Grants

National Park Service

Data Management

NICHD - Eunice Kennedy Shriver National Institute of Child Health and Human Development

It is essential to have the proper administrative infrastructure in place so that the Data Science and Analytics for Precision Rehabilitation (DAPR) Center can effectively carry out its mission to improve the rigor of medical rehabilitation research and the efficacy of clinical rehabilitation treatment. To that end, the overall goal of the DAPR Administrative Core is to provide coordinated administrative and organizational support to DAPR’s faculty, researchers, collaborators, and community. The Administrative Core has three specific aims. Aim 1 is to manage the disparate set of DAPR personnel, institutions, and initiatives in a coherent manner. To support DAPR in developing resources and promoting effective community utilization, the Administrative Core will foster intricate coordination between the DAPR Leadership Team across Cores and all key personnel and provide strong fiscal management. The Administrative Core will ensure effective collaboration between DAPR and the national and international rehabilitation communities as well as with other Medical Rehabilitation Research Centers (MRCCs). Aim 2 is to continually self-monitor, evaluate, and verify that the Center remains on track to meet the aims of each Core and Project. The Administrative Core will implement a structured meeting schedule with clear metrics, following the CDC Approach to Program Evaluation, to monitor progress and make necessary adjustments. We will facilitate communication within DAPR and maintain close contact with NICHD, the External Advisory Committee, and the Community Advisory Committee to ensure alignment with critical goals. Progress will be reviewed bi-annually, focusing on positive changes in user community behavior regarding DAPR resource adoption and support. Aim 3 is to create and administer a pilot grant program that extends DAPR’s infrastructure into new rehabilitation-specific datatypes and conditions while providing learning opportunities for early career rehabilitation researchers. Category 1 pilot grants will support researchers in annotating and publicly sharing datasets using the DAPR schema, with hands-on support for data annotation and archive identification. Category 2 pilot grants will assist researchers in applying precision rehabilitation algorithms to their data, with support for data querying and algorithm application. This two-pronged pilot program aims to maximize NIH’s research investments by enhancing the management, sharing, and utilization of rehabilitation research data for precision rehabilitation. The achievement of these aims will lead to an efficient and effective DAPR Center that promotes the use of data science, large AI/ML-ready datasets, and advanced algorithms for personalized, precision rehabilitation. A successful DAPR Center will advance a culture of open science and data science across medical rehabilitation research, focusing on improving research efficiency, reducing research waste, and meeting the needs of PWLE through data-driven approaches.

OD - NIH Office of the Director

ABSTRACT The prevalence of autism spectrum disorder (ASD) has dramatically increased in recent years to a rate of 1 in 31 U.S children. ASD risk is multifactorial, with genetic and environmental elements each playing a role. While major progress has been made in identifying causal genetic variants, environmental— particularly prenatal—exposures are increasingly recognized as critical contributors to neurodevelopmental risk. Over 200 high-production volume chemicals are routinely detected in American adults, including pregnant women. The ways in which environmental factors contribute to ASD risk (and how they interact with genetic risk) remain unclear, however. Understanding how these environmental chemicals influence neurodevelopment at the cellular and molecular level is crucial for development of mitigation strategies. Progress has been hampered by lack of large-scale data assessing the impact of chemicals at a cellular and molecular level in human neural cells that faithfully model early human brain development. A major barrier to progress has been the absence of scalable experimental platforms capable of evaluating compound exposures in ASD-relevant cell types and with comprehensive molecular readouts . Most existing studies rely on immortalized cell lines, which do not faithfully model human brain development. Here, in this project: “Data science approaches to autism: environmental modulators of the transcriptome and gene-x-environment interactions,” we address this substantial gap in the field by capitalizing on recent advances in genomics, stem cell technology, and data science. We will assess the activity of thousands of chemicals in the primary cell types implicated by ASD genetic risk, human neural progenitor cells and neurons. We leverage human stem cell-based model systems, which have been shown to accurately and reliably model key aspects of human brain development. We couple this with high-throughput culturing and robotic systems, which provide a unique opportunity to efficiently screen the entire ToxCast II/III library of ~4,700 chemical compounds to which humans are exposed, in both human primary neural progenitors (phNPCs) and induced neurons (iNeurons). We use an unbiased, genome-wide measurement at the cellular level, single-nucleus RNAseq, to comprehensively understand the effects that chemical exposures have on human neural development and to study the impacted gene regulatory networks and pathways. We will integrate this with public exposure data and molecular profiling in the human brain to find overlapping pathways. We will then evaluate how genetic background modulates cellular responses by using cell villages to model gene–environment (GxE) interactions across a cohort of 150 donors, including 110 individuals with ASD and 40 neurotypical controls. Using this framework, we will map exposure- responsive expression quantitative trait loci (eQTLs) for more than 40 chemicals with a novel method developed by one of the PIs, enabling the identification of GxE interactions at scale. We will integrate these data with publicly available data sources (e.g. PsychENCODE and ECHO). We will model the biophysical interactions between relevant chemicals and proteins, to provide molecular insights into the underlying mechanisms of action – namely, the means by which compounds induce systemic gene expression via perturbations in gene regulatory networks. We will build a public resource that will enable further mechanistic investigation. Our project will: 1) provide insight into mechanisms of action underlying known autism risk factors, 2) identify novel chemical risk factors that impact essential pathways in early brain development, and 3) query how genetic factors modulate susceptibility and resilience to chemical exposures, which will be comprehensively integrated with existing databases and resources.

