University Of Washington

NSF Awards · FY 2025 · 2025-10

Nontechnical Abstract This project aims to design a new class of engineered artificial materials, commonly known as metamaterials, that exhibit a high refractive index in ways not possible with naturally occurring substances. Refractive index determines the velocity of light, with a higher index resulting in a lower velocity. A high index helps guide light better and also bends it by a larger angle. This is crucial to get better optical components, such as lenses, which need to bend light to focus. A high nonlinear index also allows one light beam to control another light beam. These capabilities can help create faster computers, build better cameras, enhance augmented reality displays, and enable high-speed communication. These materials will be created by arranging nanometer length scale artificial materials (“nanocrystals”) in a periodic structure. Additional patterning at longer length scales will enable the development of new optical hardware. While the concept of creating such artificial materials is compelling, realizing it in practice is extremely challenging. This project addresses these challenges through a unique, multi-scale inverse design approach, driven by advanced computational modeling and machine learning. The project will also empirically validate the designed material properties, creating two testbeds: thermal imaging and nonlinear optical activation for optical information processing. Along with advancing the frontiers of optical imaging and computing, the program will train a new generation of scientists and engineers through hands-on interdisciplinary research experiences that span physics, chemistry, computation, artificial intelligence (AI), and materials science. By engaging high school, undergraduate, and graduate students, the project will broaden participation in cutting-edge science. Technical Abstract Designing materials with high linear and nonlinear susceptibilities can unlock a vast range of applications in photonics. Metamaterials present a unique opportunity to realize a high index, beyond what is available in naturally occurring materials. For instance, by combining nanocrystals appropriately, it may be possible to design a composite material with record high susceptibilities. The effective susceptibility of this composite material can be further enhanced via wavelength-scale patterning. Such a multi-scale metamaterial would be the first of its kind, where the constituent meta-molecules also comprise a metamaterial. While the multi-scale design of metamaterials is conceptually simple, it is extremely challenging in practice to design the exact combination of materials to achieve a desired property, while ensuring that the designs can be synthesized or fabricated. Guided by fundamental bounds based on the causality and passivity constraints of physical materials, this project will identify new design rules. Using a multi-scale inverse design approach, including a physics-inspired artificial neural network, the optimal combination of nanocrystals and meta-molecule structures will be identified. While the design techniques will be applicable to many material systems, a few promising ones will be downselected for experimental realization. These composite nanocrystals will be chemically synthesized and subsequently patterned to create the metamaterial. Ellipsometry and nonlinear pump-probe spectroscopy will be used to validate the design. The experimental data will help refine the design assumptions and provide new insight. Combining computational electromagnetics, optimization theory, machine learning, chemical synthesis, nanofabrication, and optical characterization, three research thrusts will be pursued: (i) create high linear susceptibility composite materials; (ii) create high nonlinear susceptibility composite materials; and (iii) demonstrate metamaterials made of the composite materials for nonlinear optical activation in optical neural network accelerators and high-efficiency thermal imaging. 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

Teenagers are increasingly using tools that depend on generative artificial intelligence (generative AI). Social media companies are rapidly integrating generative AI into their platforms to create agents that they describe as assistants, companions, and coaches. However, research on how the use of generative AI agents affects teen well-being lags behind teens' exposure to these systems. In this project, the research team will study how teens are interacting with generative AI agents today and how this use impacts teens' well-being. The team will also explore how technology designers can use this knowledge to create more useful, safer generative AI agents for teens. The contributions of this work will include new scientific understanding of how teens are using generative AI and new guidance for maximizing the benefits of these systems for young people. The research is organized around three main activities. In the first activity, the team will recruit teenagers who use generative AI who are willing to share selected parts of their interactions with these systems. Through analyzing the chat histories and using them to guide personalized interviews with teenagers, the team will develop a nuanced understanding of what kinds of conversations teens have with generative AI systems that impact their well-being. In the second activity, the team will ask therapists who work with teens to themselves interact with generative AI systems and review findings from the first study. This will allow the research team to develop an expert perspective on the benefits and risks of current generative AI systems as well as design principles for systems that are more likely to improve teens' well-being. In the final activity, the team will conduct co-design activities with teens to create generative AI agents based on the new knowledge and principles developed. These agents will be assessed through a comparative field study where teens interact with these agents versus commonly available ones and report on their interactions and well-being during the study. 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

