAH’s two nearly identical sister proteins (VASP and EVL). Hwang and her colleagues saw that the amino acids flanking PCARE’s core SliM caused ENAH to change shape slightly when the two made contact, allowing the binding sites to latch onto one another. VASP and EVL, by contrast, could not undergo this structural change, so the PCARE SliM did not bind to either of them as tightly.

      Inspired by this unique interaction, Hwang designed her own protein that bound to ENAH with unprecedented affinity and specificity. “It was exciting that we were able to come up with such a specific binder,” she says. “This work lays the foundation for designing synthetic molecules with the potential to disrupt protein-protein interactions that cause disease — or to help scientists learn more about ENAH and other SLiM-binding proteins.”  

      Ylva Ivarsson, a professor of biochemistry at Uppsala University who was not involved with the study, says that understanding how proteins find their binding partners is a question of fundamental importance to cell function and regulation. The two eLife studies, she explains, show that extended SLiMs play an underappreciated role in determining the affinity and specificity of these binding interactions.

      “The studies shed light on the idea that context matters, and provide a screening strategy for a variety of context-dependent binding interactions,” she says. “Hwang and co-authors have created valuable tools for dissecting the cellular function of proteins and their binding partners. Their approach could even inspire ENAH-specific inhibitors for therapeutic purposes.”

      Hwang’s biggest takeaway from the project is that things are not always as they seem: even short, simple protein segments can play complex roles in the cell. As she puts it: “We should really appreciate SLiMs more.” More

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      Professors Elchanan Mossel and Rosalind Picard named 2021 ACM Fellows

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      The Association for Computing Machinery (ACM) has named MIT professors Elchanan Mossel and Rosalind Picard as fellows for outstanding accomplishments in computing and information technology.

      The ACM Fellows program recognizes wide-ranging and fundamental contributions in areas including algorithms, computer science education, cryptography, data security and privacy, medical informatics, and mobile and networked systems, among many other areas. The accomplishments of the 2021 ACM Fellows underpin important innovations that shape the technologies we use every day.

      Elchanan Mossel

      Mossel is a professor of mathematics and a member at the Statistics and Data Science Center of the MIT Institute for Data, Systems and Society. His research in discrete functional inequalities, isoperimetry, and hypercontractivity led to the proof that Majority is Stablest and confirmed the optimality of the Goemans-Williamson MAX-CUT algorithm under the unique games conjecture from computational complexity. His work on the reconstruction problem on trees provides optimal algorithms and bounds for phylogenetic reconstruction in molecular biology and has led to sharp results in the analysis of Gibbs samplers from statistical physics and inference problems on graphs. His research has resolved open problems in computational biology, machine learning, social choice theory, and economics.Mossel received a BS from the Open University in Israel in 1992, and MS (1997) and PhD (2000) degrees in mathematics from the Hebrew University of Jerusalem. He was a postdoc at the Microsoft Research Theory Group and a Miller Fellow at University of California at Berkeley. He joined the UC Berkeley faculty in 2003 as a professor of statistics and computer science, and spent leaves as a professor at the Weizmann Institute and at the Wharton School before joining MIT in 2016 as a full professor.

      In 2020, he received the Vannevar Bush Faculty Fellowship of the U.S. Department of Defense. Other distinctions include being named a Simons Investigator in Mathematics in 2019, being selected as a fellow of the AMS, and receiving a Sloan Research Fellowship, NSF CAREER Award, and the Bergmann Memorial Award from the U.S.-Israel Binational Science Foundation.

      “I am honored by this award,” says Mossel. “It makes me realize how fortunate I’ve been, working with creative and generous colleagues, and mentoring brilliant young minds.”

      Rosalind Picard

      Picard is a scientist, engineer, author, and professor of media arts and sciences at the MIT Media Lab. She is recognized as the founder of the field of affective computing, and has carried this research forward as head of the Media Lab’s Affective Computing research group. She is also a founding faculty chair of MIT’s MindHandHeart Initiative, and a faculty member of the MIT Center for Neurobiological Engineering. Picard is an IEEE fellow, and a member of the National Academy of Engineering. 

