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    Advancing technology for aquaculture

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    According to the National Oceanic and Atmospheric Administration, aquaculture in the United States represents a $1.5 billion industry annually. Like land-based farming, shellfish aquaculture requires healthy seed production in order to maintain a sustainable industry. Aquaculture hatchery production of shellfish larvae — seeds — requires close monitoring to track mortality rates and assess health from the earliest stages of life. 

    Careful observation is necessary to inform production scheduling, determine effects of naturally occurring harmful bacteria, and ensure sustainable seed production. This is an essential step for shellfish hatcheries but is currently a time-consuming manual process prone to human error. 

    With funding from MIT’s Abdul Latif Jameel Water and Food Systems Lab (J-WAFS), MIT Sea Grant is working with Associate Professor Otto Cordero of the MIT Department of Civil and Environmental Engineering, Professor Taskin Padir and Research Scientist Mark Zolotas at the Northeastern University Institute for Experiential Robotics, and others at the Aquaculture Research Corporation (ARC), and the Cape Cod Commercial Fishermen’s Alliance, to advance technology for the aquaculture industry. Located on Cape Cod, ARC is a leading shellfish hatchery, farm, and wholesaler that plays a vital role in providing high-quality shellfish seed to local and regional growers.

    Two MIT students have joined the effort this semester, working with Robert Vincent, MIT Sea Grant’s assistant director of advisory services, through the Undergraduate Research Opportunities Program (UROP). 

    First-year student Unyime Usua and sophomore Santiago Borrego are using microscopy images of shellfish seed from ARC to train machine learning algorithms that will help automate the identification and counting process. The resulting user-friendly image recognition tool aims to aid aquaculturists in differentiating and counting healthy, unhealthy, and dead shellfish larvae, improving accuracy and reducing time and effort.

    Vincent explains that AI is a powerful tool for environmental science that enables researchers, industry, and resource managers to address challenges that have long been pinch points for accurate data collection, analysis, predictions, and streamlining processes. “Funding support from programs like J-WAFS enable us to tackle these problems head-on,” he says. 

    ARC faces challenges with manually quantifying larvae classes, an important step in their seed production process. “When larvae are in their growing stages they are constantly being sized and counted,” explains Cheryl James, ARC larval/juvenile production manager. “This process is critical to encourage optimal growth and strengthen the population.” 

    Developing an automated identification and counting system will help to improve this step in the production process with time and cost benefits. “This is not an easy task,” says Vincent, “but with the guidance of Dr. Zolotas at the Northeastern University Institute for Experiential Robotics and the work of the UROP students, we have made solid progress.” 

    The UROP program benefits both researchers and students. Involving MIT UROP students in developing these types of systems provides insights into AI applications that they might not have considered, providing opportunities to explore, learn, and apply themselves while contributing to solving real challenges.

    Borrego saw this project as an opportunity to apply what he’d learned in class 6.390 (Introduction to Machine Learning) to a real-world issue. “I was starting to form an idea of how computers can see images and extract information from them,” he says. “I wanted to keep exploring that.”

    Usua decided to pursue the project because of the direct industry impacts it could have. “I’m pretty interested in seeing how we can utilize machine learning to make people’s lives easier. We are using AI to help biologists make this counting and identification process easier.” While Usua wasn’t familiar with aquaculture before starting this project, she explains, “Just hearing about the hatcheries that Dr. Vincent was telling us about, it was unfortunate that not a lot of people know what’s going on and the problems that they’re facing.”

    On Cape Cod alone, aquaculture is an $18 million per year industry. But the Massachusetts Division of Marine Fisheries estimates that hatcheries are only able to meet 70–80 percent of seed demand annually, which impacts local growers and economies. Through this project, the partners aim to develop technology that will increase seed production, advance industry capabilities, and help understand and improve the hatchery microbiome.

    Borrego explains the initial challenge of having limited data to work with. “Starting out, we had to go through and label all of the data, but going through that process helped me learn a lot.” In true MIT fashion, he shares his takeaway from the project: “Try to get the best out of what you’re given with the data you have to work with. You’re going to have to adapt and change your strategies depending on what you have.”

    Usua describes her experience going through the research process, communicating in a team, and deciding what approaches to take. “Research is a difficult and long process, but there is a lot to gain from it because it teaches you to look for things on your own and find your own solutions to problems.”

    In addition to increasing seed production and reducing the human labor required in the hatchery process, the collaborators expect this project to contribute to cost savings and technology integration to support one of the most underserved industries in the United States. 

    Borrego and Usua both plan to continue their work for a second semester with MIT Sea Grant. Borrego is interested in learning more about how technology can be used to protect the environment and wildlife. Usua says she hopes to explore more projects related to aquaculture. “It seems like there’s an infinite amount of ways to tackle these issues.” More

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      MIT researchers remotely map crops, field by field

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      Crop maps help scientists and policymakers track global food supplies and estimate how they might shift with climate change and growing populations. But getting accurate maps of the types of crops that are grown from farm to farm often requires on-the-ground surveys that only a handful of countries have the resources to maintain.

      Now, MIT engineers have developed a method to quickly and accurately label and map crop types without requiring in-person assessments of every single farm. The team’s method uses a combination of Google Street View images, machine learning, and satellite data to automatically determine the crops grown throughout a region, from one fraction of an acre to the next. 

