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Two patients with the same kind of tumor can have very different experiences. One patient’s cancer may progress quickly while the other grows slowly. Treatments may shrink tumors or have no effect at all. And some patients survive while others don’t.
Efforts to profile tumors at the DNA, RNA, or epigenetic levels have revealed subtypes of tumors that help oncologists diagnose and prognose cancer, but it’s often unclear how to turn that molecular knowledge into new therapeutics. The problem is especially acute for cancers with no clear genetic cause like medulloblastoma, a pediatric brain tumor with toxic treatments and unpredictable outcomes. Now a new effort to look beyond the genome and analyze the tumor’s proteins — the functional players of the cell — has revealed previously unrecognized subtypes of medulloblastoma that could be relevant in the clinic.
Led by scientists at MIT, Boston Children’s Hospital, and the Broad Institute of MIT and Harvard, the study combined clinical, technical, and bioinformatic expertise to show that “multi-omic” approaches aimed at not just genetic material, but also proteins and their modifications, can discover new biomarkers or drug targets.
“We have known since the early days of molecular biology that critical regulatory events take place after genes are transcribed into proteins,” says Ernest Fraenkel, a co-senior author of the new paper, an associate member of the Broad Institute, and professor of biological engineering at MIT. “It’s always been a high priority to look for these events in cancer.”
The researchers describe their findings in the journal Cancer Cell. They have also generated a rich dataset and shared it with the scientific community.
To systematically examine protein modifications in medulloblastoma, the team began by collecting 45 samples of the tumor, with clinical leadership from co-senior author Scott Pomeroy, who is neurologist-in-chief at Boston Children’s Hospital and the Bronson Crothers Professor of Neurology at Harvard Medical School, and co-first author Tenley Archer, a postdoctoral researcher at Children’s Hospital.
The samples were sent to a team in the Broad Institute Proteomics Platform, led by platform senior director and co-senior author Steven Carr. With leadership from co-first author Filip Mundt, the team used a workflow developed at the Broad to perform global measurement of the tumors’ proteins, as well as several ways in which proteins can be modified in the cell after they’re made, such as phosphorylation and acetylation. This proteomics data was then integrated with global measurements of the DNA and RNA by Broad proteomics computational scientists D.R. Mani and Karsten Krug in collaboration with the German Cancer Research Center.
“Recent advances in proteomic technology, especially with respect to analyses of modifications like phosphorylation, enabled us to conduct one of the most comprehensive proteomic studies of medulloblastoma to date, giving us a deeper view of this disease than we could get using genomic methods alone,” says Carr.
Although only a few dozen samples were tested, the results include hundreds of thousands of diverse measurements. This complex dataset was analyzed by a team of computational biologists led by co-senior author Jill Mesirov, a Broad senior institute fellow and professor of medicine at the University of California at San Diego School of Medicine, and Ernest Fraenkel.
Within the four existing subgroups of medulloblastoma — wingless (WNT), sonic hedgehog (SHH), group 3, and group 4 — the researchers found new subtypes.
“Adding data on proteins and their modifications allowed us to see differences in medulloblastoma tumor sub-groups that we can’t see through RNA analyses,” says Pomeroy.
The data showed that group 3 tumors can be divided into two subtypes, including one that is driven by mutations related to the MYC oncogene and has very poor prognosis.
"Notably, the proteomic data improved our ability to stratify the group-3, poorest outcome patients, compared to our previous work using gene expression data,” says Mesirov.
Proteomic analysis of SHH tumors showed different sets of activated pathways. One subtype was strongly driven by the sonic hedgehog pathway, and the other not as much.
To help interpret the data, Fraenkel and co-first author Tobias Ehrenberger led development of a new approach to predict key regulatory proteins that caused changes seen in the proteomic data. Dubbed “kinome analysis,” the methods led them to discover a protein, PRKDC, that when inactivated made one subtype of the tumors more sensitive to radiation treatment.
“Kinome analysis suggested potential therapeutic avenues for type 3 medulloblastoma, a subtype that is known to have poor outcomes — namely, a target that may sensitize tumor cells to radiation,” Pomeroy says.
Fraenkel says patients with medulloblastoma “need better treatments.”
“We hope that insights from this study may lead to therapeutic strategies that result in fewer long-term side effects,” he says.
Connor Coley, currently pursuing his graduate degree in chemical engineering at MIT, has been selected as one of 2018’s “Talented Twelve” by Chemical and Engineering News (C&EN), the weekly magazine of the American Chemical Society. Coley was recognized for his work in “reprogramming the way chemists design drugs.”
Currently a member of the Klavs Jensen and William Green research groups, Coley is focused on improving automation and computer assistance in synthesis planning and reaction optimization with medicinal chemistry applications. He is more broadly interested in the design and construction of automated microfluidic platforms for analytics (e.g. kinetic or process understanding) and on-demand synthesis. Coley’s work is an integral part of the new MIT-industry consortium, Machine Learning for Pharmaceutical Discovery and Synthesis.
As described in C&EN, “Machine learning aims to create artificial intelligence systems that make decisions with little intervention from people. Coley's efforts in this arena have blossomed into a collaboration between MIT and eight drug industry partners, known as the Machine Learning for Pharmaceutical Discovery and Synthesis Consortium. While most other chemists working in the field of machine learning and chemical synthesis use rules devised by experts to guide their systems, Coley relies on reactions in databases, such as those in U.S. patent filings, to teach the computer what transformations will and won't take place without being influenced by human bias.”
Earlier this year, Coley was also named a 2018 “Riser” by the U.S. Defense Advanced Research Projects Agency (DARPA).
To find its annual Talented Twelve, C&EN consulted a panel of industry advisers, the publication's advisory board, and Talented Twelve alumni to nominate prospects aged 42 or younger “who are taking risks in the early stages of their career.” They also accepted nominations from readers through an online form. The team researched and evaluated more than 350 candidates before finalizing the 2018 Talented Twelve.
Professors Brady Olsen and Fikile Brushett, also of MIT Chemical Engineering, have previously been named to this group.
Subtropical gyres are huge, sustained currents spanning thousands of kilometers across the Pacific and Atlantic oceans, where very little grows.
With nutrients in short supply, phytoplankton, the microscopic plants that form the basis of the marine food chain, struggle to thrive.
However, some phytoplankton do live within the hostile environment of these gyres, and exactly how they obtain their nutrients has long been a mystery.
Now research by Edward Doddridge, a postdoc in the Department of Earth, Atmospheric and Planetary Sciences at MIT, has found that phytoplankton growth in subtropical gyres is affected by a layer of water well below the ocean surface, which allows nutrients to be recycled back to the surface.
Working with David Marshall at Oxford University, Doddridge has developed a model to investigate the mechanism behind phytoplankton growth within the gyres, which appears in the Journal of Geophysical Research: Oceans.
According to the textbooks, winds push surface waters into the center of the gyres and then downward, taking nutrients away from the sunlit zone and therefore preventing phytoplankton from thriving.
But previous research by Doddridge has suggested that this view is too simplistic, and that the motion of eddies — the ocean equivalent of weather systems — within the gyres acts against this movement, preventing the water from being pushed far downward.
To investigate this further, the researchers developed a simple computer model, in which they split the ocean into two layers: the sunlit layer and a layer of homogenous water below it, called mode water. Beneath this layer of mode water is the abyss, which was not included in the model.
Within the model, the researchers included both the wind-led process of water convergence from the sides of the gyre and then downward, and the way that eddies should act against this movement.
When they ran the model, its results broadly mirrored observations of the gyres themselves, with higher nutrient concentration and phytoplankton productivity at the edges of the gyres, and lower productivity in the center.
They then began varying the different parameters of the model, to investigate what effect this would have on nutrient levels and phytoplankton productivity.
They first varied a mechanism proposed previously by researchers and known as eddy pumping, in which the swirling motion of circular currents draws colder, nutrient-rich water up from below.
“We changed how much fluid this mechanism could swap between the sunlit layer and the homogenous layer below, and we found that as we increased the eddy pumping, the nutrient concentration went up, as suggested by previous research,” says Doddridge.
However, the effect of this eddy pumping began to plateau at higher levels. The more the researchers increased the eddy pumping mechanism, the smaller the increase in nutrient concentration became.
They then varied the process of horizontal water convergence and downward pumping within the gyres, known as residual Ekman transport. They found this process had a considerable impact on nutrient concentration.
Finally, the researchers varied the thickness of the layer of homogenous water below the sunlit layer, which they also found to have a significant impact on nutrient concentration.
Previous research had suggested that as this layer of mode water gets thicker, it blocks nutrients coming up from below, resulting in lower productivity levels in the sunlit zone. However, the results of the model suggest the opposite is the case, with a thicker mode layer leading to greater nutrient concentration. This was particularly the case when the level of Ekman transport was low, Doddridge says.
“When phytoplankton and other things living in the sunlit layer die, or get eaten and excreted, they start falling down through the ocean, and their nutrients are absorbed back into the water,” Doddridge says.
“So the thicker that homogenous layer is, the longer it takes these particles to fall through it, and the more of their nutrients are absorbed into the fluid, to be recycled as food.”
