Aurelia Butler

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    in Security

    Rover images confirm Jezero crater is an ancient Martian lake

    The first scientific analysis of images taken by NASA’s Perseverance rover has now confirmed that Mars’ Jezero crater — which today is a dry, wind-eroded depression — was once a quiet lake, fed steadily by a small river some 3.7 billion years ago.

    The images also reveal evidence that the crater endured flash floods. This flooding was energetic enough to sweep up large boulders from tens of miles upstream and deposit them into the lakebed, where the massive rocks lie today.

    The new analysis, published today in the journal Science, is based on images of the outcropping rocks inside the crater on its western side. Satellites had previously shown that this outcrop, seen from above, resembled river deltas on Earth, where layers of sediment are deposited in the shape of a fan as the river feeds into a lake.

    Perseverance’s new images, taken from inside the crater, confirm that this outcrop was indeed a river delta. Based on the sedimentary layers in the outcrop, it appears that the river delta fed into a lake that was calm for much of its existence, until a dramatic shift in climate triggered episodic flooding at or toward the end of the lake’s history.

    “If you look at these images, you’re basically staring at this epic desert landscape. It’s the most forlorn place you could ever visit,” says Benjamin Weiss, professor of planetary sciences in MIT’s Department of Earth, Atmospheric and Planetary Sciences and a member of the analysis team. “There’s not a drop of water anywhere, and yet, here we have evidence of a very different past. Something very profound happened in the planet’s history.”

    As the rover explores the crater, scientists hope to uncover more clues to its climatic evolution. Now that they have confirmed the crater was once a lake environment, they believe its sediments could hold traces of ancient aqueous life. In its mission going forward, Perseverance will look for locations to collect and preserve sediments. These samples will eventually be returned to Earth, where scientists can probe them for Martian biosignatures.

    “We now have the opportunity to look for fossils,” says team member Tanja Bosak, associate professor of geobiology at MIT. “It will take some time to get to the rocks that we really hope to sample for signs of life. So, it’s a marathon, with a lot of potential.”

    Tilted beds

    On Feb. 18, 2021, the Perseverance rover landed on the floor of Jezero crater, a little more than a mile away from its western fan-shaped outcrop. In the first three months, the vehicle remained stationary as NASA engineers performed remote checks of the rover’s many instruments.

    During this time, two of Perseverance’s cameras, Mastcam-Z and the SuperCam Remote Micro-Imager (RMI), captured images of their surroundings, including long-distance photos of the outcrop’s edge and a formation known as Kodiak butte, a smaller outcop that planetary geologists surmise may have once been connected to the main fan-shaped outcrop but has since partially eroded.

    Once the rover downlinked images to Earth, NASA’s Perseverance science team processed and combined the images, and were able to observe distinct beds of sediment along Kodiak butte in surprisingly high resolution. The researchers measured each layer’s thickness, slope, and lateral extent, finding that the sediment must have been deposited by flowing water into a lake, rather than by wind, sheet-like floods, or other geologic processes.

    The rover also captured similar tilted sediment beds along the main outcrop. These images, together with those of Kodiak, confirm that the fan-shaped formation was indeed an ancient delta and that this delta fed into an ancient Martian lake.

    “Without driving anywhere, the rover was able to solve one of the big unknowns, which was that this crater was once a lake,” Weiss says. “Until we actually landed there and confirmed it was a lake, it was always a question.”

    Boulder flow

    When the researchers took a closer look at images of the main outcrop, they noticed large boulders and cobbles embedded in the youngest, topmost layers of the delta. Some boulders measured as wide as 1 meter across, and were estimated to weigh up to several tons. These massive rocks, the team concluded, must have come from outside the crater, and was likely part of bedrock located on the crater rim or else 40 or more miles upstream.

    Judging from their current location and dimensions, the team says the boulders were carried downstream and into the lakebed by a flash-flood that flowed up to 9 meters per second and moved up to 3,000 cubic meters of water per second.

    “You need energetic flood conditions to carry rocks that big and heavy,” Weiss says. “It’s a special thing that may be indicative of a fundamental change in the local hydrology or perhaps the regional climate on Mars.”

    Because the huge rocks lie in the upper layers of the delta, they represent the most recently deposited material. The boulders sit atop layers of older, much finer sediment. This stratification, the researchers say, indicates that for much of its existence, the ancient lake was filled by a gently flowing river. Fine sediments — and possibly organic material — drifted down the river, and settled into a gradual, sloping delta.

    However, the crater later experienced sudden flash floods that deposited large boulders onto the delta. Once the lake dried up, and over billions of years wind eroded the landscape, leaving the crater we see today.

    The cause of this climate turnaround is unknown, although Weiss says the delta’s boulders may hold some answers.

    “The most surprising thing that’s come out of these images is the potential opportunity to catch the time when this crater transitioned from an Earth-like habitable environment, to this desolate landscape wasteland we see now,” he says. “These boulder beds may be records of this transition, and we haven’t seen this in other places on Mars.”