      The researchers used the technique to automatically generate the first nationwide crop map of Thailand — a smallholder country where small, independent farms make up the predominant form of agriculture. The team created a border-to-border map of Thailand’s four major crops — rice, cassava, sugarcane, and maize — and determined which of the four types was grown, at every 10 meters, and without gaps, across the entire country. The resulting map achieved an accuracy of 93 percent, which the researchers say is comparable to on-the-ground mapping efforts in high-income, big-farm countries.

      The team is applying their mapping technique to other countries such as India, where small farms sustain most of the population but the type of crops grown from farm to farm has historically been poorly recorded.

      “It’s a longstanding gap in knowledge about what is grown around the world,” says Sherrie Wang, the d’Arbeloff Career Development Assistant Professor in MIT’s Department of Mechanical Engineering, and the Institute for Data, Systems, and Society (IDSS). “The final goal is to understand agricultural outcomes like yield, and how to farm more sustainably. One of the key preliminary steps is to map what is even being grown — the more granularly you can map, the more questions you can answer.”

      Wang, along with MIT graduate student Jordi Laguarta Soler and Thomas Friedel of the agtech company PEAT GmbH, will present a paper detailing their mapping method later this month at the AAAI Conference on Artificial Intelligence.

      Ground truth

      Smallholder farms are often run by a single family or farmer, who subsist on the crops and livestock that they raise. It’s estimated that smallholder farms support two-thirds of the world’s rural population and produce 80 percent of the world’s food. Keeping tabs on what is grown and where is essential to tracking and forecasting food supplies around the world. But the majority of these small farms are in low to middle-income countries, where few resources are devoted to keeping track of individual farms’ crop types and yields.

      Crop mapping efforts are mainly carried out in high-income regions such as the United States and Europe, where government agricultural agencies oversee crop surveys and send assessors to farms to label crops from field to field. These “ground truth” labels are then fed into machine-learning models that make connections between the ground labels of actual crops and satellite signals of the same fields. They then label and map wider swaths of farmland that assessors don’t cover but that satellites automatically do.

      “What’s lacking in low- and middle-income countries is this ground label that we can associate with satellite signals,” Laguarta Soler says. “Getting these ground truths to train a model in the first place has been limited in most of the world.”

      The team realized that, while many developing countries do not have the resources to maintain crop surveys, they could potentially use another source of ground data: roadside imagery, captured by services such as Google Street View and Mapillary, which send cars throughout a region to take continuous 360-degree images with dashcams and rooftop cameras.

      In recent years, such services have been able to access low- and middle-income countries. While the goal of these services is not specifically to capture images of crops, the MIT team saw that they could search the roadside images to identify crops.

      Cropped image

      In their new study, the researchers worked with Google Street View (GSV) images taken throughout Thailand — a country that the service has recently imaged fairly thoroughly, and which consists predominantly of smallholder farms.

      Starting with over 200,000 GSV images randomly sampled across Thailand, the team filtered out images that depicted buildings, trees, and general vegetation. About 81,000 images were crop-related. They set aside 2,000 of these, which they sent to an agronomist, who determined and labeled each crop type by eye. They then trained a convolutional neural network to automatically generate crop labels for the other 79,000 images, using various training methods, including iNaturalist — a web-based crowdsourced  biodiversity database, and GPT-4V, a “multimodal large language model” that enables a user to input an image and ask the model to identify what the image is depicting. For each of the 81,000 images, the model generated a label of one of four crops that the image was likely depicting — rice, maize, sugarcane, or cassava.

      The researchers then paired each labeled image with the corresponding satellite data taken of the same location throughout a single growing season. These satellite data include measurements across multiple wavelengths, such as a location’s greenness and its reflectivity (which can be a sign of water). 

      “Each type of crop has a certain signature across these different bands, which changes throughout a growing season,” Laguarta Soler notes.

      The team trained a second model to make associations between a location’s satellite data and its corresponding crop label. They then used this model to process satellite data taken of the rest of the country, where crop labels were not generated or available. From the associations that the model learned, it then assigned crop labels across Thailand, generating a country-wide map of crop types, at a resolution of 10 square meters.

      This first-of-its-kind crop map included locations corresponding to the 2,000 GSV images that the researchers originally set aside, that were labeled by arborists. These human-labeled images were used to validate the map’s labels, and when the team looked to see whether the map’s labels matched the expert, “gold standard” labels, it did so 93 percent of the time.

      “In the U.S., we’re also looking at over 90 percent accuracy, whereas with previous work in India, we’ve only seen 75 percent because ground labels are limited,” Wang says. “Now we can create these labels in a cheap and automated way.”

      The researchers are moving to map crops across India, where roadside images via Google Street View and other services have recently become available.

      “There are over 150 million smallholder farmers in India,” Wang says. “India is covered in agriculture, almost wall-to-wall farms, but very small farms, and historically it’s been very difficult to create maps of India because there are very sparse ground labels.”

      The team is working to generate crop maps in India, which could be used to inform policies having to do with assessing and bolstering yields, as global temperatures and populations rise.

