3 Questions: Investigating a long-standing neutrino mystery

Neutrinos are one of the most mysterious members of the Standard Model, a framework for describing fundamental forces and particles in nature. While they are among the most abundant known particles in the universe, they interact very rarely with matter, making their detection a challenging experimental feat. One of the long-standing puzzles in neutrino physics comes from the Mini Booster Neutrino Experiment (MiniBooNE), which ran from 2002 to 2017 at the Fermi National Accelerator Laboratory, or Fermilab, in Illinois. MiniBooNE observed significantly more neutrino interactions that produce electrons than one would expect given our best knowledge of the Standard Model — and physicists are trying to understand why.

In 2007, researchers developed the idea for a follow-up experiment, MicroBooNE, which recently finished collecting data at Fermilab. MicroBooNE is an ideal test of the MiniBooNE excess thanks to its use of a novel detector technology known as the liquid argon time projection chamber (LArTPC), which yields high-resolution pictures of the particles that get created in neutrino interactions.  

Physics graduate students Nicholas Kamp and Lauren Yates, along with Professor Janet Conrad, all within the MIT Laboratory for Nuclear Science, have played a leading role in MicroBooNE’s deep-learning-based search for an excess of neutrinos in the Fermilab Booster Neutrino Beam. In this interview, Kamp discusses the future of the MiniBooNE anomaly within the context of MicroBooNE’s latest findings.

Q: Why is the MiniBooNE anomaly a big deal?

A: One of the big open questions in neutrino physics concerns the possible existence of a hypothetical particle called the “sterile neutrino.” Finding a new particle would be a very big deal because it can give us clues to the larger theory that explains the many particles we see. The most common explanation of the MiniBooNE excess involves the addition of such a sterile neutrino to the Standard Model. Due to the effects of neutrino oscillations, this sterile neutrino would manifest itself as an enhancement of electron neutrinos in MiniBooNE.

There are many additional anomalies seen in neutrino physics that indicate this particle might exist. However, it is difficult to explain these anomalies along with MiniBooNE through a single sterile neutrino — the full picture doesn’t quite fit. Our group at MIT is interested in new physics models that can potentially explain this full picture.

Q: What is our current understanding of the MiniBooNE excess?

A: Our understanding has progressed significantly of late thanks to developments in both the experimental and theoretical realms.

Our group has worked with physicists from Harvard, Columbia, and Cambridge universities to explore new sources of photons that can appear in a theoretical model that also has a 20 percent electron signature. We developed a “mixed model” that involves two types of exotic neutrinos — one which morphs to electron flavor and one which decays to a photon. This work is forthcoming in Physical Review D.

On the experimental end, more recent MicroBooNE results — including a deep-learning-based analysis in which our MIT group played an important role — observed no excess of neutrinos that produce electrons in the MicroBooNE detector. Keeping in mind the level at which MicroBooNE can make the measurement, this suggests that the MiniBooNE excess cannot be attributed entirely to extra neutrino interactions. If it isn’t electrons, then it must be photons, because that is the only particle that can produce a similar signature in MiniBooNE. But we are sure it is not photons produced by interactions that we know about because those are restricted to a low level. So, they must be coming from something new, such as the exotic neutrino decay in the mixed model. Next, MicroBooNE is working on a search that could isolate and identify these additional photons. Stay tuned!

Q: You mentioned that your group is involved in deep-learning-based MicroBooNE analysis. Why use deep learning in neutrino physics?

A: When humans look at images of cats, they can tell the difference between species without much difficulty. Similarly, when physicists look at images coming from a LArTPC, they can tell the difference between the particles produced in neutrino interactions without much difficulty. However, due to the nuance of the differences, both tasks turn out to be difficult for conventional algorithms.

MIT is a nexus of deep-learning ideas. Recently, for example, it became the site of the National Science Foundation AI Institute for Artificial Intelligence and Fundamental Interactions. It made sense for our group to build on the extensive local expertise in the field. We have also had the opportunity to work with fantastic groups at SLAC, Tufts University, Columbia University, and IIT, each with a strong knowledge base in the ties between deep learning and neutrino physics.

One of the key ideas in deep learning is that of a “neutral network,” which is an algorithm that makes decisions (such as identifying particles in a LArTPC) based on previous exposure to a suite of training data. Our group produced the first paper on particle identification using deep learning in neutrino physics, proving it to be a powerful technique. This is a major reason why the recently-released results of MicroBooNE’s deep learning-based analysis place strong constraints on an electron neutrino interpretation of the MiniBooNE excess.