NSF

There are substantial opportunities in the construction industry to reduce energy costs, decrease global material use, and decarbonize our society. On opportunity lies in the design stages of a building. Key decisions affecting a building’s long-term environmental impact, such as material selection and design for disassembly, are often made at a late stage when there is limited flexibility to make changes. If these decisions were made earlier, it could help mitigate unnecessary waste, resource depletion, and environmental impacts. This project will address this challenge by leveraging artificial intelligence (AI) and life cycle assessment. This research will provide practical, science-based tools to guide early design decisions, directly contributing to a more resource-efficient construction industry. This project will benefit society by advancing education and workforce development, integrating its findings into university curricula, mentoring students, and engaging industry stakeholders in circular building practices. This project will develop a data-driven framework that integrates generative AI (GenAI), life cycle assessment, and circular economy principles to optimize material selection and waste reduction in the built environment. A key innovation of this work is an AI-driven system that generates technical specifications for materials and assemblies based on early-stage design parameters. These specifications will be evaluated using quantitative circularity indicators and life cycle assessment tools that measure embodied carbon, energy use, and other environmental impacts. The evaluated design alternatives will be compiled into a Circular Building Solutions Database, providing an open-access resource for sustainable building practices. Additionally, the project will develop an automated framework for circular design recommendations, enabling architects and engineers to make informed, data-driven decisions that enhance building circularity, improve resource efficiency, and minimize environmental impact. The integration of GenAI, sustainability assessment, and automated decision-making represents a transformative approach to circular building design, bridging the gap between early design flexibility and long-term sustainability goals. 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.

