Cornell University

NSF Awards · FY 2025 · 2025-10

Modern dairy farming generates enormous amounts of valuable information about milk production, animal health, and farm operations, but this data remains scattered across individual farms and difficult to share due to privacy concerns and technical barriers. This fragmentation prevents farmers from benefiting from advanced computer models that could help them test different management strategies and improve their operations without disrupting actual farm activities or animal welfare. Currently, farmers, especially those with smaller operations, lack access to sophisticated data analysis tools that could help them make better decisions about their herds. This project addresses this challenge by developing a secure digital platform that allows farmers to collaboratively benefit from advanced artificial intelligence without ever sharing their private farm data. The platform will include user-friendly computer assistants that can understand everyday language and provide personalized, data-driven recommendations to help farmers improve their operations. This work serves the national interest by promoting scientific progress in agriculture, advancing national prosperity and welfare through more efficient and sustainable food production, strengthening agricultural data security to protect critical food systems, and supporting the competitiveness of American agriculture in global markets. This project develops a comprehensive, privacy-preserving digital testbed to optimize decision-making in dairy farming through federated-learning empowered digital twins integrated with fine-tuned large language models and retrieval-augmented generation for both centralized policy-making and localized end-user decision support. The research activities include four key components. First, the team will design new or adopt existing ontologies to support interoperability across heterogeneous data sources, facilitating integration of on-farm data with historical datasets and existing artificial intelligence frameworks. Second, the project will extend existing digital twin models to cover additional phenotypes using multimodal data including genomics, sensor streams, and cow history, adapting them for modern artificial intelligence architectures. Third, the research will leverage an established network of dairy farms across Europe and the United States to implement federated training of digital twins, comparing parallel and sequential federated learning schemes against a reference standard model, with evaluation metrics including training time, convergence rates, and communication overhead. During federated training, advanced aggregation techniques will be evaluated and employed, and anomaly detection will be integrated to enhance system security. Fourth, the project will fine-tune large language models using federated instruction tuning and value alignment to support both centralized and localized applications, using parameter-efficient tuning techniques to reduce computational burden. The fine-tuned models will be integrated into retrieval-augmented generation pipelines, enabling language model agents to retrieve and reason over both local farm databases and centralized scientific repositories. 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 2025 · 2025-10

Touch and hearing convey physical properties about the world that are difficult to perceive from vision alone. The objective of this project is to give machine perception systems the ability to form cross-modal associations between these three sensory modalities, such as the ability to predict how an object will feel or sound from sight. These cross-modal associations can also be obtained directly via sensors, making them well-suited to creating autonomous systems that learn to physically interact with the world without human-provided supervision. The project's integrated education and outreach activities will also advance an understanding of multimodal machine learning for a general audience, and for students at multiple levels. This project aims to learn material properties and microgeometry through cross-modal associations between sight, sound, and touch. It does this through four research thrusts. First, it aims to capture 3D multimodal representations by registering observations from all modalities into a unified 3D model, using estimated visual geometry to obtain dense estimates of touch and sound from sparse observations. Second, it aims to generate space-time reconstructions of objects from touch and sound during physical interaction, using cross-modal visual supervision. Third, it aims to learn material representations that capture acoustic properties, as well as methods that integrate these representations into 3D sound synthesis models. Finally, it aims to simulate and learn physical interactions within captured 3D multimodal scenes. 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 2025 · 2025-10

NON-TECHNICAL SUMMARY Impact-induced erosion from high-speed particle collisions occur at the microscale in critical systems such as jet engine turbine blades, satellites, and chemical pipelines. Ultrafine-grained and nanocrystalline metals, with their superior mechanical properties hold promise for improved erosion resistance, but the underlying mechanisms remain poorly understood. This award is addressing this gap through in-situ studies of supersonic impact-induced erosion in metals across a broad range of grain sizes, from tens of microns down to the nanometer scale. Using in-situ supersonic impact testing, the project examines material response at small scales and extreme strain rates, focusing on understanding the key hardening and softening mechanisms. This research is enabling the design of new materials that can prevent erosive failures, with the potential to strengthen the national defense, automotive, aerospace, and energy industries. This activity is also engaging K–12 students to strengthen the STEM pipeline and enhance national competitiveness. This project supports education and workforce development by introducing new curricula, raising student awareness of emerging opportunities in metals, and connecting industry with the latest research advances. TECHNICAL SUMMARY Deformation localization under supersonic impact is a precursor to erosive failure and is governed by the competition between hardening and softening mechanisms at extreme strain rates. The overall goal of this project is to systematically investigate these mechanisms in ultrafine-grained and nanocrystalline metals, within a strain-rate regime that is largely inaccessible to conventional mechanical testing and as as result, remains significantly underexplored. This research further refines a novel combination of laser-induced microprojectile impact testing and spherical nanoindentation to isolate and quantify dislocation–phonon interactions which are hypothesized to be the primary hardening mechanism under erosive impact conditions. This integrated approach is being used to study how grain size influences ballistic dislocation transport and its role in impact-induced hardening. In parallel, the study is also exploring the microstructural origins of softening, focusing on two key mechanisms, adiabatic shear instability and grain coarsening. Moreover, this project is examining how these are affected by grain size. High-resolution cross-sectional microscopy is being used to characterize microstructural evolution while providing mechanistic insight. Together, this investigation is revealing how the interplay between hardening and softening mechanisms govern a material’s resistance to erosive failure and offer design guidelines to help prevent such failures. This project also supports education and workforce development by introducing new curricula, raising student awareness of emerging opportunities in metals, and connecting industry with the latest research advances. 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 2025 · 2025-10