While the nutrients remain in the homogenous layer, it does not take much energy for them to be mixed back up to the surface, Doddridge says. But if they quickly drop below it into the abyss — because the homogenous layer is thin, for example — the nutrients are essentially cut off from the surface water above, he says.
When the researchers tested the results of the model using data from satellites, autonomous robots, and ships, they found that it supported their findings, suggesting that thicker mode water does indeed enhance phytoplankton growth within subtropical gyres.
In the future, Doddridge would like to carry out further experiments using more complex models, to gain further insights into the way in which nutrients are fed into and recycled within subtropical gyres.
The nutrient-poor upper ocean waters of the subtropical gyre play globally important roles in ocean carbon uptake, with biological processes mediating a large fraction of this carbon uptake, but the processes supplying nutrients required to support net biological production in these ecosystems remains unclear, according to Matthew Church at the University of Montana, who was not involved in the research.
“The paper highlights the key role of physical processes (specifically eddies) in regulating both the upward supply of nutrients, and the downward flux of sinking organic matter,” Church says. “The authors conclude that this latter term, specifically the depth over which organic particles are remineralized, sets constraints on productivity of the overlying waters. This model-derived conclusion presents a field-testable hypothesis.”
The Massachusetts Institute of Technology Investment Management Company (MITIMCo) announced today that MIT’s unitized pool of endowment and other MIT funds generated an investment return of 13.5 percent during the fiscal year ending June 30, 2018. At the end of the fiscal year, MIT’s endowment funds totaled $16.4 billion, excluding pledges.
MIT’s endowment is intended to support current and future generations of MIT scholars with the resources needed to advance knowledge, research, and innovation. As such, endowment funds are used for Institute activities including education, research, campus renewal, faculty work, and student financial aid.
The Institute’s need-blind undergraduate admissions policy ensures that an MIT education is accessible to all qualified candidates regardless of financial resources. MIT works closely with all families who qualify for financial aid to develop an individual affordability plan tailored to their financial circumstances. In 2017-18, the average MIT scholarship was $45,530. Fifty-eight percent of MIT undergraduates received need-based financial aid, and 30 percent of MIT undergraduate students received scholarship funding from MIT and other sources sufficient to cover the total cost of tuition.
MITIMCo is a unit of MIT, created to manage and oversee the investment of the Institute’s endowment, retirement, and operating funds. As of June 30, 2018 MITIMCo had approximately $25.3 billion of assets under management.
MIT’s Report of the Treasurer for fiscal year 2018 was made available publicly today.
The Bert L and N Kuggie Vallee Foundation has named McGovern Institute investigator Mark Harnett a 2018 Vallee Scholar. The Vallee Scholars Program recognizes original, innovative, and pioneering work by early career scientists at a critical juncture in their careers and provides $300,000 in discretionary funds to be spent over four years for basic biomedical research. Harnett is among five researchers named to this year’s Vallee Scholars Program.
Harnett, who is also the Fred and Carole Middleton Career Development Assistant Professor in the Department of Brain and Cognitive Sciences, is being recognized for his work exploring how the biophysical features of neurons give rise to the computational power of the brain. By exploiting new technologies and approaches at the interface of biophysics and systems neuroscience, research in the Harnett lab aims to provide a new understanding of the biology underlying how mammalian brains learn. This may open new areas of research into brain disorders characterized by atypical learning and memory (such as dementia and schizophrenia) and may also have important implications for designing new, brain-inspired artificial neural networks.
The Vallee Foundation was established in 1996 by Bert and Kuggie Vallee to foster originality, creativity, and leadership within biomedical scientific research and medical education. The foundation’s goal to fund originality, innovation, and pioneering work “recognizes the future promise of these scientists who are dedicated to understanding fundamental biological processes.” Harnett joins a list of 24 Vallee Scholars, including McGovern investigator Feng Zhang, who have been appointed to the program since its inception in 2013.
Given only a few frames of a video, humans can usually surmise what is happening and will happen on screen. If we see an early frame of stacked cans, a middle frame with a finger at the stack’s base, and a late frame showing the cans toppled over, we can guess that the finger knocked down the cans. Computers, however, struggle with this concept.
In a paper being presented at this week’s European Conference on Computer Vision, MIT researchers describe an add-on module that helps artificial intelligence systems called convolutional neural networks, or CNNs, to fill in the gaps between video frames to greatly improve the network’s activity recognition.
The researchers’ module, called Temporal Relation Network (TRN), learns how objects change in a video at different times. It does so by analyzing a few key frames depicting an activity at different stages of the video — such as stacked objects that are then knocked down. Using the same process, it can then recognize the same type of activity in a new video.
In experiments, the module outperformed existing models by a large margin in recognizing hundreds of basic activities, such as poking objects to make them fall, tossing something in the air, and giving a thumbs-up. It also more accurately predicted what will happen next in a video — showing, for example, two hands making a small tear in a sheet of paper — given only a small number of early frames.
One day, the module could be used to help robots better understand what’s going on around them.
“We built an artificial intelligence system to recognize the transformation of objects, rather than appearance of objects,” says Bolei Zhou, a former PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) who is now an assistant professor of computer science at the Chinese University of Hong Kong. “The system doesn’t go through all the frames — it picks up key frames and, using the temporal relation of frames, recognize what’s going on. That improves the efficiency of the system and makes it run in real-time accurately.”
Co-authors on the paper are CSAIL principal investigator Antonio Torralba, who is also a professor in the Department of Electrical Engineering and Computer Science; CSAIL Principal Research Scientist Aude Oliva; and CSAIL Research Assistant Alex Andonian.
Picking up key frames
Two common CNN modules being used for activity recognition today suffer from efficiency and accuracy drawbacks. One model is accurate but must analyze each video frame before making a prediction, which is computationally expensive and slow. The other type, called two-stream network, is less accurate but more efficient. It uses one stream to extract features of one video frame, and then merges the results with “optical flows,” a stream of extracted information about the movement of each pixel. Optical flows are also computationally expensive to extract, so the model still isn’t that efficient.
“We wanted something that works in between those two models — getting efficiency and accuracy,” Zhou says.
The researchers trained and tested their module on three crowdsourced datasets of short videos of various performed activities. The first dataset, called Something-Something, built by the company TwentyBN, has more than 200,000 videos in 174 action categories, such as poking an object so it falls over or lifting an object. The second dataset, Jester, contains nearly 150,000 videos with 27 different hand gestures, such as giving a thumbs-up or swiping left. The third, Charades, built by Carnegie Mellon University researchers, has nearly 10,000 videos of 157 categorized activities, such as carrying a bike or playing basketball.
When given a video file, the researchers’ module simultaneously processes ordered frames — in groups of two, three, and four — spaced some time apart. Then it quickly assigns a probability that the object’s transformation across those frames matches a specific activity class. For instance, if it processes two frames, where the later frame shows an object at the bottom of the screen and the earlier shows the object at the top, it will assign a high probability to the activity class, “moving object down.” If a third frame shows the object in the middle of the screen, that probability increases even more, and so on. From this, it learns object-transformation features in frames that most represent a certain class of activity.
Recognizing and forecasting activities
In testing, a CNN equipped with the new module accurately recognized many activities using two frames, but the accuracy increased by sampling more frames. For Jester, the module achieved top accuracy of 95 percent in activity recognition, beating out several existing models.
It even guessed right on ambiguous classifications: Something-Something, for instance, included actions such as “pretending to open a book” versus “opening a book.” To discern between the two, the module just sampled a few more key frames, which revealed, for instance, a hand near a book in an early frame, then on the book, then moved away from the book in a later frame.
Some other activity-recognition models also process key frames but don’t consider temporal relationships in frames, which reduces their accuracy. The researchers report that their TRN module nearly doubles in accuracy over those key-frame models in certain tests.
The module also outperformed models on forecasting an activity, given limited frames. After processing the first 25 percent of frames, the module achieved accuracy several percentage points higher than a baseline model. With 50 percent of the frames, it achieved 10 to 40 percent higher accuracy. Examples include determining that a paper would be torn just a little, based how two hands are positioned on the paper in early frames, and predicting that a raised hand, shown facing forward, would swipe down.
“That’s important for robotics applications,” Zhou says. “You want [a robot] to anticipate and forecast what will happen early on, when you do a specific action.”
Next, the researchers aim to improve the module’s sophistication. The first step is implementing object recognition together with activity recognition. Then, they hope to add in “intuitive physics,” meaning helping it understand real-world physical properties of objects. “Because we know a lot of the physics inside these videos, we can train module to learn such physics laws and use those in recognizing new videos,” Zhou says. “We also open source all the code and models. Activity understanding is an exciting area of artificial intelligence right now.”
Researchers are only beginning to understand how the gut microbiome — the vital community of microorganisms that lives in our intestines — interacts with our bodies and the food we eat. For doctors and scientists, the challenge lies in predicting whether changes to gut microbes are associated with either disease, diet, or both — a complex problem due to the ever-shifting nature of gut bacteria, food consumption, and the ways in which the two interact.
