Can your phone tell if a bridge is in good shape?

Want to know if the Golden Gate Bridge is holding up well? There could be an app for that.

A new study involving MIT researchers shows that mobile phones placed in vehicles, equipped with special software, can collect useful structural integrity data while crossing bridges. In so doing, they could become a less expensive alternative to sets of sensors attached to bridges themselves.

“The core finding is that information about structural health of bridges can be extracted from smartphone-collected accelerometer data,” says Carlo Ratti, director of the MIT Sensable City Laboratory and co-author of a new paper summarizing the study’s findings.

The research was conducted, in part, on the Golden Gate Bridge itself. The study showed that mobile devices can capture the same kind of information about bridge vibrations that stationary sensors compile. The researchers also estimate that, depending on the age of a road bridge, mobile-device monitoring could add from 15 percent to 30 percent more years to the structure’s lifespan.

“These results suggest that massive and inexpensive datasets collected by smartphones could play an important role in monitoring the health of existing transportation infrastructure,” the authors write in their new paper.

The study, “Crowdsourcing Bridge Vital Signs with Smartphone Vehicle Trips,” is being published in Nature Communications Engineering.

The authors are Thomas J. Matarazzo, an assistant professor of civil and mechanical engineering at the United States Military Academy at West Point; Daniel Kondor, a postdoc at the Complexity Science Hub in Vienna; Sebastiano Milardo, a researcher at the Senseable City Lab; Soheil S. Eshkevari, a senior research scientist at DiDi Labs and a former member of Senseable City Lab; Paolo Santi, principal research scientist at the Senseable City Lab and research director at the Italian National Research Council; Shamim N. Pakzad, a professor and chair of the Department of Civil and Environmental Engineering at Lehigh University; Markus J. Buehler, the Jerry McAfee Professor in Engineering and professor of civil and environmental engineering and of mechanical engineering at MIT; and Ratti, who is also professor of the practice in MIT’s Department of Urban Studies and Planning.

Bridges naturally vibrate, and to study the essential “modal frequencies” of those vibrations in many directions, engineers typically place sensors, such as accelerometers, on bridges themselves. Changes in the modal frequencies over time may indicate changes in a bridge’s structural integrity.

To conduct the study, the researchers developed an Android-based mobile phone application to collect accelerometer data when the devices were placed in vehicles passing over the bridge. They could then see how well those data matched up with data record by sensors on bridges themselves, to see if the mobile-phone method worked.

“In our work, we designed a methodology for extracting modal vibration frequencies from noisy data collected from smartphones,” Santi says. “As data from multiple trips over a bridge are recorded, noise generated by engine, suspension and traffic vibrations, [and] asphalt, tend to cancel out, while the underlying dominant frequencies emerge.”

In the case of the Golden Gate Bridge, the researchers drove over the bridge 102 times with their devices running, and the team used 72 trips by Uber drivers with activated phones as well. The team then compared the resulting data to that from a group of 240 sensors that had been placed on the Golden Gate Bridge for three months.

The outcome was that the data from the phones converged with that from the bridge’s sensors; for 10 particular types of low-frequency vibrations engineers measure on the bridge, there was a close match, and in five cases, there was no discrepancy between the methods at all.

“We were able to show that many of these frequencies correspond very accurately to the prominent modal frequencies of the bridge,” Santi says.  

However, only 1 percent of all bridges in the U.S. are suspension bridges. About 41 percent are much smaller concrete span bridges. So, the researchers also examined how well their method would fare in that setting.

To do so, they studied a bridge in Ciampino, Italy, comparing 280 vehicle trips over the bridge to six sensors that had been placed on the bridge for seven months. Here, the researchers were also encouraged by the findings, though they found up to a 2.3 percent divergence between methods for certain modal frequencies over all 280 trips, and a 5.5 percent divergence over a smaller sample. That suggests a larger volume of trips could yield more useful data.

“Our initial results suggest that only a [modest amount] of trips over the span of a few weeks are sufficient to obtain useful information about bridge modal frequencies,” Santi says.

Looking at the method as a whole, Buehler observes, “Vibrational signatures are emerging as a powerful tool to assess properties of large and complex systems, ranging from viral properties of pathogens to structural integrity of bridges as shown in this study. It’s a universal signal found widely in the natural and built environment that we’re just now beginning to explore as a diagnostic and generative tool in engineering.”

As Ratti acknowledges, there are ways to refine and expand the research, including accounting for the effects of the smartphone mount in the vehicle, the influence of the vehicle type on the data, and more.

“We still have work to do, but we believe that our approach could be scaled up easily — all the way to the level of an entire country,” Ratti says. “It might not reach the accuracy that one can get using fixed sensors installed on a bridge, but it could become a very interesting early-warning system. Small anomalies could then suggest when to carry out further analyses.”

The researchers received support from Anas S.p.A., Allianz, Brose, Cisco, Dover Corporation, Ford, the Amsterdam Institute for Advanced Metropolitan Solutions, the Fraunhofer Institute, the former Kuwait-MIT Center for Natural Resources and the Environment, Lab Campus, RATP, Singapore–MIT Alliance for Research and Technology (SMART), SNCF Gares & Connexions, UBER, and the U.S. Department of Defense High-Performance Computing Modernization Program.