Quantum systems are gaining increasing interest in solving computationally challenging problems, enabling high-performance computing, advanced sensing, and secure communication. However, building a scalable quantum system with high fidelity faces a major challenge. Ultimately, millions of qubits are required to achieve practical quantum computing, and a significant challenge lies in their control electronics and interconnectivity. Current cryogenically cooled quantum computers are controlled from room temperature using coaxial cables, which poses a significant bottleneck for long-term scalability due to the limited cooling power of a typical refrigerator. This project aims to demonstrate an ultra-low-power cryogenic complementary metal-oxide-semiconductor (cryoCMOS) integrated architecture for controlling a novel superconducting qubit. This approach will enable scalable, low-power, low-latency control of superconducting qubits with high fidelity. In addition to the research, the education component of this interdisciplinary project addresses the increasing need of workforce development in several critical areas: integrated circuits, quantum engineering, and semiconductor manufacturing. The education plan includes integrating advanced research materials into courses, providing research opportunities for undergraduate and graduate students, advising student project teams, and conducting outreach efforts. In this project, the cryoCMOS architecture will be designed and developed to reduce the power consumption of qubit controllers without sacrificing its functionality. The proposed qubit controller replaces the conventional power-hungry digital-to-analog converter, which generates pulse shapes, with a custom-designed energy-efficient crossbar array. With this novel approach, the cryoCMOS chip can efficiently generate modulated signals required for high-fidelity quantum logical operations. In addition, the team will design a superconducting qubit architecture that reduces measurement errors, which are currently the dominant source of errors in qubit processing. To achieve higher readout and quantum non-demolition fidelity, the team will develop readout resonators with a large detuning from the transmon qubit while maintaining compatibility with standard microwave cabling. The project aims to first demonstrate single-qubit and two-qubit operations with state control and readout pulses generated with the cryoCMOS chip. With successful demonstration, the team will expand its collaborative efforts toward scalable quantum processors. 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 2025 · 2025-10

Software bugs can have disastrous consequences, ranging from financial costs to loss of human life. As a result, for high-stakes systems, software vendors are increasingly applying techniques that can prove the absence of various kinds of bugs. However, existing techniques have limitations that make them inapplicable for certain types of programs that make use of randomness, which is common in sensitive software domains such as cryptography and machine learning. This project will develop new techniques for reasoning about randomness in programs, which will make it possible to prove important properties about these programs, thereby improving software quality in these critical areas. In addition, the team of researchers will develop educational materials to make the project's ideas more broadly accessible to students, researchers, and industrial practitioners. This project targets programs that exhibit two important kinds of effectful features: concurrency and randomization. Existing formal verification techniques cannot handle the complexity and expressivity of many programming language features, and these features make it harder to write, test, and reason about programs. Establishing correctness in the presence of just one of these features is hard enough, and it only becomes more difficult when they are combined. This project will develop program logics and reasoning tools that can enable more precise, compositional analysis of concurrent randomized programs by building on a new semantic model of randomness and concurrency. The investigators will formally verify the soundness of the logic and build a framework for using it inside of an interactive theorem prover. This formalized framework will facilitate further breakthroughs in verification of concurrent randomized programs in different domains. 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 2025 · 2025-10

In every false conviction case, there came a point in the investigation when an innocent person became a suspect. Despite the countless accounts of how this presumed guilt can start a chain reaction of confirmation bias that taints all stages of the criminal justice process, little is known about what triggers suspicion about the innocent person in the first place. Drawing from existing and to-be-created databases, the overarching goal of this project is to identify and understand what demographic, behavioral, and linguistic factors initially spark suspicion and trigger dangerous confirmation bias processes leading to false convictions. We focus on one of the earliest moments in which a person might become a suspect: when they call 911. A key hypothesis is that when witnesses’ behaviors violate expectations, people “morally typecast” the witness as less capable of being a victim and more capable of being a perpetrator. Increasing basic understanding on how these expectations are formed, how they are violated, and how they interact with witnesses’ gender and race, is a critical step towards predicting whether callers are ultimately charged with the crime they are reporting. Moreover, understanding how these processes emerge will be critical in developing state-of-the-art curriculum to educate police about their expectations’ accuracy and consequences, and to educate attorneys on defending clients they believe to be innocent. Given the serious consequences that false convictions have on both innocent individuals and the public trust in the criminal justice system at large, the project’s focus has broad societal impact by systematically investigating the source of detectives’ misguided “hunches” that have anecdotally led to false convictions. This study employs a variety of data-driven methods to identify predictors of suspicion and being charged with the crime one is reporting. First, we will create a large corpus of real 911 calls to analyze linguistic and acoustic behavioral aspects of reporting a violent crime to see if laypeople and law enforcements’ expectations are accurate. We will recruit lay people, police officers, 911 operators, and trauma clinicians to listen to these calls and report their impressions of the caller to identify what aspects of reporting a violent crime generates suspicion and predicts actually becoming a suspect in the crime. We will identify psychological explanations for these effects as well as factors that might moderate these effects, such the caller’s race and gender. Across studies, we will use indirect, data-driven ways of assessing what makes people suspicious without imposing the researchers’ hypotheses on the design, such as using natural language processing machine-learning models. We will also test downstream consequences of witnesses’ emotion expression in a 911 call for the likelihood of being falsely convicted at trial for the crime they reported. Finally, we will quantitatively code cases of known innocence (exoneree case files) for what sparked suspicion about the innocent person, focusing on mentions of their behavior seeming “unusual”. 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 2025 · 2025-10

Fruit and vegetable crops are essential to a healthy diet, supplying nutrients often missing from staple grains like corn and wheat. Their high market value also offers important economic opportunities for farmers. Developing new varieties with improved disease resistance, yield, and quality benefits both producers and consumers. However, characteristics that set these crops apart, such as their perishability, also make them harder to breed. A major challenge is collecting reliable data on yield and quality, a process that is often slow, costly, and labor-intensive. This project addresses that challenge by creating faster, more precise ways to evaluate and select for these traits in three of the world’s most important horticultural crops: tomato, onion, and strawberry. It brings together researchers from the U.S., India, Japan, and Australia to apply advanced tools in imaging, machine learning, and genomics to support the development of productive, high-quality varieties that meet the needs of both growers and consumers. This project develops methods for the high-throughput, non-destructive evaluation of yield and quality using both RGB and spectral imaging. To accomplish this, data are collected from both handheld and autonomous devices and fed into deep learning-based image segmentation models to measure traits such as fruit count, size, and shape in tomato and strawberry as well as bulb shape in onion. In addition, the project investigates the ability of models incorporating high-dimensional biological data, including hyperspectral, genomic, and transcriptomic features, to predict complex traits in these crops. The research team also combines 3D modeling and gene expression data to understand and forecast growth in strawberry plants. In parallel, the project fosters international collaboration and capacity-building through research exchanges, workshops, and training opportunities focused on the use of modern phenotyping and predictive tools in plant breeding. 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 2025 · 2025-10