      “What would be interesting would be to create these maps over time,” Wang says. “Then you could start to see trends, and we can try to relate those things to anything like changes in climate and policies.” More

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      J-WAFS announces 2023 seed grant recipients

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      Today, the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) announced its ninth round of seed grants to support innovative research projects at MIT. The grants are designed to fund research efforts that tackle challenges related to water and food for human use, with the ultimate goal of creating meaningful impact as the world population continues to grow and the planet undergoes significant climate and environmental changes.Ten new projects led by 15 researchers from seven different departments will be supported this year. The projects address a range of challenges by employing advanced materials, technology innovations, and new approaches to resource management. The new projects aim to remove harmful chemicals from water sources, develop monitoring and other systems to help manage various aquaculture industries, optimize water purification materials, and more.“The seed grant program is J-WAFS’ flagship grant initiative,” says J-WAFS executive director Renee J. Robins. “The funding is intended to spur groundbreaking MIT research addressing complex issues that are challenging our water and food systems. The 10 projects selected this year show great promise, and we look forward to the progress and accomplishments these talented researchers will make,” she adds.The 2023 J-WAFS seed grant researchers and their projects are:Sara Beery, an assistant professor in the Department of Electrical Engineering and Computer Science (EECS), is building the first completely automated system to estimate the size of salmon populations in the Pacific Northwest (PNW).Salmon are a keystone species in the PNW, feeding human populations for the last 7,500 years at least. However, overfishing, habitat loss, and climate change threaten extinction of salmon populations across the region. Accurate salmon counts during their seasonal migration to their natal river to spawn are essential for fisheries’ regulation and management but are limited by human capacity. Fish population monitoring is a widespread challenge in the United States and worldwide. Beery and her team are working to build a system that will provide a detailed picture of the state of salmon populations in unprecedented, spatial, and temporal resolution by combining sonar sensors and computer vision and machine learning (CVML) techniques. The sonar will capture individual fish as they swim upstream and CVML will train accurate algorithms to interpret the sonar video for detecting, tracking, and counting fish automatically while adapting to changing river conditions and fish densities.Another aquaculture project is being led by Michael Triantafyllou, the Henry L. and Grace Doherty Professor in Ocean Science and Engineering in the Department of Mechanical Engineering, and Robert Vincent, the assistant director at MIT’s Sea Grant Program. They are working with Otto Cordero, an associate professor in the Department of Civil and Environmental Engineering, to control harmful bacteria blooms in aquaculture algae feed production.

      Aquaculture in the United States represents a $1.5 billion industry annually and helps support 1.7 million jobs, yet many American hatcheries are not able to keep up with demand. One barrier to aquaculture production is the high degree of variability in survival rates, most likely caused by a poorly controlled microbiome that leads to bacterial infections and sub-optimal feed efficiency. Triantafyllou, Vincent, and Cordero plan to monitor the microbiome composition of a shellfish hatchery in order to identify possible causing agents of mortality, as well as beneficial microbes. They hope to pair microbe data with detail phenotypic information about the animal population to generate rapid diagnostic tests and explore the potential for microbiome therapies to protect larvae and prevent future outbreaks. The researchers plan to transfer their findings and technology to the local and regional aquaculture community to ensure healthy aquaculture production that will support the expansion of the U.S. aquaculture industry.

