University Of Chicago

NSF Awards · FY 2024 · 2024-11

This qualitative research seeks to understand why communities facing similar environmental change-related threats adapt in different ways. Comparing four coastal cities facing sea level rise – one pair of cases pursuing different adaptation options in California and another pair in Florida – the researcher questions how location-specific histories might explain differing responses and what role different state-level agency configurations play in shaping risk perceptions and the type of adaptation options available to local governments. In investigating influential factors on local adaptation decisions, this project illuminates how risk perceptions develop and the varied paths and obstacles local governments face when addressing environmental change threats, highlighting for decision makers and funding agencies the constraints, points of intervention, and notable community characteristics that influence local adaptation potential. The researchers identify sites based on their environmental risk and demographic comparability, with pairings made from sites with opposing stances on whether managed retreat should be considered part of their adaptation portfolio. They collect data in three phases, incorporating ethnography and interviews, archival, and institutional analysis methods to understand risk frameworks and their effects on local adaptation decisions. First, ethnographic research at each site provides a sense of the locale and the community’s lived experience of environmental change and rising seas. Second, each site’s planning commission and city council agendas and observed recorded meetings are analyzed to outline key state-level agencies involved, establish a decision-making timeline, and identify state and local decision-makers for interviews. This is supplemented with an analysis of state policy documents. Finally, additional ethnographic research at each site allows for corroboration of the findings and access to difficult to reach stakeholders. The research offers a better understanding of what shapes risk perceptions and the adaptation options available at the local level, providing deeper insight on how people are dealing with spaces that are becoming uninhabitable. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-11

PROJECT SUMMARY/ABSTRACT Many human autoimmune diseases are associated with the expansion of self-reactive B cells and subsequent autoantibodies targeting self-constituents. Even in healthy individuals, an appreciable fraction of self-reactive B cells exists, implying the existence of extrinsic layers that restrain overt humoral autoimmunity. Foxp3+ T follicular regulatory (Tfr) cells—defined in part by their localization within the B cell follicle and the germinal center (GC)—are one such layer. Selective removal of Tfr cells in mice at the peak of an immune response results in a substantial increase of autoreactive antibodies, while low Tfr cell frequencies are negatively associated with human autoimmune diseases, further suggesting a key role in the regulation of B cell tolerance. Most previous work has studied Tfr cells at the peak of a GC reaction, meaning that their contributions at steady state and following cessation of a GC are unknown. Without knowledge of the entire life cycle of Tfr cells— including their initial development, phenotype differentiation, and maintenance outside of a GC—we will remain ignorant to additional roles that these cells likely play in maintaining immunity. Early adoptive transfer studies have suggested that the majority of Tfr cells derive from existing regulatory T cell (Treg) precursors. Alternatively, some lines of evidence controversially hint that a small subset of Tfr cells may also differentiate from Foxp3neg T helper cells of foreign antigen specificity, though such populations have not been consistently identifiable. Based on these data, we hypothesize that most Tfr-phenotype cells elicited during a GC reaction represent stable pre- existing thymus-derived Treg cells reactive to self-antigens. As proposed below, we will define the development, differentiation, and stability of Tfr cells. We will also assess the extent to which Tfr cells are reactive to foreign versus self-antigens. To accomplish these aims, we will generate T cell receptor retrogenic mice expressing monoclonal populations of Tfr biased clones of interest, allowing us to study naturally occurring Tfr cells at the clonal level. We will track the entire in vivo life cycle of individual Tfr biased clones, both at steady state and after cessation of a GC response, providing key insights into the biology of these cells. Finally, we will investigate the antigen specificity of Tfr cells by assessing their in vitro reactivity to dendritic cells or B cells loaded with self or foreign antigens. The findings obtained from this project will provide us with a clear model of the complete developmental trajectory and potential foreign reactivity of Tfr cells. This model will enable us to further study these cells in previously unappreciated contexts, thereby improving our understanding of how Tfr cells may succeed—or fail—to prevent autoimmunity.

NSF Awards · FY 2024 · 2024-10

In order to be successful, engineers need to work in diverse collaborative teams. However, it is common for inequities to arise in teamwork that hurt the performance of the team. These inequities can take many forms. One team member might have fewer opportunities to share ideas. Another team member might be interrupted more often. Yet another team member might receive fewer or different tasks to complete. These types of inequities hurt team dynamics and student learning. There are technological tools available to help teams address these problems. Existing tools include online teamwork support and careful team-building. These strategies can be effective but only work before or after teams interact. Teams need in-the-moment support, especially if they are struggling. Social robots may be able to provide that support they need with models of equity. Past research in social robotics has defined equity as all members participating equally. However, a team with equal participation might be very inequitable. For example, one team member could be rejecting another team member’s ideas when they speak. This project will create an improved model of equity that can be used with a social robot. The robot will then be able to make student engineering teams more equitable. Overall, this project will help make student engineering teams more effective. It will also improve our understanding of what makes student engineering teams fair. This project helps increase the pool of engineers who can contribute to society. The knowledge gained from this research may benefit team interactions with different teams. This could have strong impacts on diversity, equity, and inclusion across STEM fields. Collaborative teams are common in the modern engineering workplace. Learning how to work well in a team is a critical skill for engineers to learn since they take on complex problems. One common challenge teams face is inequities in communication and task allocation. Engineers need to learn how to address inequities in their teams in order to be successful. Some teamwork tools already exist to support teamwork and address inequities. However, these tools rely on team members’ opinions and do not provide real-time feedback. In this project, the research team will design a social robot to promote equity in teams. This social robot will be able to observe interactions in the team to detect inequities. Then, the social robot will intervene during team meetings to address the inequities. First, the research team will observe human engineering design teams. They will use these observations to build data-driven models to detect inequities. Next, the research team will build a machine learning model to determine when a robot should intervene in a team. This model will choose when the robot should intervene based on the inequities it can detect. Also, the research team will explore what behaviors a robot can express to promote equity. These robot behaviors are new and have not yet been tested on robots. Finally, the research team will integrate the computational model with the tested robot behaviors. They will test the integrated system in an initial small-scale user study. This study will show the positive influence of the robot’s ability to promote equity in teams. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