All in all, it's very fortunate that much of the groundwork for this analysis was done in the AI-rich environment at MIT.

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Engineers devise a way to selectively turn on RNA therapies in human cells

Researchers at MIT and Harvard University have designed a way to selectively turn on gene therapies in target cells, including human cells. Their technology can detect specific messenger RNA sequences in cells, and that detection then triggers production of a specific protein from a transgene, or artificial gene.

Because transgenes can have negative and even dangerous effects when expressed in the wrong cells, the researchers wanted to find a way to reduce off-target effects from gene therapies. One way of distinguishing different types of cells is by reading the RNA sequences inside them, which differ from tissue to tissue.

By finding a way to produce transgene only after “reading” specific RNA sequences inside cells, the researchers developed a technology that could fine-tune gene therapies in applications ranging from regenerative medicine to cancer treatment. For example, researchers could potentially create new therapies to destroy tumors by designing their system to identify cancer cells and produce a toxic protein just inside those cells, killing them in the process.

“This brings new control circuitry to the emerging field of RNA therapeutics, opening up the next generation of RNA therapeutics that could be designed to only turn on in a cell-specific or tissue-specific way,” says James Collins, the Termeer Professor of Medical Engineering and Science in MIT’s Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering and the senior author of the study.

This highly targeted approach, which is based on a genetic element used by viruses to control gene translation in host cells, could help to avoid some of the side effects of therapies that affect the entire body, the researchers say.

Evan Zhao, a research fellow at the Wyss Institute for Biologically Inspired Engineering at Harvard University, and Angelo Mao, an MIT postdoc and technology fellow at the Wyss Institute, are the lead authors of the study, which appears today in Nature Biotechnology.

RNA detection

Messenger RNA (mRNA) molecules are sequences of RNA that encode the instructions for building a particular protein. Several years ago, Collins and his colleagues developed a way to use RNA detection as a trigger to stimulate cells to produce a specific protein in bacterial cells. This system works by introducing an RNA molecule called a “toehold,” which binds to the ribosome-binding site of an mRNA molecule that codes for a specific protein. (The ribosome is where proteins are assembled based on mRNA instructions.) This binding prevents the mRNA from being translated into protein, because it can’t attach to a ribosome.

The RNA toehold also contains a sequence that can bind to a different mRNA sequence that serves as a trigger. If this target mRNA sequence is detected, the toehold releases its grip, and the mRNA that had been blocked is translated into protein. This mRNA can encode any gene, such as a fluorescent reporter molecule. That fluorescent signal gives researchers a way to visualize whether the target mRNA sequence was detected.

In the new study, the researchers set out to try to create a similar system that could be used in eukaryotic (non-bacterial) cells, including human cells.

Because gene translation is more complex in eukaryotic cells, the genetic components that they used in bacteria couldn’t be imported into human cells. Instead, the researchers took advantage of a system that viruses use to hijack eukaryotic cells to translate their own viral genes. This system consists of RNA molecules called internal ribosome entry sites (IRES), which can recruit ribosomes and initiate translation of RNA into proteins.

“These are complicated folds of RNA that viruses have developed to hijack ribosomes because viruses need to find some way to express protein,” Zhao says.

The researchers started with naturally occurring IRES from different types of viruses and engineered them to include a sequence that binds to a trigger mRNA. When the engineered IRES is inserted into a human cell in front of an output transgene, it blocks translation of that gene unless the trigger mRNA is detected inside the cell. The trigger causes the IRES to recover and allows the gene to be translated into protein.

Targeted therapeutics

The researchers used this technique to develop toeholds that could detect a variety of different triggers inside human and yeast cells. First, they showed that they could detect mRNA encoding viral genes from Zika virus and the SARS-CoV-2 virus. One possible application for this could be designing T cells that detect and respond to viral mRNA during infection, the researchers say.

They also designed toehold molecules that can detect mRNA for proteins that are naturally produced in human cells, which could help to reveal cell states such as stress. As an example, they showed they could detect expression of heat shock proteins, which cells make when they are exposed to high temperatures.

Lastly, the researchers showed that they could identify cancer cells by engineering toeholds that detect mRNA for tyrosinase, an enzyme that produces excessive melanin in melanoma cells. This kind of targeting could enable researchers to develop therapies that trigger production of a protein that initiates cell death when cancerous proteins are detected in a cell.

