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How’s the Water? South Bend Answers.

Written by MBDH

By Francie Fink

This story is part of a series on partnerships developed by the Midwest Big Data Innovation Hub with institutions across the Midwest through the Community Development and Engagement (CDE) Program.

South Bend, Indiana, is no stranger to environmental challenges: more dangerously hot days than ever before and worsening storms of all types. But South Bend also stands out amongst peer cities as a leader in climate action. As South Benders and city leaders work to adapt to the realities of a changing climate, the attention is turning to another critical resource, one that is quite literally a lifeline to residents: water. In a region practically defined by its proximity to the St. Joseph River and its watershed, water quality is a priority. South Bend, in particular, is on the national stage with its extensive, groundbreaking wastewater and stormwater network.

The St. Joseph River has been enormously influential to Northern Indiana’s history. Flowing over 200 miles from southern Michigan to Lake Michigan, the river has been a source of nourishment, transportation, and economic development for centuries. Indigenous peoples, including the Miami and Potawatomi, established villages along its banks, relying on it for fishing, agriculture, and trade. With the arrival of French explorers in the 17th century, the river became a key route for fur trading and westward expansion. Later, cities like South Bend flourished along its shores, using its power for milling, manufacturing, and transportation. Today, the St. Joseph River remains a vital part of the region’s history, recreation, and conservation efforts.

Pictured: View from the East Race walking path on the St. Joseph River in South Bend

Academics may think about water quality in a very technical and traditional way. What’s its pH? What types of and how many insects are in the water? What’s the average temperature during the four different seasons?  Policymakers and advocates, though, may think about it in a slightly different, perhaps more holistic way. In St. Joseph County, sustainability leaders and those at the St. Joseph River Basin Commission (SJRBC) think about water quality as part of a broader hydrological, ecological, and human system. They might care more about personal health and safety because, quite frankly, that’s what residents care about. 

SJRBC has served as the primary river conservation, management, and water-quality-improvement-focused body for the St. Joseph River for almost four decades. Established by the Indiana General Assembly, the SJRBC provides essential governance and services to a watershed that drains 4,682 square miles between two states (both Indiana and Michigan) to Lake Michigan. Day-to-day, the Commission works on a mix of projects, from technical assistance and watershed expertise to demonstration projects and coordination between local governments.

The public cares about visible indicators. For one, they’d like their water to look like water that they might typically drink out of a purchased bottle or swim in. The appearance of water in a river like the St. Joe might allude to more pressing concerns. If someone was to fish or swim in the water, would they be okay? That is to say, would chemical contaminants give them a bacterial infection? Or, could harmful algal blooms trigger rashes or even liver damage? 

In short, as Barbara Dale, Project Manager in the city’s Office of Sustainability, says, “We want drinkable, fishable, swimmable water.”

Dr. Kate Barrett is an aquatic ecologist and Assistant Professor of Biology at Holy Cross College. Barrett has been working with the City of South Bend Office of Sustainability and other towns within the watershed for a couple of years to try and come up with a cohesive answer to one question they often get from residents: “How’s the water?” SJRBC has some great year-to-year data on water quality through water sampling but they, like the South Bend Office of Sustainability, haven’t yet had the capacity to develop a public-education campaign.

​​Building on the success of Sense South Bend: Air Quality—the first in a novel series of environmental health initiatives—the City of South Bend launched their next ambitious project in September 2024: Sense South Bend: Water Quality. This time, the City of South Bend partnered with SJRBC. The Sense South Bend initiative, as a whole, is designed to empower the community by tracking critical environmental indicators, such as air and water quality, mobility, and extreme heat. By turning complex data into easily understandable metrics, the initiative aims to inform and inspire action, fostering a healthier, more resilient South Bend.

Like Sense South Bend: Air Quality, the Sense South Bend: Water Quality campaign focuses on effectively communicating critical environmental data in a way that is both accessible and actionable for residents. This initiative will delve into the historical water-quality trends of the St. Joseph River and its expansive watershed, painting a clear picture of how the region’s waterways have evolved over time. By combining these historical insights with a detailed characterization of the river’s current state, the campaign aims to provide residents with a comprehensive understanding of their local water resources through simple visualizations and a cohesive narrative.

South Bend has creatively tackled its environmental-health campaign by partnering with the Midwest Big Data Innovation Hub (MBDH) to support graduate students on experiential-learning science-communication projects. In spring 2024, South Bend hosted a Graduate Environmental Health Fellow for the Sense South Bend: Air Quality project. Two more Environmental Health Fellows (EHFs) were onboarded in Fall 2024 to develop a roadmap for Sense South Bend: Water Quality. Samantha Rothman and Caroline Corona, Master of Public Affairs graduate students at the University of Wisconsin–Madison (UW–Madison), were brought on to learn more about science communication and, in the process, generate recommendations for a water-quality education campaign that the City of South Bend and SJRBC can implement.