In this context of virtual reality, creating, perceiving, and editing three-dimensional (3D) shapes are at the core of activities such as product design (creating or evaluating objects for manufacturing or personal fabrication), online shopping (experiencing furniture in a room or trying on clothing), and specialized training (gaining familiarity with a remote tool). Yet, today's approaches for interacting with virtual 3D shapes are strictly visual, requiring precise manipulation and interpretation of digital designs on a screen. This project's goal is to create algorithms and interfaces that make 3D modeling easier and more effective, even in the absence of visual cues: auto-correct for 3D drawing, the ability to hear shapes, and the ability to edit 3D shapes verbally. By using senses that do not require a screen—body awareness and sound—this project aims to untether people from their screens, enabling virtual 3D perception from anywhere. The outcomes of this project are expected to have far-reaching impacts, including making computers easier to use for people with visual impairments, enhancing interface techniques for low-visibility scenarios, and creating new opportunities for human-computer interface research and do-it-yourself fabrication. The research focuses on three main objectives: developing accurate “in-air” 3D drawing tools, designing sonification (conveying information through sound) techniques for non-visual shape perception and editing, and creating verbal 3D shape editing tools and interactions. These aims will be pursued through auto-correct algorithms that account for the limits of proprioceptive (a person’s sense of their body pose and movement) accuracy, techniques to sonify shapes based on hand pose, and methods for verbal shape modification. This research sets the stage for future studies on incorporating sound and speech into 3D modeling, as well as non-visual user interfaces. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This National Science Foundation Innovations of Graduate Education (IGE) Track 2 award to the University of Chicago will support the creation of a data science credential that will enable STEM doctoral students to apply data science (DS) and artificial intelligence (AI) in their fields. The methods and tools of data science are crucial for all scientific domains, and AI and machine learning will support future advances in all disciplines by providing researchers with tools to explore new and more complex questions of importance to society. There has been limited research on formal mechanisms for creating integrative opportunities between DS/AI and other STEM disciplines or on the role that a credential from another discipline can play in STEM graduate students’ career paths and research. This new data science credential will enable STEM doctoral students to learn how to use data and AI accurately and responsibly and understand its broader impacts on social systems, make and critique data backed arguments, and become fluent in the latest computation tools. Moreover, the project will contribute to knowledge in these areas by focusing on doctoral students in STEM disciplines outside of data science (e.g. astrophysics, geophysical sciences, genetics, engineering, neuroscience). With support from their advisors, second- and third-year graduate students will enroll in three customized core courses during which they will consider ways to apply DS and AI concepts in their disciplines. They will then complete a fourth culminating course residing in their home department and focus on applying DS/AI in their own research. Students will also participate in co-curricular activities to support their professional growth. The research activities will use a mixed methods design to focus on three areas: (a) the role that participation in the credential program plays on shifting disciplinary perspectives; (b) the role that participation in the credential program plays in graduate students’ job search and career direction; and (c) the institutional and systemic processes needed to establish and implement an interdisciplinary credential and the role that credential plays in shifting institutional culture. Separate evaluation questions will inform project and course improvement through a continuous learning process. The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

In the multi-billion-dollar computer storage industry, efficient operation is essential for achieving good application accuracy, reliability, and performance. Traditionally, this efficiency has relied on heuristic algorithms with parameters that are selected by humans. However, as workloads and devices become increasingly complex, manual tuning by humans becomes impractical. The DISCO project will address how to systematically leverage data science (DS) to revolutionize the many facets of storage I/O decision making. More specifically, DISCO’s research objectives are to (a) pioneer a comprehensive data science pipeline tailored to enhance the storage I/O decision-making process by in-depth exploration of intricate concepts such as data augmentation, precise labeling, noise filtration, meticulous model engineering, drift detection, and many others; (b) target both classical I/O policies and open problems in the context of modern device features as well as venture to “uncharted territories" such as investigating what data science can reveal from billions of performance data points; and (c) comprehensively encompass high-, medium-, and low-frequency decision making and address each of their own unique challenges, but at the same time address cross-cutting concerns such as all-in-one integration. The DISCO project will bring significant broader impacts, especially in training future storage data scientists. The Data Storage Research Vision 2025 (DSRV) paper from an NSF workshop emphasized "the deficit of the professionals who are knowledgeable in both storage and AI areas" where "the number of fresh graduate students with this combination of skills is small, and training existing staff takes time and effort" and "storage companies are also experiencing significant competition from other industries that require AI/ML knowledge." In this context, the DISCO project will train graduate and undergraduate students to be part of the next-generation storage data scientists. The project will also release open ML-for-storage testbeds along with a public storage data science curriculum. In terms of technology transfer, the DSRV workshop paper also states that “storage companies are excited by the opportunities of using ML to improve performance and reliability, and develop quality products.” The DISCO project will produce sophisticated ML-for-storage solutions for solid-state drive (SSD) systems, potentially making a positive impact to the SSD market that is forecasted to reach over $50 billion by 2025. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This proposal will focus on rare and extreme climate events, such as heat waves and cold spells, which have major societal impacts. Rapid developments in AI are transforming scientific research, but are difficult to apply to rare events because too few of them occur in training sets. The proposed work will develop the essential mathematical tools to leverage AI methods to significantly improve the estimation of rare event statistics, both in climate and in other fields. The broadening participation aspect of this proposal is centered on making a positive impact on the lives and studies of veteran scholars of the United States military through college and graduate school admissions mentoring and research internships. This interdisciplinary project relies on an essential collaboration among AI, math, and climate to make transformational advances in knowledge that build on and enhance each field. This proposal will develop AI Dynamic Galerkin Approximation (AI-DGA) to extract long return periods from large-ensemble short-duration emulations. Then, it will leverage rare-event sampling in a novel hybrid and iterative use of numerical solvers and AI emulators (AI-RES) to develop additional estimates of return periods and generate more rare event data to re-train, and thus improve the emulator. The proposed work will deliver methods to improve 1) return period estimates for rare events and 2) the training of the AI emulators themselves. The proposal will focus on heat waves and cold snaps, but the methods developed will increase the usefulness of AI emulators across climate science, and geoscience broadly, by innovating new ways to apply them even on rare events they have never seen in their training set and even if the emulators are not reliable for long simulations. This award by the Division of Research, Innovation, Synergies, and Education within the Directorate for Geosciences is jointly supported by the National Discovery Cloud for Climate initiative of the Office of Advanced Cyberinfrastructure within the Directorate for Computer and Information Science and Engineering and by the Division of Mathematical Sciences within the Directorate for Mathematical and Physical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