      Picard’s inventions are in use by thousands of research teams worldwide as well as in numerous products and services. She has co-founded two companies: Affectiva (now part of Smart Eye), providing emotion AI technologies now used by more than 25 percent of the Global Fortune 500, and Empatica, providing wearable sensors and analytics to improve health. Starting from inventions by Picard and her team, Empatica created the first AI-based smart watch cleared by the FDA (in neurology for monitoring seizures), which is now helping to bring potentially lifesaving help for people with epilepsy. 

      “This award makes me think of how blessed I am to work with so many amazing people here at MIT, especially at the Media Lab,” Picard notes. “Whenever any one of us has our contributions recognized, it is also a recognition of how special a place this is.” More

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      Understanding air pollution from space

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      Climate change and air pollution are interlocking crises that threaten human health. Reducing emissions of some air pollutants can help achieve climate goals, and some climate mitigation efforts can in turn improve air quality.

      One part of MIT Professor Arlene Fiore’s research program is to investigate the fundamental science in understanding air pollutants — how long they persist and move through our environment to affect air quality.

      “We need to understand the conditions under which pollutants, such as ozone, form. How much ozone is formed locally and how much is transported long distances?” says Fiore, who notes that Asian air pollution can be transported across the Pacific Ocean to North America. “We need to think about processes spanning local to global dimensions.”

      Fiore, the Peter H. Stone and Paola Malanotte Stone Professor in Earth, Atmospheric and Planetary Sciences, analyzes data from on-the-ground readings and from satellites, along with models, to better understand the chemistry and behavior of air pollutants — which ultimately can inform mitigation strategies and policy setting.

      A global concern

      At the United Nations’ most recent climate change conference, COP26, air quality management was a topic discussed over two days of presentations.

      “Breathing is vital. It’s life. But for the vast majority of people on this planet right now, the air that they breathe is not giving life, but cutting it short,” said Sarah Vogel, senior vice president for health at the Environmental Defense Fund, at the COP26 session.

      “We need to confront this twin challenge now through both a climate and clean air lens, of targeting those pollutants that warm both the air and harm our health.”

      Earlier this year, the World Health Organization (WHO) updated its global air quality guidelines it had issued 15 years earlier for six key pollutants including ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). The new guidelines are more stringent based on what the WHO stated is the “quality and quantity of evidence” of how these pollutants affect human health. WHO estimates that roughly 7 million premature deaths are attributable to the joint effects of air pollution.

      “We’ve had all these health-motivated reductions of aerosol and ozone precursor emissions. What are the implications for the climate system, both locally but also around the globe? How does air quality respond to climate change? We study these two-way interactions between air pollution and the climate system,” says Fiore.

      But fundamental science is still required to understand how gases, such as ozone and nitrogen dioxide, linger and move throughout the troposphere — the lowermost layer of our atmosphere, containing the air we breathe.

      “We care about ozone in the air we’re breathing where we live at the Earth’s surface,” says Fiore. “Ozone reacts with biological tissue, and can be damaging to plants and human lungs. Even if you’re a healthy adult, if you’re out running hard during an ozone smog event, you might feel an extra weight on your lungs.”

      Telltale signs from space

      Ozone is not emitted directly, but instead forms through chemical reactions catalyzed by radiation from the sun interacting with nitrogen oxides — pollutants released in large part from burning fossil fuels—and volatile organic compounds. However, current satellite instruments cannot sense ground-level ozone.

      “We can’t retrieve surface- or even near-surface ozone from space,” says Fiore of the satellite data, “although the anticipated launch of a new instrument looks promising for new advances in retrieving lower-tropospheric ozone”. Instead, scientists can look at signatures from other gas emissions to get a sense of ozone formation. “Nitrogen dioxide and formaldehyde are a heavy focus of our research because they serve as proxies for two of the key ingredients that go on to form ozone in the atmosphere.”

      To understand ozone formation via these precursor pollutants, scientists have gathered data for more than two decades using spectrometer instruments aboard satellites that measure sunlight in ultraviolet and visible wavelengths that interact with these pollutants in the Earth’s atmosphere — known as solar backscatter radiation.