Students at the Center for Microbiome Informatics and Therapeutics (CMIT) and the Health Sciences and Technology program, two MIT-based initiatives, sought to discover how to stabilize the gut microbiome and separate the effects of diet and disease, thus allowing for deeper investigation into the link between the gut and human health. The study was recently published in Scientific Reports and led by Thomas Gurry, a research scientist at CMIT who received his PhD in computational and systems biology from MIT in 2015.
“A number of studies show the association between the compositions of gut microbiomes and diet, but the precise relationships between individual nutrients and their effect on composition are hard to pinpoint,” Gurry says. “We wanted to go to a higher level of resolution than studies in the past.”
Fixed diets are difficult to design and implement, especially across a group of people. Enforcing a particular eating pattern is crucial, because the absence of a single ingredient would alter the composition of that particular meal and affect the resulting stool sample, rendering results unusable.
Liquid meal replacements in the form of bottled shakes are commonly served in hospitals nationwide. They are often given as a nutritional supplement — and occasionally as the exclusive source of nutrition — to patients ranging from the elderly to those being treated for eating disorders. With this in mind, the researchers chose to use the commercially available liquid meal replacement shake Ensure as the sole food source for their subjects’ fixed diet. The advantage of a liquid shake over a preportioned meal, the researchers reasoned, is that individuals cannot intentionally or accidentally leave out any part of a meal.
In the study, healthy adult participants drank only Ensure for six days. Participants could have as much of the meal replacement shake as they wanted but no other food or drink besides water. After the third day, subjects were given a large quantity of one of a sample of nutrients, referred to as spike-ins, to see what effects this single nutrient (such as the fiber pectin) had on the microbiome and whether its influence was reproducible across subjects.
To the researchers’ surprise, day-to-day variability of the gut microbiome did not go down. Although Gurry was reluctant to say why, he was able to conclude that the standardized Ensure diet does not have the effect of creating a constant background in the gut. What was detected over the course of the study was a high degree of diet-induced stress in individual participants’ microbiota, which Gurry says could be attributed to the large amounts of processed sugar in the Ensure product.
“Moving forward, hopefully more consideration will be given to the effect of these nutritional meal replacements on the microbiome in a clinical context, as some patients being treated for Crohn’s disease, for example, are asked to eat only Ensure.”
The team was equally surprised to find that the effects of the majority of nutrients, excluding the fibers inulin and pectin, despite being administered in high doses, produced little effect on microbiome composition and no reproducible impact across subjects. “These results suggest that the major drivers of microbiome composition are dietary fibers,” Gurry says.
Ultimately, according to Gurry, more needs to be documented at the strain or subspecies level of the gut. “We now know that a standardized diet is not an effective way to clean up a signal in a study measuring associations between microbiome composition and disease, nor does it reduce day-to-day variability,” he says.
“However, we do find that different individuals have different responses to the same specific nutrient, likely due to the different strains they harbor. This offers an opportunity for precision medicine.”
Samuel W. Bodman III ScD ’65, a former MIT associate professor and life member emeritus of the MIT Corporation, who served as the U.S. Secretary of Energy and in other cabinet posts, died on Sept. 7 in El Paso, Texas, after a long illness. He was 79.
Bodman served as Secretary of Energy in the George W. Bush administration from 2005 to 2009, after being unanimously confirmed by the Senate. He had previously served as Deputy Secretary of Treasury and Deputy Secretary of Commerce. Bush said in a prepared statement, “Laura and I are deeply saddened by the death of Sam Bodman. Sam had a brilliant mind, and we are fortunate that he put his intellect to work for our country as Secretary of Energy. I am proud that he was a member of my cabinet, and I am proud that he was my friend.”
“Sam led an extraordinary life of leadership and service in business, academia, and government. MIT was the very fortunate beneficiary of his time, talent, and wisdom in so many different capacities over the years. We are saddened by his loss but grateful for his impact on the Institute and well beyond,” says Robert Millard, chair of the MIT Corporation.
Bodman was born in Chicago in 1938 and earned his undergraduate degree in chemical engineering from Cornell University in 1961. He then earned his ScD in chemical engineering at MIT in 1965, where he then began a six-year stint as an associate professor. He later became a station director of what is now called the David H. Koch School of Chemical Engineering Practice, and he served on a landmark commission on the future of MIT education. He became a member of the MIT Corporation, the Institute’s governing body, and served on its Executive and Investment Committees, ultimately becoming a lifetime trustee.
He also served on the boards of Cornell University, the Isabella Stewart Gardner Museum, the New England Aquarium, and the Carnegie Institution for Science. He was a member of the American Academy of Arts and Sciences and the National Academy of Engineering.
His family recalls that a favorite saying of his was, “Every day is another opportunity to excel,” which he liked to say every morning, according to his stepdaughter Caroline Greene.
After leaving MIT, Bodman was technical director of the American Research and Development Corporation, an early venture capital firm, and from there went to Fidelity Venture Associates. He was appointed president and COO of Fidelity Investments in 1983, and also became director of the Fidelity Group of Mutual Funds.
In 1987, he moved to the Cabot Corporation to serve as chairman and CEO, where he served until joining the Bush administration in 2001. “His mind is extraordinarily creative and innovative. He has an ability to see things in a very broad and yet comprehensive way,” Kennett F. Burnes, who worked with Bodman at Cabot and succeeded him as the company’s chief executive, told The Boston Globe at the time of his appointment to be energy secretary.
Though he was born in Chicago and had homes in Florida and Texas as well as Martha’s Vineyard, Bodman remained an avid lifelong fan of the Boston Red Sox and the New England Patriots, his family says. Even during his eight years in Washington, “he made it very plain that those were his most favorite teams,” says his wife Diane Bodman.
Reflecting on his lifelong association with MIT, first as a student, then a professor, and finally a Corporation member, Mrs. Bodman says that “he loved MIT. He thought it was the finest institution in the world of its kind. He felt MIT really changed his life.”
Part of that, she says, was that it gave him “an approach to problems which encouraged a fact-based and insightful view of a problem, whether it was a problem in thermodynamics or a practical life problem.”
“He was a very intense person — he did everything with intensity,” Greene says. “He was a remarkable force in everything he did, both in his professional and personal life.”
Mrs. Bodman adds that “he found humor in most things, most situations. He was always quick to laugh.” Although he enjoyed fly fishing and loved his dogs, “he wasn’t a man who had hobbies. Sam’s favorite activity was work,” she says.
One thing he particularly enjoyed, she says, was “mentoring young people,” whether they were younger workers in the Department of Energy or entrepreneurs he worked with in his venture capital endeavors. “He encouraged thoughtful risk-taking,” she says. “He was a motivator.”
Mrs. Bodman adds that over the years, many of those young people returned to tell him “my life was changed by you.” They would say that lessons they learned by observing the way he worked or ran meetings were something they “carried over into their professional lives, and things they learned from his leadership style they have tried to use in their own careers.”
“He brought out the best in everyone,” Greene adds. “He demanded the best, saw the best, and expected no less from everyone, including himself.”
Bodman is survived by his wife M. Diane Bodman; three children, Elizabeth Mott, Andrew Bodman, and Sarah Greenhill; two step-children, Perry Barber and Caroline Greene; and a brother, James Bodman. His first wife, Elizabeth Little Bodman, died in 1982.
Follow the money. It’s a famous phrase from the Watergate era, but it applies to everyday life in modern Washington as well. That advice just got easier for everyone to carry out, thanks to the launch of LobbyView.org, a new public database created by MIT political scientist In Song Kim.
LobbyView.org has 1.2 million public records of congressional lobbying, and a flexible interface designed to make research simple. MIT News spoke with Kim, the Class of 1956 Career Development Associate Professor of Political Science, about the project.
Q: What is LobbyView.org, and how did you create it?
A: LobbyView.org is a publicly available online database where researchers and journalists and others who are interested in politics will be able to search the universe of the political activities that have been filed and reported, so that they can understand how legislative politics, especially, works in the U.S. For example, people can easily search how, and to what extent, firms engage in political activities.
There is a legal requirement that any lobbyist or lobbying firm representing clients disclose lobbying activities performed on behalf of those clients. Such information has been available in the form of lobbying reports that are filed quarterly. Currently there are more than 1.2 million reports that have been filed since 1999. I downloaded the files from the Senate Office of Public Records and then developed a program to automatically parse those reports. LobbyView enables users to easily search and download a massive amount of lobbying data based on firms’ names, content of lobbying, legislative bills, and politicians’ names. So researchers can examine how private interests are reflected in actual policymaking and which political networks dominate legislative politics in certain issue domains.
Q: How does this relate to your research, which often focuses on international trade?
A: My work on trade politics motivates the whole data collection, because I realized the U.S. is setting trade policies at a very fine-grained product level. Each product gets different policies, reflecting different interests, as we see from the recent trade wars between China and the U.S. … I started looking at why they differ and recognized that firms are lobbying on specific products. The general pattern we see is that there are more than 12,000 bills that are introduced in each Congress. The majority of those bills are lobbied, and, interestingly, lobbied by one or two groups.