NIMH - National Institute of Mental Health

Project Summary Bipolar disorder (BD) is among the deadliest and most costly psychiatric disorders in young people due to its severe and recurrent mood episodes of depression and mania which disrupt functioning, substantially increase the risk for suicide and premature death, and frequently require emergency or inpatient care. As each subsequent mood episode worsens prognosis, prevention of severe mood events in young people with BD is central to mitigating its enormous personal and societal burden. However, prevention is hindered by the lack of widely deployable tools to identify which affected individuals are at risk of a severe mood crisis event within a specific interval and which can guide individualized care. Through this mentored K23 award, the candidate, a PhD-prepared psychiatric nurse practitioner, will build upon her background in early intervention for BD, data- driven analytic approaches, and qualitative methods. Her program of training and research are designed to leverage real-world data and advanced analytic machine learning methods to efficiently identify young individuals with BD at risk for severe mood events and develop a deployment-focused clinical decision support intervention in partnership with clinicians and patients that could be rapidly translated to clinical care (NIMH Strategic Objectives 4.1 and 4.2). Through planned training activities, the candidate will gain a strong skillset in advanced predictive analytics and machine learning using electronic health record (EHR) and administrative data, mixed methods for stakeholder engaged intervention development, embedded health systems research, and BD clinical epidemiology. She will leverage robust, longitudinal health system data from two learning healthcare systems in the Mental Health Research Network, HealthPartners and Kaiser Permanente Northern California, and engagement with clinicians and patients where care is delivered. In Aim 1, rigorous machine learning methods will be used to estimate risk of severe mood crisis events, as indicated by mood-related inpatient hospitalization or emergency visits, over six-month intervals based on rich longitudinal EHR and claims data in a large sample of over 13,200 young patients with BD aged 15-39 years. In Aim 2, to maximize the translational impact of the models, clinicians and patients will be engaged, using a modified Delphi approach and qualitative interviews, in development and evaluation of a clinical decision support tool to guide personalized prevention and early intervention for BD mood crises. This research is a critical step in the candidate's long-term goal of leveraging data-driven approaches to improve individualized, patient-centered delivery of mental health services for individuals in the early course of BD and other serious mental illnesses. Her clinical and research background, expert mentoring team, and embedded research environment ideally positions her to accomplish the research and training aims, building the foundation for a next-step R01 that will externally validate and rigorously evaluate the risk prediction models and decision support tool developed in this proposal.

NLM - National Library of Medicine

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

NON-TECHNICAL SUMMARY This award supports fundamental research aimed at improving our understanding and predictive capabilities regarding the formation and degradation of high-entropy metal oxides (HEMOs) in aqueous environments. HEMOs typically consist of four or more metal elements in near-equimolar ratios and are at the forefront of innovation in energy technologies, including batteries, fuel cells, and catalysts. However, their synthesis and aqueous stability remains poorly understood. To address these challenges, the research team will utilize high-throughput computational modeling and machine learning to identify HEMO compositions that are both readily synthesizable and highly resistant to aqueous corrosion. The supported research will be tightly integrated with educational efforts through engagement of a broad audience (including high school and undergraduate students) through augmented virtual reality learning tools, online videos, and hands-on research experiences. The project will also provide open-access databases and open-source software tools to the broader materials science community. TECHNICAL SUMMARY The supported research will address a critical knowledge gap in understanding the aqueous-phase synthesis and corrosion behavior of high entropy metal oxides (HEMOs), a class of multicomponent materials with broad potential for energy storage and conversion applications. The immense compositional design space of HEMOs, combined with limited experimental and theoretical understanding of their precipitation and dissolution processes, presents a significant barrier to their scalable development. The research team will aim to: (i) Develop a universal machine learning model to accurately estimate the free energies of HEMOs at aqueous conditions; (ii) Quantify nucleation and dissolution rates of HEMOs in aqueous solution combining machine learning with nucleation theory; and (iii) Implement a kinetic Monte Carlo (KMC) simulation framework to model real-time precipitation and corrosion dynamics. These models established will also be calibrated and validated through collaborative experimental efforts as well as text mining of the experimental literature. The project will produce an open-source database, predictive tools, and simulation codes for high-entropy materials. Broader impacts will include efforts to engage students in diverse fields including data science, solid-state chemistry, energy storage and conversion, and nanoscience. To reach a broad range of participants, the project will integrate augmented and virtual reality applications into university curricula, engage K-12 students by collaborating with Young Scholars Program at FSU, and expand access to research content via a YouTube-based educational channel. These efforts aim to foster interest in materials chemistry and provide educational opportunities across a wide range of learning communities. STATEMENT OF MERIT REVIEW 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