      David Des Marais is the Cecil and Ida Green Career Development Professor in the Department of Civil and Environmental Engineering. His 2023 J-WAFS project seeks to understand plant growth responses to elevated carbon dioxide (CO2) in the atmosphere, in the hopes of identifying breeding strategies that maximize crop yield under future CO2 scenarios.Today’s crop plants experience higher atmospheric CO2 than 20 or 30 years ago. Crops such as wheat, oat, barley, and rice typically increase their growth rate and biomass when grown at experimentally elevated atmospheric CO2. This is known as the so-called “CO2 fertilization effect.” However, not all plant species respond to rising atmospheric CO2 with increased growth, and for the ones that do, increased growth doesn’t necessarily correspond to increased crop yield. Using specially built plant growth chambers that can control the concentration of CO2, Des Marais will explore how CO2 availability impacts the development of tillers (branches) in the grass species Brachypodium. He will study how gene expression controls tiller development, and whether this is affected by the growing environment. The tillering response refers to how many branches a plant produces, which sets a limit on how much grain it can yield. Therefore, optimizing the tillering response to elevated CO2 could greatly increase yield. Des Marais will also look at the complete genome sequence of Brachypodium, wheat, oat, and barley to help identify genes relevant for branch growth.Darcy McRose, an assistant professor in the Department of Civil and Environmental Engineering, is researching whether a combination of plant metabolites and soil bacteria can be used to make mineral-associated phosphorus more bioavailable.The nutrient phosphorus is essential for agricultural plant growth, but when added as a fertilizer, phosphorus sticks to the surface of soil minerals, decreasing bioavailability, limiting plant growth, and accumulating residual phosphorus. Heavily fertilized agricultural soils often harbor large reservoirs of this type of mineral-associated “legacy” phosphorus. Redox transformations are one chemical process that can liberate mineral-associated phosphorus. However, this needs to be carefully controlled, as overly mobile phosphorus can lead to runoff and pollution of natural waters. Ideally, phosphorus would be made bioavailable when plants need it and immobile when they don’t. Many plants make small metabolites called coumarins that might be able to solubilize mineral-adsorbed phosphorus and be activated and inactivated under different conditions. McRose will use laboratory experiments to determine whether a combination of plant metabolites and soil bacteria can be used as a highly efficient and tunable system for phosphorus solubilization. She also aims to develop an imaging platform to investigate exchanges of phosphorus between plants and soil microbes.Many of the 2023 seed grants will support innovative technologies to monitor, quantify, and remediate various kinds of pollutants found in water. Two of the new projects address the problem of per- and polyfluoroalkyl substances (PFAS), human-made chemicals that have recently emerged as a global health threat. Known as “forever chemicals,” PFAS are used in many manufacturing processes. These chemicals are known to cause significant health issues including cancer, and they have become pervasive in soil, dust, air, groundwater, and drinking water. Unfortunately, the physical and chemical properties of PFAS render them difficult to detect and remove.Aristide Gumyusenge, the Merton C. Assistant Professor of Materials Science and Engineering, is using metal-organic frameworks for low-cost sensing and capture of PFAS. Most metal-organic frameworks (MOFs) are synthesized as particles, which complicates their high accuracy sensing performance due to defects such as intergranular boundaries. Thin, film-based electronic devices could enable the use of MOFs for many applications, especially chemical sensing. Gumyusenge’s project aims to design test kits based on two-dimensional conductive MOF films for detecting PFAS in drinking water. In early demonstrations, Gumyusenge and his team showed that these MOF films can sense PFAS at low concentrations. They will continue to iterate using a computation-guided approach to tune sensitivity and selectivity of the kits with the goal of deploying them in real-world scenarios.Carlos Portela, the Brit (1961) and Alex (1949) d’Arbeloff Career Development Professor in the Department of Mechanical Engineering, and Ariel Furst, the Cook Career Development Professor in the Department of Chemical Engineering, are building novel architected materials to act as filters for the removal of PFAS from water. Portela and Furst will design and fabricate nanoscale materials that use activated carbon and porous polymers to create a physical adsorption system. They will engineer the materials to have tunable porosities and morphologies that can maximize interactions between contaminated water and functionalized surfaces, while providing a mechanically robust system.Rohit Karnik is a Tata Professor and interim co-department head of the Department of Mechanical Engineering. He is working on another technology, his based on microbead sensors, to rapidly measure and monitor trace contaminants in water.Water pollution from both biological and chemical contaminants contributes to an estimated 1.36 million deaths annually. Chemical contaminants include pesticides and herbicides, heavy metals like lead, and compounds used in manufacturing. These emerging contaminants can be found throughout the environment, including in water supplies. The Environmental Protection Agency (EPA) in the United States sets recommended water quality standards, but states are responsible for developing their own monitoring criteria and systems, which must be approved by the EPA every three years. However, the availability of data on regulated chemicals and on candidate pollutants is limited by current testing methods that are either insensitive or expensive and laboratory-based, requiring trained scientists and technicians. Karnik’s project proposes a simple, self-contained, portable system for monitoring trace and emerging pollutants in water, making it suitable for field studies. The concept is based on multiplexed microbead-based sensors that use thermal or gravitational actuation to generate a signal. His proposed sandwich assay, a testing format that is appealing for environmental sensing, will enable both single-use and continuous monitoring. The hope is that the bead-based assays will increase the ease and reach of detecting and quantifying trace contaminants in water for both personal and industrial scale applications.Alexander Radosevich, a professor in the Department of Chemistry, and Timothy Swager, the John D. MacArthur Professor of Chemistry, are teaming up to create rapid, cost-effective, and reliable techniques for on-site arsenic detection in water.Arsenic contamination of groundwater is a problem that affects as many as 500 million people worldwide. Arsenic poisoning can lead to a range of severe health problems from cancer to cardiovascular and neurological impacts. Both the EPA and the World Health Organization have established that 10 parts per billion is a practical threshold for arsenic in drinking water, but measuring arsenic in water at such low levels is challenging, especially in resource-limited environments where access to sensitive laboratory equipment may not be readily accessible. Radosevich and Swager plan to develop reaction-based chemical sensors that bind and extract electrons from aqueous arsenic. In this way, they will exploit the inherent reactivity of aqueous arsenic to selectively detect and quantify it. This work will establish the chemical basis for a new method of detecting trace arsenic in drinking water.Rajeev Ram is a professor in the Department of Electrical Engineering and Computer Science. His J-WAFS research will advance a robust technology for monitoring nitrogen-containing pollutants, which threaten over 15,000 bodies of water in the United States alone.Nitrogen in the form of nitrate, nitrite, ammonia, and urea can run off from agricultural fertilizer and lead to harmful algal blooms that jeopardize human health. Unfortunately, monitoring these contaminants in the environment is challenging, as sensors are difficult to maintain and expensive to deploy. Ram and his students will work to establish limits of detection for nitrate, nitrite, ammonia, and urea in environmental, industrial, and agricultural samples using swept-source Raman spectroscopy. Swept-source Raman spectroscopy is a method of detecting the presence of a chemical by using a tunable, single mode laser that illuminates a sample. This method does not require costly, high-power lasers or a spectrometer. Ram will then develop and demonstrate a portable system that is capable of achieving chemical specificity in complex, natural environments. Data generated by such a system should help regulate polluters and guide remediation.Kripa Varanasi, a professor in the Department of Mechanical Engineering, and Angela Belcher, the James Mason Crafts Professor and head of the Department of Biological Engineering, will join forces to develop an affordable water disinfection technology that selectively identifies, adsorbs, and kills “superbugs” in domestic and industrial wastewater.Recent research predicts that antibiotic-resistance bacteria (superbugs) will result in $100 trillion in health care expenses and 10 million deaths annually by 2050. The prevalence of superbugs in our water systems has increased due to corroded pipes, contamination, and climate change. Current drinking water disinfection technologies are designed to kill all types of bacteria before human consumption. However, for certain domestic and industrial applications there is a need to protect the good bacteria required for ecological processes that contribute to soil and plant health. Varanasi and Belcher will combine material, biological, process, and system engineering principles to design a sponge-based water disinfection technology that can identify and destroy harmful bacteria while leaving the good bacteria unharmed. By modifying the sponge surface with specialized nanomaterials, their approach will be able to kill superbugs faster and more efficiently. The sponge filters can be deployed under very low pressure, making them an affordable technology, especially in resource-constrained communities.In addition to the 10 seed grant projects, J-WAFS will also fund a research initiative led by Greg Sixt. Sixt is the research manager for climate and food systems at J-WAFS, and the director of the J-WAFS-led Food and Climate Systems Transformation (FACT) Alliance. His project focuses on the Lake Victoria Basin (LVB) of East Africa. The second-largest freshwater lake in the world, Lake Victoria straddles three countries (Uganda, Tanzania, and Kenya) and has a catchment area that encompasses two more (Rwanda and Burundi). Sixt will collaborate with Michael Hauser of the University of Natural Resources and Life Sciences, Vienna, and Paul Kariuki, of the Lake Victoria Basin Commission.The group will study how to adapt food systems to climate change in the Lake Victoria Basin. The basin is facing a range of climate threats that could significantly impact livelihoods and food systems in the expansive region. For example, extreme weather events like droughts and floods are negatively affecting agricultural production and freshwater resources. Across the LVB, current approaches to land and water management are unsustainable and threaten future food and water security. The Lake Victoria Basin Commission (LVBC), a specialized institution of the East African Community, wants to play a more vital role in coordinating transboundary land and water management to support transitions toward more resilient, sustainable, and equitable food systems. The primary goal of this research will be to support the LVBC’s transboundary land and water management efforts, specifically as they relate to sustainability and climate change adaptation in food systems. The research team will work with key stakeholders in Kenya, Uganda, and Tanzania to identify specific capacity needs to facilitate land and water management transitions. The two-year project will produce actionable recommendations to the LVBC. More