“The idea is that you would be able to target any unique RNA signature and deliver a therapeutic,” Mao says. “This could be a way of limiting expression of the biomolecule to your target cells or tissue.”

The new technique represents “a conceptual quantum leap in controlling and programming mammalian cell behavior,” says Martin Fussenegger, a professor of biotechnology and bioengineering at ETH Zurich, who was not involved in the research. “This novel technology sets new standards by which human cells could be treated to sense and react to viruses such as Zika and SARS-CoV-2.”

All of the studies done in this paper were performed in cells grown in a lab dish. The researchers are now working on delivery strategies that would allow the RNA components of the system to reach target cells in animal models.

The research was funded by BASF, the National Institutes of Health, an American Gastroenterological Association Takeda Pharmaceuticals Research Scholar Award in Inflammatory Bowel Disease, and the Schmidt Science Fellows program.

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Making machine learning more useful to high-stakes decision makers

The U.S. Centers for Disease Control and Prevention estimates that one in seven children in the United States experienced abuse or neglect in the past year. Child protective services agencies around the nation receive a high number of reports each year (about 4.4 million in 2019) of alleged neglect or abuse. With so many cases, some agencies are implementing machine learning models to help child welfare specialists screen cases and determine which to recommend for further investigation.

But these models don’t do any good if the humans they are intended to help don’t understand or trust their outputs.

Researchers at MIT and elsewhere launched a research project to identify and tackle machine learning usability challenges in child welfare screening. In collaboration with a child welfare department in Colorado, the researchers studied how call screeners assess cases, with and without the help of machine learning predictions. Based on feedback from the call screeners, they designed a visual analytics tool that uses bar graphs to show how specific factors of a case contribute to the predicted risk that a child will be removed from their home within two years.

The researchers found that screeners are more interested in seeing how each factor, like the child’s age, influences a prediction, rather than understanding the computational basis of how the model works. Their results also show that even a simple model can cause confusion if its features are not described with straightforward language.

These findings could be applied to other high-risk fields where humans use machine learning models to help them make decisions, but lack data science experience, says senior author Kalyan Veeramachaneni, principal research scientist in the Laboratory for Information and Decision Systems (LIDS) and senior author of the paper.

“Researchers who study explainable AI, they often try to dig deeper into the model itself to explain what the model did. But a big takeaway from this project is that these domain experts don’t necessarily want to learn what machine learning actually does. They are more interested in understanding why the model is making a different prediction than what their intuition is saying, or what factors it is using to make this prediction. They want information that helps them reconcile their agreements or disagreements with the model, or confirms their intuition,” he says.

Co-authors include electrical engineering and computer science PhD student Alexandra Zytek, who is the lead author; postdoc Dongyu Liu; and Rhema Vaithianathan, professor of economics and director of the Center for Social Data Analytics at the Auckland University of Technology and professor of social data analytics at the University of Queensland. The research will be presented later this month at the IEEE Visualization Conference.

Real-world research

The researchers began the study more than two years ago by identifying seven factors that make a machine learning model less usable, including lack of trust in where predictions come from and disagreements between user opinions and the model’s output.

With these factors in mind, Zytek and Liu flew to Colorado in the winter of 2019 to learn firsthand from call screeners in a child welfare department. This department is implementing a machine learning system developed by Vaithianathan that generates a risk score for each report, predicting the likelihood the child will be removed from their home. That risk score is based on more than 100 demographic and historic factors, such as the parents’ ages and past court involvements.

“As you can imagine, just getting a number between one and 20 and being told to integrate this into your workflow can be a bit challenging,” Zytek says.

They observed how teams of screeners process cases in about 10 minutes and spend most of that time discussing the risk factors associated with the case. That inspired the researchers to develop a case-specific details interface, which shows how each factor influenced the overall risk score using color-coded, horizontal bar graphs that indicate the magnitude of the contribution in a positive or negative direction.

Based on observations and detailed interviews, the researchers built four additional interfaces that provide explanations of the model, including one that compares a current case to past cases with similar risk scores. Then they ran a series of user studies.

The studies revealed that more than 90 percent of the screeners found the case-specific details interface to be useful, and it generally increased their trust in the model’s predictions. On the other hand, the screeners did not like the case comparison interface. While the researchers thought this interface would increase trust in the model, screeners were concerned it could lead to decisions based on past cases rather than the current report.   