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The “last mile” from credentials to employment

Academic digital credentials — the cryptographically verifiable assertion that an individual holds a degree, certificate, or other credential — have been available for the better part of a decade. Yet despite the potential value of these data-rich, transportable credentials to graduates, employers, and academic institutions, digital credentials have by no means become the standard in presenting or verifying skills and qualifications.

A new report from the Digital Credentials Consortium (DCC), housed at MIT Open Learning, explores this gap between the promise of digital credentials and their widespread adoption.

Digital credentials in education include those issued by universities and professional training providers (MIT has offered digital diplomas since 2017, for example), and microcredentials from online course providers. The researchers note that widespread adoption of digital credentials would improve both workers’ and employers’ agility in responding to changing workforce needs. Currently, employers view digital credentials on a resume as a value-add, but there is no standardized approach to issuing, storing, verifying, or submitting them in the job application process.

Through semi-structured interviews with leading experts and decision-makers in North America and Europe, desk research, and expert analysis, the researchers set out to map the ecosystem of stakeholders and better understand the barriers to using digital academic credentials in the “last mile” of education-to-employment pathways.

“There’s a lot of interest in the potential of digital credentials to improve the way we design education programs, manage hiring processes, and support employees throughout their careers,” says principal investigator Philipp Schmidt, an MIT research scientist and director of the DCC. “In this report we tried to lay out a few concrete strategies to address the existing barriers that limit uptake of this technology.”

“Digital credentials have the potential to connect skilled workers with jobs, but given their complexity, we need to determine how to bring educators, employers, and governments together to accelerate adoption,” adds Sean Murphy, a director of opportunity at Walmart, which funded the report. “The technology can create more equitable pathways for workers entering the workforce and upskilling, and the Last Mile report lays out a clear set of recommendations on how to address remaining barriers to adoption.”

Key findings: Navigating complex needs and expectations

The researchers conducted interviews with over 20 experts from universities, corporations, nonprofits, and consultancies with diverse perspectives on education and training, human resources and career development, and corporate technology and innovation. Across the board, the panelists agreed that the landscape of stakeholders is too complex to sort into simple categories. Dividing stakeholders into issuers, holders, and validators or educational institutions, learners, and employers does not adequately reflect the reality of the ecosystem. Each of these groups is highly diverse, and different sub-groups have different relationships with credentials. They often will not face the same problems or require the same solutions.

With that in mind, the researchers worked to identify baseline sets of challenges to widespread adoption across different sectors. These include:

• Employers need evidence that the credentials can be used to identify and match skills to jobs.
• A gap in what current credentials on the market can say about their holders and the information employers are looking for to improve their hiring and career development decisions, needs to be closed.
• More detailed information, some of which institutions already track and store internally, needs to be included in credentials.
• Employers need reasonable belief that a plurality of job candidates will hold digital credentials in the near future. 

From an employer perspective, the main use case for digital credentials is matching applicants’ job skills to open positions. Employers envision analyzing applicants’ skills and widening access to talent by data-mining credential databases. However, there is very little implementation of systems that use digital credentials this way; there’s a disconnect between the value digital credentials provide today and employers’ expectations for skills-based hiring.

Employers use different trust models when evaluating applicants, relying on proxies such as accreditation bodies, ranking lists, or social mechanisms. Resumes are still the lingua franca of most human resource processes. Shifting to digital credentials would not replace the resume, but would make the information in resumes more easily verifiable, and give applicants new ways of extending their employment profiles.

Skill-based hiring is held back by a lack of coordination among different stakeholder groups — education, standardization, human resources, and technology startups are separate communities that only sometimes overlap. This makes it harder to take a coordinated, integrated approach toward adoption of digital credential technologies. For example, digital credentials would need to be integrated into existing human-resource management systems (HRMS). Until institutions issue these verifiable digital credentials at scale, or employers demand them from their applicants, HRMS vendors lack incentives to invest in upgrading their technology to support them.

Recommendations and next steps

So what would incentivize better coordination, improved integration, and greater adoption of digital credentials so that they could actually achieve their potential in matching skilled workers with jobs?

The report identifies a number of strategies to accelerate the adoption of digital credentials. These include academic institutions issuing traditional credentials (diplomas, certificates, transcripts) in digital formats, and developing partnerships with employers to design new types of credentials that better represent an individual’s skills, competencies, and abilities. The researchers offer tailored recommendations for different stakeholder groups — issuers, employers, governments, and trust providers — as well as for joint action.

Perhaps most urgent is the researchers’ call for stakeholders to collaborate on a shared vision and roadmap for an integrated skills system. Working together, they could create mechanisms to coordinate the development of the sector in an integrated way.

The Digital Credentials Consortium continues to turn the recommendations from the report into practice. MIT and Walmart entered into a new grant agreement in October 2022, extending the analysis of the Last Mile report through the development of a roadmap aimed at addressing the barriers to widespread adoption.  