The dominant theory in international political economy has been that industry-level lobbying has been driving trade politics. However I found there is significant variation at the firm level, even when firms belong to the same industry.
It’s been difficult to study firms’ political activities, and policymakers won’t necessarily disclose that they have met with lobbyists to discuss legislation, even though we all know this is an important part of how K Street [where many lobbying firms in Washington are located] works. So LobbyView.org is designed to increase transparency based on publicly available information, establishing links among firms, lobbyists, and politicians.
Q: Having now publicly launched LobbyView.org, what are the next steps for using it, beyond your own research?
A: I am trying to incorporate this in teaching at MIT, in the class that I created last semester, 17.835 (Machine Learning and Data Science in Politics). The idea is that a lot of MIT students have various technical skills to analyze and visualize data in general. What I’m trying to do is bring these data into class, so students can learn about politics using data. This is a nice application of big data analysis, where you can use automated data collection and apply algorithms to identify patterns, and this opens the door for a lot of interesting research. Various students have been involved in this project through UROP [MIT’s Undergraduate Research Opportunities Program], or through group projects in the class. And I’m looking forward to expanding this database to cover campaign contributions in electoral politics, so money in politics can be further studied by researchers and students at MIT.
In recognition of their exceptional potential to be leaders in the life sciences, three MIT postdocs at the Koch, Picower, and Whitehead institutes at MIT are among 15 young researchers to earn Hanna Gray Fellowships from the Howard Hughes Medical Institute (HHMI), the Chevy Chase, Maryland-based organization has announced.
The early-career awards are given to individuals around the U.S. who are from racial, gender, ethnic, and other groups underrepresented in the life sciences. The fellowships will support their work with up to $1.4 million in funding over eight years, enough to last well into a tenure track position after they complete their postdoctoral studies.
“This program will help us retain the most diverse talent in science,” HHMI President Erin O’Shea said in the announcement. “We feel it’s critically important in academia to have exceptional people from all walks of life, all cultures, and all backgrounds — people who can inspire the next generation of scientists.”
MIT’s three Hanna Gray Fellows are Matheus Victor, who works in the lab of Li-Huei Tsai in the Picower Institute for Learning and Memory; Quinton Smith, who works in Sangeeta Bhatia’s lab at the Koch Institute for Integrative Cancer Research; and Jarrett Smith, who works in the David Bartel Lab at the Whitehead Institute for Biomedical Research.
Jarrett Smith was always interested in science, but no one in his family had ever received a PhD, making biology research feel like an unlikely career path for him. Nevertheless, he followed his passion, which led him to his PhD program at the Johns Hopkins University School of Medicine. Despite his strong academic performance, Smith began graduate school with doubts about his ability to become a scientist. His mentors were incredible teachers, he says, but their self-assuredness could be intimidating.
“They were absolutely my role models, but I didn’t think of them as having gone through what I was going through. In the first few years, I felt like I had a lot of catching up to do,” Smith says.
Now, as a postdoc in David Bartel's lab at the Whitehead Institute, he studies how cells respond to stress. When a cell is exposed to environmental stressors such as heat, UV radiation, or viral infection, proteins and RNAs in the cell may clump together into dense aggregates called stress granules. The exact function of stress granules and their potential role in disease are unknown, so Smith is investigating changes in the cell linked to their formation. His findings could shed light on a potential role for stress granules in cancer, infection, and neurodegenerative disease.
“I’m grateful for the support that the fellowship will provide during the formative years of my career,” Smith says. “This kind of opportunity gives you the confidence to set ambitious research goals and find out what you can accomplish.”
Quinton Smith fell in love with regenerative medicine the summer after his sophomore year of college. Working on an epidemiological study at the University of New Mexico Cancer Research Facility in his hometown of Albuquerque, Smith found himself extracting DNA from hundreds of mouthwash samples in order to answer overarching questions about the biological and behavioral factors that contribute to cancer.
The experience left him with a strong appreciation for the complexities of human disease, and an even stronger desire to translate his knowledge into better interventions for patients. To this end, he completed his PhD at Johns Hopkins University, exploring a variety of engineering techniques to control and probe the differentiation of pluripotent stem cells. In 2017 he joined the laboratory of Sangeeta Bhatia, the John J. and Dorothy Wilson Professor at MIT’s Institute for Medical Engineering and Science and Electrical Engineering and Computer Science and director of the Marble Center for Cancer Nanomedicine.
By then, the lab had already demonstrated success developing implantable “mini-livers” able to engraft and respond to regenerative stimuli in mice with damaged livers. However, the design is incomplete and Smith wants to incorporate a biliary tree to guide hepato-secreted bile acids that aid in the breakdown of fats but pose as a potential toxin to these therapeutic grafts. He is working to build such a tree with an unexpected tool — microfluidic vessels, forged by biomaterial-encapsulated acupuncture needles that, once removed, can be used to create a network of biliary-lined tunnels through which bile can flow.
The ability to bolster cutting-edge research is but one benefit of Smith’s HHMI award. He and Bhatia praise the seeds the program plants within the academic environment, providing much-needed visibility that achievements in STEM are, and will continue to be, driven by scientists from all walks of life.
“I think this program is directing a shift in what a researcher looks like, offering a motivating exposure to the next generation of students who have the potential to impact science regardless of their background,” Smith says. “This is an incredible and humbling opportunity to explore my passion for the promise of regenerative medicine, and to have the resources to tinker and extend my creativity around translational research.”
As a graduate student at Washington University in St. Louis, Matheus Victor, a native of Recife, Brazil, who came to Florida at the age of 15, learned that in the U.S., Latino immigrants are rare among scientific researchers. But there he was, pursuing his dreams to become a neuroscientist. The realization inspired him to lead a Latin American student group at WashU and to conduct outreach activities including creating bilingual curricula for local students.
“I was so privileged to be in a top tier graduate program pursuing my interest,” he said. “How many people get to pursue an interest? We live in a world where you have to earn money and you have to feed your family.”
Now he’s a new postdoc at MIT’s Picower Institute for Learning and Memory in the lab of Institute director and Picower Professor Li-Huei Tsai, with a prestigious fellowship that will support him as he embarks on two investigations of the role of specific cell types in brain aging and cognitive decline.
In one, he plans to turn human induced pluripotent stem cells into microglia, an immune cell of the nervous system increasingly implicated in Alzheimer’s disease, and implant them in the brains of mice where the original microglia have been removed. With this chimera model Victor can test how microglia with different genetic variations act in a mammalian brain to see how those variations might contribute to disease pathology.
In the other, he is interested in studying how inhibitory interneurons change in the aging brain. The neurons are of particular interest because they are the source of a crucial brain rhythm that is notably reduced in Alzheimer’s disease. Understanding more about how they function and falter could help explain that important change.
Luis von Ahn, Carnegie Mellon University consulting professor and CEO of Duolingo, has just been announced as the winner of the 2018 $500,000 Lemelson-MIT Prize for invention. Von Ahn has been a pioneer in cybersecurity as a co-inventor of CAPTCHA and reCAPTCHA. He is also the co-founder of Duolingo, the most popular language-learning platform worldwide with the mission of making education free and accessible to everyone. Von Ahn joins a long lineage of inventors to receive the Lemelson-MIT Prize, the largest cash prize for invention in the United States.
CAPTCHA, or Completely Automated Public Turing test to tell Computers and Humans Apart, is a now universally recognizable cybersecurity system, which websites use to prevent automated programs from inflicting large-scale abuse. CAPTCHA requires users to complete a computer-generated test of characters. While these tests are easy for humans to complete, computers cannot yet do so. CAPTCHA applications include, but are not limited to: authenticating website registration, protecting email addresses from scrapers, and blocking scalpers from buying or reselling tickets in large quantities. Von Ahn later invented reCAPTCHA, a new form of CAPTCHA that digitizes books and archives.
Currently, von Ahn is working full-time at his company, Duolingo, which offers 82 language courses to over 300 million users worldwide. Through Duolingo, von Ahn is making language education free, fun, and accessible to everyone.
Von Ahn’s dedication to improving the world through technology, as well as his commitment to mentorship and education, earned him the Lemelson-MIT Prize. The Lemelson-MIT Prize honors outstanding mid-career inventors improving the world through technological invention and demonstrating a commitment to mentorship in science, technology, engineering and mathematics (STEM).
“Luis has created a novel resource for people around the world who need to learn a new language,” says Stephanie Couch, executive director of the Lemelson-MIT Program. “For some users, Duolingo is key to survival in a new country. Others use Duolingo to learn a language for business, leisure, or travel. Luis’ dedication to harnessing the power and promise of technology to engage and empower learners of all types is truly inspiring.”
“I am incredibly honored and grateful to receive the Lemelson-MIT Prize,” says von Ahn. “Throughout my career, I’ve been passionate about using technology and invention to make a positive difference in the world – previously with reCAPTCHA and now with Duolingo. Earning this prize is a great testament to the work that the entire Duolingo team does in creating technology that’s made education free and accessible to millions of people worldwide.”