“The most interesting result to me was that, the features we showed them — the information that the model uses — had to be really interpretable to start. The model uses more than 100 different features in order to make its prediction, and a lot of those were a bit confusing,” Zytek says.

Keeping the screeners in the loop throughout the iterative process helped the researchers make decisions about what elements to include in the machine learning explanation tool, called Sibyl.

As they refined the Sibyl interfaces, the researchers were careful to consider how providing explanations could contribute to some cognitive biases, and even undermine screeners’ trust in the model.

For instance, since explanations are based on averages in a database of child abuse and neglect cases, having three past abuse referrals may actually decrease the risk score of a child, since averages in this database may be far higher. A screener may see that explanation and decide not to trust the model, even though it is working correctly, Zytek explains. And because humans tend to put more emphasis on recent information, the order in which the factors are listed could also influence decisions.

Improving interpretability

Based on feedback from call screeners, the researchers are working to tweak the explanation model so the features that it uses are easier to explain.

Moving forward, they plan to enhance the interfaces they’ve created based on additional feedback and then run a quantitative user study to track the effects on decision making with real cases. Once those evaluations are complete, they can prepare to deploy Sibyl, Zytek says.

“It was especially valuable to be able to work so actively with these screeners. We got to really understand the problems they faced. While we saw some reservations on their part, what we saw more of was excitement about how useful these explanations were in certain cases. That was really rewarding,” she says.

This work is supported, in part, by the National Science Foundation.

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One autonomous taxi, please

If you don’t get seasick, an autonomous boat might be the right mode of transportation for you. 

Scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Senseable City Laboratory, together with Amsterdam Institute for Advanced Metropolitan Solutions (AMS Institute) in the Netherlands, have now created the final project in their self-navigating trilogy: a full-scale, fully autonomous robotic boat that’s ready to be deployed along the canals of Amsterdam. 

“Roboat” has come a long way since the team first started prototyping small vessels in the MIT pool in late 2015. Last year, the team released their half-scale, medium model that was 2 meters long and demonstrated promising navigational prowess. 

This year, two full-scale Roboats were launched, proving more than just proof-of-concept: these craft can comfortably carry up to five people, collect waste, deliver goods, and provide on-demand infrastructure. 

The boat looks futuristic — it’s a sleek combination of black and gray with two seats that face each other, with orange block letters on the sides that illustrate the makers' namesakes. It’s a fully electrical boat with a battery that’s the size of a small chest, enabling up to 10 hours of operation and wireless charging capabilities. 

“We now have higher precision and robustness in the perception, navigation, and control systems, including new functions, such as close-proximity approach mode for latching capabilities, and improved dynamic positioning, so the boat can navigate real-world waters,” says Daniela Rus, MIT professor of electrical engineering and computer science and director of CSAIL. “Roboat’s control system is adaptive to the number of people in the boat.” 

To swiftly navigate the bustling waters of Amsterdam, Roboat needs a meticulous fusion of proper navigation, perception, and control software. 

Using GPS, the boat autonomously decides on a safe route from A to B, while continuously scanning the environment to  avoid collisions with objects, such as bridges, pillars, and other boats.

To autonomously determine a free path and avoid crashing into objects, Roboat uses lidar and a number of cameras to enable a 360-degree view. This bundle of sensors is referred to as the “perception kit” and lets Roboat understand its surroundings. When the perception picks up an unseen object, like a canoe, for example, the algorithm flags the item as “unknown.” When the team later looks at the collected data from the day, the object is manually selected and can be tagged as “canoe.” 

The control algorithms — similar to ones used for self-driving cars — function a little like a coxswain giving orders to rowers, by translating a given path into instructions toward the “thrusters,” which are the propellers that help the boat move.  

If you think the boat feels slightly futuristic, its latching mechanism is one of its most impressive feats: small cameras on the boat guide it to the docking station, or other boats, when they detect specific QR codes. “The system allows Roboat to connect to other boats, and to the docking station, to form temporary bridges to alleviate traffic, as well as floating stages and squares, which wasn’t possible with the last iteration,” says Carlo Ratti, professor of the practice in the MIT Department of Urban Studies and Planning (DUSP) and director of the Senseable City Lab. 