Observing and modeling the merger of neutron stars with each other and with black holes are challenging tasks that push understanding of the universe as embodied by Einstein’s equations for general relativity. Observatories such as National Science Foundation-funded Laser Interferometer Gravitational-Wave Observatory (LIGO) need the results of computational models to help understand the signals they receive. Computational modeling of neutron star mergers in turn is extremely demanding and requires the use of the most advanced supercomputers. Even on these unique computing resources operated by the US Department of Energy and the National Science Foundation, computer models may still require weeks to run. Improvements to gravitational wave modeling software that take advantage of improved algorithms have the potential to reduce this execution time down to hours. This project improves the open-source gravitational wave-modeling software SpECTRE to use new algorithms and to make optimal use of one-of-a-kind supercomputing resources. The results from these computations are needed for scientists to understand black holes, the expansion of the universe, and how stars explode and leave behind black holes. The transformative techniques used by SpECTRE have the potential to also be applied to research areas in fluid dynamics, geoscience, plasma physics, and nuclear physics and engineering. The project is training the next generation of computational astrophysicists on the use and extension of SpECTRE through summer schools. The investigating team engages the public through visualizations and movies posted on social media and through public outreach events. The new SpECTRE code uses a hybrid finite difference-discontinuous Galerkin method, task-based parallelism, and the U.S. cyberinfrastructure Graphical Processing Unit (GPU) abstraction library Kokkos to accomplish its goals. This framework will allow multiphysics applications to be treated both accurately and efficiently on the new architectures of petascale and exascale machines. The code is designed to scale to over a million cores for efficient exploration of the parameter space of potential sources and allowed physics, and for the high-fidelity predictions needed to realize the promise of multi-messenger astronomy. The software design separates parallelism and physics capabilities in a way that makes adopting new computing paradigms and libraries possible without rewriting the physics modules. The code will allow astrophysicists to understand electromagnetic transients and gravitational-wave phenomena in compact objects, to reveal the dense matter equation of state, and to perform binary black hole simulations at the accuracies necessary for next-generation detectors. The key algorithmic innovations in the code, the hybrid finite difference-discontinuous Galerkin method coupled with task-based parallelism and GPU offloading, promise revolutionary impact in other fields relying on numerical solution of partial differential equations at the exascale. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Physics and the Division of Astronomy in the Mathematical and Physical Sciences Directorate. 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 2025 · 2025-10

This project aims to serve the national interest by enhancing chemical and biomolecular engineering education in the Robert Frederick Smith School of Chemical and Biomolecular Engineering at Cornell University and beyond, through the re-envisioning of how engineering education can integrate a living laboratory ecosystem and prepare graduates for a rapidly changing workforce. A living laboratory ecosystem is a culture that values and rewards students, faculty, and staff for asking new questions, testing innovative approaches, embracing failure as a learning opportunity, and continuously enhancing our knowledge and methods to tackle local, national, and global challenges. While this iterative process has been a goal of engineering education, its implementation throughout an entire curriculum has not been realized. This Track 3 Revolutionizing Engineering Departments Innovation project seeks to create this living laboratory ecosystem to address four challenges currently faced in chemical and biomolecular engineering education: 1) a rigid and packed curriculum; 2) a sequence of courses students struggle to make connections across; 3) a culture designed to reward success but that does not accommodate failure as a part of learning or being human; and 4) a community of individual excellence that lacks a shared agreement on common and discipline-specific improvement using informed risk-taking in the classroom and pedagogical innovation broadly. The project has two goals: 1) deconstruct our traditional approach to teaching and learning by revolutionizing content, assessment, and pedagogy to become a living laboratory ecosystem that is responsive and dynamic to current events, individual learning trajectories, and student and faculty needs and 2) reshape the culture of the School to value a living laboratory ecosystem as a way of being and doing that extends beyond the classroom. Communities of transformation, comprised of faculty, staff, and students, will be organized to design and prototype structural innovations both inside and outside the classroom, complementing School-wide professional development opportunities during School meetings. This project will not only transform this specific School but also provide an evidence-based model for implementing this education effort more broadly. In addition to transforming the School's culture and approach to educating engineers, this project will also generate new knowledge about how students develop and evolve the evidence-based practices for creating more responsive and dynamic education systems that can adapt to changing technologies, areas of national priority interests, and individual student needs. Our research questions are grouped into questions that 1) articulate the revolution's impact on students and the student experience as well as the educational innovations developed, and 2) document the transformation of School culture, student, faculty, and staff development, and how change occurs. In answering these questions, the project will articulate how a living laboratory ecosystem revolutionizes student development and learning, particularly during the middle two years of education, and how departmental culture can be transformed to embrace this approach. The results of this work will provide transferable models of change in understanding what levers are most successful for supporting community engagement in change, what structural changes work for whom and why, and how cultural transformation can be supported as individuals move in, through, and out of significant transitions in their ways of being, doing, and learning. We will employ a multi-methods approach over five years to gather evidence from surveys, interviews, focus groups, observations, and education and policy artifacts on how the transformation influences the School's culture and the members who create and reinforce it - students, faculty, and staff. The results of this project will be disseminated to chemical engineering departments and industry, as well as to engineering educators, engineering education scholars, and administrators, through archival scholarly publications, workshops, and webinars. The IUSE/Professional Formation of Engineers: Revolutionizing Engineering Departments Program supports research and development projects to improve the effectiveness of STEM education for all students. 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 · 2025-09