“We are excited to recognize Luis for his significant contribution to solving modern challenges such as cyber-security and global migration,” said Carol Dahl, executive director of The Lemelson Foundation. “His inventions underpinning reCAPTCHA highlight the fact that, even as machines get smarter, there is still an enormous need for human intelligence individually and collectively.”
Von Ahn will speak at EmTech MIT, the annual conference on emerging technologies hosted by MIT Technology Review at the MIT Media Lab on Wednesday, Sept. 12 at 5:30 p.m.
The Lemelson-MIT Program is now seeking nominations for the 2019 $500,000 Lemelson-MIT Prize. Please contact the Lemelson-MIT Program at firstname.lastname@example.org for more information or visit the Lemelson-MIT website to learn more.
Dopamine, a signaling molecule used throughout the brain, plays a major role in regulating our mood, as well as controlling movement. Many disorders, including Parkinson’s disease, depression, and schizophrenia, are linked to dopamine deficiencies.
MIT neuroscientists have now devised a way to measure dopamine in the brain for more than a year, which they believe will help them to learn much more about its role in both healthy and diseased brains.
“Despite all that is known about dopamine as a crucial signaling molecule in the brain, implicated in neurologic and neuropsychiatric conditions as well as our abilty to learn, it has been impossible to monitor changes in the online release of dopamine over time periods long enough to relate these to clinical conditions,” says Ann Graybiel, an MIT Institute Professor, a member of MIT’s McGovern Institute for Brain Research, and one of the senior authors of the study.
Michael Cima, the David H. Koch Professor of Engineering in the Department of Materials Science and Engineering and a member of MIT’s Koch Institute for Integrative Cancer Research, and Rober Langer, the David H. Koch Institute Professor and a member of the Koch Institute, are also senior authors of the study. MIT postdoc Helen Schwerdt is the lead author of the paper, which appears in the Sept. 12 issue of Communications Biology.
Dopamine is one of many neurotransmitters that neurons in the brain use to communicate with each other. Traditional systems for measuring dopamine — carbon electrodes with a shaft diameter of about 100 microns — can only be used reliably for about a day because they produce scar tissue that interferes with the electrodes’ ability to interact with dopamine.
In 2015, the MIT team demonstrated that tiny microfabricated sensors could be used to measure dopamine levels in a part of the brain called the striatum, which contains dopamine-producing cells that are critical for habit formation and reward-reinforced learning.
Because these probes are so small (about 10 microns in diameter), the researchers could implant up to 16 of them to measure dopamine levels in different parts of the striatum. In the new study, the researchers wanted to test whether they could use these sensors for long-term dopamine tracking.
“Our fundamental goal from the very beginning was to make the sensors work over a long period of time and produce accurate readings from day to day,” Schwerdt says. “This is necessary if you want to understand how these signals mediate specific diseases or conditions.”
To develop a sensor that can be accurate over long periods of time, the researchers had to make sure that it would not provoke an immune reaction, to avoid the scar tissue that interferes with the accuracy of the readings.
The MIT team found that their tiny sensors were nearly invisible to the immune system, even over extended periods of time. After the sensors were implanted, populations of microglia (immune cells that respond to short-term damage), and astrocytes, which respond over longer periods, were the same as those in brain tissue that did not have the probes inserted.
In this study, the researchers implanted three to five sensors per animal, about 5 millimeters deep, in the striatum. They took readings every few weeks, after stimulating dopamine release from the brainstem, which travels to the striatum. They found that the measurements remained consistent for up to 393 days.
“This is the first time that anyone’s shown that these sensors work for more than a few months. That gives us a lot of confidence that these kinds of sensors might be feasible for human use someday,” Schwerdt says.
Paul Glimcher, a professor of physiology and neuroscience at New York University, says the new sensors should enable more researchers to perform long-term studies of dopamine, which is essential for studying phenomena such as learning, which occurs over long time periods.
“This is a really solid engineering accomplishment that moves the field forward,” says Glimcher, who was not involved in the research. “This dramatically improves the technology in a way that makes it accessible to a lot of labs.”
If developed for use in humans, these sensors could be useful for monitoring Parkinson’s patients who receive deep brain stimulation, the researchers say. This treatment involves implanting an electrode that delivers electrical impulses to a structure deep within the brain. Using a sensor to monitor dopamine levels could help doctors deliver the stimulation more selectively, only when it is needed.
The researchers are now looking into adapting the sensors to measure other neurotransmitters in the brain, and to measure electrical signals, which can also be disrupted in Parkinson’s and other diseases.
“Understanding those relationships between chemical and electrical activity will be really important to understanding all of the issues that you see in Parkinson’s,” Schwerdt says.
The research was funded by the National Institute of Biomedical Imaging and Bioengineering, the National Institute of Neurological Disorders and Stroke, the Army Research Office, the Saks Kavanaugh Foundation, the Nancy Lurie Marks Family Foundation, and Dr. Tenley Albright.
For as long as he can remember, Bruno Perreau hoped to teach others.
“Being a teacher was something I wanted from the youngest age,” says Perreau, recalling his childhood in France. That wish has come true: Perreau taught for a decade in the French university system and is now the Cynthia L. Reed Associate Professor of French Studies and Language at MIT.
But Perreau is also an accomplished researcher with two well-received books to his name in English, and several other books and edited volumes to his credit in French. In France, he worked as an activist while entering academia. As an intellectual he has weighed in on public debates, especially those involving adoption policy and gay rights.
In short, Perreau is many things at once: teacher, scholar, author, public commentator. That seems fitting, because, as Perreau wrote in one of his books, people tend to “combine several types and levels of identity” in modern life. Indeed, much of Perreau’s work is about how personal identity interacts with states and institutions.
For instance, Perreau’s 2014 book, “The Politics of Adoption,” examined restrictive policies that limited adoption rights in France for much of the postwar era, and his 2017 book “Queer Theory: The French Response” was a close look at the intellectual landscape surrounding France’s 2013 law that opened marriage and adoption to gay couples.
In that case, as Perreau wrote in the book, states should recognize “the multipositional nature of minorities” in society. As he notes, one can be, for instance, gay, black, and a parent at the same time, and such identities vary considerably, depending on the person and circumstances. Accounting for such considerations may seem straightforward, but in practice can be a major challenge for the law.
To answer this challenge, France has long propounded a blanket universalism that formally downplays social differences. In some ways, this has helped France establish principles of equality. In others, this “logic of unity,” as Perreau calls it, has made it harder for the French to construct equal rights while explicitly acknowledging particular social differences.
On the other hand, the institution of academia is often structured to let people take on multiple roles at once. Thus, for his scholarship, teaching, public engagement, and more, MIT granted Perreau tenure in 2017.
“MIT is extremely flexible and supportive,” Perreau says.
“They invested everything in me”
Perreau grew up in Chalon-sur-Saône, France, in the southern part of Burgundy. His mother was a teacher. Growing up, Perreau was one of the few children in an extended family of modest means, and received strong moral support from relatives who wanted him to succeed.
“They didn’t have much social capital, but they invested everything in me,” Perreau says. “And they tell you, ‘You’re going to do great things,’ and you end up believing it. And you don’t want to disappoint them.”
Meanwhile, Perreau found that as a good student, classroom success had unexpected benefits.
“As a kid, when I was 7, 8, 9 years old, I would be asked by younger kids to teach them things in school,” Perreau says. “When I was 11 years old, at night, the other kids would call me and say, ‘Can you tell me, how should I understand this math problem?’ It was great for me. I really enjoyed doing this. It gave me a social identity that made me a little different, a little more likeable. … It was probably a way for me to avoid some bullying as an effeminate boy. I partially managed to avoid that, but not fully.”
Indeed, as Perreau sees it, this helped shape his long-term identity: He could become a strong student without acute worries about conforming to the crowd. As such, he kept earning scholarships that took him through school, then to college at the Institute of Political Studies (Sciences Po) in Lyon, and to a master’s program at Loughborough University in England.
At the time, Perreau was studying political institutions broadly. “How institutions work, how they help us, because we don’t have to redefine the rules of the game every time, is fascinating to me,” he says. Before he continued his studies, though, Perreau moved to Paris and became an LGBT activist, and instantly liked it.
“Suddenly I was surrounded by people involved in the same struggle,” Perreau says. But he also decided to pursue a PhD, at the University of Paris 1, the Panthéon-Sorbonne. There, Perreau took an inspiring class on the history of political ideas in relation to parity laws, gender, and more, taught by Evelyne Pisier, which steered him in a new direction.
“It was a total revelation for me because I had no idea that my own personal experience, combined with activism, could also resonate in the university system,” Perreau says. Closely examining how institutions and forms of civil rights evolve, Perreau got his doctorate and took a job at Sciences Po Paris, until he joined MIT in 2010.
Perreau’s activism and scholarship have continued to intersect as his career has unfolded. His activist work — combined with the efforts of many others, he notes — helped open up the discussion that ultimately led to France’s 2013 law on civil unions. This has made him exceptionally well-placed to write about the subject and to comment publicly about state policy, in newspaper opinion pieces and on television and radio.