    This research was supported, in part, by NASA. More

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    in Security

    A robot that finds lost items

    A busy commuter is ready to walk out the door, only to realize they’ve misplaced their keys and must search through piles of stuff to find them. Rapidly sifting through clutter, they wish they could figure out which pile was hiding the keys.

    Researchers at MIT have created a robotic system that can do just that. The system, RFusion, is a robotic arm with a camera and radio frequency (RF) antenna attached to its gripper. It fuses signals from the antenna with visual input from the camera to locate and retrieve an item, even if the item is buried under a pile and completely out of view.

    The RFusion prototype the researchers developed relies on RFID tags, which are cheap, battery-less tags that can be stuck to an item and reflect signals sent by an antenna. Because RF signals can travel through most surfaces (like the mound of dirty laundry that may be obscuring the keys), RFusion is able to locate a tagged item within a pile.

    Using machine learning, the robotic arm automatically zeroes-in on the object’s exact location, moves the items on top of it, grasps the object, and verifies that it picked up the right thing. The camera, antenna, robotic arm, and AI are fully integrated, so RFusion can work in any environment without requiring a special set up.

    While finding lost keys is helpful, RFusion could have many broader applications in the future, like sorting through piles to fulfill orders in a warehouse, identifying and installing components in an auto manufacturing plant, or helping an elderly individual perform daily tasks in the home, though the current prototype isn’t quite fast enough yet for these uses.

    “This idea of being able to find items in a chaotic world is an open problem that we’ve been working on for a few years. Having robots that are able to search for things under a pile is a growing need in industry today. Right now, you can think of this as a Roomba on steroids, but in the near term, this could have a lot of applications in manufacturing and warehouse environments,” said senior author Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science and director of the Signal Kinetics group in the MIT Media Lab.

    Co-authors include research assistant Tara Boroushaki, the lead author; electrical engineering and computer science graduate student Isaac Perper; research associate Mergen Nachin; and Alberto Rodriguez, the Class of 1957 Associate Professor in the Department of Mechanical Engineering. The research will be presented at the Association for Computing Machinery Conference on Embedded Networked Senor Systems next month.

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    Sending signals

    RFusion begins searching for an object using its antenna, which bounces signals off the RFID tag (like sunlight being reflected off a mirror) to identify a spherical area in which the tag is located. It combines that sphere with the camera input, which narrows down the object’s location. For instance, the item can’t be located on an area of a table that is empty.

    But once the robot has a general idea of where the item is, it would need to swing its arm widely around the room taking additional measurements to come up with the exact location, which is slow and inefficient.

    The researchers used reinforcement learning to train a neural network that can optimize the robot’s trajectory to the object. In reinforcement learning, the algorithm is trained through trial and error with a reward system.

    “This is also how our brain learns. We get rewarded from our teachers, from our parents, from a computer game, etc. The same thing happens in reinforcement learning. We let the agent make mistakes or do something right and then we punish or reward the network. This is how the network learns something that is really hard for it to model,” Boroushaki explains.

    In the case of RFusion, the optimization algorithm was rewarded when it limited the number of moves it had to make to localize the item and the distance it had to travel to pick it up.

    Once the system identifies the exact right spot, the neural network uses combined RF and visual information to predict how the robotic arm should grasp the object, including the angle of the hand and the width of the gripper, and whether it must remove other items first. It also scans the item’s tag one last time to make sure it picked up the right object.

    Cutting through clutter

    The researchers tested RFusion in several different environments. They buried a keychain in a box full of clutter and hid a remote control under a pile of items on a couch.

    But if they fed all the camera data and RF measurements to the reinforcement learning algorithm, it would have overwhelmed the system. So, drawing on the method a GPS uses to consolidate data from satellites, they summarized the RF measurements and limited the visual data to the area right in front of the robot.

    Their approach worked well — RFusion had a 96 percent success rate when retrieving objects that were fully hidden under a pile.

    “Sometimes, if you only rely on RF measurements, there is going to be an outlier, and if you rely only on vision, there is sometimes going to be a mistake from the camera. But if you combine them, they are going to correct each other. That is what made the system so robust,” Boroushaki says.

    In the future, the researchers hope to increase the speed of the system so it can move smoothly, rather than stopping periodically to take measurements. This would enable RFusion to be deployed in a fast-paced manufacturing or warehouse setting.

    Beyond its potential industrial uses, a system like this could even be incorporated into future smart homes to assist people with any number of household tasks, Boroushaki says.

    “Every year, billions of RFID tags are used to identify objects in today’s complex supply chains, including clothing and lots of other consumer goods. The RFusion approach points the way to autonomous robots that can dig through a pile of mixed items and sort them out using the data stored in the RFID tags, much more efficiently than having to inspect each item individually, especially when the items look similar to a computer vision system,” says Matthew S. Reynolds, CoMotion Presidential Innovation Fellow and associate professor of electrical and computer engineering at the University of Washington, who was not involved in the research. “The RFusion approach is a great step forward for robotics operating in complex supply chains where identifying and ‘picking’ the right item quickly and accurately is the key to getting orders fulfilled on time and keeping demanding customers happy.”