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In machine learning, synthetic data can offer real performance improvements

Teaching a machine to recognize human actions has many potential applications, such as automatically detecting workers who fall at a construction site or enabling a smart home robot to interpret a user’s gestures.

To do this, researchers train machine-learning models using vast datasets of video clips that show humans performing actions. However, not only is it expensive and laborious to gather and label millions or billions of videos, but the clips often contain sensitive information, like people’s faces or license plate numbers. Using these videos might also violate copyright or data protection laws. And this assumes the video data are publicly available in the first place — many datasets are owned by companies and aren’t free to use.

So, researchers are turning to synthetic datasets. These are made by a computer that uses 3D models of scenes, objects, and humans to quickly produce many varying clips of specific actions — without the potential copyright issues or ethical concerns that come with real data.

But are synthetic data as “good” as real data? How well does a model trained with these data perform when it’s asked to classify real human actions? A team of researchers at MIT, the MIT-IBM Watson AI Lab, and Boston University sought to answer this question. They built a synthetic dataset of 150,000 video clips that captured a wide range of human actions, which they used to train machine-learning models. Then they showed these models six datasets of real-world videos to see how well they could learn to recognize actions in those clips.

The researchers found that the synthetically trained models performed even better than models trained on real data for videos that have fewer background objects.

This work could help researchers use synthetic datasets in such a way that models achieve higher accuracy on real-world tasks. It could also help scientists identify which machine-learning applications could be best-suited for training with synthetic data, in an effort to mitigate some of the ethical, privacy, and copyright concerns of using real datasets.

“The ultimate goal of our research is to replace real data pretraining with synthetic data pretraining. There is a cost in creating an action in synthetic data, but once that is done, then you can generate an unlimited number of images or videos by changing the pose, the lighting, etc. That is the beauty of synthetic data,” says Rogerio Feris, principal scientist and manager at the MIT-IBM Watson AI Lab, and co-author of a paper detailing this research.

The paper is authored by lead author Yo-whan “John” Kim ’22; Aude Oliva, director of strategic industry engagement at the MIT Schwarzman College of Computing, MIT director of the MIT-IBM Watson AI Lab, and a senior research scientist in the Computer Science and Artificial Intelligence Laboratory (CSAIL); and seven others. The research will be presented at the Conference on Neural Information Processing Systems.   

Building a synthetic dataset

The researchers began by compiling a new dataset using three publicly available datasets of synthetic video clips that captured human actions. Their dataset, called Synthetic Action Pre-training and Transfer (SynAPT), contained 150 action categories, with 1,000 video clips per category.

They selected as many action categories as possible, such as people waving or falling on the floor, depending on the availability of clips that contained clean video data.

Once the dataset was prepared, they used it to pretrain three machine-learning models to recognize the actions. Pretraining involves training a model for one task to give it a head-start for learning other tasks. Inspired by the way people learn — we reuse old knowledge when we learn something new — the pretrained model can use the parameters it has already learned to help it learn a new task with a new dataset faster and more effectively.

They tested the pretrained models using six datasets of real video clips, each capturing classes of actions that were different from those in the training data.

The researchers were surprised to see that all three synthetic models outperformed models trained with real video clips on four of the six datasets. Their accuracy was highest for datasets that contained video clips with “low scene-object bias.”

Low scene-object bias means that the model cannot recognize the action by looking at the background or other objects in the scene — it must focus on the action itself. For example, if the model is tasked with classifying diving poses in video clips of people diving into a swimming pool, it cannot identify a pose by looking at the water or the tiles on the wall. It must focus on the person’s motion and position to classify the action.

“In videos with low scene-object bias, the temporal dynamics of the actions is more important than the appearance of the objects or the background, and that seems to be well-captured with synthetic data,” Feris says.

“High scene-object bias can actually act as an obstacle. The model might misclassify an action by looking at an object, not the action itself. It can confuse the model,” Kim explains.

Boosting performance

Building off these results, the researchers want to include more action classes and additional synthetic video platforms in future work, eventually creating a catalog of models that have been pretrained using synthetic data, says co-author Rameswar Panda, a research staff member at the MIT-IBM Watson AI Lab.

“We want to build models which have very similar performance or even better performance than the existing models in the literature, but without being bound by any of those biases or security concerns,” he adds.

They also want to combine their work with research that seeks to generate more accurate and realistic synthetic videos, which could boost the performance of the models, says SouYoung Jin, a co-author and CSAIL postdoc. She is also interested in exploring how models might learn differently when they are trained with synthetic data.

“We use synthetic datasets to prevent privacy issues or contextual or social bias, but what does the model actually learn? Does it learn something that is unbiased?” she says.

Now that they have demonstrated this use potential for synthetic videos, they hope other researchers will build upon their work.

“Despite there being a lower cost to obtaining well-annotated synthetic data, currently we do not have a dataset with the scale to rival the biggest annotated datasets with real videos. By discussing the different costs and concerns with real videos, and showing the efficacy of synthetic data, we hope to motivate efforts in this direction,” adds co-author Samarth Mishra, a graduate student at Boston University (BU).