PROJECT SUMMARY An immunomodulatory and vascularized cell delivery device for type 1 diabetes cell therapy The overall goal of this proposal is to develop a cell-retaining, subcutaneous device that allows blood vessel penetration and establishment of an immunotolerant environment for islet delivery for type 1 diabetes cell replacement therapy. Transplantation of human cadaveric islets and more recently human stem cell-derived insulin-producing cells has shown great promise as a curative therapy for T1D patients. However, delivering the cells safely and maintaining their long-term function without immunosuppression remains a daunting challenge. In principle, encapsulating the cells in a semi-permeable biomaterial or device can protect the cells from the immune system but the approach introduces a physical barrier and prevents direct vascularization, diminishing the mass transfer and cellular function over time. On the other hand, the “open” approach aims to support cell engraftment by establishing an immunotolerant local environment and allowing host integration and vascularization. We recently developed a cell-mediated “open” approach where we co-transplanted genetically engineered “nurse” cells (engineered mesenchymal stromal cells over-expressing PD-L1/CTLA-4Ig, or eMSCs) together with islets to induce local immunotolerance for immunosuppression-free allogeneic islet engraftment in mouse kidney capsules. However, a caveat in our approach that limits its long-term efficacy is that the eMSCs, similar to numerous MSC products that are currently evaluated in clinical trials, are migratory and they do not stay co-localized with islets. To overcome this cell retention problem, we engineered an electrospun nonwoven cell delivery device that retained the MSC cells while allowing capillary penetration in subcutaneous space of mice. The objective of this project is to test the hypothesis that improving the retention of eMSCs and their co- localization with islets in this cell-retaining, vascularized “open” device will enhance the immunomodulatory effects and prolong the therapeutic efficacy of allogeneic islet transplantation in a clinically attractive subcutaneous site. Successful completion of this project will result in the development of an immunomodulatory device that can be subcutaneously implanted, enabling both capillary penetration and establishment of an immunotolerant environment for islet engraftment without requiring chronic systemic immunosuppression.

NIH Research Projects · FY 2025 · 2025-09

The objective of the proposed project is to develop a data extraction, analysis, and reporting pipeline for sustainable companion animal antimicrobial use (AMU) surveillance. Previous surveillance efforts have been restricted to snapshots of AMU in specific veterinary practices or at limited timepoints because AMU data is time- consuming and difficult to collect. In addition, there have been substantial time lags between AMU data collection and publication because each companion animal AMU surveillance study has conducted a bespoke analysis of AMU data that requires a high-level of statistical, data visualization, and software skills. Recent advances in natural language processing (NLP) and machine learning, particularly large-language models (LLMs), have made efficient AMU data collection possible. However, even with new models, challenges remain to create a scalable system for long-term data collection. The proposed project will overcome these challenges by creating an LLM to extract AMU data that is agnostic to the veterinary electronic health record (EHR) system used and create a dashboard to streamline AMU analysis. The first aim is to make an LLM that is scalable and generalizable across EHRs by developing it on free text from multiple EHRs and validating it against existing gold-standard AMU datasets collected from manual EHR review. The LLM will be available to participating practices on a secure platform to ensure data privacy and security. Practices can process their own EHR data and share only de-identified record-level AMU data, including antimicrobial drug(s) used, dosage, duration, patient weight, and indication for AMU, plus the number of animal visits. The second aim creates an RShiny dashboard, the AMU Data Visualizer, to streamline AMU data analysis and generate standardized AMU reports. The dashboard will take the anonymous information generated by the LLM, or by other AMU data extraction methods, and generate AMU metrics, such as prevalence and number of defined daily doses, with customizable filters to understand AMU across all companion animal sectors (e.g., primary care vs. emergency care, young animals vs. geriatric animals). The AMU Data Visualizer will allow anyone, regardless of their statistical software skills, to create AMU reports, aggregated tables, and figures for their practice. The third aim is to collect anonymous, aggregated AMU reports from participating veterinary practices to identify national trends in companion animal AMU. Aggregated AMU tables from the AMU Data Visualizer will be combined into annual, public AMU reports with benchmarks for different practice types and trend analyses. The proposed project is conceptually innovative because it develops an EHR-agnostic AMU data collection method and a reproducible analysis pipeline, and does not require practices to share EHR free text that could contain sensitive or identifiable information. The project is technically innovative because it advances LLM data extraction by having the LLM “show its work” for easy data verification. Overall, the LLM, AMU Data Visualizer, and aggregated reports will reveal the full picture of companion animal AMU.