Roboat, by design, is also versatile. The team created a universal “hull” design — that’s the part of the boat that rides both in and on top of the water. While regular boats have unique hulls, designed for specific purposes, Roboat has a universal hull design where the base is the same, but the top decks can be switched out depending on the use case.

“As Roboat can perform its tasks 24/7, and without a skipper on board, it adds great value for a city. However, for safety reasons it is questionable if reaching level A autonomy is desirable,” says Fabio Duarte, a principal research scientist in DUSP and lead scientist on the project. “Just like a bridge keeper, an onshore operator will monitor Roboat remotely from a control center. One operator can monitor over 50 Roboat units, ensuring smooth operations.”

The next step for Roboat is to pilot the technology in the public domain. "The historic center of Amsterdam is the perfect place to start, with its capillary network of canals suffering from contemporary challenges, such as mobility and logistics,” says Stephan van Dijk, director of innovation at AMS Institute. 

Previous iterations of Roboat have been presented at the IEEE International Conference on Robotics and Automation. The boats will be unveiled on Oct. 28 in the waters of Amsterdam. 

Ratti, Rus, Duarte, and Dijk worked on the project alongside Andrew Whittle, MIT’s Edmund K Turner Professor in civil and environmental engineering; Dennis Frenchman, professor at MIT’s Department of Urban Studies and Planning; and Ynse Deinema of AMS Institute. The full team can be found at Roboat's website. The project is a joint collaboration with AMS Institute. The City of Amsterdam is a project partner.

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3 Questions: Administering elections in a hyper-partisan era

Charles Stewart III is the Kenan Sahin Distinguished Professor of Political Science at MIT and a renowned expert on U.S. election administration. A founding member of the influential Caltech/MIT Voting Technology Project, Stewart also founded MIT’s Election Data and Science Lab, which recently teamed up with the American Enterprise Institute to release a major report: Lessons Learned from the 2020 Election. MIT SHASS Communications asked Stewart to share some additional insights on the state of U.S. elections in advance of November voting.

Q: The United States has a decentralized system of election administration, which means local jurisdictions have a lot of control over how votes are collected and counted. What are the pros and cons of this system — particularly at this moment, when partisan political efforts are highly focused on election administration?

A: The advantage of the American decentralized system is that the basic parameters of how people vote get decided locally. This has helped create a great deal of trust among voters about how their own votes are counted. Historically, the greatest disadvantage has been that the anti-democratic pockets of America — think of the pre-Voting Rights Act Deep South — have been able to suppress voting, sometimes brutally.

At present, however, all issues of consequence have become nationalized, and all policy choices — including those around voting — are therefore seen through the lens of the national parties, not local needs. This nationalization of politics has left little room for local election officials to experiment with new technologies and methodologies, and it has made election administration particularly toxic. Now, even those who trust how votes are counted in their own backyards are often deeply distrustful of how votes are counted elsewhere.

Thus, the question about what is best for Arizona or Georgia or California is not left simply to residents of those states, as in the past; today, it is the subject of attention from (often angry) partisan zealots elsewhere in the country. In such an environment, America’s decentralized — and thus naturally inconsistent — system can be a liability.

Another way that the decentralization of the system hurts election administration is often overlooked. Because each state is autonomous and often devolves authority down to the local level, it has been difficult to create standardized voting systems. This means there is no national market for technology and business solutions to the challenges of election administration — and yet, innovation is sorely needed. The American system of election administration was designed for voting in the 1880s, but the 2020s present a very different set of problems.

Many other policies that used to be hyper-local — public education, water and sewer service, public health, etc. — have often been consolidated into larger government units and there has been greater cooperation across towns and counties. States and the federal government have taken on a bigger role in funding them. But not elections. The result is that election administration often throws antiquated solutions at modern problems or, as in the case of the cybersecurity threat, is slow to react.

The reaction to the challenges of voting during the pandemic saw some movements toward a more modern and coordinated management of election administration. States stepped in and provided centralized services, such as printing and processing mail ballots or developing online portals for voters to track mail ballots. The federal government provided nearly half-a-billion dollars to shore up security and meet the many demands on election managers as they quickly pivoted to new election modalities. One hopes that this momentum will carry into the near future, but efforts to re-litigate the 2020 election are a major distraction.

Q: What safeguards exist to ensure that future elections remain free of interference — particularly from those at the top echelons of political power?