Additional co-authors include Hilde Kuehne, professor of computer science at Goethe University in Germany and an affiliated professor at the MIT-IBM Watson AI Lab; Leonid Karlinsky, research staff member at the MIT-IBM Watson AI Lab; Venkatesh Saligrama, professor in the Department of Electrical and Computer Engineering at BU; and Kate Saenko, associate professor in the Department of Computer Science at BU and a consulting professor at the MIT-IBM Watson AI Lab.

This research was supported by the Defense Advanced Research Projects Agency LwLL, as well as the MIT-IBM Watson AI Lab and its member companies, Nexplore and Woodside.

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Nanosensors target enzymes to monitor and study cancer

Cancer is characterized by a number of key biological processes known as the “hallmarks of cancer,” which remodel cells and their immediate environment so that tumors can form, grow, and thrive. Many of these changes are mediated by specific genes and proteins, working in tandem with other cellular processes, but the specifics vary from cancer type to cancer type, and even from patient to patient.

Sensitive tools for measuring protein or gene expression, even on the single cell level, have helped researchers understand the different cell types present in a tumor’s microenvironment and how this composition changes after treatments. However, these assays don’t necessarily show which proteins are active or relevant to tumor progression, or allow clinicians to noninvasively monitor the progress of the disease or its response to treatment. A protein could be present in a cancer cell as a bystander, for example, but not an active participant in its cellular transformations. Enzymes, which catalyze biochemical reactions inside cells, may give a clearer picture of which genes or proteins to target at a particular time.

In work recently published in Nature Communications, researchers from the MIT Koch Institute for Integrative Cancer Research have developed a set of enzyme-targeting nanoscale tools to monitor cancer progression and treatment response in real time, map enzyme activity to precise locations within a tumor, and isolate relevant cell populations for analysis.

“We hope that this new suite of tools can be useful in the clinic and the lab alike,” says Sangeeta Bhatia, the John J. and Dorothy Wilson Professor of Health Sciences and Technology, professor of electrical engineering the computer science, and senior author of the study. “With further development, the nanosensors could be used by clinicians to tailor treatments to a patient’s specific cancer, and to monitor cancer progression and treatment response, while researchers could use them to better understand the molecular biology of cancer and develop new tools to diagnose, track, and treat the disease.”

Bhatia is also a member of MIT’s Koch Institute for Integrative Cancer Research and Institute for Medical Engineering and Science. The study, conducted in collaboration with the laboratory of Tyler Jacks, was led by Ava Amini (Soleimany) ’16, a former graduate student from the Bhatia laboratory; and postdoc Jesse Kirkpatrick, also from the Bhatia lab.

Tracking tumors in real time

For several years, the Bhatia laboratory has been developing noninvasive urine tests for the detection of cancer, including colon, ovarian, and lung cancer. The tests rely on nanoparticles that interact with tumor proteins called proteases. Proteases are a type of enzyme that act as molecular scissors to cleave proteins and break them down into smaller components. Proteases help cancer cells escape from tumors by cutting through the extracellular network of proteins that holds cells in place.

The nanoparticles are coated with peptides (short protein fragments) that target cancer-linked proteases. When the nanoparticles arrive at the tumor site, the peptides are cut and release biomarkers that can be detected in the urine.

In the current study, the researchers tested whether they could use this technology not just to detect cancer, but to track the development of cancer and its response to treatments accurately and sensitively over time. The team created a panel of 14 nanoparticles designed to target proteases overexpressed in non-small cell lung cancer induced in a mouse model. These nanoparticles had been adapted to release barcoded peptides when they encounter dysregulated enzymes in the tumor microenvironment.

Each nanosensor was able to track different patterns of protease activity, which changed dramatically as the tumor progressed. After treatment with a lung cancer-targeting drug, the researchers were able to find signs tumor regression quickly, within just three days of administering treatment.

Cell maps and populations

While the existing nanosensor technique could be used to track tumor progression and treatment response in general, by itself, it could not shed any light on the specific cellular process at work.

“Like many of the tools available to assess molecular markers for cancer, our urine reporter treats the body like a black box,” says Kirkpatrick. “While we get some information about the state of the disease, we wanted to know more about the cells or proteins that are causing the disease to behave in a particular way.”

Having identified nanosensors of interest, researchers mapped where in the tumor microenvironment the enzymes acting on these sensors were active. They adapted their nanoprobes to leave behind fluorescent tags when they are cleaved from the nanosensor, assigning different tags to different proteases. After applying the nanoprobes to samples of lung tissue, they looked for patterns in how the tags were distributed.

One tag resulted in a curious spindle-like pattern that turned out to belong to the tumor vasculature. Researchers pinpointed the protease activity to specific types of cells: endothelial cells, which line blood vessels, and pericytes, which regulate vascular function and are actively recruited in angiogenesis — one of the archetypal hallmarks of cancer cell growth. Angiogenesis allows tumor cells to recruit existing blood vessels and stimulate new ones to form, in order to obtain the nutrients needed for tumor formation and progression.