The Fellows’ involvement with South Bend began as a learning and mentorship experience. During their first year in the graduate program at UW–Madison, Rothman and Corona developed an interest in the intersection of science communication, data analysis, and environmental issues. Eager to apply their coursework to real-world challenges, they engaged in informational interviews with South Bend experts and explored the current state of the St. Joseph River.

For Corona, the opportunity to put her Life Sciences Communication studies into practice was invaluable. “This experience has bridged the gap between theory and practice, showing us how effectively communicating key data points can foster public understanding of shared resources.”

The Fellows benefited from mentorship by local government leaders in the City of South Bend Office of Sustainability and water-quality experts from organizations such as the SJRBC and local universities. Rothman and Corona also examined existing resources, including watershed boundaries, public-communication efforts, and key water-quality indicators such as dissolved oxygen, macroinvertebrate life, and turbidity.

With a solid understanding of South Bend’s water systems, the Fellows turned to best-practice research. They investigated how other cities communicate water-quality data to residents, reviewing municipal websites, media releases, and outreach campaigns. Their analysis identified common strategies used to inform the public about water conditions, including stormwater runoff volume, clarity, and chlorophyll content.

In January 2025, Rothman and Corona presented their findings to the City of South Bend Office of Sustainability and the Department of Innovation and Technology, along with the SJRBC. They also compiled a comprehensive research report (pdf) for stakeholders involved in the Sense South Bend (SenseSB) initiative, outlining key insights and actionable recommendations.

Two central themes emerged from their research. First, the health of the St. Joseph River Basin has improved dramatically over the past several decades. Once considered “dead,” the river is now clear and thriving. Second, the watershed is a vital resource, serving economic, agricultural, residential, and recreational needs, while providing drinking water to 1.5 million people.

Based on their findings, the Fellows proposed four potential communication strategies for the City of South Bend:

  1. Interactive Dashboard – A digital platform displaying real-time water-quality data based on regular sampling
  2. Annual Report Card – A simplified grading system to help the public understand water-quality trends
  3. Historical Timeline – A visual representation of the river basin’s history, cultural significance, and environmental progress
  4. Arts Campaign – A creative initiative, inviting local artists to celebrate the health of the St. Joseph River and its waterways.

Each proposal included a summary, resource requirements, and case studies from other cities.

Patrick McGuire, South Bend’s Director of Innovation and Technology, emphasized the importance of the Fellows’ contributions to the SenseSB project. “A community identity built around ecological systems already exists in South Bend, and we can capitalize on that in communications and outreach.” McGuire and Dale were particularly drawn to the idea of a water-quality “report card,” recognizing its potential as a simple, yet highly effective, public-engagement tool.

Reflecting on their experience, the Fellows noted that transparent communication of environmental data is becoming increasingly critical for cities across the USA. “It was fascinating to see how different cities conveyed baseline information on fishability and swimability. Each approached water-quality communication with a different level of effort,” said Rothman.

Corona highlighted South Bend’s unique opportunity to share its environmental-success stories. “The number of fish species in the St. Joseph River has increased, thanks in part to reintroduction efforts. There’s an incredible opportunity to communicate that progress.”

With the Fellows’ research in hand, the City of South Bend is now prepared to implement one or more of the proposed communication strategies as part of SenseSB: Water Quality, further strengthening public engagement with the region’s waterways.

Learn More/Get Involved:

Stay tuned for water-quality updates to the City of South Bend’s SenseSB website. Visit SJRBC’s site to learn more about the St. Joseph River Basin Commission’s role in protecting and managing the watershed.

Contact the Midwest Big Data Innovation Hub to learn more about other Community Development and Engagement projects we’ve supported. The Midwest Big Data Innovation Hub is an NSF-funded partnership of the University of Illinois at Urbana-Champaign, Indiana University, Iowa State University, the University of Michigan, the University of Minnesota, and the University of North Dakota, and is focused on developing collaborations in the 12-state Midwest region. Learn more about the national NSF Big Data Hubs community.

Big Data & Health, blog, CDE, Smart, Connected, & Resilient Communities, water

Using Deep Learning to Accelerate Protein-Folding Prediction

Written by MBDH

By Jas Mehta

For decades, a fundamental question in biology remained largely unanswered: how do proteins fold? Proteins, large, complex molecules, play crucial roles in virtually every biological process within our cells. These building blocks, the workhorses of our cells, contort their amino acid chains into intricate 3D shapes that dictate their function. Unveiling these structures has been a slow and expensive endeavor, hindering progress in medicine, drug discovery, and our understanding of life itself.