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      MIT PhD students honored for their work to solve critical issues in water and food

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      In 2017, the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) initiated the J-WAFS Fellowship Program for outstanding MIT PhD students working to solve humankind’s water-related challenges. Since then, J-WAFS has awarded 18 fellowships to students who have gone on to create innovations like a pump that can maximize energy efficiency even with changing flow rates, and a low-cost water filter made out of sapwood xylem that has seen real-world use in rural India. Last year, J-WAFS expanded eligibility to students with food-related research. The 2022 fellows included students working on micronutrient deficiency and plastic waste from traditional food packaging materials. 

      Today, J-WAFS has announced the award of the 2023-24 fellowships to Gokul Sampath and Jie Yun. A doctoral student in the Department of Urban Studies and planning, Sampath has been awarded the Rasikbhai L. Meswani Fellowship for Water Solutions, which is supported through a generous gift from Elina and Nikhil Meswani and family. Yun, who is in the Department of Civil and Environmental Engineering, received a J-WAFS Fellowship for Water and Food Solutions, which is funded by the J-WAFS Research Affiliate Program. Currently, Xylem, Inc. and GoAigua are J-WAFS’ Research Affiliate companies. A review committee comprised of MIT faculty and staff selected Sampath and Yun from a competitive field of outstanding graduate students working in water and food who were nominated by their faculty advisors. Sampath and Yun will receive one academic semester of funding, along with opportunities for networking and mentoring to advance their research.

      “Both Yun and Sampath have demonstrated excellence in their research,” says J-WAFS executive director Renee J. Robins. “They also stood out in their communication skills and their passion to work on issues of agricultural sustainability and resilience and access to safe water. We are so pleased to have them join our inspiring group of J-WAFS fellows,” she adds.

      Using behavioral health strategies to address the arsenic crisis in India and Bangladesh

      Gokul Sampath’s research centers on ways to improve access to safe drinking water in developing countries. A PhD candidate in the International Development Group in the Department of Urban Studies and Planning, his current work examines the issue of arsenic in drinking water sources in India and Bangladesh. In Eastern India, millions of shallow tube wells provide rural households a personal water source that is convenient, free, and mostly safe from cholera. Unfortunately, it is now known that one-in-four of these wells is contaminated with naturally occurring arsenic at levels dangerous to human health. As a result, approximately 40 million people across the region are at elevated risk of cancer, stroke, and heart disease from arsenic consumed through drinking water and cooked food. 

      Since the discovery of arsenic in wells in the late 1980s, governments and nongovernmental organizations have sought to address the problem in rural villages by providing safe community water sources. Yet despite access to safe alternatives, many households still consume water from their contaminated home wells. Sampath’s research seeks to understand the constraints and trade-offs that account for why many villagers don’t collect water from arsenic-safe government wells in the village, even when they know their own wells at home could be contaminated.