    The research is sponsored by the National Science Foundation, a Sloan Research Fellowship, NTT DATA, Toppan, Toppan Forms, and the Abdul Latif Jameel Water and Food Systems Lab. More

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    in Environment

    New “risk triage” platform pinpoints compounding threats to US infrastructure

    Over a 36-hour period in August, Hurricane Henri delivered record rainfall in New York City, where an aging storm-sewer system was not built to handle the deluge, resulting in street flooding. Meanwhile, an ongoing drought in California continued to overburden aquifers and extend statewide water restrictions. As climate change amplifies the frequency and intensity of extreme events in the United States and around the world, and the populations and economies they threaten grow and change, there is a critical need to make infrastructure more resilient. But how can this be done in a timely, cost-effective way?

    An emerging discipline called multi-sector dynamics (MSD) offers a promising solution. MSD homes in on compounding risks and potential tipping points across interconnected natural and human systems. Tipping points occur when these systems can no longer sustain multiple, co-evolving stresses, such as extreme events, population growth, land degradation, drinkable water shortages, air pollution, aging infrastructure, and increased human demands. MSD researchers use observations and computer models to identify key precursory indicators of such tipping points, providing decision-makers with critical information that can be applied to mitigate risks and boost resilience in infrastructure and managed resources.

    At MIT, the Joint Program on the Science and Policy of Global Change has since 2018 been developing MSD expertise and modeling tools and using them to explore compounding risks and potential tipping points in selected regions of the United States. In a two-hour webinar on Sept. 15, MIT Joint Program researchers presented an overview of the program’s MSD research tool set and its applications.  

    MSD and the risk triage platform

    “Multi-sector dynamics explores interactions and interdependencies among human and natural systems, and how these systems may adapt, interact, and co-evolve in response to short-term shocks and long-term influences and stresses,” says MIT Joint Program Deputy Director C. Adam Schlosser, noting that such analysis can reveal and quantify potential risks that would likely evade detection in siloed investigations. “These systems can experience cascading effects or failures after crossing tipping points. The real question is not just where these tipping points are in each system, but how they manifest and interact across all systems.”

    To address that question, the program’s MSD researchers have developed the MIT Socio-Environmental Triage (MST) platform, now publicly available for the first time. Focused on the continental United States, the first version of the platform analyzes present-day risks related to water, land, climate, the economy, energy, demographics, health, and infrastructure, and where these compound to create risk hot spots. It’s essentially a screening-level visualization tool that allows users to examine risks, identify hot spots when combining risks, and make decisions about how to deploy more in-depth analysis to solve complex problems at regional and local levels. For example, MST can identify hot spots for combined flood and poverty risks in the lower Mississippi River basin, and thereby alert decision-makers as to where more concentrated flood-control resources are needed.

    Successive versions of the platform will incorporate projections based on the MIT Joint Program’s Integrated Global System Modeling (IGSM) framework of how different systems and stressors may co-evolve into the future and thereby change the risk landscape. This enhanced capability could help uncover cost-effective pathways for mitigating and adapting to a wide range of environmental and economic risks.  

    MSD applications

    Five webinar presentations explored how MIT Joint Program researchers are applying the program’s risk triage platform and other MSD modeling tools to identify potential tipping points and risks in five key domains: water quality, land use, economics and energy, health, and infrastructure. 

    Joint Program Principal Research Scientist Xiang Gao described her efforts to apply a high-resolution U.S. water-quality model to calculate a location-specific, water-quality index over more than 2,000 river basins in the country. By accounting for interactions among climate, agriculture, and socioeconomic systems, various water-quality measures can be obtained ranging from nitrate and phosphate levels to phytoplankton concentrations. This modeling approach advances a unique capability to identify potential water-quality risk hot spots for freshwater resources.

    Joint Program Research Scientist Angelo Gurgel discussed his MSD-based analysis of how climate change, population growth, changing diets, crop-yield improvements and other forces that drive land-use change at the global level may ultimately impact how land is used in the United States. Drawing upon national observational data and the IGSM framework, the analysis shows that while current U.S. land-use trends are projected to persist or intensify between now and 2050, there is no evidence of any concerning tipping points arising throughout this period.  

    MIT Joint Program Research Scientist Jennifer Morris presented several examples of how the risk triage platform can be used to combine existing U.S. datasets and the IGSM framework to assess energy and economic risks at the regional level. For example, by aggregating separate data streams on fossil-fuel employment and poverty, one can target selected counties for clean energy job training programs as the nation moves toward a low-carbon future. 

    “Our modeling and risk triage frameworks can provide pictures of current and projected future economic and energy landscapes,” says Morris. “They can also highlight interactions among different human, built, and natural systems, including compounding risks that occur in the same location.”  

    MIT Joint Program research affiliate Sebastian Eastham, a research scientist at the MIT Laboratory for Aviation and the Environment, described an MSD approach to the study of air pollution and public health. Linking the IGSM with an atmospheric chemistry model, Eastham ultimately aims to better understand where the greatest health risks are in the United States and how they may compound throughout this century under different policy scenarios. Using the risk triage tool to combine current risk metrics for air quality and poverty in a selected county based on current population and air-quality data, he showed how one can rapidly identify cardiovascular and other air-pollution-induced disease risk hot spots.