Whatever progress that law represents, Perreau is hardly complacent about it. In “Queer Theory: The French Response,” he elaborates on the idea that citizenship itself does not come from universal norms. Rather, Perreau writes, “a feeling of belonging stems from a challenge to, rather than a sanctification of, the social order.”
At any given time, a legal code will not “fully grasp reality,” as he puts it. A challenge for citizens, then, is to identify the mismatches between laws, norms, and complex reality. In this sense citizenship exists in a state of tension with the established order, not by conforming to it.
Currently this idea is the center of one of the books Perreau is now working on, about “minority democracy.” By that, Perreau says, he means “how we can develop systems of representation and presence for minorities in the public space that do not require them to abandon who they are.”
If citizenship is about seeking to improve our systems of governing, then Perreau’s work helps identify him another way: as a citizen. It is one more descriptor to add to the list, along with activist, scholar, and, yes, teacher.
The Abdul Latif Jameel World Water and Food Security Lab (J-WAFS) has announced two new J-WAFS Solutions grant recipients, who are developing technologies that will provide powerful solutions for improving food and water access in the Global South.
One technology turns agricultural waste into inexpensive fertilizer, and another will enable rural communities in developing countries to test Escherichia coli (E. coli) levels in drinking water via an affordable and accessible mobile kit. J-WAFS Solutions grants provide one year of financial support to MIT principal investigators with bench-scale, market-ready technologies. The funding is accompanied by mentorship from industry partners and additional networking and guidance, supporting project teams as they advance their technologies toward commercialization.
Since the start of the program in 2015, J-WAFS Solutions grants have already been instrumental in the launch of three MIT startups. The first of these was Via Separations, a company spun out from a 2015 J-WAFS Solutions grant which has since been supported by The Engine, has received numerous innovation prizes, and has been recognized by other startup accelerators within and outside of MIT.
“As we face a future increasingly shaped by climate change, urbanization, and population increase, we can’t ensure the sustainability of our water and food systems without considerable innovation,” says J-WAFS Executive Director Renee J. Robins. “But innovation alone doesn’t solve problems. J-WAFS Solutions grants help turn MIT discoveries and inventions into water and food sector products and services that will have real world impact.”
Improving agricultural efficiency where fertilizer access is limited
Currently, most of the world’s fertilizers are produced in capital-intensive and energy-intensive centralized facilities in North America, Europe, and China. As a result, rural farmers in the Global South often pay two to three times the cost of fertilizer elsewhere.
Ahmed Ghoniem, the Ronald C. Crane (1972) Professor in the Department of Mechanical Engineering, has a solution. He and his team have been awarded a commercialization grant to develop a technology that downsizes and decentralizes soil enhancement production in order for it to be carried out on a small-scale basis in rural villages.
The process, called decentralized biomass torrefaction, uses small-scale reactor units developed in Ghoniem’s lab to heat agricultural residues (husks, stalks, and other organic materials that are otherwise considered waste) using conditions that turn this biomass into an alkaline carbon-rich substance. When added to soils, it promotes plant growth and improves soil retention of nutrients and moisture. The portable reactors can be latched onto the back of tractors or inside standard shipping containers and can perform the biomass processing in the field rather than at a centralized plant, reducing the cost of fertilizer and enabling rural, small-scale farmers to increase both their yield and their net income.
Improving water safety in Nepal using affordable monitoring kits
In spring 2016, the MIT-Nepal Initiative funded the production and shipping of 2,000 low-cost, easy to use, and highly accurate water-testing kits to Nepal. The wearable kits were designed by MIT D-Lab lecturer Susan Murcott and provide a simple, accessible way to test the presence of E. coli in drinking water. That year the prototypes were used by the Environment and Public Health Organization (ENPHO), a Nepali non-governmental organization, to test water found in food trucks and mobile water tanks in the Kathmandu Valley in the wake of the April 2015 earthquake.
Building on the success of that initial collaboration, a J-WAFS Solutions grant will support a collaboration of the MIT-Nepal Initiative, led by Murcott and Professor Jeffrey Ravel, with the NGO ENPHO and its business subsidiary EcoConcern. The goal is to refine the design of these kits based on feedback from users in Kathmandu in 2016 as well as Murcott’s subsequent kit design and implementation in Ghana, the Philippines, and in Puerto Rico after Hurricane Maria.
The team plans to develop a regionally-appropriate manufacturing and marketing plan that ensures that the kits are accessible to rural and urban communities in Nepal and elsewhere in South Asia which face the risk of water-related diseases from unsafe drinking water. Their vision is for the profits from the sale of these kits to support ENPHO’s social and environmental objectives that promote safely managed water, sanitation, and hygiene in the region.
The J-WAFS Solutions program aims to help MIT faculty and students commercialize promising innovations for our water and food systems. By speeding the development of new products and services, J-WAFS Solutions grants help advance MIT technologies that will increase the safety, supply, efficiency, and accessibility of water and food, and bring tangible economic and societal benefits to the communities where they are deployed.
The program is part of a research partnership with Community Jameel — the social enterprise organization that co-founded J-WAFS with MIT in 2015, and is administered in partnership with the MIT Deshpande Center for Technological Innovation.
“With rising populations, climate change, and urbanization, we need to start taking action now to meet the world's future needs for food and water,” Community Jameel International President Fady Jameel says. “Community Jameel is proud to be a key partner of MIT in tackling some of the most pressing issues related to food and water safety and security in the Middle East and around the world.”
Murcott says her team “is thrilled to have J-WAFS support for manufacturing and marketing the E.coli test kit.”
“Early stage ideas can more readily get funding, whereas support for commercialization is harder to come by,” she says. “This is doubly the case when one considers commercialization of a product like ours in a low-income economy such as Nepal. Yet, it is exactly in such contexts where this product is most needed. So the J-WAFS Solutions grant provides us with a huge opportunity.”
The two new 2018 J-WAFS Solutions grant recipients and their projects are:
“Decentralized torrefaction for producing high-yield, irrigation-saving fertilizer”
PI: Ahmed Ghoniem, the Ronald C. Crane (1972) Professor in the Department of Mechanical Engineering
“Manufacturing and Marketing E. coli Test Kits to Promote Safely Managed Drinking Water and Improved Public Health in Nepal”
PIs: Jeffrey S. Ravel, professor in the Department of History; and Susan Murcott, lecturer in the MIT D-Lab
A child is presented with a picture of various shapes and is asked to find the big red circle. To come to the answer, she goes through a few steps of reasoning: First, find all the big things; next, find the big things that are red; and finally, pick out the big red thing that’s a circle.
We learn through reason how to interpret the world. So, too, do neural networks. Now a team of researchers from MIT Lincoln Laboratory's Intelligence and Decision Technologies Group has developed a neural network that performs human-like reasoning steps to answer questions about the contents of images. Named the Transparency by Design Network (TbD-net), the model visually renders its thought process as it solves problems, allowing human analysts to interpret its decision-making process. The model performs better than today’s best visual-reasoning neural networks.
Understanding how a neural network comes to its decisions has been a long-standing challenge for artificial intelligence (AI) researchers. As the neural part of their name suggests, neural networks are brain-inspired AI systems intended to replicate the way that humans learn. They consist of input and output layers, and layers in between that transform the input into the correct output. Some deep neural networks have grown so complex that it’s practically impossible to follow this transformation process. That's why they are referred to as "black box” systems, with their exact goings-on inside opaque even to the engineers who build them.
With TbD-net, the developers aim to make these inner workings transparent. Transparency is important because it allows humans to interpret an AI's results.
It is important to know, for example, what exactly a neural network used in self-driving cars thinks the difference is between a pedestrian and stop sign, and at what point along its chain of reasoning does it see that difference. These insights allow researchers to teach the neural network to correct any incorrect assumptions. But the TbD-net developers say the best neural networks today lack an effective mechanism for enabling humans to understand their reasoning process.
"Progress on improving performance in visual reasoning has come at the cost of interpretability,” says Ryan Soklaski, who built TbD-net with fellow researchers Arjun Majumdar, David Mascharka, and Philip Tran.
The Lincoln Laboratory group was able to close the gap between performance and interpretability with TbD-net. One key to their system is a collection of "modules," small neural networks that are specialized to perform specific subtasks. When TbD-net is asked a visual reasoning question about an image, it breaks down the question into subtasks and assigns the appropriate module to fulfill its part. Like workers down an assembly line, each module builds off what the module before it has figured out to eventually produce the final, correct answer. As a whole, Tb-D net utilizes one AI technique that interprets human language questions and breaks those sentences into subtasks, followed by multiple computer vision AI techniques that interpret the imagery.
Majumdar says: "Breaking a complex chain of reasoning into a series of smaller subproblems, each of which can be solved independently and composed, is a powerful and intuitive means for reasoning."
Each module's output is depicted visually in what the group calls an "attention mask." The attention mask shows heat-map blobs over objects in the image that the module is identifying as its answer. These visualizations let the human analyst see how a module is interpreting the image.
Take, for example, the following question posed to TbD-net: “In this image, what color is the large metal cube?" To answer the question, the first module locates large objects only, producing an attention mask with those large objects highlighted. The next module takes this output and finds which of those objects identified as large by the previous module are also metal. That module's output is sent to the next module, which identifies which of those large, metal objects is also a cube. At last, this output is sent to a module that can determine the color of objects. TbD-net’s final output is “red,” the correct answer to the question.