An atmospheric blocking event describes the occurrence of a large-scale, quasi-stationary, high-pressure system that persists between 5 days to a few weeks in the extratropics. Its appearance diverts the jet stream flow (altering nearby weather) and can contribute to extreme events like heat waves and droughts. Predicting blocking events is difficult. Our understanding of blocking events is poor, and complete theory for blocking still does not exist. The outcome of this project will advance our knowledge of blocking, which will enhance our capabilities to better forecast midlatitude weather extremes and project the response of blocking to climate change. In addition to training doctoral students, the project will develop (1) a Research Experiences for Teachers (RET) program with workshops focused on developing innovative teaching materials on climate science, and (2) a course to educate college students from non-geoscience quantitative majors about climate science. Specifically, this proposal seeks to understand blocking dynamics and their spatiotemporal variability as well as their response to climate change. The research objectives are to: (1) assess the role of positive eddy-blocking feedback mechanism in blocking persistence, (2) evaluate the impact of large-scale circulation on blocking characteristics, (3) examine the impact of climate change to blocking, and (4) investigate the roles of latent heating in the previous objectives. The research approach includes an innovative use of the linear response function theory, Buckingham-pi scale analysis, and wavelet analysis in a hierarchy of models, from dry/moist two-layer quasi-geostrophic to dry/moist idealized general circulation models (GCMs) to large-ensemble simulations from fully coupled GCMs. The RET program will provide 7 research positions for high-school science teachers, who will develop introductory lessons on climate change. These lessons will be shared with 100s of science teachers through workshops and other venues and will be taught to 10000s of Houston public school students, many from the underrepresented groups. The developed class will result in novel materials for introducing climate science/research to a broad group of STEM students, strengthening efforts aimed at training a skilled future workforce in climate science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Since the invention of DNA sequencing in 1977, genomic data has grown exponentially due to decreasing sequencing costs. Unfortunately, many bioinformatics systems lag behind in adopting state-of-the-art computing principles, resulting in wasted computing potential. Such adoption is challenging due to domain-specific expertise requirements and the limited resources available for many bioinformatics projects. Exciting research areas, including workload characterization, performance modeling, resource optimization, scheduling, and leveraging advanced hardware accelerators, remain largely unexplored in bioinformatics systems. The All-in-One (AIO) collaborative research project aims to build a next-generation genomic data processing system that incorporates state-of-the-art systems design principles. Toward this end, the project focuses on three key innovations: (1) cluster scheduling policy improvement, which uses the characterization of genomic workloads to build an execution time predictor and guide scheduling design; (2) machinery for independently-scheduled genomic tasks that support resource-aware and failure-aware directed acyclic graph-based (DAG) scheduling; and (3) a meta-compiler for a cloud-and-language agnostic processing system, which allows automated performance tuning for various domain-specific languages and cloud execution environments. The project will transfer expertise from the systems community to bioinformatics, addressing the growing computational demands for genomic data processing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The goal of this project is to train a cohort of early- and mid-career scholars to become leaders in applying advanced quantitative and computational methods to STEM education research. Specifically, the institute supported by this project will seek to (a) deepen participants’ methodological training to rigorously apply cutting-edge methods to STEM education research, (b) provide participants with sustained methodological support in research planning, data analysis, and publication, and (c) broaden participation in a community of emerging scholars poised to take leadership in advancing STEM education research. The project team will recruit on a national scale to select a diverse cohort of participants whose research is focused on understanding the sources of unequal access to STEM learning opportunities and evaluating strategies for transforming STEM education to advance equity and inclusion. The theme of the institute will be to use cutting-edge methods to advance research for promoting equality and equity in STEM education. The institute is a collaboration between the University of Chicago and Michigan State University and will focus on integrating existing and novel quantitative methods with critical perspectives to examine STEM education opportunities and outcomes. Specifically, instructors will teach introductory modules in year 1 and advanced modules in year 2 in: (1) research designs and causal inference, (2) measurement, (3) social network analysis/computational methods, (4) multilevel modeling, and (5) causal moderation and mediation analyses. Structured discussions of participants’ ongoing studies will be supplemented by guest speaker presentations and round-table discussions organized around how to innovatively address some of the most challenging research questions about how to reduce inequality and inequity in STEM. Fellows will present their capstone projects in the summer of year 3. In addition to an annual summer institute, participants will meet in virtual monthly colloquia and small groups with instructors to discuss the application of methods. This work builds on a previous institute in advanced quantitative and computational methods for STEM education research. Accordingly, the project team will facilitate opportunities for the current participants to forge meaningful professional connections with participants from the previous institute through the organization of symposia and events at major educational conferences. By fostering these connections, the project will contribute to cultivating a critical mass of highly engaged and skilled researchers dedicated to addressing STEM education challenges for underrepresented groups. Continuous formative and summative assessments will be employed to enhance program activities and ensure equality. A customized website will disseminate training content and research products to the broader STEM education research community. The project is supported by NSF’s EDU Core Research Building Capacity in STEM Education Research (ECR: BCSER) program, which is designed to build investigators’ capacity to carry out high-quality STEM education research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Quantum information science (QIS) is an area of discovery and technology, with implications for national security and overall competitiveness. Yet, there are few ways for future innovators to learn about QIS and consider career pathways prior to college. While there are ongoing efforts to solve this by bringing QIS into the formal classroom, developing the readiness among informal educators offers a parallel, scalable approach for connecting kids to this exciting area. This project builds on Quander, a multi-game online platform that introduces middle-school students to fundamental QIS concepts. It seeks to expand Quander into a full-scale learning ecosystem tailored to afterschool settings through (1) developing and delivering professional development (PD) to informal educators, (2) integrating a rich set of incentives and rewards within the online game, and (3) creating a suite of QIS offline activities. The project partners with the Boys & Girls Clubs of Chicago to engage historically underserved youth in quantum learning. Blending online and offline play will further enable students and mentors with diverse backgrounds and identities to learn about QIS, regardless of their geography or access to formal quantum coursework. The PD will serve over 60 informal educators locally and 1000 online, and the games and activities will serve hundreds of students locally and thousands nationally through integration with the QuanTime national event. This project will advance understanding of (1) how to empower informal educators so they feel comfortable and confident in leading QIS activities, (2) what motivational incentives extend game play and deepen engagement with educative materials, and (3) how to develop offline activities that foster awareness and interest in QIS among middle grade youth. While there is emerging research on single, facilitated activities, research on multi-session informal QIS learning experiences is scant. With an existing suite of games with QIS-related learning objectives and accompanying initial studies on efficacy, the project will explore more deeply to what extent those learning objectives are age-appropriate for informal settings. In addition, the development of new offline games and activities will increase understanding of varied methods for introducing QIS to young audiences. These studies will be performed through in-person and on-line data collection of learner engagement with the activities. Engagement will be measured by the in-game behaviors in the online games, educator and researcher observations, and learner focus groups. Finally, not only is teaching pre-college students QIS concepts in its infancy, but there is no published research on QIS-related PD for informal educators, who may not have STEM degrees. To increase engagement with QIS, it is critical to better understand how to develop their confidence in teaching QIS. The study will perform pre- and post-assessments of facilitator knowledge and confidence. In addition, long-term participation will track facilitator deployments, analyzing the degree to which PD resulted in learning opportunities. This Integrating Research and Practice project is funded by the Advancing Informal STEM Learning (AISL) program, which supports projects that: (a) contribute to research and practice that considers informal STEM learning's role in equity and belonging in STEM; (b) promote personal and educational success in STEM; (c) advance public engagement in scientific discovery; (d) foster interest in STEM careers; (e) create and enhance the theoretical and empirical foundations for effective informal STEM learning; (f) improve community vibrancy; and/or (g) enhance science communication and the public's engagement in and understanding of STEM and STEM processes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Chameleon is a deeply reconfigurable experimental edge to cloud testbed supporting Computer Science (CS) systems experimentation, education, and emergent applications. The platform consists of