Using their nanoprobes to label and sort cells based on their enzymatic activity, the team identified populations of cells associated with vasculature that displayed heightened expression of genes related to angiogenesis. The researchers also found evidence of signaling between pericytes and the endothelial cells that together comprise angiogenic blood vessels in vascular tissue.

Hallmark observations

In future work, the team seeks to identify the specific protease active in pericytes and dissect its role in angiogenesis. With this knowledge, they hope to develop formulations of therapies that can be delivered to patients to disrupt the recruitment and formation of blood vessels associated with tumor growth.

Ultimately, however, the team envisions panels of nanoprobes targeting several important features of cancer simultaneously and noninvasively in patients. Other hallmarks of cancer include proliferative signaling, the evasion of growth suppressors, genome instability, resistance to cell death, deregulated metabolism, and activation of invasion and metastasis. Because cancer alters protease activity across all of these processes, the team’s nanoprobes could be designed to target these different processes, with the aim of providing a comprehensive picture of tumor activity driving the disease. The approach could be used by researchers looking to investigate key biological phenomena in cancer models, as well as by clinicians seeking to monitor cancer progression noninvasively and select treatments for their patients.

The study was supported, in part, by the Virginia and D.K. Ludwig Fund for Cancer Research, the Koch Institute Frontier Research Program through a gift from Upstage Lung Cancer, the Koch Institute’s Marble Center for Cancer Nanomedicine, and Johnson & Johnson.

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Methane research takes on new urgency at MIT

One of the most notable climate change provisions in the 2022 Inflation Reduction Act is the first U.S. federal tax on a greenhouse gas (GHG). That the fee targets methane (CH₄), rather than carbon dioxide (CO₂), emissions is indicative of the urgency the scientific community has placed on reducing this short-lived but powerful gas. Methane persists in the air about 12 years — compared to more than 1,000 years for CO₂ — yet it immediately causes about 120 times more warming upon release. The gas is responsible for at least a quarter of today’s gross warming. 

“Methane has a disproportionate effect on near-term warming,” says Desiree Plata, the director of MIT Methane Network. “CH₄ does more damage than CO₂ no matter how long you run the clock. By removing methane, we could potentially avoid critical climate tipping points.” 

Because GHGs have a runaway effect on climate, reductions made now will have a far greater impact than the same reductions made in the future. Cutting methane emissions will slow the thawing of permafrost, which could otherwise lead to massive methane releases, as well as reduce increasing emissions from wetlands.  

“The goal of MIT Methane Network is to reduce methane emissions by 45 percent by 2030, which would save up to 0.5 degree C of warming by 2100,” says Plata, an associate professor of civil and environmental engineering at MIT and director of the Plata Lab. “When you consider that governments are trying for a 1.5-degree reduction of all GHGs by 2100, this is a big deal.” 

Under normal concentrations, methane, like CO₂, poses no health risks. Yet methane assists in the creation of high levels of ozone. In the lower atmosphere, ozone is a key component of air pollution, which leads to “higher rates of asthma and increased emergency room visits,” says Plata. 

Methane-related projects at the Plata Lab include a filter made of zeolite — the same clay-like material used in cat litter — designed to convert methane into CO₂ at dairy farms and coal mines. At first glance, the technology would appear to be a bit of a hard sell, since it converts one GHG into another. Yet the zeolite filter’s low carbon and dollar costs, combined with the disproportionate warming impact of methane, make it a potential game-changer.

The sense of urgency about methane has been amplified by recent studies that show humans are generating far more methane emissions than previously estimated, and that the rates are rising rapidly. Exactly how much methane is in the air is uncertain. Current methods for measuring atmospheric methane, such as ground, drone, and satellite sensors, “are not readily abundant and do not always agree with each other,” says Plata.  

The Plata Lab is collaborating with Tim Swager in the MIT Department of Chemistry to develop low-cost methane sensors. “We are developing chemiresisitive sensors that cost about a dollar that you could place near energy infrastructure to back-calculate where leaks are coming from,” says Plata.  

The researchers are working on improving the accuracy of the sensors using machine learning techniques and are planning to integrate internet-of-things technology to transmit alerts. Plata and Swager are not alone in focusing on data collection: the Inflation Reduction Act adds significant funding for methane sensor research. 

Other research at the Plata Lab includes the development of nanomaterials and heterogeneous catalysis techniques for environmental applications. The lab also explores mitigation solutions for industrial waste, particularly those related to the energy transition. Plata is the co-founder of an lithium-ion battery recycling startup called Nth Cycle. 

On a more fundamental level, the Plata Lab is exploring how to develop products with environmental and social sustainability in mind. “Our overarching mission is to change the way that we invent materials and processes so that environmental objectives are incorporated along with traditional performance and cost metrics,” says Plata. “It is important to do that rigorous assessment early in the design process.”