    Finally, MIT Joint Program research affiliate Alyssa McCluskey, a lecturer at the University of Colorado at Boulder, showed how the risk triage tool can be used to pinpoint potential risks to roadways, waterways, and power distribution lines from flooding, extreme temperatures, population growth, and other stressors. In addition, McCluskey described how transportation and energy infrastructure development and expansion can threaten critical wildlife habitats.

    Enabling comprehensive, location-specific analyses of risks and hot spots within and among multiple domains, the Joint Program’s MSD modeling tools can be used to inform policymaking and investment from the municipal to the global level.

    “MSD takes on the challenge of linking human, natural, and infrastructure systems in order to inform risk analysis and decision-making,” says Schlosser. “Through our risk triage platform and other MSD models, we plan to assess important interactions and tipping points, and to provide foresight that supports action toward a sustainable, resilient, and prosperous world.”

    This research is funded by the U.S. Department of Energy’s Office of Science as an ongoing project. More

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    in Environment

    For campus “porosity hunters,” climate resilience is the goal

    At MIT, it’s not uncommon to see groups navigating campus with smartphones and measuring devices in hand, using the Institute as a test bed for research. During one week this summer more than a dozen students, researchers, and faculty, plus an altimeter, could be seen doing just that as they traveled across MIT to measure the points of entry into campus buildings — including windows, doors, and vents — known as a building’s porosity.

    Why measure campus building porosity?

    The group was part of the MIT Porosity Hunt, a citizen-science effort that is using the MIT campus as a place to test emerging methodologies, instruments, and data collection processes to better understand the potential impact of a changing climate — and specifically storm scenarios resulting from it — on infrastructure. The hunt is a collaborative effort between the Urban Risk Lab, led by director and associate professor of architecture and urbanism Miho Mazereeuw, and the Office of Sustainability (MITOS), aimed at supporting an MIT that is resilient to the impacts of climate change, including flooding and extreme heat events. Working over three days, members of the hunt catalogued openings in dozens of buildings across campus to better support flood mapping and resiliency planning at MIT.

    For Mazereeuw, the data collection project lies at the nexus of her work with the Urban Risk Lab and as a member of MIT’s Climate Resiliency Committee. While the lab’s mission is to “develop methods, prototypes, and technologies to embed risk reduction and preparedness into the design of cities and regions to increase resilience,” the Climate Resiliency Committee — made up of faculty, staff, and researchers — is focused on assessing, planning, and operationalizing a climate-resilient MIT. The work of both the lab and the committee is embedded in the recently released MIT Climate Resiliency Dashboard, a visualization tool that allows users to understand potential flooding impacts of a number of storm scenarios and drive decision-making.

    While the debut of the tool signaled a big advancement in resiliency planning at MIT, some, including Mazereeuw, saw an opportunity for enhancement. In working with Ken Strzepek, a MITOS Faculty Fellow and research scientist at the MIT Center for Global Change Science who was also an integral part of this work, Mazereeuw says she was surprised to learn that even the most sophisticated flood modeling treats buildings as solid blocks. With all buildings being treated the same, despite varying porosity, the dashboard is limited in some flood scenario analysis. To address this, Mazereeuw and others got to work to fill in that additional layer of data, with the citizen science efforts a key factor of that work. “Understanding the porosity of the building is important to understanding how much water actually goes in the building in these scenarios,” she explains.

    Though surveyors are often used to collect and map this type of information, Mazereeuw wanted to leverage the MIT community in order to collect data quickly while engaging students, faculty, and researchers as resiliency stewards for the campus. “It’s important for projects like this to encourage awareness,” she explains. “Generally, when something fails, we notice it, but otherwise we don’t. With climate change bringing on more uncertainty in the scale and intensity of events, we need everyone to be more aware and help us understand things like vulnerabilities.”

    To do this, MITOS and the Urban Risk Lab reached out to more than a dozen students, who were joined by faculty, staff, and researchers, to map porosity of 31 campus buildings connected by basements. The buildings were chosen based on this connectivity, understanding that water that reaches one basement could potentially flow to another.

    Urban Risk Lab research scientists Aditya Barve and Mayank Ojha aided the group’s efforts by creating a mapping app and chatbot to support consistency in reporting and ease of use. Each team member used the app to find buildings where porosity points needed to be mapped. As teams arrived at the building exteriors, they entered their location in the app, which then triggered the Facebook and LINE-powered chatbot on their phone. There, students were guided through measuring the opening, adjusting for elevation to correlate to the City of Cambridge base datum, and, based on observable features, noting the materials and quality of the opening on a one-through-three scale. Over just three days, the team, which included Mazereeuw herself, mapped 1,030 porosity points that will aid in resiliency planning and preparation on campus in a number of ways.

    “The goal is to understand various heights for flood waters around porous spots on campus,” says Mazereeuw. “But the impact can be different depending on the space. We hope this data can inform safety as well as understanding potential damage to research or disruption to campus operations from future storms.”