Researchers have grappled with the challenge of deciphering protein structures using methods such as X-ray crystallography and computational modeling. However, these approaches often fell short in terms of accuracy and efficiency. Scientists and software developers using artificial intelligence (AI) concepts are creating powerful new tools to address this challenge. One example, called AlphaFold, was developed by the DeepMind subsidiary of Google’s parent company, Alphabet. AlphaFold represents a paradigm shift in protein structure prediction, building upon decades of research engaging with the intricate puzzle of protein folding, and leveraging the power of deep learning to achieve near-atomic accuracy in predicting 3D protein structures from amino acid sequences. (See the image below for an example of how researchers are computing protein structure from amino acid sequence data.)

Computing protein structure from amino acid sequence

A graphic of how researchers compute protein structure from amino acid sequence data.
[Source]


This breakthrough has not only streamlined the process, reducing prediction times from months to minutes, but has also opened new avenues for drug discovery and biomedical research, promising to revolutionize our understanding of proteins and their functions within cells. This represents a monumental leap compared to traditional methods like X-ray crystallography, which can take months or even years. This breakthrough not only accelerates research cycles and slashes costs but also holds profound implications for fields ranging from medicine to materials science.

In the 14th Critical Assessment of Protein Structure Prediction (CASP), a biennial competition, AlphaFold achieved a staggering feat. It matched or surpassed the accuracy of experimental methods for a whopping 90% of proteins, showcasing the immense power of deep learning for this complex task. Historically, determining a protein structure could cost upwards of $100,000 and take months. AlphaFold slashes this time to minutes, with a projected cost per prediction of mere cents. This translates to significant cost savings and faster research cycles.

Designing drugs often hinges on knowing a protein’s structure. AlphaFold’s speed and accuracy streamline this process. A recent study used AlphaFold to identify a potential drug target for a baffling neurodegenerative disease, a process that would have taken significantly longer using traditional methods. Moving beyond snapshots, the next frontier is understanding how proteins fold, move, and interact within the cell. This will provide invaluable insights into cellular processes and protein function. Deep learning thrives on data. Integrating protein interaction databases, cellular environment data, and real-time folding kinetics will further enhance the accuracy and applicability of protein structure prediction. Open-source platforms like AlphaFold are making these powerful tools accessible to researchers worldwide. This fosters collaboration and accelerates scientific progress across disciplines.

The success of AlphaFold stands as a testament to the indispensable role played by the Protein Data Bank (PDB), a vast repository housing experimentally determined protein structures. Mr. Darnell Granberry, a distinguished machine-learning (ML) engineer at the New York Structural Biology Center, sheds light on the critical importance of open data in driving groundbreaking advancements in protein research. “The PDB contains nearly all of the protein structures that have been experimentally determined, and the fact that it’s open source is a major enabler of AlphaFold and other protein ML models,” remarks Mr. Granberry. “If we didn’t have it, I think we’d likely have been limited to in-house models developed at pharma/biologics companies on proprietary data.”

His insights offer a nuanced understanding of the symbiotic relationship between computational methods and protein research, emphasizing the transformative impact of accessible data on scientific innovation. Furthermore, Mr. Granberry eloquently articulates a foundational principle of biology, stating, “There’s that central dogma of biology: DNA to RNA, RNA to protein, protein to function. So basically, anything that you’re interested in, basically in any living thing, is going to be rooted in some sort of protein or complex of proteins, or collection of them that interact with each other.”

In his words, we discern a profound appreciation for the pivotal role played by proteins in shaping the essence of life itself, underscoring the fundamental importance of unraveling their structures and functions in driving progress across diverse realms of scientific inquiry.

In a recently published study, researchers used AlphaFold to predict the structure of a protein implicated in amyotrophic lateral sclerosis (ALS), a debilitating neurodegenerative disease. The predicted structure revealed a never-before-seen binding site, paving the way for the design of drugs that could potentially slow or halt disease progression. This exemplifies AlphaFold’s potential to revolutionize drug discovery, particularly for complex and previously untreatable diseases.

A graph showing the protein-folding prediction accuracy from 2006 to 2020.
[Source]


The chart above depicts the median accuracy of protein-folding predictions in the free-modeling category of the CASP competition over the years. As you can see, there was a significant jump in accuracy in 2018 and 2020, coinciding with the introduction of DeepMind’s AlphaFold systems. This dramatic improvement highlights the transformative power of deep learning in protein-folding prediction.

Deep learning has irrevocably transformed protein-folding prediction. As we delve deeper into protein dynamics and leverage the power of big data, the potential applications are truly boundless. From developing new medicines and biomaterials to a fundamental understanding of how life works at the molecular level, AlphaFold and its successors promise to usher in a new era of biological discovery.