MIT amps up methane research 

The MIT Methane Network brings together 26 researchers from MIT along with representatives of other institutions “that are dedicated to the idea that we can reduce methane levels in our lifetime,” says Plata. The organization supports research such as Plata’s zeolite and sensor projects, as well as designing pipeline-fixing robots, developing methane-based fuels for clean hydrogen, and researching the capture and conversion of methane into liquid chemical precursors for pharmaceuticals and plastics. Other members are researching policies to encourage more sustainable agriculture and land use, as well as methane-related social justice initiatives. 

“Methane is an especially difficult problem because it comes from all over the place,” says Plata. A recent Global Carbon Project study estimated that half of methane emissions are caused by humans. This is led by waste and agriculture (28 percent), including cow and sheep belching, rice paddies, and landfills.  

Fossil fuels represent 18 percent of the total budget. Of this, about 63 percent is derived from oil and gas production and pipelines, 33 percent from coal mining activities, and 5 percent from industry and transportation. Human-caused biomass burning, primarily from slash-and-burn agriculture, emits about 4 percent of the global total.  

The other half of the methane budget includes natural methane emissions from wetlands (20 percent) and other natural sources (30 percent). The latter includes permafrost melting and natural biomass burning, such as forest fires started by lightning.  

With increases in global warming and population, the line between anthropogenic and natural causes is getting fuzzier. “Human activities are accelerating natural emissions,” says Plata. “Climate change increases the release of methane from wetlands and permafrost and leads to larger forest and peat fires.”  

The calculations can get complicated. For example, wetlands provide benefits from CO₂ capture, biological diversity, and sea level rise resiliency that more than compensate for methane releases. Meanwhile, draining swamps for development increases emissions. 

Over 100 nations have signed onto the U.N.’s Global Methane Pledge to reduce at least 30 percent of anthropogenic emissions within the next 10 years. The U.N. report estimates that this goal can be achieved using proven technologies and that about 60 percent of these reductions can be accomplished at low cost. 

Much of the savings would come from greater efficiencies in fossil fuel extraction, processing, and delivery. The methane fees in the Inflation Reduction Act are primarily focused on encouraging fossil fuel companies to accelerate ongoing efforts to cap old wells, flare off excess emissions, and tighten pipeline connections.  

Fossil fuel companies have already made far greater pledges to reduce methane than they have with CO₂, which is central to their business. This is due, in part, to the potential savings, as well as in preparation for methane regulations expected from the Environmental Protection Agency in late 2022. The regulations build upon existing EPA oversight of drilling operations, and will likely be exempt from the U.S. Supreme Court’s ruling that limits the federal government’s ability to regulate GHGs. 

Zeolite filter targets methane in dairy and coal 

The “low-hanging fruit” of gas stream mitigation addresses most of the 20 percent of total methane emissions in which the gas is released in sufficiently high concentrations for flaring. Plata’s zeolite filter aims to address the thornier challenge of reducing the 80 percent of non-flammable dilute emissions. 

Plata found inspiration in decades-old catalysis research for turning methane into methanol. One strategy has been to use an abundant, low-cost aluminosilicate clay called zeolite.  

“The methanol creation process is challenging because you need to separate a liquid, and it has very low efficiency,” says Plata. “Yet zeolite can be very efficient at converting methane into CO₂, and it is much easier because it does not require liquid separation. Converting methane to CO₂ sounds like a bad thing, but there is a major anti-warming benefit. And because methane is much more dilute than CO₂, the relative CO₂ contribution is minuscule.”  

Using zeolite to create methanol requires highly concentrated methane, high temperatures and pressures, and industrial processing conditions. Yet Plata’s process, which dopes the zeolite with copper, operates in the presence of oxygen at much lower temperatures under typical pressures. “We let the methane proceed the way it wants from a thermodynamic perspective from methane to methanol down to CO₂,” says Plata. 

Researchers around the world are working on other dilute methane removal technologies. Projects include spraying iron salt aerosols into sea air where they react with natural chlorine or bromine radicals, thereby capturing methane. Most of these geoengineering solutions, however, are difficult to measure and would require massive scale to make a difference.  

Plata is focusing her zeolite filters on environments where concentrations are high, but not so high as to be flammable. “We are trying to scale zeolite into filters that you could snap onto the side of a cross-ventilation fan in a dairy barn or in a ventilation air shaft in a coal mine,” says Plata. “For every packet of air we bring in, we take a lot of methane out, so we get more bang for our buck.”  

The major challenge is creating a filter that can handle high flow rates without getting clogged or falling apart. Dairy barn air handlers can push air at up to 5,000 cubic feet per minute and coal mine handlers can approach 500,000 CFM. 

Plata is exploring engineering options including fluidized bed reactors with floating catalyst particles. Another filter solution, based in part on catalytic converters, features “higher-order geometric structures where you have a porous material with a long path length where the gas can interact with the catalyst,” says Plata. “This avoids the challenge with fluidized beds of containing catalyst particles in the reactor. Instead, they are fixed within a structured material.”  