    The porosity data collection is complete for this round — future hunts will likely be conducted to confirm and converge data — but one team member’s work continues at the basement level of MIT. Katarina Boukin, a PhD student in civil and environmental engineering and PhD student fellow with MITOS, has been focused on methods of collecting data beneath buildings at MIT to understand how they would be impacted if flood water were to enter. “We have a number of connected basements on campus, and if one of them floods, potentially all of them do,” explains Boukin. “By looking at absolute elevation and porosity, we’re connecting the outside to the inside and tracking how much and where water may flow.” With the added data from the Porosity Hunt, a complete picture of vulnerabilities and resiliency opportunities can be shared.

    Synthesizing much of this data is where Eva Then ’21 comes in. Then was among the students who worked to capture data points over the three days and is now working in ArcGIS — an online mapping software that also powers the Climate Resiliency Dashboard — to process and visualize the data collected. Once completed, the data will be incorporated into the campus flood model to increase the accuracy of projections on the Climate Resiliency Dashboard. “Over the next decades, the model will serve as an adaptive planning tool to make campus safe and resilient amid growing climate risks,” Then says.

    For Mazereeuw, the Porosity Hunt and data collected additionally serve as a study in scalability, providing valuable insight on how similar research efforts inspired by the MIT test bed approach could be undertaken and inform policy beyond MIT. She also hopes it will inspire students to launch their own hunts in the future, becoming resiliency stewards for their campus and dorms. “Going through measuring and documenting turns on and shows a new set of goggles — you see campus and buildings in a slightly different way,” she says, “Having people look carefully and document change is a powerful tool in climate and resiliency planning.” 

    Mazereeuw also notes that recent devastating flooding events across the country, including those resulting from Hurricane Ida, have put a special focus on this work. “The loss of life that occurred in that storm, including those who died as waters flooded their basement homes  underscores the urgency of this type of research, planning, and readiness.” More

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    in Environment

    The language of change

    Ryan Conti came to MIT hoping to find a way to do good things in the world. Now a junior, his path is pointing toward a career in climate science, and he is preparing by majoring in both math and computer science and by minoring in philosophy.

    Language for catalyzing change

    Philosophy matters to Conti not only because he is interested in ethics — questions of right and wrong — but because he believes the philosophy of language can illuminate how humans communicate, including factors that contribute to miscommunication. “I care a lot about climate change, so I want to do scientific work on it, but I also want to help work on policy — which means conveying arguments well and convincing people so that change can occur,” he says.Conti says a key reason he came to MIT was because the Institute has such a strong School of Humanities, Arts, and Social Sciences (MIT SHASS). “One of the big factors in my choosing MIT is that the humanities departments here are really, really good,” says Conti, who was named a 2021 Burchard Scholar in honor of his excellence in the Institute’s humanistic fields. “I was considering literature, writing, philosophy, linguistics, all of that.”Revitalizing endangered indigenous languages

    Within MIT SHASS, Conti has focused academically on the philosophy of language, and he is also personally pursuing another linguistic passion — the preservation and revitalization of endangered indigenous languages. Raised in Plano, Texas, Conti is a citizen of the Chickasaw Nation, which today has fewer than 50 first-language speakers left.“I’ve been studying the language on my own. It’s something I really care about a lot, the entire endeavor of language revitalization,” says Conti, who credits his maternal grandmother with instilling his appreciation for his heritage. “She would always tell me that I should be proud of it,” he says. “As I got older and understood the history of things, the precarious nature of our language, I got more invested.” Conti says working to revitalize the Chickasaw language “could be one of the most important things I do with my life.”Already, MIT has given him an opportunity — through the MIT Solve initiative — to participate in a website project for speakers of Makah, an endangered indigenous language of the Pacific Northwest. “The thrust at a high level is trying to use AI [artificial intelligence] to develop speech-to-text software for languages in the Wakashan language family,” he says. The project taught him a lot about natural language processing and automatic speech recognition, he adds, although his website design was not chosen for implementation.

    Glacier dynamics, algorithms — and Quizbowl!

    MIT has also given Conti some experience on the front lines of climate change. Through the Undergraduate Research Opportunities Program, he has been working in MIT’s Glacier Dynamics and Remote Sensing Group, developing machine learning algorithms to improve iceberg detection using satellite imagery. After graduation, Conti plans to pursue a PhD in climate science, perhaps continuing to work in glaciology.He also hopes to participate in a Chickasaw program that pairs students with native speakers to become fluent. He says he sees some natural overlap between his two passions. “Issues of indigenous sovereignty and language preservation are inherently linked with climate change, because the effects of climate change fall unequally on poor communities, which are oftentimes indigenous communities,” he says.For the moment, however, those plans still lie at least two years in the future. In the meantime, Conti is having fun serving as vice president of the MIT Quizbowl Team, an academic quiz team that competes across the region and often participate in national tournaments. What are Conti’s competition specialties? Literature and philosophy. 