      Satellites, such as NASA’s Aura, carry instruments like the Ozone Monitoring Instrument (OMI). OMI, along with European-launched satellites such as the Global Ozone Monitoring Experiment (GOME) and the Scanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY), and the newest generation TROPOspheric Monitoring instrument (TROPOMI), all orbit the Earth, collecting data during daylight hours when sunlight is interacting with the atmosphere over a particular location.

      In a recent paper from Fiore’s group, former graduate student Xiaomeng Jin (now a postdoc at the University of California at Berkeley), demonstrated that she could bring together and “beat down the noise in the data,” as Fiore says, to identify trends in ozone formation chemistry over several U.S. metropolitan areas that “are consistent with our on-the-ground understanding from in situ ozone measurements.”

      “This finding implies that we can use these records to learn about changes in surface ozone chemistry in places where we lack on-the-ground monitoring,” says Fiore. Extracting these signals by stringing together satellite data — OMI, GOME, and SCIAMACHY — to produce a two-decade record required reconciling the instruments’ differing orbit days, times, and fields of view on the ground, or spatial resolutions. 

      Currently, spectrometer instruments aboard satellites are retrieving data once per day. However, newer instruments, such as the Geostationary Environment Monitoring Spectrometer launched in February 2020 by the National Institute of Environmental Research in the Ministry of Environment of South Korea, will monitor a particular region continuously, providing much more data in real time.

      Over North America, the Tropospheric Emissions: Monitoring of Pollution Search (TEMPO) collaboration between NASA and the Smithsonian Astrophysical Observatory, led by Kelly Chance of Harvard University, will provide not only a stationary view of the atmospheric chemistry over the continent, but also a finer-resolution view — with the instrument recording pollution data from only a few square miles per pixel (with an anticipated launch in 2022).

      “What we’re very excited about is the opportunity to have continuous coverage where we get hourly measurements that allow us to follow pollution from morning rush hour through the course of the day and see how plumes of pollution are evolving in real time,” says Fiore.

      Data for the people

      Providing Earth-observing data to people in addition to scientists — namely environmental managers, city planners, and other government officials — is the goal for the NASA Health and Air Quality Applied Sciences Team (HAQAST).

      Since 2016, Fiore has been part of HAQAST, including collaborative “tiger teams” — projects that bring together scientists, nongovernment entities, and government officials — to bring data to bear on real issues.

      For example, in 2017, Fiore led a tiger team that provided guidance to state air management agencies on how satellite data can be incorporated into state implementation plans (SIPs). “Submission of a SIP is required for any state with a region in non-attainment of U.S. National Ambient Air Quality Standards to demonstrate their approach to achieving compliance with the standard,” says Fiore. “What we found is that small tweaks in, for example, the metrics we use to convey the science findings, can go a long way to making the science more usable, especially when there are detailed policy frameworks in place that must be followed.”

      Now, in 2021, Fiore is part of two tiger teams announced by HAQAST in late September. One team is looking at data to address environmental justice issues, by providing data to assess communities disproportionately affected by environmental health risks. Such information can be used to estimate the benefits of governmental investments in environmental improvements for disproportionately burdened communities. The other team is looking at urban emissions of nitrogen oxides to try to better quantify and communicate uncertainties in the estimates of anthropogenic sources of pollution.

      “For our HAQAST work, we’re looking at not just the estimate of the exposure to air pollutants, or in other words their concentrations,” says Fiore, “but how confident are we in our exposure estimates, which in turn affect our understanding of the public health burden due to exposure. We have stakeholder partners at the New York Department of Health who will pair exposure datasets with health data to help prioritize decisions around public health.

      “I enjoy working with stakeholders who have questions that require science to answer and can make a difference in their decisions.” Fiore says. More

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      in Data Management & Statistics

      At UN climate change conference, trying to “keep 1.5 alive”

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      After a one-year delay caused by the Covid-19 pandemic, negotiators from nearly 200 countries met this month in Glasgow, Scotland, at COP26, the United Nations climate change conference, to hammer out a new global agreement to reduce greenhouse gas emissions and prepare for climate impacts. A delegation of approximately 20 faculty, staff, and students from MIT was on hand to observe the negotiations, share and conduct research, and launch new initiatives.