Competing technologies for removing methane from mine shafts “operate at temperatures of 1,000 to 1,200 degrees C, requiring a lot of energy and risking explosion,” says Plata. “Our technology avoids safety concerns by operating at 300 to 400 degrees C. It reduces energy use and provides more tractable deployment costs.” 

Potentially, energy and dollar costs could be further reduced in coal mines by capturing the heat generated by the conversion process. “In coal mines, you have enrichments above a half-percent methane, but below the 4 percent flammability threshold,” says Plata. “The excess heat from the process could be used to generate electricity using off-the-shelf converters.” 

Plata’s dairy barn research is funded by the Gerstner Family Foundation and the coal mining project by the U.S. Department of Energy. “The DOE would like us to spin out the technology for scale-up within three years,” says Plata. “We cannot guarantee we will hit that goal, but we are trying to develop this as quickly as possible. Our society needs to start reducing methane emissions now.” 

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A career in biochemistry unfolds

Rita Anoh's first exposure to college-level research was not something she recognized as a path she could follow. While in high school, the daughter of Anoh’s Advanced Placement biology teacher presented a poster to her class about what she was working on in graduate school. “At the time, actually, it did not click to me what she was presenting,” Anoh laughs. “Because I didn't know that you could do research as such, I just didn't put it together.”

Instead, Anoh traces the start of her journey to science back to her childhood in Ghana, where she enjoyed spending summers assisting in a health clinic run by her grandmother, a nurse. Anoh especially loved the problem-solving and teamwork involved. “Every time, people would leave like, ‘Problem solved!’ or ‘Oh, my problem is not solved, but I know where to go next.’”

Anoh’s enthusiasm for finding solutions to complex problems shifted from medicine to research when she arrived as an undergraduate at Mount Saint Mary’s University (The Mount) in Maryland, and later as a participant in the 2022 Bernard S. and Sophie G. Gould MIT Summer Research Program in Biology (BSG-MSRP-Bio).

As a first-year majoring in biology at The Mount, Anoh applied to a summer research program with the encouragement of Patrick Lombardi, assistant professor of chemistry. She earned an internship to work in his lab exploring how DNA damage in cells is detected and repaired. Then, the summer following her sophomore year, she participated in the Caltech WAVE Fellows program in the lab of Douglas Rees, the Roscoe Gilkey Dickinson Professor of Chemistry, focusing on the structures and mechanisms of complex metalloproteins and integral membrane proteins. Anoh was also awarded a Barry M. Goldwater Scholarship for students intending to pursue research careers in natural science, mathematics, and engineering. “I was the first sophomore to receive at my school, so that was very exciting,” adds Anoh.

“It’s been a blast”

Eager to continue building her science skills and experience a new city, Anoh quickly accepted an offer to join the BSG-MSRP-Bio program at MIT this past summer.

Anoh spent 10 weeks in the lab of assistant professor of biology Joey Davis, whose lab works to uncover how cells construct and degrade complex molecular machines rapidly and efficiently. Anoh also worked with Robert Sauer, the Salvador E. Luria Professor of Biology at MIT, who studies the relationship between protein structure, function, sequence, and folding. 

“It’s been a blast,” says Anoh.

Specifically, her project centered on a complex in the cell that helps oversee proteolysis, or the breakdown of proteins into peptides, or strings of amino acids, and further into amino acids for recycling by the cell. Called ClpXP, this molecular machine is made up of two substructures: ClpX and ClpP. First, ClpX identifies and unfolds peptide sequences in the protein substrate to be broken down; then ClpP breaks the unfolded peptides down into smaller fragments.

In her research, Anoh looked at the degradation of a protein RseA by ClpXP bound to another piece of molecular machinery called SspB. This “adapter protein” delivers the targeted protein to ClpXP to begin breaking it down. By degrading RseA, ClpXP plays an essential role in the signaling pathway in bacteria allowing the bacterial cell to respond to stress. Along with her mentor, she examined samples under a cryo‐electron microscope (Cryo-EM) at MIT.nano and collected data to determine its 3D map, shedding light on how ClpXP with the help of SspB breaks down proteins within a cell.

In addition to her gain of technical and research skills, one of Anoh’s takeaways from her summer at MIT was “how collaborative and dynamic science is in general,” she says, especially with mentors such as Alireza Ghanbarpour, a joint postdoc in the Davis and Sauer labs.

“During her time at our lab, she became friends with everyone,” says Ghanbarpour, who mentored Anoh and another undergraduate student whom Anoh befriended. “Rita developed a great relationship with her and, on many occasions, helped her with her project.”

Anoh attended group meetings, lab retreats, and conferences. In MSRP seminars, she heard from MIT researchers about their own experiences solving problems using advice from fellow scientists.

“I talk to my peers about what we're all doing, and how different people at the same lab work together, or how different labs work together,” Anoh says. “I’ve learned different ways to achieve the same goal.”

Ghanbarpour also assisted Anoh in deepening her understanding of the material beyond the bench. Passionate about structural biology and biochemistry, he provided explanations and connected Anoh with materials to expand her knowledge of relevant researchers and concepts. “I was learning not just stuff in the lab but actually the meaning of what I was doing, so that was pretty cool,” she says.