    Story prepared by MIT SHASS CommunicationsEditor, Designer: Emily Hiestand, Communications DirectorSenior Writer: Kathryn O’Neill, Associate News Manager More

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    in Environment

    Zeroing in on the origins of Earth’s “single most important evolutionary innovation”

    Some time in Earth’s early history, the planet took a turn toward habitability when a group of enterprising microbes known as cyanobacteria evolved oxygenic photosynthesis — the ability to turn light and water into energy, releasing oxygen in the process.

    This evolutionary moment made it possible for oxygen to eventually accumulate in the atmosphere and oceans, setting off a domino effect of diversification and shaping the uniquely habitable planet we know today.  

    Now, MIT scientists have a precise estimate for when cyanobacteria, and oxygenic photosynthesis, first originated. Their results appear today in the Proceedings of the Royal Society B.

    They developed a new gene-analyzing technique that shows that all the species of cyanobacteria living today can be traced back to a common ancestor that evolved around 2.9 billion years ago. They also found that the ancestors of cyanobacteria branched off from other bacteria around 3.4 billion years ago, with oxygenic photosynthesis likely evolving during the intervening half-billion years, during the Archean Eon.

    Interestingly, this estimate places the appearance of oxygenic photosynthesis at least 400 million years before the Great Oxidation Event, a period in which the Earth’s atmosphere and oceans first experienced a rise in oxygen. This suggests that cyanobacteria may have evolved the ability to produce oxygen early on, but that it took a while for this oxygen to really take hold in the environment.

    “In evolution, things always start small,” says lead author Greg Fournier, associate professor of geobiology in MIT’s Department of Earth, Atmospheric and Planetary Sciences. “Even though there’s evidence for early oxygenic photosynthesis — which is the single most important and really amazing evolutionary innovation on Earth — it still took hundreds of millions of years for it to take off.”

    Fournier’s MIT co-authors include Kelsey Moore, Luiz Thiberio Rangel, Jack Payette, Lily Momper, and Tanja Bosak.

    Slow fuse, or wildfire?

    Estimates for the origin of oxygenic photosynthesis vary widely, along with the methods to trace its evolution.

    For instance, scientists can use geochemical tools to look for traces of oxidized elements in ancient rocks. These methods have found hints that oxygen was present as early as 3.5 billion years ago — a sign that oxygenic photosynthesis may have been the source, although other sources are also possible.

    Researchers have also used molecular clock dating, which uses the genetic sequences of microbes today to trace back changes in genes through evolutionary history. Based on these sequences, researchers then use models to estimate the rate at which genetic changes occur, to trace when groups of organisms first evolved. But molecular clock dating is limited by the quality of ancient fossils, and the chosen rate model, which can produce different age estimates, depending on the rate that is assumed.

    Fournier says different age estimates can imply conflicting evolutionary narratives. For instance, some analyses suggest oxygenic photosynthesis evolved very early on and progressed “like a slow fuse,” while others indicate it appeared much later and then “took off like wildfire” to trigger the Great Oxidation Event and the accumulation of oxygen in the biosphere.

    “In order for us to understand the history of habitability on Earth, it’s important for us to distinguish between these hypotheses,” he says.

    Horizontal genes

    To precisely date the origin of cyanobacteria and oxygenic photosynthesis, Fournier and his colleagues paired molecular clock dating with horizontal gene transfer — an independent method that doesn’t rely entirely on fossils or rate assumptions.

    Normally, an organism inherits a gene “vertically,” when it is passed down from the organism’s parent. In rare instances, a gene can also jump from one species to another, distantly related species. For instance, one cell may eat another, and in the process incorporate some new genes into its genome.

    When such a horizontal gene transfer history is found, it’s clear that the group of organisms that acquired the gene is evolutionarily younger than the group from which the gene originated. Fournier reasoned that such instances could be used to determine the relative ages between certain bacterial groups. The ages for these groups could then be compared with the ages that various molecular clock models predict. The model that comes closest would likely be the most accurate, and could then be used to precisely estimate the age of other bacterial species — specifically, cyanobacteria.

    Following this reasoning, the team looked for instances of horizontal gene transfer across the genomes of thousands of bacterial species, including cyanobacteria. They also used new cultures of modern cyanobacteria taken by Bosak and Moore, to more precisely use fossil cyanobacteria as calibrations. In the end, they identified 34 clear instances of horizontal gene transfer. They then found that one out of six molecular clock models consistently matched the relative ages identified in the team’s horizontal gene transfer analysis.

    Fournier ran this model to estimate the age of the “crown” group of cyanobacteria, which encompasses all the species living today and known to exhibit oxygenic photosynthesis. They found that, during the Archean eon, the crown group originated around 2.9 billion years ago, while cyanobacteria as a whole branched off from other bacteria around 3.4 billion years ago. This strongly suggests that oxygenic photosynthesis was already happening 500 million years before the Great Oxidation Event (GOE), and that cyanobacteria were producing oxygen for quite a long time before it accumulated in the atmosphere.

    The analysis also revealed that, shortly before the GOE, around 2.4 billion years ago, cyanobacteria experienced a burst of diversification. This implies that a rapid expansion of cyanobacteria may have tipped the Earth into the GOE and launched oxygen into the atmosphere.