This project investigates how coupled processes evolve on complex networks, which is a fundamental question with broad significance across science and engineering. Such systems are central to varied fields, including epidemiology, social science, power grids, traffic flow, and statistical physics. In these systems, individual components (nodes) update their states by interacting with neighboring nodes through intricate and heterogeneous connections (edges), giving rise to complex, large scale dynamics that are difficult to model and predict due to randomness, limited data, and structural complexity. This project tackles these challenges by developing efficient, data-driven methods to simulate and learn from large systems of coupled agents. This project also promotes STEM education by training students at all levels and engaging broader community through collaboration, workshops, and scientific conferences. To improve model accuracy and computational efficiency, the project develops model-reduction methods tailored for heterogeneous network systems. These include heterogeneous mean-field and pair approximations, as well as coarse-graining techniques. The project also focuses on inferring unknown functional parameters from partial data and limited information exchange by incorporating latent variables into the learning framework, combined with PDE-constrained optimization. The proposed methods are calibrated and validated using real-world datasets from social sciences and epidemiology. The project also designs scalable algorithms to handle large datasets from varied practical scenarios. 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.

NIGMS - National Institute of General Medical Sciences

Abstract The estrogen receptor (ER or ERα) plays a fundamental role in breast cancer through complex molecular mechanisms involving its N-terminal transactivation domain (NTD), DNA-binding domain (DBD), and ligand- binding domain (LBD). Understanding their structural dynamics is crucial for developing next-generation ER- targeted therapeutics. Our research employs advanced biophysical techniques to decode two fundamental aspects of ER regulation. Project-1 will characterize the structural properties of the NTD through nuclear magnetic resonance spectroscopy and small-angle X-ray scattering (SAXS), generating comprehensive structural insights. Project-2 will examine the dynamic interactions between DBD and LBD using cryo-electron microscopy combined with solution-state SAXS analysis, revealing how various factors influence domain communication. These studies will establish a detailed framework for understanding ER regulation in normal and disease states, with broad implications for the nuclear receptor family. Our data-driven computational strategy will illuminate new molecular mechanisms underlying receptor function. The methodological advances from this research will enhance our understanding of protein dynamics and domain interactions across the nuclear receptor family, potentially leading to innovative therapeutic strategies for hormone-dependent cancers.

NIGMS - National Institute of General Medical Sciences

Project Abstract In order to survive nutrient deprivation and fluctuating environments, microbes adopt quiescent (non-growing) physiological states. Some quiescent states, such as bacterial spores, are remarkably resilient and can re- initiate growth after surviving centuries as a dormant spore. Compared to the exponential growth phase of bacteria, relatively little is known about quiescent states. For example, quantitative relations between growth rates and cellular physiology have been established for the exponential growth phase, but no equivalent relations have been formulated for non-growing states. To that end, the central goal of the proposed work is to uncover such relations for non-growing states through the development of data-driven, predictive biophysical models of molecular mechanisms which characterize cellular quiescence. These models will incorporate data from a variety of experimental studies, which will improve our current understanding of the interplay between certain physiological traits and molecular processes that have been observed in non-growing states. This proposal builds on our previous works on rate-limiting molecular processes in exponentially growing unicellular organisms, in which data-informed biophysical models allowed us to deduce previously unrecognized growth laws and invariant quantities. In a similar spirit, here we aim to deduce quantitative relations characterizing cellular quiescence, to provide a new framework for understanding non-growing states. Over the next five years, we will focus on three main research thrusts, all motivated by experimental observations: (i) regulation of protein synthesis and ribosome abundance in quiescent states; (ii) the influence of pH on critical metabolic reactions; and (iii) the effect of protein composition on germination rates of bacterial spores and its implications for population growth. Our biophysical models and computational analyses will be validated against data provided by experimental collaborators. This five-year plan will enable us to lay the foundations for our long- term vision to uncover principles of dormancy and deliver predictive models that can be applied in the laboratory. The proposed work will provide molecular-scale insights into the physical processes that support cellular quiescence, and will contribute to the discovery of new methods to target non-growing cells. In particular, antibiotic-tolerant persister cells present one such application.