NIH Research Projects · FY 2025 · 2025-09

Human milk provides optimal nutrition for infants; however, only 25% of U.S. mothers achieve the breastfeeding targets recommended by the Centers for Disease Control, which emphasize six months of exclusive breastfeeding. Suboptimal breastfeeding practices have profound public health consequences, costing the U.S. an estimated $13 billion annually and resulting in the preventable deaths of over 900 infants. These outcomes are often attributed to early breastfeeding cessation or the inability to sustain exclusive breastfeeding, with low milk production being the most frequently cited reason. Additional challenges include concerns about milk composition and quality. Insufficient milk production negatively impacts infant growth, maternal mental health, and maternal experiences overall, yet the physiological mechanisms underlying these issues remain poorly understood, and treatment options are very limited. The proposed research seeks to address these critical issues by employing a randomized clinical trial design alongside molecular insights into mammary gland biology. This study will include three intervention arms: (1) instructions to pump after breastfeeding session 6-8 times per day, (2) instructions to pump following breastfeeding sessions only 3-4 times per day, and (3) no specific instructions to add pumping sessions. The trial will span 14 days, during which maternal milk production, mammary gland transcriptomic changes, and milk composition alterations will be tracked and analyzed. Participants will provide milk and blood samples, detailed records of expressed milk volume, and daily questionnaires tracking breastfeeding and pumping schedules. This research combines a robust randomized trial design with innovative molecular analyses, such as RNA-seq of milk-derived transcripts profiling, to elucidate changes in mammary gland function and secretory processes resulting from these clinical interventions. This interdisciplinary strategy directly aligns with the mission of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) to advance knowledge of maternal and infant health. By integrating clinical and molecular approaches, this project seeks to generate evidence-based strategies to improve breastfeeding outcomes, providing crucial insights into both the physiological and clinical factors underlying low milk production and lactation challenges.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Cryogenic-sample electron microscopy (cryo-EM) presents a unique opportunity to understand protein conformational change. In cryo-EM experiments, a solution of biomolecules is snap-frozen, trapping them in conformations close to the ones they adopt in solution. Modulo the effects of freezing on the conformational ensemble, we should be able to quantitatively recover the probabilities of protein conformational states from cryo-EM. In prior work, we developed a framework for addressing this challenge. For a hypothesized conformational ensemble, we simulate a cryo-EM experiment and see how well the resulting images match the experimental data. The better the match, the more likely the conformational ensemble is to be correct. Preliminary results suggest that this approach can recover accurate conformational probabilities from cryo- EM data, even in high noise regimes when it is impossible to accurately classify individual images. We plan to build on these promising preliminary results by developing new algorithms that will aid us in recovering conformational probabilities accurately and at scale. To scale our formalism to larger datasets and more conformations, we first aim to accelerate the comparison of simulated and experimental cryo-EM images using a combination of hierarchical clustering and heuristic search. By leveraging these twin strategies, we can efficiently match experimental to simulated images, minimizing the computational power required by our formalism. We also seek to improve the way we represent our conformational ensemble. Diffusion models, a family of generative neural networks, have seen considerable practical use for protein design. By training our diffusion model to match experimental data, we gain a flexible, fast, and accurate way to encode the ensemble of protein conformations. Moreover, our work will open the door to foundation models for protein conformational ensembles: generic models that can be used to predict conformational changes for all proteins. In parallel with our algorithmic work, we will attempt to push the limits of heterogeneity analysis for cryo-EM by attempting to recover the conformational ensemble of intrinsically disordered regions (IDRs). Historically, this has been seen as an impossible task due to the prohibitive difficulty of determining what IDR conformation a given cryo-EM image represents. But our formalism doesn’t require solving this problem: it is enough to recover the statistical properties of the image set. Consequently, we believe our method can succeed where traditional approaches have failed. Ultimately, the proposed work will transform cryo-EM into a tool that can efficiently recover protein conformational ensembles with quantitative probability estimates. This will shed new light into how proteins move to accomplish their biological tasks, helping scientists understand protein function, facilitate the design of new proteins, and discover new druggable mechanisms.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY / ABSTRACT 3.9 billion people are estimated to be at risk of dengue and other viruses transmitted by Aedes aegypti mosquitoes. Without effective therapeutics, disease management relies on mosquito control. Optimizing the deployment and efficacy of mosquito control requires understanding key drivers of population structure and dispersal. Population structure is known to affect the spread of both beneficial (e.g., gene drives) and detrimental genes (e.g., insecticide resistance alleles), and to affect the probability of reintroduction or resurgence following local elimination. Because Ae. aegypti is a weak flyer, dispersal is comprised of natural movement across very fine spatial scales (~100-200m) punctuated by long-distance dispersal events due to human movement. However, current approaches used to characterize mosquito population structure and movement are limited. Genomic approaches (e.g., mtDNA) can estimate connectivity between populations on evolutionary timescales and across large spatial scales. In contrast, while close-kin mark recapture methods (CKMR) can characterize movement on fine-spatial scales, this method cannot logistically be scaled up to even intermediate spatial scales (e.g. city districts). In this application, we propose a novel, highly innovative approach that will yield a unified framework to estimate mosquito population structure and dispersal rate across varying spatial scales to inform control. Recent studies have shown that Ae. aegypti is host to more than 27 unique viruses. We aim to leverage genetic variation in insect specific viruses to identify the relevant drivers of Ae. aegypti dispersal and population structure at regional (AIM 1) and local scales (AIM 2). We will estimate population structure and dispersal at each scale using genetic diversity within the virome or within individual viruses and will compare our results with current gold standard techniques that estimate mosquito connectivity from variation in the mosquito genome. This work will be done on St. Kitts where Ae. aegypti is abundant island-wide and has driven substantial arbovirus outbreaks. To determine Ae. aegypti population structure at regional scales (AIM 1), we will sample Ae. aegypti at 25 sites across five different landcovers monthly in two consecutive months each season, using metrics common to landscape genomics to quantify population structure based on variation within the Ae. aegypti genome and virome. In addition, we will develop phylogeographic models of four target viruses that are commonly found in Ae. aegypti. We will then compare our results to predictions from theoretical models of dispersal on the island. To estimate dispersal on local to intermediate scales (AIM 2), we will compare CKMR methods to our approach that estimates dispersal using phylogeographic models based on target viruses in mosquitoes sampled from a transect across an urban center, Basseterre. We predict Ae. aegypti dispersal will be dominated by human-mediated dispersal at the regional scale, with natural dispersal occurring between nearby, environmentally similar sites. We believe that the use of phylogeographic models of target viruses will provide a single method capable of estimating dispersal and population structure across multiple spatial scales.

NIH Research Projects · FY 2025 · 2025-09

Research Summary Despite numerous large-scale metagenomic case-control studies that associate microbial taxa and genes with specific diseases, surprisingly little is known about the molecular mechanisms underlying these associations. Much of the mechanistic microbiome research to date cites the production of small molecules, immune interactions and indirect interactions with food or drugs. However, direct protein-protein interactions (PPIs) have emerged as a novel facet of host-microbiome interaction underlying microbiome-associated disorders. Building from an initial analysis of host-microbiome PPIs that leveraged existing PPI databases, we have now expanded this approach to predict novel host-microbiome PPIs. We have applied protein language models to develop a novel commensal host-microbiome interaction prediction (CHIP) tool. Our final model is capable of screening through the 1011 possible host-microbiome PPIs with high precision, albeit low sensitivity. This initial step cuts down on the search space, enabling refinement steps on a much smaller subset, using structure prediction models, such as AlphaFold-multimer. This proposal aims to further develop our pipeline and validate it computationally and experimentally. Our overall goal is to accelerate data-driven and experimental discovery of host-microbiome PPIs. Our first aim is to further improve our CHIP model and analyze the host-microbiome interaction network. Our second aim is to utilize structure predictions, specifically focusing on interfaces, to infer functions. As shared interfaces between PPIs or protein-molecule interfaces suggests a common underlying function, we will examine whether microbiome proteins bind human proteins at important sites vis à vis the interactor's human protein binding partners. Our third aim is to apply proximity labeling to identify novel host receptor-bacterial protein ligand interactions, addressing a major gap in the existing data on host-microbe PPIs. Our fourth aim is to validate subsets of the host-microbiome interaction network that represent important hubs involving proteins situated within disease-relevant pathways. These include investigating the role of host-microbiome PPIs in intestinal barrier integrity, ubiquitination pathways, and inflammation signaling pathways. Overall, we aim to vastly expand our annotations of microbiome-derived proteins in human disease-relevant pathways. We anticipate these findings will lead to the identification of novel therapeutics, diagnostics and drug targets.