When tested, TbD-net achieved results that surpass the best-performing visual reasoning models. The researchers evaluated the model using a visual question-answering dataset consisting of 70,000 training images and 700,000 questions, along with test and validation sets of 15,000 images and 150,000 questions. The initial model achieved 98.7 percent test accuracy on the dataset, which, according to the researchers, far outperforms other neural module network–based approaches.
Importantly, the researchers were able to then improve these results because of their model's key advantage — transparency. By looking at the attention masks produced by the modules, they could see where things went wrong and refine the model. The end result was a state-of-the-art performance of 99.1 percent accuracy.
"Our model provides straightforward, interpretable outputs at every stage of the visual reasoning process,” Mascharka says.
Interpretability is especially valuable if deep learning algorithms are to be deployed alongside humans to help tackle complex real-world tasks. To build trust in these systems, users will need the ability to inspect the reasoning process so that they can understand why and how a model could make wrong predictions.
Paul Metzger, leader of the Intelligence and Decision Technologies Group, says the research “is part of Lincoln Laboratory’s work toward becoming a world leader in applied machine learning research and artificial intelligence that fosters human-machine collaboration.”
The details of this work are described in the paper, “Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning," which was presented at the Conference on Computer Vision and Pattern Recognition (CVPR) this summer.
Artists may soon have at their disposal a new MIT-developed tool that could help them create digital characters, logos, and other graphics more quickly and easily.
Many digital artists rely on image vectorization, a technique that converts a pixel-based image into an image comprising groupings of clearly defined shapes. In this technique, points in the image are connected by lines or curves to construct the shapes. Among other perks, vectorized images maintain the same resolution when either enlarged or shrunk down.
To vectorize an image, artists often have to hand-trace each stroke using specialized software, such as Adobe Illustrator, which is laborious. Another option is using automated vectorization tools in those software packages. Often, however, these tools lead to numerous tracing errors that take more time to rectify by hand. The main culprit: mismatches at intersections where curves and lines meet.
In a paper being published in the journal ACM Transactions on Graphics, MIT researchers detail a new automated vectorization algorithm that traces intersections without error, greatly reducing the need for manual revision. Powering the tool is a modified version of a new mathematical technique in the computer-graphics community, called “frame fields,” used to guide tracing of paths around curves, sharp corners, and messy parts of drawings where many lines intersect.
The tool could save digital artists significant time and frustration. “A rough estimate is that it could save 20 to 30 minutes from automated tools, which is substantial when you think about animators who work with multiple sketches,” says first author Mikhail Bessmeltsev, a former Computer Science and Artificial Intelligence Laboratory (CSAIL) postdoc associate who is now an assistant professor at the University of Montreal. “The hope is to make automated vectorization tools more practical for artists who care about the quality of their work.”
Co-author on the paper is Justin Solomon, an assistant professor in CSAIL and in the Department of Electrical Engineering and Computer Science, and a principal investigator in the Geometric Data Processing Group.
Guiding the lines
Many modern tools used to model 3-D shapes directly from artist sketches, including Bessmeltsev’s previous research projects, require vectorizing the drawings first. Automated vectorization “never worked for me, so I got frustrated,” he says. Those tools, he says, are fine for rough alignments but aren’t designed for precision: “Imagine you’re an animator and you drew a couple frames of animation. They’re pretty clean sketches, and you want to edit or color them on a computer. For that, you really care how well your vectorization aligns with your pencil drawing.”
Many errors, he noted, come from misalignment between the original and vectorized image at junctions where two curves meet — in a type of “X” junction — and where one line ends at another — in a “T” junction. Previous research and software used models incapable of aligning the curves at those junctions, so Bessmeltsev and Solomon took on the task.
The key innovation came from using frame fields to guide tracing. Frame fields assign two directions to each point of a 2-D or 3-D shape. These directions overlay a basic structure, or topology, that can guide geometric tasks in computer graphics. Frame fields have been used, for instance, to restore destroyed historical documents and to convert triangle meshes — networks of triangles covering a 3-D shape — into quadrangle meshes — grids of four-sided shapes. Quad meshes are commonly used to create computer-generated characters in movies and video games, and for computer-aided design (CAD) for better real-world design and simulation.
Bessmeltsev, for the first time, applied frame fields to image vectorization. His frame fields assign two directions to every dark pixel on an image. This keeps track of the tangent directions — where a curve meets a line — of nearby drawn curves. That means, at every intersection of a drawing, the two directions of the frame field align with the directions of the intersecting curves. This drastically reduces the roughness, or noise, surrounding intersections, which usually makes them difficult to trace.
“At a junction, all you have to do is follow one direction of the frame field and you get a smooth curve. You do that for every junction, and all junctions will then be aligned properly,” Bessmeltsev says.
When given an input of a pixeled raster 2-D drawing with one color per pixel, the tool assigns each dark pixel a cross that indicates two directions. Starting at some pixel, it first chooses a direction to trace. Then, it traces the vector path along the pixels, following the directions. After tracing, the tool creates a graph capturing connections between the solid strokes in the drawn image. Using this graph, the tool matches the necessary lines and curves to those strokes and automatically vectorizes the image.
In their paper, the researchers demonstrated their tool on various sketches, such as cartoon animals, people, and plants. The tool cleanly vectorized all intersections that were traced incorrectly using traditional tools. With traditional tools, for instance, lines around facial features, such as eyes and teeth, didn’t stop where the original lines did or ran through other lines.
One example in the paper shows pixels making up two slightly curved lines leading to the tip of a hat worn by a cartoon elephant. There’s a sharp corner where the two lines meet. Each dark pixel contains a cross that’s straight or slightly slanted, depending on the curvature of the line. Using those cross directions, the traced line could easily follow as it swooped around the sharp turn.
“Many artists still enjoy and prefer to work with real media (for example, pen, pencil, and paper). … The problem is that the scanning of such content into the computer often results in a severe loss of information,” says Nathan Carr, a principal researcher in computer graphics at Adobe Systems Inc., who was not involved in the research. “[The MIT] work relies on a mathematical construct known as ‘frame fields,’ to clean up and disambiguate scanned sketches to gain back this loss of information. It’s a great application of using mathematics to facilitate the artistic workflow in a clean well-formed manner. In summary, this work is important, as it aids in the ability for artists to transition between the physical and digital realms.”
Next, the researchers plan to augment the tool with a temporal-coherence technique, which extracts key information from adjacent animation frames. The idea would be to vectorize the frames simultaneously, using information from one to adjust the line tracing on the next, and vice versa. “Knowing the sketches don’t change much between the frames, the tool could improve the vectorization by looking at both at the same time,” Bessmeltsev says.
New and returning students cooled off from the late-summer heat by waging the Institute's annual water war — an MIT tradition that began in the early 2000s and is an official part of Residential Exploration (REX). The invasion pits residents from East Campus and West Campus for ultimate bragging rights.
Preparation for the battlefield is no joke. Students spent days filling up water balloons, designing shields, building the coveted Trojan Duck, and more for their Aug. 28 faceoff.
As dusk approached, chanting and the sounds of vuvuzelas could be heard echoing from a distance as residents from East Campus and West Campus marched onto Killian Court — signaling the imminent water battle. Once the troops were assembled on each side, REX Chairs convened in the middle for their traditional handshake and announced the official start of war. Within seconds, students armed for battle ran towards each other and a massive explosion of water balloons ensued.
Despite their intensity, water wars usually only last for 10 minutes or so before a ceasefire is called. No victor is ever officially declared, but there is always a sure winner. Who that is depends on which side you ask.
Timothy Grove, the Robert R. Shrock Professor of Earth and Planetary Sciences, has been recognized with the 2018 Harry H. Hess Medal by the American Geophysical Union (AGU). The medal is awarded annually for what AGU calls “outstanding achievements in research on the constitution and evolution of the Earth and other planets.”
A past president of the AGU himself (2008-2010), Grove is a geologist who explores the processes that have led to the chemical evolution of Earth and other planets and objects in the solar system including the moon, Mars, Mercury, and meteorite parent bodies. His approach to understanding planetary differentiation is to combine field, petrological, and geochemical studies of igneous rocks with high-pressure, high-temperature experimental petrology.
On Earth, his research focuses on mantle melting and subsequent crustal-level magma differentiation at both mid-ocean ridges and subduction zones. For mid-ocean ridges, he is interested in the influence of mantle convection and lithospheric cooling on melt generation and modification. In subduction zone environments, he is interested in understanding the critical role of water on melting and differentiation processes.
On the moon, his work focuses on understanding the chemical differentiation of the early lunar magma ocean and the subsequent remelting of its cumulates to create lunar mare basalts. He applies his experimental approach to meteorites from the earliest formed planetesimals in the Solar System to understand the melting and chemical differentiation processes that occurred in these asteroidal bodies.
Grove has been a member of the MIT faculty since 1979, and served as EAPS associate department head for eight years from 2010 to 2018.