      Before coming to MIT, Sampath received a master’s degree in Middle East, South Asian, and African studies from Columbia University, as well as a bachelor’s degree in microbiology and history from the University of California at Davis. He has long worked on water management in India, beginning in 2015 as a Fulbright scholar studying households’ water source choices in arsenic-affected areas of the state of West Bengal. He also served as a senior research associate with the Abdul Latif Jameel Poverty Action Lab, where he conducted randomized evaluations of market incentives for groundwater conservation in Gujarat, India. Sampath’s advisor, Bishwapriya Sanyal, the Ford International Professor of Urban Development and Planning at MIT, says Sampath has shown “remarkable hard work and dedication.” In addition to his classes and research, Sampath taught the department’s undergraduate Introduction to International Development course, for which he received standout evaluations from students.

      This summer, Sampath will travel to India to conduct field work in four arsenic-affected villages in West Bengal to understand how social influence shapes villagers’ choices between arsenic-safe and unsafe water sources. Through longitudinal surveys, he hopes to connect data on the social ties between families in villages and the daily water source choices they make. Exclusionary practices in Indian village communities, especially the segregation of water sources on the basis of caste and religion, has long been suspected to be a barrier to equitable drinking water access in Indian villages. Yet despite this, planners seeking to expand safe water access in diverse Indian villages have rarely considered the way social divisions within communities might be working against their efforts. Sampath hopes to test whether the injunctive norms enabled by caste ties constrain villagers’ ability to choose the safest water source among those shared within the village. When he returns to MIT in the fall, he plans to dive into analyzing his survey data and start work on a publication.

      Understanding plant responses to stress to improve crop drought resistance and yield

      Plants, including crops, play a fundamental role in Earth’s ecosystems through their effects on climate, air quality, and water availability. At the same time, plants grown for agriculture put a burden on the environment as they require energy, irrigation, and chemical inputs. Understanding plant/environment interactions is becoming more and more important as intensifying drought is straining agricultural systems. Jie Yun, a PhD student in the Department of Civil and Environmental Engineering, is studying plant response to drought stress in the hopes of improving agricultural sustainability and yield under climate change.  Yun’s research focuses on genotype-by-environment interaction (GxE.) This relates to the observation that plant varieties respond to environmental changes differently. The effects of GxE in crop breeding can be exploited because differing environmental responses among varieties enables breeders to select for plants that demonstrate high stress-tolerant genotypes under particular growing conditions. Yun bases her studies on Brachypodium, a model grass species related to wheat, oat, barley, rye, and perennial forage grasses. By experimenting with this species, findings can be directly applied to cereal and forage crop improvement. For the first part of her thesis, Yun collaborated with Professor Caroline Uhler’s group in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society. Uhler’s computational tools helped Yun to evaluate gene regulatory networks and how they relate to plant resilience and environmental adaptation. This work will help identify the types of genes and pathways that drive differences in drought stress response among plant varieties.  David Des Marais, the Cecil and Ida Green Career Development Professor in the Department of Civil and Environmental Engineering, is Yun’s advisor. He notes, “throughout Jie’s time [at MIT] I have been struck by her intellectual curiosity, verging on fearlessness.” When she’s not mentoring undergraduate students in Des Marais’ lab, Yun is working on the second part of her project: how carbon allocation in plants and growth is affected by soil drying. One result of this work will be to understand which populations of plants harbor the necessary genetic diversity to adapt or acclimate to climate change. Another likely impact is identifying targets for the genetic improvement of crop species to increase crop yields with less water supply. Growing up in China, Yun witnessed environmental issues springing from the development of the steel industry, which caused contamination of rivers in her hometown. On one visit to her aunt’s house in rural China, she learned that water pollution was widespread after noticing wastewater was piped outside of the house into nearby farmland without being treated. These experiences led Yun to study water supply and sewage engineering for her undergraduate degree at Shenyang Jianzhu University. She then went on to complete a master’s program in civil and environmental engineering at Carnegie Mellon University. It was there that Yun discovered a passion for plant-environment interactions; during an independent study on perfluorooctanoic sulfonate, she realized the amazing ability of plants to adapt to environmental changes, toxins, and stresses. Her goal is to continue researching plant and environment interactions and to translate the latest scientific findings into applications that can improve food security. More

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      Reducing food waste to increase access to affordable foods

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      About a third of the world’s food supply never gets eaten. That means the water, labor, energy, and fertilizer that went into growing, processing, and distributing the food is wasted.

      On the other end of the supply chain are cash-strapped consumers, who have been further distressed in recent years by factors like the Covid-19 pandemic and inflation.

      Spoiler Alert, a company founded by two MIT alumni, is helping companies bridge the gap between food waste and food insecurity with a platform connecting major food and beverage brands with discount grocers, retailers, and nonprofits. The platform helps brands discount or donate excess and short-dated inventory days, weeks, and months before it expires.

      “There is a tremendous amount of underutilized data that exists in the manufacturing and distribution space that results in good food going to waste,” says Ricky Ashenfelter MBA ’15, who co-founded the company with Emily Malina MBA ’15.

      Spoiler Alert helps brands manage distressed inventory data, create offers for potential buyers, and review and accept bids. The platform is designed to work with companies’ existing inventory and fulfillment systems, using automation and pricing intelligence to further streamline sales.

      “At a high level, we’re a waste-prevention software built for sales and supply-chain teams,” Ashenfelter says. “You can think of it as a private [business-to-business] eBay of sorts.”