NSF Awards · FY 2025 · 2025-09

With support from the Macromolecular, Supramolecular, and Nanochemistry Program in the Division of Chemistry, Professors Tristan Lambert and Brett Fors of Cornell University will develop a metal-free variant of a powerful polymerization reaction known as ring-opening metathesis polymerization (ROMP). Materials produced by ROMP are critically important for a range of applications; however, current ROMP methods rely exclusively on metal-based catalysts that are often toxic, costly, and limited in availability. As a result, ROMP materials typically contain residual metal impurities, requiring difficult and labor-intensive purification procedures—especially problematic for applications such as medical implants, drug delivery systems, and microelectronics, where metal contamination is unacceptable. The new ROMP methodology developed by Lambert and Fors will employ an organic catalyst composed solely of carbon, nitrogen, and hydrogen, making it inexpensive, non-toxic, and sustainable. The broader impact of this work includes the creation of a metal-free platform for preparing ROMP polymers, thereby enabling their use in sensitive and high-value applications, and training graduate students in the areas of polymer chemistry and catalyst development. More technically, this program aims to develop and apply 1,2-dialkylhydrazines as organocatalysts for the ROMP of a wide range of cyclic olefins. A central goal is to elucidate the structural features of these hydrazine catalysts that govern their reactivity and selectivity, and to use these insights to design more efficient and broadly applicable catalysts. The project will integrate empirical and computational approaches to achieve a detailed fundamental mechanistic understanding of key steps in the catalytic cycle, including cycloaddition, cycloreversion, and chain transfer. Catalysts with enhanced reactivity and control will be exploited to expand the scope of ROMP reactions to a more diverse array of monomers, thereby opening new avenues for the synthesis of functional polymeric materials without metal contamination. 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 · 2025-09

PROJECT SUMMARY / ABSTRACT T lymphocytes are adaptive immune cells that protect the host against a diverse array of pathogens. In the last decade, immunologists have started to learn how to harness the function of T cells for immune benefit, which has revolutionized the field of immunology and changed the way we treat a variety of immune-mediated diseases, including cancer. However, recent discoveries in the fields of immunology and hematopoiesis suggest that our current model of T cell development is out-of-date, which may be contributing to inefficient treatment approaches to immune-mediated diseases. Up until now, the dogma in the field has been that all T cells are generated from bone marrow-derived hematopoietic stem cells (HSCs), and that the T cell compartment is comprised of a single lineage of T cells that are created equally. However, stem cell biologists have recently discovered that there are a wide variety of non-HSC progenitors at different anatomical sites in early life that can produce T cells. In separate studies, immunologists have learned that T cells made in early life persist into adulthood and exhibit unique roles during infection and disease. The stem cell biologists have yet to characterize the T cells made by non-HSC progenitors, and the immunologists have yet to map the precise hematopoietic origins of T cells found in adulthood. Essentially, the fields of hematopoiesis and immunology are not in sync, preventing us from updating the current model of T cell development. Our goal is thus to sync the fields, dissecting out the unique lineages of T cells made from different non-HSC and HSC progenitors in early life to determine how the function of T cells in adults is linked to their developmental origins. In Aim 1, we will establish what types of T cells can be made from various embryonic progenitors. In Aim 2, we will focus on a particular type of T cells (CD8+ T cells) and examine how their immune function and programming correspond to their derivation from non-HSC and HSC progenitors in early life. To accomplish these goals, we will employ embryo dissections, artificial thymic organoids, cell transplantations, and single cell sequencing, ultimately propelling us toward a new paradigm for T cell development. Once this paradigm is established, we expect to (i) uncover lineages of T cells ideally suited for immunotherapies, (ii) predict how patients will respond to current treatment strategies, and (iii) learn how to fully regenerate the T cell compartment after immune reconstitution therapies.

NSF Awards · FY 2025 · 2025-09

With support from the Chemical Mechanism, Function, and Properties (CMFP) program in the Division of Chemistry, Professors Andrew Musser and Phillip Milner of Cornell University are using ultrafast laser techniques to study how molecular coherence can be controlled. Coherence can arise when the electronic or vibrational states of two or more molecules synchronize, and it plays in central role in how light interacts with matter, from the physics of vision to photosynthesis to new systems for generating electricity from light. Proper control of such coherences could lead to devices that more efficiently harvest and transport energy, or to advanced applications of quantum mechanics in sensing and information science. And yet, despite its ubiquity, very little is known about how to tune such coherences or identify their unique contribution to photochemical processes. In this project, the teams of Musser and Milner will tackle this question in the context of singlet fission, a process in which one photoexcited state splits in two, with major implications for technologies such as quantum information science and light harvesting devices. Professor Milner and his students will prepare systematic libraries of crystalline sponge-like materials known as metal-organic frameworks – effectively molecular tinker toys whose modularity lets the team dial in specific interactions between photoactive molecules. Professor Musser and his students will use cutting-edge ultrafast laser-based measurements to watch the molecules in these frameworks share energy and move in real time and identify conditions where they achieve coherent synchronization. These studies will lead to new design principles to control and steer coherent singlet fission dynamics for next-generation devices. The team will additionally train multiple graduate and undergraduate students in interdisciplinary research spanning organic chemistry to spectroscopy, and they will develop middle-school outreach programs on how light interacts with matter. Recent advances in spectroscopic techniques have made it ever easier to identify the presence of coherence in a host of processes driven by light-matter interactions, and it is associated with ultrafast, high-efficiency transfer and conversion dynamics. But it has proved more challenging to identify the unique impact of coherence itself: the general conditions that enable superpositions of quantum states (e.g. energetic resonance) also favor efficient incoherent processes. By studying coherent dynamics in well-defined solids where the interactions are tuned with molecular-level precision, the team aims to establish structural guidelines to harness and tune intermolecular coherence. The team will probe this behavior in the context of singlet fission, in which an excited singlet separates into two low-energy triplets. The initial ultrafast conversion into an intermediate triplet-pair state is often described in terms of vibronic coherence, while the product triplets exhibit persistent spin coherence. Both types of coherence present potential control knobs in terms of vibrational environment, orbital overlap, and intermolecular transport pathways. By incorporating fission-active molecules (such as monomeric or dimeric pentacenes, rylene diimides) into modular crystalline frameworks with varied structure, the team will systematically tune these parameters. The project will use ultrafast electronic spectroscopy to track coherent vibronic wavepackets through the initial fission process and monitor variation in long-time spin coherence through magnetic-field effects. Comparing these measurements across the structural library, the team will develop empirical guidelines to enhance intermolecular coherence. The insights generated will enable the design of more effective singlet fission materials for optoelectronic devices. 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 2025 · 2025-09