A: The 2020 election showed the resilience of the fact-based part of the election administration system — election administrators, judges, and research institutions (including universities) — that have stood for the rule of law in the face of illiberal attacks on election administration. Opponents of fair elections recognize this and have attacked all parts of this fact-based bulwark. They are physically threatening election workers, trying to remove judicial oversight of election administration, and creating so-called “election integrity” think tanks to perpetuate disinformation about elections in America.

The fact-based part of election administration is robust, but we can’t be complacent about its health. The federal government is stepping up the protection of election workers and officials; states should do this as well. Unfortunately, some states have taken action that is less than helpful by passing laws that try to strip authority from local election officials and empower state legislatures to overturn the results of free and fair elections. I think there’s every reason to be concerned about these laws, but not because I think they will achieve these worrisome ends. The biggest worry is that such laws encourage doubt about outcomes and give those who lost elections a platform to sow that doubt.
 
It’s important to keep in mind that the first principles of election laws have not been overturned in these states, nor have constitutional guarantees. There is a judicial principle that says that if an election has been run under a set of rules established before the election, that election’s results must stand even if some of the rules may have been contestable beforehand. If partisan election officials or state legislatures want to throw out an election result because they don’t like the outcome, or on a pretext of unproven fraud, the courts will intervene.

Similarly, if local election officials are replaced for pretextual reasons, it’s hard to imagine a state or federal court letting this stand. Still, democracy will be damaged regardless of the outcomes of such disputes. Politicians will be given more opportunities to denigrate the voting process, and baseless conspiracy theories will be given a megaphone.

For now, I’m more worried about the culture of democracy than I am about whether winners will be properly certified. That can change if the assaults on neutral election administration continues.

One of the final challenges facing the free conduct of elections is how to stanch the stream of disinformation about elections that is the source of the populist energy centered on attacking the system. Even when the clown-show of a ballot review in Arizona had to conclude that Joe Biden legitimately won that state in the 2020 presidential election, the release of the reviewers’ report was used by an array of manipulative pundits to continue to sow doubt.

This is a misinformation problem that infects American public life generally: It’s not confined to election administration alone, or even to politics. Insisting on responsible behavior by the social media platforms is a necessary first step toward addressing the plague of misinformation, but it’s not likely to be enough. I think we are seeing the consequences of the half-century-long destruction of responsibly curated news sources in the name of economic disruption, and that problem and its consequences extend far beyond the administration of elections.

Q: Can you suggest some efforts — either by citizens, legislators, scholars, and/or pro-democracy organizations — that could effectively protect and strengthen democracy at this moment in the nation’s history?

A: The greatest effort to protect and strengthen democracy is voting itself. The consensus on the ground in states like North Carolina and Texas, where efforts have been made in the recent past to raise barriers to voting, is that the efforts of state legislatures in these states have actually served to mobilize pro-election forces. Donating to candidates who are pro-democracy and working for their election is probably the most important thing citizens can do.

For scholars, we need to be laser-focused on what we do uniquely best, which is documenting the actual consequences of election laws on participation. Legislators who support barriers to voting often mis-estimate the consequences of such laws. Citizen groups who are hypervigilant about threats to democracy also may overestimate the power of some election laws to suppress or expand the vote. As scholars, we need to be independent voices in identifying the worst of the barriers, and we need to act to redress anti-democratic efforts, either through our publications or through litigation. If we don’t ground advocacy in science, those of us who study the workings of our democracy squander what we have to offer that is distinct.

Finally, I think we all have to be clear that the illiberal wind is blowing at a gale force in just one of the political parties. This is not a partisan statement, but a fact. Working to isolate the illiberal fringe of the Republican Party and protect those in the party who value open elections and political competition may be the most important thing of all, although how to do that is still not clear. My liberal friends, of whom I have hundreds, don’t like to hear it, but I think that saving the Republican Party from extreme illiberalism may be the most important pro-democracy activity in America. 

At the moment, it’s not clear how that might happen, but ideas are being suggested. In a recent New York Times op-ed, Miles Taylor and Christine Todd Whitman, both strong Republicans, argued that the best path to change is for Republicans to vote for Democrats in 2022. They also alluded to the possibility of creating a conservative third party based on more traditional Republican values, not anti-democratic ones. Changes to election laws that discourage victory by extreme candidates of the left and the right might also work. (Rank-choice voting is one such popular reform.) 