three core operating sites, at University of Chicago (UC), Texas Advanced Computing Center (TACC), and the National Center for Atmospheric Research (NCAR), and partners with the Renaissance Computing Institute (RENCI) at the University of North Carolina on networking capability development. The hardware ranges from an investment in diverse high-end datacenter nodes including graphic processing units (GPUs), field programmable gate arrays (FPGAs), and specialized architectures, to single board computers (SBCs) that can be cost-effectively deployed at the edge. Users can reconfigure this hardware at bare-metal level, through virtualization, or container deployment depending on their experimental needs and the used hardware. In Phase-IV, Chameleon will continue its mission to provide an experimental platform for computer science research and education, and evolve further to better satisfy emergent needs in current research. In particular, the team will implement a flexible combination of bare metal reconfiguration and virtualization in order to support more projects in artificial intelligence (AI) and machine learning (ML), while preserving the bare metal reconfiguration capability for projects that need it. Furthermore, CHI@Edge capability will be extended allowing users to create edge to cloud experimental scenarios, by improving its networking, ease of use, and its ability to support diverse Internet of Things (IoT) peripherals. Lastly, services and interactions will be improved allowing the community to be more productive and impactful, specifically Chameleon’s support for reproducibility and sharing of the digital artifacts and by promoting translational approach to the computer science research. To use Chameleon or learn more about the system, visit www.chameleoncloud.org. For anyone interested in the development side of the system or the packaging of Chameleon, i.e., CHI-in-a-Box, visit the github repository at https://github.com/ChameleonCloud/chi-in-a-box/wiki. For anyone interested in exploring traces from Chameleon to date, see https://www.scienceclouds.org/cloud-traces/. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Post-traumatic stress disorder (PTSD) is an anxiety disorder that can follow from witnessing or directly experiencing an event involving injury, threat to life, or death. People with PTSD re-experience the traumatic event through flashbacks or nightmares and may feel anxious, numb, or hyperaroused. PTSD can last for years and severely impair day-to-day functioning. The condition exacts an enormous toll on trauma survivors, their families, and society. Moreover, we may be underestimating the individual and societal costs of PTSD, given growing appreciation that PTSD contributes to other chronic and costly physical health problems such as cardiovascular disease. Although treatments for PTSD exist, it remains a difficult and stubborn disorder to treat with many patients resistant to current methods. This issue is in part due to a lack of understanding of the neuronal mechanisms involved in the retrieval of traumatic memories and how they are suppressed or extinguished in normal subjects. It has recently come to light that a subregion of the thalamus called the Nucleus Reuniens (NR) plays a key role in the suppression of fear memories and their extinction – a process in which animals learn that a previously fearful situation is now safe and thus is no longer feared. Understanding how the NR suppresses and extinguishes fear memories is therefore essential for developing effective treatments for PTSD, but this is currently unknown. The hippocampus is a central brain region for the formation, consolidation, and retrieval of contextual fear memories, those that become maladaptive in PTSD patients. NR must therefore interact with the hippocampus to suppress contextual fear memories and support extinction processes. Anatomically, there is a direct connection between NR and the CA1 region of the hippocampus, but physiologically is remains unknown how this pathway interacts with contextual fear memory processing in CA1. To address this, we have developed a new paradigm whereby contextual fear memories can be induced, later retrieved, and eventually extinguished in head-fixed mice exploring virtual reality contexts. This allows us to use 2-photon microscopy (as mice are head fixed) to image the activity of NR inputs directly in CA1 during fear memory retrieval and throughout extinction. We can also manipulate the activity of these inputs using optogenetics and measure the effects on hippocampal population dynamics and single cell activity. It also allows us to image the dendritic activity of CA1 cells during NR input manipulations. Together, this strategy will uncover how NR interacts with hippocampus to suppress fear memory retrieval and promote extinction at the synaptic, single cell, population, and circuit level. These finding will provide new insights that can be used to help develop novel strategies for the treatment of PTSD.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY HIV-1 must assemble and disassemble into different morphologies throughout its life cycle to infect new cells and replicate. Some examples of this include assembly and budding of the immature virion, maturation of the viral core, and uncoating. Inhibiting or otherwise disrupting these transitions, which are critical for viral infectivity, is an effective strategy in the design of antiretroviral therapies. For example, a class of drugs known as allosteric integrase inhibitors disrupts maturation by preventing packaging of viral RNA in the mature core resulting in a loss of infectivity. A full understanding of the process by which viral RNA is packaged would greatly aid in a mechanistic understanding of how these drugs work. This mechanistic understanding would also aid in the design of future therapeutics targeting RNA packaging just as would for drugs targeting budding and uncoating. In this work, I propose to computationally investigate these three morphological transitions in HIV, focusing on the role of factors beyond the capsid domain proteins including RNA, host cell factors, and potential therapeutics. After expression by an infected cell, Gag polyproteins assemble at the plasma membrane into the 100nm, quasi- spherical immature virion that buds outward from the plasma membrane. The formation and budding of the virion were thought to be driven by interactions of the Gag CA domain, however, recent experiments paint a more complex picture. I will investigate the role of membrane and membrane bending IBAR protein IRSp53 in budding and assembly of the immature virion. I will incorporate IRSp53 into a coarse-grained (CG) model, which could not previously generate the curvature necessary for budding to occur. The maturation of the virion begins when HIV-1 protease cleaves the Gag polyprotein, freeing its different domains such as the capsid (CA) domain. CA then assembles into the mature, conical capsid which packages the ribonucleoprotein complex (RNP) composed of viral RNA, Integrase (IN), and nucleocapsid (NC) protein among other components. Using CG models of different resolution, I will investigate how the polymer-like RNP affects assembly of the mature focusing on the physical properties necessary for encapsulation of the RNP. The final CG model will be built based off experimental data showing contacts between RNA, NC, and IN with and without IN. Following import of the mature capsid into the nucleus, the mature capsid uncoats and releases the viral DNA for integration. Uncoating occurs as a result of reverse transcriptase transcribing viral RNA into more rigid DNA. This creates on outward pressure on the capsid which leads to makes it unstable. I will investigate the pathway of uncoating using a UCG model of reverse transcription that changes the rigidity of the RNP and a bottom-up CG model of the mature capsid. This model will include host factors such as cleavage and polyadenylation specificity factor 6 (CPSF6) as well as potentially therapeutic molecules that rigidify the capsid, possibly inhibiting the rupture of the capsid and release of DNA.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT Pancreatic cancer is one of the deadliest cancers, and its mortality rate is only predicted to rise. The deadliness of this disease arises, in part, from the latency of symptoms, which typically do not present until the disease is at an advanced stage. Advanced stage pancreatic tumors are rarely resectable, with ineffective chemotherapies as the only option for treatment. Considering this, a better understanding of pancreatic cancer initiation and development could improve patient outcomes. Therefore, there is a critical need for research that could lead to earlier detection, identification of biomarkers, and new therapeutic targets for pancreatic cancer. Using novel experimental approaches, the proposed work aims to interrogate in mechanistic detail key regulatory steps in the earliest stages of pancreatic ductal adenocarcinoma (PDAC), the deadliest form of pancreatic cancer, addressing an unmet need in the field of pancreatic cancer biology. Transcription factor Atf4, a key regulator of the cellular response to various stresses, has not previously been directly linked to PDAC development; however, my unpublished data shown here suggest that Atf4 plays a critical role in two of the earliest steps in PDAC initiation, acinar to ductal metaplasia (ADM), and development of pancreatic intra-epithelial neoplasia (PanINs). First, I observe Atf4 down-regulation in control acinar cells induced to undergo ADM ex vivo. Additionally, ADM is accelerated and more extensive in vivo in pancreas from mice with inducible and pancreas-specific deletion of Atf4. I also observe that deletion of Atf4 in cells undergoing ADM results in significant upregulation of MMP7, a known regulator of ADM. Second, PanIN formation and development of pancreatic ductal adenocarcinoma (PDAC) is blocked in mouse pancreas with inducible and conditional Atf4 deletion. Initial data show that this may be due to induction of p53 target genes and cellular senescence when Atf4 is lost. Lastly, Atf4 deficiency leads to accumulation of lipid in the pancreas of these mice, a pathology known as fatty pancreas in humans. The results of these data have led to my central hypothesis that Atf4 plays two distinct, critical roles in PDAC tumorigenesis, one in ADM, and the other in the transition to precancerous PanIN lesions. To test this central hypothesis, I will use three novel mouse models, first, an inducible pancreatic acinar cell specific deletion of Atf4 and two others where this strain is crossed to two different widely used PDAC mouse models. To connect these mouse studies to human disease, I will also make use of human PDAC patient data and samples. For this project, I propose two aims: 1. Define the role of Atf4 in acinar to ductal metaplasia, and 2. Determine the role of Atf4 in PanIN development. This work will link novel functions of Atf4 to PDAC tumorigenesis and potentially validate targeting the Atf4 pathway for therapeutic purpose in PDAC.