      On Saturday, Nov. 13, following two weeks of negotiations in the cavernous Scottish Events Campus, countries’ representatives agreed to the Glasgow Climate Pact. The pact reaffirms the goal of the 2015 Paris Agreement “to pursue efforts” to limit the global average temperature increase to 1.5 degrees Celsius above preindustrial levels, and recognizes that achieving this goal requires “reducing global carbon dioxide emissions by 45 percent by 2030 relative to the 2010 level and to net zero around mid-century.”

      “On issues like the need to reach net-zero emissions, reduce methane pollution, move beyond coal power, and tighten carbon accounting rules, the Glasgow pact represents some meaningful progress, but we still have so much work to do,” says Maria Zuber, MIT’s vice president for research, who led the Institute’s delegation to COP26. “Glasgow showed, once again, what a wicked complex problem climate change is, technically, economically, and politically. But it also underscored the determination of a global community of people committed to addressing it.”

      An “ambition gap”

      Both within the conference venue and at protests that spilled through the streets of Glasgow, one rallying cry was “keep 1.5 alive.” Alok Sharma, who was appointed by the UK government to preside over COP26, said in announcing the Glasgow pact: “We can now say with credibility that we have kept 1.5 degrees alive. But, its pulse is weak and it will only survive if we keep our promises and translate commitments into rapid action.”

      In remarks delivered during the first week of the conference, Sergey Paltsev, deputy director of MIT’s Joint Program on the Science and Policy of Global Change, presented findings from the latest MIT Global Change Outlook, which showed a wide gap between countries’ nationally determined contributions (NDCs) — the UN’s term for greenhouse gas emissions reduction pledges — and the reductions needed to put the world on track to meet the goals of the Paris Agreement and, now, the Glasgow pact.

      Pointing to this ambition gap, Paltsev called on all countries to do more, faster, to cut emissions. “We could dramatically reduce overall climate risk through more ambitious policy measures and investments,” says Paltsev. “We need to employ an integrated approach of moving to zero emissions in energy and industry, together with sustainable development and nature-based solutions, simultaneously improving human well-being and providing biodiversity benefits.”

      Finalizing the Paris rulebook

      A key outcome of COP26 (COP stands for “conference of the parties” to the UN Framework Convention on Climate Change, held for the 26th time) was the development of a set of rules to implement Article 6 of the Paris Agreement, which provides a mechanism for countries to receive credit for emissions reductions that they finance outside their borders, and to cooperate by buying and selling emissions reductions on international carbon markets.

      An agreement on this part of the Paris “rulebook” had eluded negotiators in the years since the Paris climate conference, in part because negotiators were concerned about how to prevent double-counting, wherein both buyers and sellers would claim credit for the emissions reductions.

      Michael Mehling, the deputy director of MIT’s Center for Energy and Environmental Policy Research (CEEPR) and an expert on international carbon markets, drew on a recent CEEPR working paper to describe critical negotiation issues under Article 6 during an event at the conference on Nov. 10 with climate negotiators and private sector representatives.

      He cited research that finds that Article 6, by leveraging the cost-efficiency of global carbon markets, could cut in half the cost that countries would incur to achieve their nationally determined contributions. “Which, seen from another angle, means you could double the ambition of these NDCs at no additional cost,” Mehling noted in his talk, adding that, given the persistent ambition gap, “any such opportunity is bitterly needed.”

      Andreas Haupt, a graduate student in the Institute for Data, Systems, and Society, joined MIT’s COP26 delegation to follow Article 6 negotiations. Haupt described the final days of negotiations over Article 6 as a “roller coaster.” Once negotiators reached an agreement, he says, “I felt relieved, but also unsure how strong of an effect the new rules, with all their weaknesses, will have. I am curious and hopeful regarding what will happen in the next year until the next large-scale negotiations in 2022.”

      Nature-based climate solutions

      World leaders also announced new agreements on the sidelines of the formal UN negotiations. One such agreement, a declaration on forests signed by more than 100 countries, commits to “working collectively to halt and reverse forest loss and land degradation by 2030.”

      A team from MIT’s Environmental Solutions Initiative (ESI), which has been working with policymakers and other stakeholders on strategies to protect tropical forests and advance other nature-based climate solutions in Latin America, was at COP26 to discuss their work and make plans for expanding it.