Get Involved

Contact the Midwest Big Data Innovation Hub if you’re aware of other people or projects we should profile here, or to participate in any of our community-led Priority Areas. The MBDH has a variety of ways to get involved with our community and activities. The Midwest Big Data Innovation Hub is an NSF-funded partnership of the University of Illinois at Urbana-Champaign, Indiana University, Iowa State University, the University of Michigan, the University of Minnesota, and the University of North Dakota, and is focused on developing collaborations in the 12-state Midwest region. Learn more about the national NSF Big Data Hubs community.

AI, Big Data & Health, blog

Toward Building Quality Relationships: How Chatbots Can Help Us Practice Self-Disclosure

Written by MBDH

By Qining Wang

Under the turmoil of social events, from global pandemics to wars and social unrests, mental health is becoming an increasingly greater concern among the public.

According to the Anxiety and Depression Association of America (AADA), anxiety disorders are the most common mental illness in the USA, affecting 40 million adults. Another common mental health illness, depression, affects 16 million adults in the USA, according to statistics from the Centers for Disease Control and Prevention (CDC). The greater awareness and gradual destigmatization of mental health issues have led more people to seek professional help to improve their overall mental well-being.

When working with mental health professionals, self-disclosure is vital to finding the roots and triggers of mental health issues. Self-disclosure is a process through which a person reveals personal or sensitive information to others. It is a crucial way to relieve stress, anxiety, and depression.

Meanwhile, self-disclosure is a skill that one needs to cultivate through practice. It’s a skill we can only practice through constant self-exploration and the courage to be vulnerable.

To investigate alternative ways of practicing self-disclosure, a research team at the University of Illinois at Urbana-Champaign (UIUC) explored chatbots and conversational AIs as potential mediators in the self-disclosure process in a study in 2020. The team leader, Dr. Yun Huang, is an assistant professor in the School of Information Sciences at UIUC and the co-director of the Social Computing Systems (SALT) Lab. The team is mainly interested in context-based social computing system research.

Chatbots are ubiquitous in today’s online world. They are computer programs interacting with humans back-and-forth, like having a conversation. Some chatbots are task-oriented. An example can be a frequently-asked-questions (FAQ) chatbot that recognizes the keywords a person types and spits out a preset answer according to the keywords. Other more sophisticated chatbots, such as Apple’s Siri and Amazon’s Alexa, are data-driven. They are more contextually aware and can tailor their responses based on user input. Both are ideal qualities for designing an empathetic and tone-aware chatbot capable of self-disclosure.

As such, Dr. Huang’s team built a self-disclosing chatbot that can engage in conversation more naturally and spontaneously. The chatbot would initiate self-disclosure during small-talk sessions. It would gradually move to more sensitive questions that encourage users to self-disclose.

To study how chatbots’ self-disclosure can affect humans’ willingness to self-disclose, the team recruited university students and divided them into three groups. Each group would interact with the chatbot at different levels of self-disclosure, from no self-disclosure to low and high levels of self-disclosure.

During the four-week study, the student participants would interact with the chatbot every day for 7–10 minutes. At the end of the third week, the chatbot would recommend that students interact with a human mental health specialist. The researchers would then evaluate students’ willingness to self-disclose to the professional.

The team found that the groups that self-disclosed to the chatbot reported greater trust in the mental health professional than the control group. Participants felt “confused” when the chatbot brought up the human professional. In the experimental groups, they felt that they could listen to the chatbot and share sensitive experiences.

The team noted that, for participants interacting with the chatbot with the highest level of self-disclosure, their trust for the mental health professional stemmed from the trust of the chatbot. Participants’ trust was mainly directed toward the research team and professionals behind the chatbot for the other two groups.

This study highlights how chatbots can be a great tool to help users practice self-disclosure, making them more comfortable seeking human professionals. It is worth noting that, regardless of how sophisticated chatbots can be, they are just mediators between users and mental health professionals.

At the end of the day, the most meaningful kind of self-disclosure can only be found through care, empathy, and understanding. Human to human.

Get Involved

Contact the Midwest Big Data Innovation Hub if you’re aware of other people or projects we should profile here, or to participate in any of our community-led Priority Areas. The MBDH has a variety of ways to get involved with our community and activities. The Midwest Big Data Innovation Hub is an NSF-funded partnership of the University of Illinois at Urbana-Champaign, Indiana University, Iowa State University, the University of Michigan, the University of Minnesota, and the University of North Dakota, and is focused on developing collaborations in the 12-state Midwest region. Learn more about the national NSF Big Data Hubs community.

AI, Big Data & Health, blog