Enormous black holes a billion times more massive than the Sun orbit and merge with each other in the hearts of distant galaxies. These mergers produce gravitational waves, ripples in the fabric of space-time itself, with periods of years. Recently, the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) collaboration confirmed the existence of these ripples. The collaboration has been observing cosmic clocks, called "millisecond pulsars", for 15 years. Gravitational waves stretch and squeeze space-time, making these clocks appear to speed up and slow down. The observation that these clocks vary in concert, not independently, reveals the existence of these gravitational waves. Making these observations requires radio telescopes of enormous sensitivity. The NANOGrav collaboration has been partnering with the DSA-2000 project to build a telescope that can continue these observations. Graduate and undergraduate students will receivehands-on training on the development of hardware and algorithms. The results will be presented widely to the scientific community as well as the broader public. This award contributes to the goals of NSF's "Windows on the Universe: The Era of Multi-Messenger Astrophysics" meta-program by including the development of metrics to evaluate millisecond pulsars for their timing suitability, and the selection of an expanded sample for timing observations. It will support the development and deployment of pulsar timing instrumentation and pipelines, and its commissioning on prototype hardware and the DSA-2000 telescope as construction proceeds. As a result, the infrastructure will be in place to accurately characterize the low-frequency gravitational wave background, and thus to characterize the astrophysics of supermassive black holes, as well as to potentially identify individual black hole binary systems. 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 2025 · 2025-09

The world is becoming a hybrid society, where people interact with each other and with artificial intelligence (AI) agents. These human-AI relationships are hard to study. To enhance such investigations, the researchers build new online tools that let thousands of humans and AI “bots” meet and interact. These new tools are integrated into existing platforms for the behavioral sciences (Dallinger and PsyNet). This project helps scientists run large, realistic experiments to see exactly how humans and AI influence each other and learn how to make teamwork with AI more productive for everyone. The goal of this project is to make it easier for scientists across many fields to study how AI and humans can work together through three related aims. First, scientists build new tools to run social network experiments that combine people and AI agents. Second, there are three case studies showing different ways humans and AI can interact in group settings. Third, training and educational materials help researchers use the platform. This research helps understand hybrid societies and helps design better businesses and industries where people and AI collaborate effectively. 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 2025 · 2025-09

Agriculture is under increasing pressure from shifting weather patterns, more frequent flooding, and temperature extremes that threaten traditional crops and rural livelihoods. This project explores a promising alternative: introducing rice farming to the region. Rice is naturally suited to wetter conditions and offers a way for farmers to diversify their operations while strengthening their resilience. By establishing two regional rice hubs and a research and development site, the project supports a sustainable and community-centered approach to growing rice in temperate areas. Through direct collaboration with farmers, agricultural advisors, and local organizations, the project helps lower adoption barriers and promotes practices that improve soil and water quality, support rural economies, and provide new pathways for agricultural innovation. The research combines agronomy, environmental science, engineering, and community development to evaluate the feasibility and broader impacts of rice farming in NYS. It includes field trials to test soil and water management techniques that reduce greenhouse gas emissions, lower arsenic uptake, and enhance nutrient use efficiency. A quasi-experimental study will assess farmer participation and decision-making, while cost-benefit analyses will compare irrigation strategies for environmental and economic outcomes. By working with Cornell Cooperative Extension and other partners, the project will build knowledge-sharing networks, deliver training and decision-support tools, and engage with policymakers to explore institutional support for rice as a strategic crop. The outcomes will offer a science-based and community-informed model for agricultural diversification in regions facing increasing environmental variability. 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 2025 · 2025-09

Extreme precipitation events impact infrastructure, including dams, culverts, nuclear power plants, and highways, that must be built and maintained to withstand these events over their lifetime. This project will address three fundamental questions aimed at advancing understanding of extreme precipitation: 1) how does the precipitation distribution depend on spatial and temporal scales? 2) what are the comparative roles of aerosol and greenhouse gas forcing in determining the precipitation distribution? and 3) how do stratospheric aerosol injections impact extreme precipitation changes? These questions are specifically motivated by the need to better understand the value of high-resolution modeling for understanding precipitation extremes, to understand if aerosol forcing can explain the discrepancy between observed and modeled changes to extreme precipitation events over the past few decades, and to understand why extreme precipitation decreases despite holding temperatures fixed in model projections of stratospheric aerosol injection. The scientific goals of this project are in line with the principal investigator’s goal to build a career around understanding precipitation extremes as they unfold over the next decades. An educational component is also integrated into this project providing research experiences for undergraduates related to the science objectives; training students on the use of data for real world problems co-identified in workshops with highway engineers; and integrating the science results into graduate and undergraduate level courses. The principal investigator has previously developed a novel approach to the analysis of the precipitation distribution by defining shift and intensity modes of the distribution. The shift mode is defined as a uniform shift in the amount distribution to higher or lower intensity by a set amount, while the intensity mode is defined as an increase in the amount distribution by the same factor across intensity bins. In the context of these two modes of the precipitation distribution, this project will test the hypotheses that 1) increasing the resolution of the precipitation dataset shifts the precipitation distribution to higher intensities without changing its shape; 2) aerosol forcing over the past several decades has had a larger impact on the intensity mode of the precipitation distribution relative to the shift mode, and that this impact differs from that of greenhouse gases; and 3) stratospheric aerosol injection masks the temperature effects on the precipitation distribution, leaving only the direct response to determine the distribution change, which differs from that of the temperature response in terms of its impact on the shift and increase modes. Results of this work will be used to produce information co-designed during workshops with Northeast US highway engineers, with an integrated educational component to allow students to gain experience in addressing stakeholder needs related to precipitation extremes. In addition to supporting the principal investigator, the project requests funds for a postdoctoral researcher, two graduate students, and one undergraduate student researcher each summer. 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 2025 · 2025-09