    Fournier plans to apply horizontal gene transfer beyond cyanobacteria to pin down the origins of other elusive species.

    “This work shows that molecular clocks incorporating horizontal gene transfers (HGTs) promise to reliably provide the ages of groups across the entire tree of life, even for ancient microbes that have left no fossil record … something that was previously impossible,” Fournier says. 

    This research was supported, in part, by the Simons Foundation and the National Science Foundation. More

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    in Environment

    Taylor Perron receives 2021 MacArthur Fellowship

    Taylor Perron, professor of geology and associate department head for education in MIT’s Department of Earth, Atmospheric, and Planetary Sciences, has been named a recipient of a 2021 MacArthur Fellowship.

    Often referred to as “genius grants,” the fellowships are awarded by the John D. and Catherine T. MacArthur Foundation to talented individuals in a variety of fields. Each MacArthur fellow receives a $625,000 stipend, which they are free to use as they see fit. Recipients are notified by the foundation of their selection shortly before the fellowships are publicly announced.

    “After I had absorbed what they were saying, the first thing I thought was, I couldn’t wait to tell my wife, Lisa,” Perron says of receiving the call. “We’ve been a team through all of this and have had a pretty incredible journey, and I was just eager to share that with her.”

    Perron is a geomorphologist who seeks to understand the mechanisms that shape landscapes on Earth and other planets. His work combines mathematical modeling and computer simulations of landscape evolution; analysis of remote-sensing and spacecraft data; and field studies in regions such as the Appalachian Mountains, Hawaii, and the Amazon rainforest to trace how landscapes evolved over time and how they may change in the future.

    “If we can understand how climate and life and geological processes have interacted over a long time to create the landscapes we see now, we can use that information to anticipate where the landscape is headed in the future,” Perron says.

    His group has developed models that describe how river systems generate intricate branching patterns as a result of competing erosional processes, and how climate influences erosion on continents, islands, and reefs.

    Perron has also applied his methods beyond Earth, to retrace the evolution of the surfaces of Mars and Saturn’s moon Titan. His group has used spacecraft images and data to show how features on Titan, which appear to be active river networks, were likely carved out by raining liquid methane. On Mars, his analyses have supported the idea that the Red Planet once harbored an ocean and that the former shoreline of this Martian ocean is now warped as a result of a shift in the planet’s spin axis.

    He is continuing to map out the details of Mars and Titan’s landscape histories, which he hopes will provide clues to their ancient climates and habitability.

    “I think answers to some of the big questions about the solar system are written in planetary landscapes,” Perron says. “For example, why did Mars start off with lakes and rivers, but end up as a frozen desert? And if a world like Titan has weather like ours, but with a methane cycle instead of a water cycle, could an environment like that have supported life? One thing we try to do is figure out how to read the landscape to find the answers to those questions.”

    Perron has expanded his group’s focus to examine how changing landscapes affect biodiversity, for instance in Appalachia and in the Amazon — both freshwater systems that host some of the most diverse populations of life on the planet.

    “If we can figure out how changes in the physical landscape may have generated regions of really high biodiversity, that should help us learn how to conserve it,” Perron says.

    Recently, his group has also begun to investigate the influence of landscape evolution on human history. Perron is collaborating with archaeologists on projects to study the effect of physical landscapes on human migration in the Americas, and how the response of rivers to ice ages may have helped humans develop complex farming societies in the Amazon.

    Looking ahead, he plans to apply the MacArthur grant toward these projects and other “intellectual risks” — ideas that have potential for failure but could be highly rewarding if they succeed. The fellowship will also provide resources for his group to continue collaborating across disciplines and continents.

    “I’ve learned a lot from reaching out to people in other fields — everything from granular mechanics to fish biology,” Perron says. “That has broadened my scientific horizons and helped us do innovative work. Having the fellowship will provide more flexibility to allow us to continue connecting with people from other fields and other parts of the world.”

    Perron holds a BA in earth and planetary sciences and archaeology from Harvard University and a PhD in earth and planetary science from the University of California at Berkeley. He joined MIT as a faculty member in 2009. More

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    in Environment

    Making roadway spending more sustainable

    The share of federal spending on infrastructure has reached an all-time low, falling from 30 percent in 1960 to just 12 percent in 2018.

    While the nation’s ailing infrastructure will require more funding to reach its full potential, recent MIT research finds that more sustainable and higher performing roads are still possible even with today’s limited budgets.

    The research, conducted by a team of current and former MIT Concrete Sustainability Hub (MIT CSHub) scientists and published in Transportation Research D, finds that a set of innovative planning strategies could improve pavement network environmental and performance outcomes even if budgets don’t increase.

    The paper presents a novel budget allocation tool and pairs it with three innovative strategies for managing pavement networks: a mix of paving materials, a mix of short- and long-term paving actions, and a long evaluation period for those actions.

    This novel approach offers numerous benefits. When applied to a 30-year case study of the Iowa U.S. Route network, the MIT CSHub model and management strategies cut emissions by 20 percent while sustaining current levels of road quality. Achieving this with a conventional planning approach would require the state to spend 32 percent more than it does today. The key to its success is the consideration of a fundamental — but fraught — aspect of pavement asset management: uncertainty.