NIH Research Projects · FY 2025 · 2024-09

The Summer Healthcare Experience in Oncology is a virtual, multi-institutional STEM enrichment program for high school students, with a mission to grow the cancer biomedical workforce. The curriculum offers hands-on research experience, career exploration, mentorship, and leadership training, drawing strategically from the unique strengths and resources of five of the nation’s top cancer centers: University of Chicago Medicine Comprehensive Cancer Center, University of Kentucky Markey Cancer Center, University of Michigan Rogel Cancer Center, University of Pennsylvania Abramson Cancer Center, and the University of Texas at Austin Livestrong Cancer Institutes. Programming is delivered collaboratively and synchronously across the five sites, connecting trainees to an expansive network of peers, mentors, and opportunities in cancer research and care. The program’s multi-institutional structure also creates rich context for the study of social determinants of health, access to care, and other factors driving cancer health outcomes within the centers’ respective catchment areas (and participants’ respective communities). In summers 2021 and 2022, 169 participants completed a pilot version of the Summer Healthcare Experience in Oncology program. Pilot outcomes reflect significant gains in trainees’ scientific knowledge, biomedical career awareness, and confidence and sense of belonging in STEM. Trainees also report that the program enhanced important generalizable skills including critical thinking, self-directed learning, and the ability to communicate scientific concepts. This proposal details plans to expand the Summer Healthcare Experience in Oncology in fundamental ways including year-round programming, formalized support for program alumni, and rigorous evaluation to validate and strengthen the program model for expansion to new institutional cohorts nationwide.