To be clear, changing voting rules to box out extremists or withholding votes from illiberal candidates will not purge the country of extreme anti-democratic movements. But, if we think that the biggest threat to upholding democratic values is the fact that political leaders believe they must appeal to anti-democratic elements, at least we can work to reduce the payoff to appealing to the fringes.

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Chronicles of the epic mission to deliver Covid vaccines to the world

The race to deliver a Covid-19 vaccine has been likened to a moonshot, but in several ways landing a man on the moon was easier. In his new book, “A Shot in the Arm: How Science, Engineering, and Supply Chains Converged to Vaccinate the World” (MIT CTL Media, 2021), MIT Professor Yossi Sheffi recounts the vaccine’s extraordinary journey from scientific breakthroughs to coronavirus antidote and mass vaccination. And he explores how the mission could transform the fight against deadly diseases and other global-scale challenges.

“The historic Apollo moonshots built a dozen or so rockets to carry astronauts to a single location. In contrast, vaccine mission teams mass-produced billions of doses of a complex medication from a standing start, and delivered them to billions of individuals across the globe,” says Sheffi. This is a story of bold innovation and risk-taking he notes, “and interdisciplinary teamwork that involved experts vital to the mission’s success, such as manufacturing engineers and supply chain managers.” 

Like previous moonshot quests, this one was founded on revolutionary science. The book describes how the effort built on decades of biochemistry and microbiology research to develop Covid mRNA vaccines. The vaccines teach the human body how to recognize coronavirus invaders and neutralize them before they convert the body’s cells into virus factories.

However, a weapon is impotent without the means to make and distribute it. The book explains how governments joined forces with the scientific community and industry to fund, produce, and deliver the vaccine to a world in danger of losing the battle against the pandemic. The author characterizes this monumental endeavor as the greatest product launch in history. Along the way, the mission teams broke new ground in their respective fields.

The teams also made mistakes, and the book shows how these failures will inform future campaigns. Other obstacles in the way included disinformation, public mistrust of science and government, and political opportunism. Sheffi explores the root causes of these opposing forces and the societal implications.

“Yossi Sheffi offers strategic lessons behind the record-breaking development, production, and global delivery of the Covid vaccines and what they mean for the future,” says Bob Langer, the David H. Koch Institute Professor at MIT and co-founder of Moderna.

"A Shot in the Arm" ends on an optimistic note with a look at the Covid vaccine mission’s formidable legacy. In addition to providing templates for fighting pandemics, the effort has advanced immunology and highlighted the breathtaking potential of mRNA-based vaccines. Future vaccines could cure life-threatening illnesses including cancer, and when combined with other technologies, spur innovations in other fields such as agriculture. The book argues that the convergence of multiple disciplines, industries, and sectors — which resulted in the vaccine — provides a blueprint for humanity for tackling global challenges. These include poverty, food and water security, and climate change, particularly in getting from R&D and lab work to scaling innovations addressing such challenges.

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MIT Energy Initiative awards seven Seed Fund grants for early-stage energy research

The MIT Energy Initiative (MITEI) has awarded seven Seed Fund grants to support novel, early-stage energy research by faculty and researchers at MIT. The awardees hail from a range of disciplines, but all strive to bring their backgrounds and expertise to address the global climate crisis by improving the efficiency, scalability, and adoption of clean energy technologies.

“Solving climate change is truly an interdisciplinary challenge,” says MITEI Director Robert C. Armstrong. “The Seed Fund grants foster collaboration and innovation from across all five of MIT’s schools and one college, encouraging an ‘all hands on deck approach’ to developing the energy solutions that will prove critical in combatting this global crisis.”

This year, MITEI’s Seed Fund grant program received 70 proposals from 86 different principal investigators (PIs) across 25 departments, labs, and centers. Of these proposals, 31 involved collaborations between two or more PIs, including 24 that involved multiple departments.

The winning projects reflect this collaborative nature with topics addressing the optimization of low-energy thermal cooling in buildings; the design of safe, robust, and resilient distributed power systems; and how to design and site wind farms with consideration of wind resource uncertainty due to climate change.

Increasing public support for low-carbon technologies

One winning team aims to leverage work done in the behavioral sciences to motivate sustainable behaviors and promote the adoption of clean energy technologies.

“Objections to scalable low-carbon technologies such as nuclear energy and carbon sequestration have made it difficult to adopt these technologies and reduce greenhouse gas emissions,” says Howard Herzog, a senior research scientist at MITEI and co-PI. “These objections tend to neglect the sheer scale of energy generation required and the inability to meet this demand solely with other renewable energy technologies.”