      Marcela Angel, a research associate at ESI, moderated a panel discussion featuring John Fernández, professor of architecture and ESI’s director, focused on protecting and enhancing natural carbon sinks, particularly tropical forests such as the Amazon that are at risk of deforestation, forest degradation, and biodiversity loss.

      “Deforestation and associated land use change remain one of the main sources of greenhouse gas emissions in most Amazonian countries, such as Brazil, Peru, and Colombia,” says Angel. “Our aim is to support these countries, whose nationally determined contributions depend on the effectiveness of policies to prevent deforestation and promote conservation, with an approach based on the integration of targeted technology breakthroughs, deep community engagement, and innovative bioeconomic opportunities for local communities that depend on forests for their livelihoods.”

      Energy access and renewable energy

      Worldwide, an estimated 800 million people lack access to electricity, and billions more have only limited or erratic electrical service. Providing universal access to energy is one of the UN’s sustainable development goals, creating a dual challenge: how to boost energy access without driving up greenhouse gas emissions.

      Rob Stoner, deputy director for science and technology of the MIT Energy Initiative (MITEI), and Ignacio Pérez-Arriaga, a visiting professor at the Sloan School of Management, attended COP26 to share their work as members of the Global Commission to End Energy Poverty, a collaboration between MITEI and the Rockefeller Foundation. It brings together global energy leaders from industry, the development finance community, academia, and civil society to identify ways to overcome barriers to investment in the energy sectors of countries with low energy access.

      The commission’s work helped to motivate the formation, announced at COP26 on Nov. 2, of the Global Energy Alliance for People and Planet, a multibillion-dollar commitment by the Rockefeller and IKEA foundations and Bezos Earth Fund to support access to renewable energy around the world.

      Another MITEI member of the COP26 delegation, Martha Broad, the initiative’s executive director, spoke about MIT research to inform the U.S. goal of scaling offshore wind energy capacity from approximately 30 megawatts today to 30 gigawatts by 2030, including significant new capacity off the coast of New England.

      Broad described research, funded by MITEI member companies, on a coating that can be applied to the blades of wind turbines to prevent icing that would require the turbines’ shutdown; the use of machine learning to inform preventative turbine maintenance; and methodologies for incorporating the effects of climate change into projections of future wind conditions to guide wind farm siting decisions today. She also spoke broadly about the need for public and private support to scale promising innovations.

      “Clearly, both the public sector and the private sector have a role to play in getting these technologies to the point where we can use them in New England, and also where we can deploy them affordably for the developing world,” Broad said at an event sponsored by America Is All In, a coalition of nonprofit and business organizations.

      Food and climate alliance

      Food systems around the world are increasingly at risk from the impacts of climate change. At the same time, these systems, which include all activities from food production to consumption and food waste, are responsible for about one-third of the human-caused greenhouse gas emissions warming the planet.

      At COP26, MIT’s Abdul Latif Jameel Water and Food Systems Lab announced the launch of a new alliance to drive research-based innovation that will make food systems more resilient and sustainable, called the Food and Climate Systems Transformation (FACT) Alliance. With 16 member institutions, the FACT Alliance will better connect researchers to farmers, food businesses, policymakers, and other food systems stakeholders around the world.

      Looking ahead

      By the end of 2022, the Glasgow pact asks countries to revisit their nationally determined contributions and strengthen them to bring them in line with the temperature goals of the Paris Agreement. The pact also “notes with deep regret” the failure of wealthier countries to collectively provide poorer countries $100 billion per year in climate financing that they pledged in 2009 to begin in 2020.

      These and other issues will be on the agenda for COP27, to be held in Sharm El-Sheikh, Egypt, next year.

      “Limiting warming to 1.5 degrees is broadly accepted as a critical goal to avoiding worsening climate consequences, but it’s clear that current national commitments will not get us there,” says ESI’s Fernández. “We will need stronger emissions reductions pledges, especially from the largest greenhouse gas emitters. At the same time, expanding creativity, innovation, and determination from every sector of society, including research universities, to get on with real-world solutions is essential. At Glasgow, MIT was front and center in energy systems, cities, nature-based solutions, and more. The year 2030 is right around the corner so we can’t afford to let up for one minute.” More