NIH Research Projects · FY 2025 · 2024-09

Project Summary. Sub-Saharan Africa is home to >70% of people with HIV (PWH) globally. It is also burdened with rising rates of tobacco use, high rates of unhealthy alcohol use, and high rates of tobacco and alcohol co-use. These disparities are amplified in PWH in this region. Tobacco users drink more and alcohol users smoke more and quit less than mono-users. Tobacco combines with alcohol to exert a destructive synergy expressed, in particular, in extremely high rates of aerodigestive (especially esophageal) cancers. Kenya is the 7th most populous country in Africa, with 54 million people, 1.6 million of whom are PWH. Tobacco and alcohol use rates in Kenya, including Kenyan PWH, are higher than those in most sub-Saharan nations. Incidence rates of esophageal cancer in Kenya are 2.9 times those of the US in men and 8.4 times those of the US in women. There is an enormous need for better strategies to manage tobacco and alcohol co-use in Kenya and throughout the world. Our group has been engaged in tobacco treatment research for PWH in Nairobi, Kenya since 2018. We are completing a 2 x 2 factorial design randomized controlled trial (RCT) of Positively Smoke Free intensive behavioral counseling ± bupropion, and we have collected detailed information on alcohol usage in Kenyan PWH in the course of that study. We have created and piloted a new version of Positively Smoke Free counseling to include content related to alcohol co-use and strategies to cut down or quit alcohol use. Cytisine is a nicotinic acetylcholine receptor partial agonist that has been used for tobacco treatment since the 1960’s, mostly in Eastern Europe, Russia, and Canada. Multiple trials of cytisine, conducted almost exclusively in White participants, have demonstrated that its efficacy in promoting smoking cessation is comparable to varenicline, the first line tobacco treatment in the US. Cytisine is inexpensive, safe, and does not interact with antiretroviral therapies. It is a potent treatment for alcohol dependence in animal models, but data to support its use for this purpose in humans are scarce. There is virtually no published experience with cytisine on the African continent, but Kenya’s Ministry of Health has expressed interest in this affordable agent if it is proven effective in a Kenyan population. A placebo controlled trial of intensive counseling ± cytisine for Kenyan PWH who smoke and drink alcohol heavily will advance the science of tobacco and alcohol co-use, it will strengthen the case for cytisine use in Kenya and other low- and middle-income nations if effective, and it will address significant health disparities in a resource-constrained area of the world. In this application to renew 1R01CA225419, we propose a 2 x 2 factorial RCT comparing Positively Smoke Free including alcohol-referent content to standard care and comparing cytisine to placebo. We will study both tobacco and alcohol use endpoints as primary and secondary outcomes. We will also examine putative mediators of intervention effects on study outcomes. Finally, we will complete detailed cost analyses to estimate the cost-effectiveness of the trial interventions in relation to the trial’s tobacco and alcohol use outcomes.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY This project will develop, test, apply, and disseminate multilevel statistical models and software for estimating effects of intraindividual means, variances, slopes generated from multi-burst and continuous ILD designs to predict cancer control behaviors and outcomes. Cancer remains a leading cause of mortality. Approximately 42% of new cancer cases in the U.S. are viewed as potentially avoidable including 19% caused by smoking and 18% caused by excess body weight, physical inactivity, excess alcohol consumption, and poor nutrition. Intensive Longitudinal Data (ILD) methods, which collect many assessments captured at high density on a micro- timescale (e.g., seconds, minutes, hours) using real-time data capture methodologies (e.g., Ecological Momentary Assessment [EMA] and accelerometry), offer enormous opportunities for insight into the dynamic nature of cancer control behaviors and outcomes. In ILD studies, it is common to have hundreds to thousands of observations per subject, and this allows us to model intraindividual parameters comprised of time-varying variables such as means (e.g., how unhappy is a subject, on average, across occasions?), variances (e.g., how erratic is a subject’s mood across occasions?), and slopes (e.g., is a subject’s mood related to feelings of energy across occasions?). In our prior work, we developed a software, called MixWILD, consisting of a series of two- stage multilevel statistical models testing the effects of intraindividual means, variances, and slopes on time- varying and subject-level outcomes. The next generation of ILD studies has begun to use multi-burst (e.g., multiple day EMA periods interspersed with days with no assessment) and continuous (e.g., 24-hour/days per week smartwatch accelerometry) measurement designs, allowing the entire study to extend across months or years. However, available data analysis techniques cannot address common substantive questions that arise with multi-burst and continuous ILD designs such as does momentary mood variability increase across a year? Also, do month-to-month increases in momentary mood variability predict declines in sleep duration over a year? To address these gaps, we will develop multilevel models capable of (Aim 1) jointly estimating how within-burst means, variability, and slopes differ between bursts and/or subjects, (Aim 2) testing predictors (either occasion, burst-, person-level) of how within-burst means, variability, and slopes differ between bursts and/or subjects, and (Aim 3) testing whether random effects from Aim 1 predict subject- and burst-level cancer control outcomes. We will test and apply these statistical features by conducting secondary analyses of data from a multi-burst ILD study of cancer control behaviors and outcome, which conducted mobile sensing, EMA, and accelerometry from 246 emerging adults (ages 18-29) across 12 months. We will also develop, test, and disseminate a stand-alone software with GUI capable of running these statistical models to be used by applied behavioral and social science researchers. The methods to be developed can easily generalize to a variety of other disease areas such asthma, disordered eating, suicide prevention, HIV risk, medication adherence, and environmental exposures.