This interdisciplinary team — which includes researchers from MITEI, the Department of Nuclear Science and Engineering, and the MIT Sloan School of Management — plans to convene industry professionals and academics, as well as behavioral scientists, to identify common objections, design messaging to overcome them, and prove that these messaging campaigns have long-lasting impacts on attitudes toward scalable low-carbon technologies.

“Our aim is to provide a foundation for shifting the public and policymakers’ views about these low-carbon technologies from something they, at best, tolerate, to something they actually welcome,” says co-PI David Rand, the Erwin H. Schell Professor and professor of management science and brain and cognitive sciences at MIT Sloan School of Management.

Siting and designing wind farms

Michael Howland, an assistant professor of civil and environmental engineering, will use his seed fund grant to develop a foundational methodology for wind farm siting and design that accounts for the uncertainty of wind resources resulting from climate change.

“The optimal wind farm design and its resulting cost of energy is inherently dependent on the wind resource at the location of the farm,” says Howland. “But wind farms are currently sited and designed based on short-term climate records that do not account for the future effects of climate change on wind patterns.”

Wind farms are capital-intensive infrastructure that cannot be relocated and often have lifespans exceeding 20 years — all of which make it especially important that developers choose the right locations and designs based not only on wind patterns in the historical climate record, but also based on future predictions. The new siting and design methodology has the potential to replace current industry standards to enable a more accurate risk analysis of wind farm development and energy grid expansion under climate change-driven energy resource uncertainty.

Membraneless electrolyzers for hydrogen production

Producing hydrogen from renewable energy-powered water electrolyzers is central to realizing a sustainable and low-carbon hydrogen economy, says Kripa Varanasi, a professor of mechanical engineering and a Seed Fund award recipient. The idea of using hydrogen as a fuel has existed for decades, but it has yet to be widely realized at a considerable scale. Varanasi hopes to change that with his Seed Fund grant.

“The critical economic hurdle for successful electrolyzers to overcome is the minimization of the capital costs associated with their deployment,” says Varanasi. “So, an immediate task at hand to enable electrochemical hydrogen production at scale will be to maximize the effectiveness of the most mature, least complex, and least expensive water electrolyzer technologies.”

To do this, he aims to combine the advantages of existing low-temperature alkaline electrolyzer designs with a novel membraneless electrolyzer technology that harnesses a gas management system architecture to minimize complexity and costs, while also improving efficiency. Varanasi hopes his project will demonstrate scalable concepts for cost-effective electrolyzer technology design to help realize a decarbonized hydrogen economy.

Since its establishment in 2008, the MITEI Seed Fund Program has supported 194 energy-focused seed projects through grants totaling more than $26 million. This funding comes primarily from MITEI’s founding and sustaining members, supplemented by gifts from generous donors.

Recipients of the 2021 MITEI Seed Fund grants are:

• “Design automation of safe, robust, and resilient distributed power systems” — Chuchu Fan of the Department of Aeronautics and Astronautics
• “Advanced MHD topping cycles: For fission, fusion, solar power plants” — Jeffrey Freidberg of the Department of Nuclear Science and Engineering and Dennis Whyte of the Plasma Science and Fusion Center
• “Robust wind farm siting and design under climate-change‐driven wind resource uncertainty” — Michael Howland of the Department of Civil and Environmental Engineering
• “Low-energy thermal comfort for buildings in the Global South: Optimal design of integrated structural-thermal systems” — Leslie Norford of the Department of Architecture and Caitlin Mueller of the departments of Architecture and Civil and Environmental Engineering
• “New low-cost, high energy-density boron-based redox electrolytes for nonaqueous flow batteries” — Alexander Radosevich of the Department of Chemistry
• “Increasing public support for scalable low-carbon energy technologies using behavorial science insights” — David Rand of the MIT Sloan School of Management, Koroush Shirvan of the Department of Nuclear Science and Engineering, Howard Herzog of the MIT Energy Initiative, and Jacopo Buongiorno of the Department of Nuclear Science and Engineering
• “Membraneless electrolyzers for efficient hydrogen production using nanoengineered 3D gas capture electrode architectures” — Kripa Varanasi of the Department of Mechanical Engineering

de MIT News https://ift.tt/3jBzexK