Now in her senior year back at The Mount, Anoh intends to keep an open mind. An open mind, after all, is why she acted on her professor’s suggestion when she was a first-year student to apply to the program that set her on her current path to a research career. Without a doubt, though, Anoh says she plans to pursue a PhD in biochemistry and mentor young researchers like herself along the way.

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A new control system for synthetic genes

Using an approach based on CRISPR proteins, MIT researchers have developed a new way to precisely control the amount of a particular protein that is produced in mammalian cells.

This technique could be used to finely tune the production of useful proteins, such as the monoclonal antibodies used to treat cancer and other diseases, or other aspects of cellular behavior. In their new study, which appears in Nature Communications, the researchers showed that this system can work in a variety of mammalian cells, with very consistent results.

“It’s a highly predictable system that we can design up front and then get the expected outcome,” says William C.W. Chen, a former MIT research scientist. “It’s a very tunable system and suitable for many different biomedical applications in different cell types.”

Chen, who is now an assistant professor of biomedical sciences at the University of South Dakota, is one of the lead authors of the new study, along with former MIT Research Scientist Leonid Gaidukov and postdoc Yong Lai. Senior author Timothy Lu led the research as an MIT associate professor of biological engineering and of electrical engineering and computer science.

Gene control

Many therapeutic proteins, including monoclonal antibodies, are produced in large bioreactors containing mammalian cells that are engineered to generate the desired protein. Several years ago, researchers in MIT’s Synthetic Biology Center, including Lu’s lab, began working with Pfizer Inc. on a project to develop synthetic biology tools that could be used to boost the production of these useful proteins.

To do so, the researchers targeted the promoters of the genes they wanted to upregulate. In all mammalian cells, genes have a promoter region that binds to transcription factors — proteins that initiate the transcription of the gene into messenger RNA.

In previous work, scientists have designed synthetic transcription factors, including proteins called zinc fingers, to help activate target genes. However, zinc fingers and most other types of synthetic transcription factors have to be redesigned for each gene that they target, making them challenging and time-consuming to develop.

In 2013, researchers in Lu’s lab developed a CRISPR-based transcription factor that allowed them to more easily control transcription of naturally occurring genes in mammalian and yeast cells. In the new study, the researchers set out to build on that work to create a library of synthetic biological parts that would allow them to deliver a transgene — a gene not normally expressed by the cell — and precisely control its expression.

“The idea is to have a full-spectrum synthetic promoter system that can go from very low to very high, to accommodate different cellular applications,” Chen says.

The system that the researchers designed includes several components. One is the gene to be transcribed, along with an “operator” sequence, which consists of a series of artificial transcription factor binding sites. Another component is a guide RNA that binds to those operator sequences. Lastly, the system also includes a transcription activation domain attached to a deactivated Cas9 protein. When this deactivated Cas9 protein binds to the guide RNA at the synthetic promoter site, the CRISPR-based transcription factor can turn on gene expression.

The promoter sites used for this synthetic system were designed to be distinct from naturally occurring promoter sites, so that the system won’t affect genes in the cells’ own genomes. Each operator includes between two and 16 copies of the guide RNA binding site, and the researchers found that their system could initiate gene transcription at rates that linearly correspond to the number of binding sites, allowing them to precisely control the amount of the protein produced.

High consistency

The researchers tested their system in several types of mammalian cells, including Chinese hamster ovary (CHO) cells, which are commonly used to produce therapeutic proteins in industrial bioreactors. They found very similar results in CHO cells and the other cells they tested, including mouse and rat myoblasts (precursors to muscle cells), human embryonic kidney cells, and human induced pluripotent stem cells.

“The system has very high consistency over different cell types and different target genes,” Chen says. “This is a good starting point for thinking about regulating gene expression and cell behavior with a highly tunable, predictable artificial system.”

After first demonstrating that they could use the new system to induce cells to produce expected amounts of fluorescent proteins, the researchers showed they could also use it to program the production of the two major segments of a monoclonal antibody known as JUG444.

The researchers also programmed CHO cells to produce different quantities of a human antibody called anti-PD1. When human T cells were exposed to these cells, they became more potent tumor cell killers if there was a larger amount of the antibody produced.

Although the researchers were able to obtain a high yield of the desired antibodies, further work would be needed to incorporate this system into industrial processes, they say. Unlike the cells used in industrial bioreactors, the cells used in this study were grown on a flat surface, rather than in a liquid suspension.

“This is a system that is promising to be used in industrial applications, but first we have to adapt this into suspended cells, to see if they make the proteins the same way. I suspect it should be the same, because there’s no reason that it shouldn’t, but we still need to test it,” Chen says.

The research was funded by the Pfizer-MIT RCA Synthetic Biology Program, the National Science Foundation, the National Institutes of Health, the University of South Dakota Sanford School of Medicine, an NIH Ruth L. Kirschstein NRSA postdoctoral fellowship, and the U.S. Department of Defense.

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