NIH Research Projects · FY 2024 · 2024-09

Project Summary/Abstract Children's community violence exposure (CVE) is a public health crisis, and racial and ethnic disparities are well-documented. As a source of toxic stress, CVE can lead to poor mental and physical health outcomes, disproportionately affects children in racially and economically segregated communities, and perpetuates health disparities. Concentrated neighborhood disadvantage and limited access to youth services (in combination with family and individual factors) influence and exacerbate disparities in CVE11-12. Over the past 7 years we have developed a novel, interdisciplinary, pediatric healthcare model that provides psychological and psychiatric trauma-informed assessment, social work-focused case management, and treatment planning across a short period (3-sessions) within a general medical center. Our target population are pediatric patients and their families who are affected by community violence, whether or not they have been injured. The University of Chicago Medicine REACT (Recovery & Empowerment After Community Trauma) Clinic4 is a trauma-informed intervention focused on healing, rather than risk, to empower minoritized and systematically oppressed CVE urban families. The REACT Clinic is located on Chicago’s South Side in one of the most racially and economically segregated cities in the US.5-7 The REACT Clinic is innovative because it is the only SAMHSA funded clinic in the US that provides Black youth, within the Chicago metropolitan area, with free, accessible, high-quality, anti-racist, structurally and culturally responsive, strengths-based, interdisciplinary care to specifically address effects of community violence and trauma. To date, the impact of the REACT program has not been systematically evaluated. Both pilot data from REACT families and a robust prior literature8-10 suggest that the mechanisms most associated with mental healthcare utilization (MHCU) are mental health stigma, healthcare distrust, and mental health literacy. Thus, the objective of this two-year proposal is to systematically test whether the REACT Clinic model is related to changes in the these three mechanisms after completion of services and at 3-month follow- up. The study proposes a longitudinal, within-person, mixed-method approach to collect and analyze data from a sample of 48 Black youth aged 12 -18 and their primary caregivers who have received REACT service following CVE. The study has three goals: (1) employ a mixed methods design to examine acceptability, feasibility, and adherence to the REACT Clinic model; (2) examine the impact of the REACT Clinic on proximal outcomes (MHS, MHL, HCD) as reported by caregivers (n=48), at baseline, post-intervention, and at 3 month follow-up; and 3) establish initial estimates of MHCU (distal outcome) and explore whether metrics of acceptability and adherence and/or changes in proximal outcomes are associated with MHCU at 3-month follow-up.

NIH Research Projects · FY 2025 · 2024-09

Most adults with Type 1 Diabetes (T1D) do not achieve optimal glycemic control, and thus have increased cardiovascular risk. Obstructive sleep apnea (OSA) is a highly common sleep disorder that is strongly linked to cardiometabolic dysregulation. About 50% of adults with T1D have OSA. Among adults with T1D, those with OSA have poorer glycemic control and have higher risk of diabetes-related complications. Despite strong biological plausibility, it is unknown how OSA impacts glycemic control in T1D. Our overall goal is to investigate the role OSA in glycemic dysregulation in adults with T1D. We hypothesize that OSA exerts negative effects on glucose regulation in T1D, leading to suboptimal glycemic control and thus increasing cardiovascular risk in T1D. We further hypothesize that suboptimal glycemic control in adults with T1D occurs in part through OSA- induced alterations in counterregulatory hormone release and lipid metabolism, subsequently worsening glycemic control. To test these hypotheses, we propose a rigorous sleep intervention study with a within- subject, cross-over design in adults with T1D and OSA (n=40) who are using an insulin pump and continuous glucose monitoring (CGM). Participants will be studied under two 14-day study conditions in randomized order with a 4-week washout: untreated OSA and treated OSA. We will perform the same assessments in each study condition. We will apply all-night CPAP intervention under continuous in-lab supervision to achieve complete resolution of OSA. Thus, we will use CPAP as a tool to eliminate OSA i.e., “turn off” the disease state. Participants will continue their daily routine activities outside the lab. Sleep will be objectively assessed during the study. We will determine to what extent OSA contributes to suboptimal glycemic control in adults with T1D (Aim1). We will compare 14-day CGM profiles (key clinical CGM metrics) between untreated OSA vs. treated OSA conditions. We will also determine how alterations in counterregulatory hormone release and lipid metabolism in the setting of OSA account for suboptimal glycemic control in adults with T1D (Aim 2). We will compare 24-hour blood profiles under controlled in-lab conditions with standardized meals between untreated OSA vs. treated OSA conditions and determine how specific hormonal (e.g., glucagon, catecholamines) and metabolic factors contribute to glycemic dysregulation. We will concurrently assess additional biological and behavioral factors: sleep stages (e.g., slow wave sleep), insulin dose, subjective sleepiness and sleep quality, and diabetes self-management behaviors. We will also measure resting heart rate and 24-hour ambulatory blood pressure, and fasting lipids to determine how OSA impacts these intermediate markers of cardiovascular risk. The proposed work will be the first intervention study to rigorously examine the role of OSA in glycemic dysregulation in adults with T1D. Our study will combine real-life and in-lab assessments to determine specific biological and behavioral factors that may contribute to suboptimal glycemic control in adults with T1D and OSA. The findings may help identify sleep as a novel intervention target to improve T1D management.

NSF Awards · FY 2024 · 2024-09

This collaborative research will study how organizations strengthen governance. It will trace the efforts of organizations that have sought to resolve international crises from the end of World War I to contemporary times. The research will draw on history, international relations, sociology, and political science to study the dynamics of organizations to understand how institutions sustain governance and trust around the world. It will study how domestic politics have shaped nations’ attitudes toward these bodies. The project will also study how non-governmental organizations (NGOs) have tried to use these organizations to achieve their objectives. The results of this research will provide inputs into policies regarding international organizations to make governance work better for future generations. The interdisciplinary project team will pursue two lines of inquiry. Specifically, it will examine campaigns that have sought to create, reform, transform, or abolish international organizations. This will highlight the potentials and shortfalls of organizations. It will also investigate attempts of NGOs to enlist the support of international bodies in response to obstacles they encounter domestically. These findings will draw on the past using historical methodologies to strengthen understanding of how future cooperation can be reimagined and will advance fundamental science in sociology and political science. The team will produce a co-authored monograph and engage stakeholders through papers and workshops. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