Grove is only the second member of the MIT faculty to earn the Hess Medal; Vice President for Research Maria Zuber, the E. A. Griswold Professor of Geophysics, was awarded the prize in 2012. The medal will be presented at the 2018 Union Awards Ceremony on Dec. 12 at the 2018 AGU Fall Meeting in Washington.
The Hess Medal was established in 1984 and is named in honor of Harry H. Hess, who made many seminal contributions to geology, mineralogy, and geophysics. His achievements include constraining the mechanisms of seafloor spreading and the formation of flat-topped seamounts (guyots), conducting detailed mineralogic and petrologic studies of peridotites, and originating scientific ocean drilling by the Mohole Project. Hess served multiple terms as an AGU section president for both Geodesy (1950–1953) and Tectonophysics (1956–1959).
The AGU's full 2018 awards announcement is available online.
MIT Materials Research Laboratory (MRL) interns covered a wide gamut of challenges this summer, working with materials as soft as silk to as hard as iron and at temperatures from as low as that of liquid helium (-452.47 degrees Fahrenheit) to as high as that of melted copper (1,984 F).
Summer Scholars and other interns participated on the MIT campus through the MRL’s Materials Research Science and Engineering Center, with support from the National Science Foundation, the AIM Photonics Academy, the MRL Collegium, and the Guided Academic Industry Network (GAIN) program.
Simon Egner, from the University of Illinois at Urbana-Champaign, made samples of lead tin telluride to detect mid-infrared light at wavelengths from 4 to 7 microns for integrated photonic applications. Egner measured several materials properties of the samples, including the concentration and mobility of electrons. “One thing we have come up with recently is adding lead oxide to try to decrease the amount of noise we get when sensing light with our detectors,” Egner says.
Lead tin telluride is an alloy of lead telluride and tin telluride, explains Peter Su, a materials science and engineering graduate student in the lab of MIT Materials Research Laboratory Principal Research Scientist Anuradha Agarwal. “If you have a lot of carriers already present in your material, you get a lot of extra noise, a lot of background signal, above which it’s really hard to detect the new carriers generated by the light striking your material,” Su says. “We’re trying to lower that noise level by lowering the carrier concentration and we’re trying to do that by adding lead oxide to that alloy.”
Thin films for photonics
Summer Scholar Alvin Chang, from Oregon State University, created chalcogenide thin films with non-linear properties for photonics applications. He worked with postdoc Samuel Serna in the lab of associate professor of materials science and engineering Juejun Hu. Chang varied the thickness of two different compositions, one of germanium, antimony and sulfur (GSS) and the other of germanium, antimony, and selenium (GSSE), creating a gradient, or ratio, between the two across the length of the film.
“The GSS and GSSE both have different advantages and disadvantages,” Chang explains. “We're hoping that by merging the two together in a film we can sort of optimize both their advantages and disadvantages so that they would be complementary with each other.”
These materials, known as chalcogenide glasses, can be used for infrared sensing and imaging. Anyone interested in learning more about Chang's work can watch this video.
Both Roxbury Community College chemistry and biotechnology Professor Kimberly Stieglitz and Roxbury Community College student Credoritch Joseph worked in the lab of assistant professor in materials science and engineering Robert J. Macfarlane. The Macfarlane Lab grafts DNA to nanoparticles, which enable precise control over self-assembly of molecular structures. The lab is also creating a new class of chemical building blocks that it alls Nanocomposite Tectons, or NCTs, which present new opportunities for self-assembly of composite materials.
Joseph learned the multi-step process of creating self-assembled DNA-nanoparticle aggregates, and used the ones he preparted to study the stability of the aggregates when exposed to different chemicals. Stieglitz created NCTs consisting of clusters of gold nanoparticles with attached polymers and examined their melting behavior in polymer solutions. "They're actually nanoparticles that are linked together through hydrogen bonding networks," Stieglitz explains.
Strengthening aerospace composites
Abigail Nason, from the University of Florida, studied the potential benefits of incorporating carbon nanotubes into carbon fiber reinforced plastic [CFRP] via a process termed “nanostitching” in the lab of Brian L. Wardle, professor of aeronautics and astronautics.
Bundles of carbon microfibers, which are known as tows, are used to make sheets of aerospace-grade carbon fiber reinforced plastic. Working with graduate student Reed Kopp, Nason took 3-D scans of composite laminate samples to reveal their structure. Areas between sheets of the laminate are called the interlaminar region. Traditional composites have no reinforcement in this interlaminar region, and carbon nanotubes provide nano-scale fiber reinforcement in the nano-stitch version.
Kopp notes that despite the high level of resolution required to elucidate an intricate architecture of micro-scale features, the 3-D scans can’t distinguish the carbon nanotubes from the epoxy resin because they have similar density and elemental composition. “Since they absorb X-rays similarly, we can’t actually detect X-ray interaction differences that would indicate the locations of reinforcing carbon nanotube forests, but we can visualize how they affect the shape of the interlaminar region, such as how they may push fibers apart and change the shape of inherent resin-rich regions caused during carbon fiber reinforced plastic layer manufacturing.”
Nason adds: “It’s really interesting to see that there isn’t a lot of information out there about how composites fail and why they fail the way they do. But it’s really cool and interesting to be at the forefront of seeing this new technology and being able to look so closely at the composite layers and quantifying critical micro-scale material features that influence failure.”
Synthesizing electronic materials
Summer Scholar Michael Molinski, from the University of Rhode Island, and Roxbury Community College student Bruce Quinn worked in the lab of assistant professor of materials science and engineering Rafael Jaramillo. Working with graduate students Stephen Filippone and Kevin Ye, both Molinski and Quinn made solid materials, producing powders of compounds such as barium zirconium sulfide, which are desireable for their optical and electrical properties.
The process involves mixing together the chemical ingredients to produce the powders in a quartz tupe in the absense of air and sealing it. The first GAIN program participant, Quinn hot pressed the powders into pellets. Molinski also grew crystals, and both examined their powders with X-ray diffraction.
Developing multiple sclerosis models
Summer Scholar Fernando Nieves Muñoz, from the University of Puerto Rico at Mayagüez, worked in the lab of Krystyn Van Vliet, the Michael (1949) and Sonja Koerner Professor of Materials Science and Engineering, to develop mechanical models of multiple sclerosis (MS) lesions. Nieves Muñoz worked closely with research scientist Anna Jagielska and chemical engineering graduate student Daniela Espinosa-Hoyos.
“We are trying to find a way to stimulate repair of myelin in MS patients so that neurological function can be restored. To better understand how remyelination works, we are developing polymer-based materials to engineer models of MS lesions that mimic mechanical stiffness of real lesions in the brain,” Jagielska explains.
Nieves Muñoz used stereolithography 3-D printing to create cross-linked polymers with varying degrees of mechanical stiffness and conducted atomic force microscopy studies to determine the stiffness of his samples. “Our long-term goal is to use these models of lesions and brain tissue to develop drugs that can stimulate myelin repair,” Nieves Muñoz says. “As a mechanical engineering major, it has been exciting to work and learn from people with diverse backgrounds.”
Other MIT Materials Research Laboratory interns tackled projects including superconducting thin films, quantum dots for solar, spinning particles with magnetism, carbon-activated silk fibers, water-based iron flow batteries, and polymer-based neuro fibers.
A version of this post, including additional MRL summer intern success stories, originally appeared on the Materials Research Laboratory website.
U.S. News and World Report has placed MIT third in its annual rankings of the nation’s best colleges and universities, which were announced today. The Institute has moved up from the No. 5 spot it occupied last year. Columbia University, the University of Chicago, and Yale University also share the No. 3 ranking.
MIT’s engineering program continues to top of the magazine’s list of undergraduate engineering programs at a doctoral institution. The Institute also placed first in five out of 12 engineering disciplines. No other institution is No. 1 in more than one discipline.
MIT also remains the No. 2 undergraduate business program, sharing the spot with the University of California at Berkeley. Among business subfields, MIT is ranked No. 1 in three specialties.
In the overall institutional rankings, U.S. News placed Princeton University in the No. 1 spot, followed by Harvard University.
MIT ranks as the third most innovative university in the nation, according to the U.S. News peer assessment survey of top academics. The Institute is also third on the magazine’s list of national universities that offer students the best value, based on the school’s ranking and the net cost of attendance for a student who received the average level of need-based financial aid, and other variables.
MIT placed first in five engineering specialties: aerospace/aeronautical/astronautical engineering; chemical engineering; electrical/electronic/communication engineering; materials engineering; and mechanical engineering. It placed second in biomedical engineering and computer engineering.
Other schools in the top five overall for undergraduate engineering programs are Stanford University, Berkeley, Caltech, and Georgia Tech.
Among undergraduate business specialties, the MIT Sloan School of Management leads in management information systems; production/operations management; and quantitative analysis/methods. It ranked second in entrepreneurship and third in finance and supply chain management/logistics.
The No. 1-ranked undergraduate business program overall is at the University of Pennsylvania; the top five also include Berkeley, the University of Michigan at Ann Arbor, and New York University.