      Spoiler Alert is working with global companies like Nestle, Kraft Heinz, and Danone, as well as discount grocers like the United Grocery Outlet and Misfits Market. Those brands are already using the platform to reduce food waste and get more food on people’s tables.

      “Project Drawdown [a nonprofit working on climate solutions] has identified food waste as the number one priority to address the global climate crisis, so these types of corporate initiatives can be really powerful from an environmental standpoint,” Ashenfelter says, noting the nonprofit estimates food waste accounts for 8 percent of global greenhouse gas emissions. “Contrast that with growing levels of food insecurity and folks not being able to access affordable nutrition, and you start to see how tackling supply-chain inefficiency can have a dramatic impact from both an environmental and a social lens. That’s what motivates us.”

      Untapped data for change

      Ashenfelter came to MIT’s Sloan School of Management after several years in sustainability software and management consulting within the retail and consumer products industries.

      “I was really attracted to transitioning into something much more entrepreneurial, and to leverage not only Sloan’s focus on entrepreneurship, but also the broader MIT ecosystem’s focus on technology, entrepreneurship, clean tech innovation, and other themes along that front,” he says.

      Ashenfelter met Malina at one of Sloan’s admitted students events in 2013, and the founders soon set out to use data to decrease food waste.

      “For us, the idea was clear: How do we better leverage data to manage excess and short-dated inventory?” Ashenfelter says. “How we go about that has evolved over the last six years, but it’s all rooted in solving an enormous climate problem, solving a major food insecurity problem, and from a capitalistic standpoint, helping businesses cut costs and generate revenue from otherwise wasted products.”

      The founders spent many hours in the Martin Trust Center for MIT Entrepreneurship with support from the Sloan Sustainability Initiative, and used Spoiler Alert as a case study in nearly every class they took, thinking through product development, sales, marketing, pricing, and more through their coursework.

      “We brought our idea into just about every action learning class that we could at Sloan and MIT,” Ashenfelter says.

      They also participated in the MIT $100K Entrepreneurship Competition and received support from the Venture Mentoring Service and the IDEAS Global Challenge program.

      Upon graduation, the founders initially began building a platform to facilitate donations of excess inventory, but soon learned big companies’ processes for discounting that inventory were also highly manual. Today, more than 90 percent of Spoiler Alert’s transaction volume is discounted, with the remainder donated.

      Different teams within an organization can upload excess inventory reports to Spoiler Alert’s system, eliminating the need to manually aggregate datasets and preparing what the industry refers to as “blowout lists” to sell. Spoiler Alert uses machine-learning-based tools to help both parties with pricing and negotiations to close deals more quickly.

      “Companies are taking pretty manual and slow approaches to deciding [what to do with excess inventory],” Ashenfelter says. “And when you have slow decision-making, you’re losing days or even weeks of shelf life on that product. That can be the difference between selling product versus donating, and donating versus dumping.”

      Once a deal has been made, Spoiler Alert automatically generates the forms and workflows needed by fulfillment teams to get the product out the door. The relationships companies build on the platform are also a major driver for cutting down waste.

      “We’re providing suppliers with the ability to control where their discounted and donated product ends up,” Ashenfelter says. “That’s really powerful because it allows these CPG brands to ensure that this product is, in many cases, getting to affordable nutrition outlets in underserved communities.”

      Ashenfelter says the majority of inventory goes to regional and national discount grocers, supplemented with extensive purchasing from local and nonprofit grocery chains.

      “Everything we do is oriented around helping sell as much product as possible to a reputable set of buyers at the most fair, equitable prices possible,” Ashenfelter says.

      Scaling for impact

      The pandemic has disrupted many aspects of the food supply chains. But Ashenfelter says it has also accelerated the adoption of digital solutions that can better manage such volatility.

      When Campbell began using Spoiler Alert’s system in 2019, for instance, it achieved a 36 percent increase in discount sales and a 27 percent increase in donations over the first five months.

      Ashenfelter says the results have proven that companies’ sustainability targets can go hand in hand with initiatives that boost their bottom lines. In fact, because Spoiler Alert focuses so much on the untapped revenue associated with food waste, many customers don’t even realize Spoiler Alert is a sustainability company until after they’ve signed on.

      “What’s neat about this program is that it becomes an incredibly powerful case study internally for how sustainability and operational outcomes aren’t in conflict and can drive both business results as well as overall environmental impact,” Ashenfelter says.

      Going forward, Spoiler Alert will continue building out algorithmic solutions that could further cut down on waste internationally and across a wider array of products.

      “At every step in our process, we’re collecting a tremendous amount of data in terms of what is and isn’t selling, at what price point, to which buyers, out of which geographies, and with how much remaining shelf life,” Ashenfelter explains. “We are only starting to scratch the surface in terms of bringing our recommendations engine to life for our suppliers and buyers. Ultimately our goal is to power the waste-free economy, and rooted in that is making better decisions faster, in collaboration with a growing ecosystem of supply chain partners, and with as little manual intervention as possible.” More

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      in Data Management & Statistics

      Research collaboration puts climate-resilient crops in sight

      by

      Any houseplant owner knows that changes in the amount of water or sunlight a plant receives can put it under immense stress. A dying plant brings certain disappointment to anyone with a green thumb. 