ABSTRACT Fentanyl is a widely used opioid in the clinic for relieving chronic pain. Fentanyl abuse leads to mortality due to opioid-induced respiratory depression (OIRD). OIRD is a major health concern in the USA and there is an unmet need for developing therapeutic strategies for mitigating OIRD. Breathing is regulated by brainstem neurons generating respiratory rhythm. Sensory feedback from carotid body (CB) chemoreceptors, which stimulate breathing, is an important regulator of breathing. Current evidence suggests that OIRD is in part due to fentanyl-induced inhibition of brainstem neurons by µ-opioid receptors (MORs). the effects of fentanyl on CB are not known. Preliminary data showed fentanyl stimulates CB sensory nerve (CSN) activity acting on κ- opioid receptors (κ-ORs) and co-administration of fentanyl and a κ-OR agonist prevent OIRD. Based on these observations, we will test the hypotheses: a) fentanyl stimulates CB acting as a partial κ-OR agonist by elevating [Ca2+]I in glomus cells through κ-OR-Gαq signaling and b) full activation of CB with co-application of fentanyl and a κ agonist mitigate OIRD without compromising analgesic effects of fentanyl. These hypotheses are tested in rats and mice using repertoire of approaches. Studies in AIM 1 determine relative abundance of κ-ORs in the rat and murine CBs and assess their role in CB activation by fentanyl. AIM 2 delineate the role of κ-OR-Gαq signaling in [Ca2+]I elevation by fentanyl in glomus cells and assess its significance in CB activation. AIM 3 tests the hypothesis that co-application of fentanyl and κ-OR agonist mitigate OIRD without compromising analgesic efficacy of fentanyl and assess whether CB is the site of action. Major conceptual and technical innovations include that a) Fentanyl activates CB through κ-ORs as opposed to inhibition of brainstem neurons by µOR; b) Mitigating OIRD with κ-OR agonist is a hitherto unexplored concept that may have important therapeutic implications and c) Integrating various standard and state-of-the-art approaches provide information at the systems level and cellular insights. The proposed studies provide much-needed framework for developing novel therapeutic strategies for mitigating OIRD.

NSF Awards · FY 2024 · 2024-09

The world is increasingly facing displacements due to conflicts and climate change. These displacements, especially those caused by violence, profoundly affect children's cognitive and emotional development, with potential long-term societal repercussions. This project examines how displacement impacts the development of social preferences and trust in children. By studying the influence of these displacements on trust, cooperation, and social norms, this research advances social sciences and informs policies promoting prosocial behavior and trust. The findings aim to guide community initiatives that support displaced families, fostering social cohesion and economic integration. Ultimately, the goal is to build more cohesive and resilient communities, thereby enhancing societal welfare, national stability, prosperity, and overall societal benefits. The project studies how displacement affects children's and adolescents’ social preferences, trust, and social norms, particularly focusing on those displaced by intergroup conflicts. Through behavioral economics experiments and a randomized controlled trial (RCT) involving children, adolescents, and their parents, the project will evaluate fairness, trust, trustworthiness, cooperation, and gender-specific social norms. Including adults in the study aims to understand how social preferences and norms are transmitted across generations. This multidisciplinary project integrates expertise in education, behavioral economics, and developmental psychology. The research seeks to provide evidence-based recommendations for policy interventions that enhance prosocial motivations, trust, and cooperation, ultimately supporting governance and societal well-being. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-09

PROJECT SUMMARY Clinical predictors are now firmly incorporated into routine standard-of-care in many fields of medicine, in contrast with Psychiatry where quantitative predictors that guide clinical decision-making remain extremely limited. Psychosis-related disorders are responsible for a substantial public health burden, for which there are significant unmet needs that would be subserved by clinical predictors. For example, long-term outcomes vary widely and identifying individuals with poor or advantageous future outcomes would help to optimize treatment planning and resource allocation. Furthermore, antipsychotics are associated with adverse side effects, such as increased risk of diabetes. In this application, we propose to use machine learning approaches to build predictors and identify subtypes of clinical outcomes among individuals with schizophrenia, through integration of longitudinal electronic health records (EHRs), dimensional phenotyping, and genetic analyses. We will also explore the psychosocial and ethical implications of psychiatric clinical predictors. Our long-term objective is to advance the goals of Precision Psychiatry to achieve individualized treatment planning, outcome monitoring, and preventive interventions. We propose the following specific aims: Aim 1: Leverage two independent EHR databases for outcome prediction and sub-classification of psychosis-related disorders. (a) We will use the longitudinal PSYCKES and MarketScan databases to build machine learning-based individual-level prediction models to forecast the onset of four major prognostic outcomes: treatment response (antipsychotic resistance), illness severity (long-term hospitalization), medical comorbidity (diabetes), and diagnostic transition from a psychosis-related disorder to schizophrenia. (b) We will perform cohort-level analyses using unsupervised methods to discover novel psychosis-related diagnosis and prognosis subtypes. Aim 2: Enhance predictive modeling through dimensional phenotyping and whole genome sequencing. (a) We will recruit n = 10,000 patients with schizophrenia from the PSYCKES database population for enriched data collection: 1) dimensional phenotyping (cognition, exposome, and social determinants of health), and 2) whole genome sequencing to enable calling of rare variants, structural variants, and common variants (polygenic risk). (b) We will investigate the extent to which dimensional phenotypes and genomic data can improve the models developed in Aim 1. Aim 3: Explore the psychosocial and ethical implications of psychiatric clinical predictors. (a) We will survey a subset of patients and their clinicians regarding their attitudes towards implementation of clinical outcome predictors. (b) We will return pathogenic findings to patients through genetic counseling and survey the experience of patients and their clinicians on their emotional reactions and perceptions of impairment, treatability, and life-planning.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT Lynch syndrome (LS), the most common inherited cause of colorectal cancer (CRC) affecting ~1.2 million Amer- icans, carries 3-8x higher lifetime CRC risk than the general population. Colonoscopy starting early in adulthood and repeated yearly or biennially is the only recommended surveillance strategy which translates into ~25-50 lifetime colonoscopies. This intensive colonoscopy surveillance is not ideal because: a) CRC risk varies widely by LS gene and age, b) colonoscopies are invasive, costly, and not readily accessible to all patients, and c) adherence is suboptimal. Personalized surveillance strategies in LS that integrate non-invasive tests with colon- oscopy would address limitations and barriers in current clinical practice but have not been previously studied. To address these gaps, this multi-center, prospective trial will examine performance of fecal immunochemical test (FIT) in LS patients undergoing colonoscopy surveillance. In addition to measuring FIT test characteristics (specificity, sensitivity, negative and positive predictive value), this study will examine LS patients' attitudes and acceptability of non-invasive tests (Aim 1). The proposal will evaluate personalized surveillance strategies in LS that integrate non-invasive tests and colonoscopy using simulation modeling (Aim 2). An important deliverable is the 1st well-annotated, prospective LS biobank in the US for future studies of promising biomarkers and clinical outcomes (Aim 3). This study is timely because of the unique opportunity to harmonize data with the UK “FIT for Lynch” study that began recruitment in 2023 in order to evaluate FIT performance in a large, international LS cohort. Taken together, the proposal is significant because it has potential to change paradigms of CRC surveil- lance in order to improve the lives and longevity of LS patients.