NSF

Computational imaging, particularly involving radiography and tomography, has a critical role in a variety of industries including security systems, materials science, non-destructive testing, seismic imaging, and medical imaging. Current methods for generating images rely on conventional physical models or on AI-based models that learn patterns in data. These current methods are limited in applications with lower quality data, or where the patterns in the data are highly complex, or where the collection of additional data is expensive. This research seeks to develop improved methods that rely on novel combinations of conventional models and AI-based models. These combinations offer the ability to generate quantitatively accurate and reproducible images from current systems and from future lower cost systems. This project develops physics and learning-based models and methods for tomographic imaging reconstruction and acquisition in extremely limited and scatter-corrupted measurement setups. Developing effective machine learning approaches to enable high-quality object reconstructions in tomography with extremely sparse/limited or highly corrupted (by scatter, noise) measurements is a critical area of ongoing research. A key challenge is that many existing deep learning based methods often underperform in the extremely sparse or highly corrupted measurements regime, and often require large training sets, which can be time-consuming or impractical to generate in applications (e.g., medical imaging, or accurate scatter or hydrodynamic simulations). Moreover, machine learning methods for challenging applications of tomographic imaging (limited-views acquisition, scatter correction, and quantitative density or dynamic reconstruction) remain in early stages. This research aims to significantly advance the current algorithms for data acquisition, data correction, and image reconstruction and analysis in tomography with very limited/corrupted data by incorporating learning-based algorithms with limited training data throughout the imaging process. These techniques will both enable prediction of the optimal limited tomographic views to acquire and leverage a unified learning-based framework for scatter correction and image reconstruction that combines locally learned or robust feature-based scatter correction models and limited-view/low-dose reconstruction models that effectively combine learned neural network priors, sparsity-based models, and physics and statistical imaging models. In summary, this research develops and evaluates a machine learning-driven tomographic imaging framework that employs a unified approach combining machine learned and data-adaptive priors and physics and statistical models and simulations. 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.

Department of Public Health

DUPLICATE FEDAID TABLE

Department of Public Health

DUPLICATE FEDAID TABLE

City of Richmond

DR Transport

City of Richmond

David I Lopez Miranda

Sound Portraits Productions

David Isay - The Voices Of The Forgotten Ones

New York Foundation for the Arts

David Licata - Tango Octogenario

DECD

David M. Hunt Library improvements

NICHD - Eunice Kennedy Shriver National Institute of Child Health and Human Development

Project Summary/Abstract: For over 45 years, the David W. Smith Workshop on Malformations and Morphogenesis has brought together dysmorphologists, developmental biologists, embryologists, anatomists, geneticists and others who work on developing a better understanding of how human malformations occur and their underlying mechanisms of morphogenesis. The workshops are held annually in celebration of the career of David W. Smith who was the father of human clinical dysmorphology and first to initiate these meetings. Meetings are held over 4-5 days in the summer and have an attendance of 130-150 individuals who are selected following submission of abstracts reviewed by the scientific program committee. Attendance is predicated on having an abstract accepted for presentation. The workshop is intended to be an interactive exchange and thus is limited only to those presenting that year. Each year three to five central topics are selected by the organizers. The selection of topics takes into account when the topic had last been a focus, and the timeliness of the topic based on new scientific developments and emerging fields. The meetings consist of a workshop format in which short presentations of hypothesis-driven findings are then followed by an intense discussion period. There are also typically 3-4 plenary sessions presented by internationally recognized experts in the fields relevant to the central focus topic of that given workshop. The meetings have a long history of wide-ranging and stimulating discussion and catalyzing collaborations as well as providing an especially important focus for the development and recruitment of residents, fellows and junior faculty into the field of morphogenesis, malformation and birth defects research.

City of Richmond

David's Chariot Mobility Transportation

Daviess County Economic Development Foundation Inc

Daviess County Economic Development Foundation Inc is an active grantmaking foundation based in Washington, IN. Focus: community development. Contact the foundation directly for current funding opportunities.

Davis Conservation Foundation

Davis Conservation Foundation is an active grantmaking foundation based in Yarmouth, ME. Focus: environmental. Contact the foundation directly for current funding opportunities.

Davis Education Foundation

Davis Education Foundation is an active grantmaking foundation based in Farmington, UT. Focus: general. Contact the foundation directly for current funding opportunities.

River Arts Repertory Co, Inc.

Dawn Saito - Blood Cherries