    Predicting unpredictability

    The average road must last many years and support the traffic of thousands — if not millions — of vehicles. Over that time, a lot can change. Material prices may fluctuate, budgets may tighten, and traffic levels may intensify. Climate (and climate change), too, can hasten unexpected repairs.

    Managing these uncertainties effectively means looking long into the future and anticipating possible changes.

    “Capturing the impacts of uncertainty is essential for making effective paving decisions,” explains Fengdi Guo, the paper’s lead author and a departing CSHub research assistant.

    “Yet, measuring and relating these uncertainties to outcomes is also computationally intensive and expensive. Consequently, many DOTs [departments of transportation] are forced to simplify their analysis to plan maintenance — often resulting in suboptimal spending and outcomes.”

    To give DOTs accessible tools to factor uncertainties into their planning, CSHub researchers have developed a streamlined planning approach. It offers greater specificity and is paired with several new pavement management strategies.

    The planning approach, known as Probabilistic Treatment Path Dependence (PTPD), is based on machine learning and was devised by Guo.

    “Our PTPD model is composed of four steps,” he explains. “These steps are, in order, pavement damage prediction; treatment cost prediction; budget allocation; and pavement network condition evaluation.”

    The model begins by investigating every segment in an entire pavement network and predicting future possibilities for pavement deterioration, cost, and traffic.

    “We [then] run thousands of simulations for each segment in the network to determine the likely cost and performance outcomes for each initial and subsequent sequence, or ‘path,’ of treatment actions,” says Guo. “The treatment paths with the best cost and performance outcomes are selected for each segment, and then across the network.”

    The PTPD model not only seeks to minimize costs to agencies but also to users — in this case, drivers. These user costs can come primarily in the form of excess fuel consumption due to poor road quality.

    “One improvement in our analysis is the incorporation of electric vehicle uptake into our cost and environmental impact predictions,” Randolph Kirchain, a principal research scientist at MIT CSHub and MIT Materials Research Laboratory (MRL) and one of the paper’s co-authors. “Since the vehicle fleet will change over the next several decades due to electric vehicle adoption, we made sure to consider how these changes might impact our predictions of excess energy consumption.”

    After developing the PTPD model, Guo wanted to see how the efficacy of various pavement management strategies might differ. To do this, he developed a sophisticated deterioration prediction model.

    A novel aspect of this deterioration model is its treatment of multiple deterioration metrics simultaneously. Using a multi-output neural network, a tool of artificial intelligence, the model can predict several forms of pavement deterioration simultaneously, thereby, accounting for their correlations among one another.

    The MIT team selected two key metrics to compare the effectiveness of various treatment paths: pavement quality and greenhouse gas emissions. These metrics were then calculated for all pavement segments in the Iowa network.

    Improvement through variation

     The MIT model can help DOTs make better decisions, but that decision-making is ultimately constrained by the potential options considered.

    Guo and his colleagues, therefore, sought to expand current decision-making paradigms by exploring a broad set of network management strategies and evaluating them with their PTPD approach. Based on that evaluation, the team discovered that networks had the best outcomes when the management strategy includes using a mix of paving materials, a variety of long- and short-term paving repair actions (treatments), and longer time periods on which to base paving decisions.

    They then compared this proposed approach with a baseline management approach that reflects current, widespread practices: the use of solely asphalt materials, short-term treatments, and a five-year period for evaluating the outcomes of paving actions.

    With these two approaches established, the team used them to plan 30 years of maintenance across the Iowa U.S. Route network. They then measured the subsequent road quality and emissions.

    Their case study found that the MIT approach offered substantial benefits. Pavement-related greenhouse gas emissions would fall by around 20 percent across the network over the whole period. Pavement performance improved as well. To achieve the same level of road quality as the MIT approach, the baseline approach would need a 32 percent greater budget.

    “It’s worth noting,” says Guo, “that since conventional practices employ less effective allocation tools, the difference between them and the CSHub approach should be even larger in practice.”

    Much of the improvement derived from the precision of the CSHub planning model. But the three treatment strategies also play a key role.

    “We’ve found that a mix of asphalt and concrete paving materials allows DOTs to not only find materials best-suited to certain projects, but also mitigates the risk of material price volatility over time,” says Kirchain.

    It’s a similar story with a mix of paving actions. Employing a mix of short- and long-term fixes gives DOTs the flexibility to choose the right action for the right project.

    The final strategy, a long-term evaluation period, enables DOTs to see the entire scope of their choices. If the ramifications of a decision are predicted over only five years, many long-term implications won’t be considered. Expanding the window for planning, then, can introduce beneficial, long-term options.

    It’s not surprising that paving decisions are daunting to make; their impacts on the environment, driver safety, and budget levels are long-lasting. But rather than simplify this fraught process, the CSHub method aims to reflect its complexity. The result is an approach that provides DOTs with the tools to do more with less.

    This research was supported through the MIT Concrete Sustainability Hub by the Portland Cement Association and the Ready Mixed Concrete Research and Education Foundation. More