      But for farmers who make their living by successfully growing plants, and whose crops may nourish hundreds or thousands of people, the devastation of failing flora is that much greater. As climate change is poised to cause increasingly unpredictable weather patterns globally, crops may be subject to more extreme environmental conditions like droughts, fluctuating temperatures, floods, and wildfire. 

      Climate scientists and food systems researchers worry about the stress climate change may put on crops, and on global food security. In an ambitious interdisciplinary project funded by the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS), David Des Marais, the Gale Assistant Professor in the Department of Civil and Environmental Engineering at MIT, and Caroline Uhler, an associate professor in the MIT Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society, are investigating how plant genes communicate with one another under stress. Their research results can be used to breed plants more resilient to climate change.

      Crops in trouble

      Governing plants’ responses to environmental stress are gene regulatory networks, or GRNs, which guide the development and behaviors of living things. A GRN may be comprised of thousands of genes and proteins that all communicate with one another. GRNs help a particular cell, tissue, or organism respond to environmental changes by signaling certain genes to turn their expression on or off.

      Even seemingly minor or short-term changes in weather patterns can have large effects on crop yield and food security. An environmental trigger, like a lack of water during a crucial phase of plant development, can turn a gene on or off, and is likely to affect many others in the GRN. For example, without water, a gene enabling photosynthesis may switch off. This can create a domino effect, where the genes that rely on those regulating photosynthesis are silenced, and the cycle continues. As a result, when photosynthesis is halted, the plant may experience other detrimental side effects, like no longer being able to reproduce or defend against pathogens. The chain reaction could even kill a plant before it has the chance to be revived by a big rain.

      Des Marais says he wishes there was a way to stop those genes from completely shutting off in such a situation. To do that, scientists would need to better understand how exactly gene networks respond to different environmental triggers. Bringing light to this molecular process is exactly what he aims to do in this collaborative research effort.

      Solving complex problems across disciplines

      Despite their crucial importance, GRNs are difficult to study because of how complex and interconnected they are. Usually, to understand how a particular gene is affecting others, biologists must silence one gene and see how the others in the network respond. 

      For years, scientists have aspired to an algorithm that could synthesize the massive amount of information contained in GRNs to “identify correct regulatory relationships among genes,” according to a 2019 article in the Encyclopedia of Bioinformatics and Computational Biology. 

      “A GRN can be seen as a large causal network, and understanding the effects that silencing one gene has on all other genes requires understanding the causal relationships among the genes,” says Uhler. “These are exactly the kinds of algorithms my group develops.”

      Des Marais and Uhler’s project aims to unravel these complex communication networks and discover how to breed crops that are more resilient to the increased droughts, flooding, and erratic weather patterns that climate change is already causing globally.

      In addition to climate change, by 2050, the world will demand 70 percent more food to feed a booming population. “Food systems challenges cannot be addressed individually in disciplinary or topic area silos,” says Greg Sixt, J-WAFS’ research manager for climate and food systems. “They must be addressed in a systems context that reflects the interconnected nature of the food system.”

      Des Marais’ background is in biology, and Uhler’s in statistics. “Dave’s project with Caroline was essentially experimental,” says Renee J. Robins, J-WAFS’ executive director. “This kind of exploratory research is exactly what the J-WAFS seed grant program is for.”

      Getting inside gene regulatory networks

      Des Marais and Uhler’s work begins in a windowless basement on MIT’s campus, where 300 genetically identical Brachypodium distachyon plants grow in large, temperature-controlled chambers. The plant, which contains more than 30,000 genes, is a good model for studying important cereal crops like wheat, barley, maize, and millet. For three weeks, all plants receive the same temperature, humidity, light, and water. Then, half are slowly tapered off water, simulating drought-like conditions.

      Six days into the forced drought, the plants are clearly suffering. Des Marais’ PhD student Jie Yun takes tissues from 50 hydrated and 50 dry plants, freezes them in liquid nitrogen to immediately halt metabolic activity, grinds them up into a fine powder, and chemically separates the genetic material. The genes from all 100 samples are then sequenced at a lab across the street.

      The team is left with a spreadsheet listing the 30,000 genes found in each of the 100 plants at the moment they were frozen, and how many copies there were. Uhler’s PhD student Anastasiya Belyaeva inputs the massive spreadsheet into the computer program she developed and runs her novel algorithm. Within a few hours, the group can see which genes were most active in one condition over another, how the genes were communicating, and which were causing changes in others. 

      The methodology captures important subtleties that could allow researchers to eventually alter gene pathways and breed more resilient crops. “When you expose a plant to drought stress, it’s not like there’s some canonical response,” Des Marais says. “There’s lots of things going on. It’s turning this physiologic process up, this one down, this one didn’t exist before, and now suddenly is turned on.” 

      In addition to Des Marais and Uhler’s research, J-WAFS has funded projects in food and water from researchers in 29 departments across all five MIT schools as well as the MIT Schwarzman College of Computing. J-WAFS seed grants typically fund seven to eight new projects every year.

      “The grants are really aimed at catalyzing new ideas, providing the sort of support [for MIT researchers] to be pushing boundaries, and also bringing in faculty who may have some interesting ideas that they haven’t yet applied to water or food concerns,” Robins says. “It’s an avenue for researchers all over the Institute to apply their ideas to water and food.”

      Alison Gold is a student in MIT’s Graduate Program in Science Writing. More