Computer networks are critical for distributed software systems, and their failure can lead to major disruptions. Tools for verifying networked behavior can help catch bugs and improve trust, but there is a fundamental challenge that limits the applicability of formal methods in networking: the need for precise models of the devices, topologies, and systems being verified. This project will develop new techniques that enable automatic construction of such models, which will enable broader use of formal verification in networking, ensuring that networks satisfy specified correct properties and are aligned with the intents of human operators. The project will also pursue education and outreach efforts to share results with the broader community and will seek to transition the fundamental research into practice through collaborations with industrial partners. Despite many recent successes of verification in networking, many important properties remain challenging to formally reason about. One challenge is the lack of models that automatically evolve as changes are made to network devices, configuration, and topology. Another challenge is the lack of models that capture essential quantitative properties such as latency and reliability. This project will further the vision of automated inference of models and provide efficient techniques for quantitative model learning that do not require access to source code or handcrafted models of network behavior. At the core of the development will be a unique integration of new closed-box learning algorithms and symbolic techniques crucial to tame the scale of network models. The outcomes will be a new generation of formal verification techniques based on closed-box learning and grounded in applications in networking. 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 · 2025-09

Investigators: Abigail Loucks, MS (PhD Student), Aimee Colbath, VMD, PhD, DACVS-LA (Sponsor), Tristan Maerz, PhD (Co-Sponsor). Contributors: Adam Moeser, MS, PhD, DVM; Dana Orange, MD, M.Sc. Context: With identification of synovitis as a driver of osteoarthritis (OA), recent studies have placed an increasing focus on the synovium and the role of intra-articular inflammation in OA pathogenesis. Mast cells (MCs) are long-lived tissue-resident immune cells that act as early responders to infection or injury. Here we are looking to evaluate the temporal role of MCs in post-traumatic osteoarthritis (PTOA) progression and a potential mechanism via IL-33 signaling to alter MC phenotype to mitigate disease severity following an injury. In PTOA patients, OA may be initiated by an isolated event; therefore, a window may exist for therapeutic intervention by immune modulation before the disease progresses. Therefore, the overall objective of this proposal is to further elucidate the phenotype and functions of MCs in the acute and chronic phases of PTOA. Our central hypothesis is that MCs are pro-inflammatory in the acute phase following injury, but prolonged stimulation of MCs by IL-33 results in an immunomodulatory phenotype, mitigating joint inflammation. Specific Aims: To test our hypothesis and achieve our long-term goal to enhance endogenous immunomodulatory activity of MCs following joint injury through manipulation of IL-33 signaling we aim to (i) identify the temporal role of MCs in PTOA and (ii) determine the role of IL-33 in mediating MC phenotypes during disease progression. Research Plan: Aim 1) The effect of MCs on PTOA progression during the acute and chronic phase of the disease will be evaluated by ablating MCs in an MCPT5-Cre x iDTR mouse model 24hrs prior or 10 days after mice undergo a non-invasive anterior cruciate ligament rupture (ACLR). Histology grading of hind limbs will be used to evaluate PTOA severity and single cell RNA sequencing to unbiasedly assess transcriptional changes surrounding MCs in PTOA. Aim 2) We will assess the long- and short-term effect of IL-33 on MC phenotype in both murine bone marrow derived mast cells (mBMMCs) and a human mast cell line (LADR) by culturing cells with recombinant IL-33 for either 24hrs or 7 days followed by stimulation with both IgG and IgE or IgE, respectively. Expression of a known activation marker, CD63, and expression of well-known MC-associated genes will be analyzed via flow cytometry and RT-qPCR, respectively. Media will be analyzed for cytokine and chemokine release via multiplex assay. An IL-33 conditional knockout (Pdgfra-CreER; IL33 f/f) will be utilized to study the progression of PTOA with or without IL-33 released by synovial fibroblasts (SFs) 24hrs prior or 10 days after mice undergo (ACLR) procedure based on histology grading of PTOA severity. Bulk RNA-seq of flow sorted MCs from synovium of these mice will identify alterations in biological processes related to immune cell recruitment and synovitis as associated with MCs present within the joint.

NSF Awards · FY 2025 · 2025-09

Marine mass mortality events can drastically alter the structure and function of marine habitats, shifting the balance between organisms and leading to ecosystem degradation. The long-spined sea urchin Diadema antillarum plays a critical role in Caribbean coral reefs by maintaining the balance between corals and algae. Mass mortality of this urchin species has been linked to a pathogenic ciliate from the Diadema scuticociliatosis Philaster clade (DaScPc). Despite identifying the agent responsible, we still lack a fundamental understanding of the disease and its progression. By combining field monitoring, laboratory experiments, and molecular approaches, this project investigates how DaScPc, environmental conditions, and the microbiomes of Diadema interact to shape disease outcomes. These findings will be essential for predicting and potentially mitigating future urchin die-offs, thereby protecting the sensitive coral reef habitats they inhabit. This project includes the training and mentoring of graduate, undergraduate and high school students across four institutions, and the development of a global network of collaborators to facilitate monitoring and early detection of the disease. This project addresses two major questions surrounding mass mortality of the long-spined sea urchin Diadema antillarum. Aim 1 identifies environmental reservoirs of DaScPc and explores the environmental factors influencing its emergence and spread. This team conducts systematic time-series surveys of DaScPc presence in urchins, macroalgae, and corals in the U.S. Virgin Islands while monitoring physicochemical oceanographic conditions, host densities, and indicators of biological productivity. Additionally, field and experimental work assesses the potential reemergence of DaScPc from corals and other sympatric surfaces and its subsequent infection dynamics in Diadema. Aim 2 defines the growth conditions of DaScPc and determines how environmental factors and host microbiomes influence parasite-host interactions. This team assesses the effects of salinity, temperature, and nutrients on DaScPc growth and behavior in the laboratory. Co-culture experiments with bacterial strains isolated from the reef evaluates whether these microbes serve as prey or antagonists. Finally, this project characterizes the microbiomes of healthy urchins from unaffected sites and conducts experiments to determine how microbiome variation alters DaScPc-Diadema relationships. This integrative approach advances our understanding of the interactions among marine pathogens, hosts, and the environment, and provides critical tools for predicting and mitigating future outbreaks of marine diseases. 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.