Modern computer programs often use a large amount of resources and energy. Before running a complex program it is important to anticipate how many resources the program needs, to best provision these resources in order to avoid any waste, while at the same time ensuring that the program executes successfully. Database management systems often have multiple alternatives ways available on how to run a program, and the system needs to choose that alternative that uses the small amount of resources. This project develops novel techniques for estimating the amount of data produced by a program, which in turn can be used either to provision how many resources the program needs, or to chose between alternative ways to execute the program. The project develops a cardinality estimation system, which is "pessimistic", in the sense that it offers a one-sided theoretical guarantee that output cardinality of a query will never exceed that estimate. To compute this estimate, the project builds on information theory and computes a tight upper bound on the output cardinality. The estimate is computed from simple statistics on the input data, which can be collected offline, and which are already available in many current systems, such as cardinalities of base tables, number of distinct values in various columns, maximum degrees, or Lp-norms of degree sequences for various values of p. During the offline phase, the system can further refine these statistics by dividing the input data into buckets, then computing these statistics separately in each bucket, similar to how histograms are computed today by database management systems. What is novel about this project is that it uses all available statistics and computes an upper bound on the output size of a query, which is guaranteed to be the tightest upper bound possible given these statistics. The project studies new efficient ways to compute this bound, explores the potential use of Lp-sketches for computing the statistics offline and maintaining them incrementally, and extends the framework to group-by queries, for which traditional cardinality estimation methods are known to perform very poorly. 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

One of the most important events in the history of life in the Americas was when North and South America became connected by a land bridge—the Isthmus of Panama—which occurred between 15 million years ago and the present day. This connection allowed plants and animals to move between the two continents. Scientists have studied land plants to better understand when this event happened, but we still do not know how rivers played a role in how the land bridge formed. This project will study a special group of plants that live in tropical and subtropical rivers rapids and waterfalls, called riverweeds. Using information from both plant fossils and the DNA of living plants, we will study how these plants moved and evolved as the land bridge. Combining information about plants with geologic data, we hope to understand when rivers began to connect, and how these changes affected the plants living in them. This project will help science grow by training American scientists to work together with scientists in other countries and in various languages. The project will also help train new partners, strengthening the pathways for future American scientific research. This project will provide a novel lens on biotic migration during the rise of the Isthmus of Panama by leveraging the tight link between Podostemaceae plants and river evolution, adding a new element to the story of the Isthmus closure, and shifting the focus from terrestrial to unexplored freshwater systems. The traditional approach in plant evolution research is to interpret biological data using geological models. In this project, however, genomic data will be used to infer the timing and pattern of riverine plant migration across the Isthmus, which will then be coupled with geological and fossil data to build a wholistic model of river connectivity across the Isthmus of Panama. The project will use a recently developed method for the integration of distributional and genomic data to refine the resulting models of past landscape change. This interdisciplinary approach will not only clarify the tempo and mode of riverine connectivity across the Isthmus but will also fill critical gaps in our understanding of tropical biodiversity assembly in freshwater ecosystems. The project also includes opportunities for training for students ranging from high school to postdoctoral scholars. This project is co-funded by the Systematics & Biodiversity Science and Life through Environment and Time programs. 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

Artificial Intelligence (AI) has the potential to transform many areas of life – like healthcare, education, and finance – but it needs access to data to learn and improve. A lot of the most useful data in the nation is locked away in places like hospitals, research labs, and private companies, where it cannot easily be shared because of privacy concerns. This slows down the development of AI in these important domains. One promising solution is synthetic data – data that's created by computer programs trained on real data. It looks and behaves like real data but does not contain any personal information. This project aims to develop techniques to let organizations take part in the synthetic data creation process without ever revealing their real data. These techniques use AI and encryption to keep the original data secure while still helping to generate useful synthetic versions. Such technology is especially impactful in domains where real data is currently distributed across organizations, such as data of patients with rare diseases. The project builds research capacity at the University of Washington Tacoma, an emerging research institution, in partnership with the University of Central Florida. It creates valuable opportunities for students to participate in research, thereby strengthening the future AI and security workforce. This project advances the state-of-the-art for privacy-preserving data sharing through the development of Secure Multiparty Computation (MPC) protocols to train statistics-based and neural network-based synthetic data generators while keeping the training data encrypted; the development of Fully Homomorphic Encryption (FHE) protocols to train synthetic data generators over encrypted tabular data; and the design of cryptographic protocols for evaluating synthetic data in a privacy-preserving manner so that multiple synthetic data generation techniques can be run and compared against real data without consuming differential privacy budget. The algorithms and cryptographic protocols for the generation of synthetic data will be implemented in open-source MPC and FHE libraries. 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

Traditional Hunger Relief Organizations (HROs) have gaps in their ability to serve food-insecure households, operating from a limited number of locations and for limited hours, and often relying on purchased food that doesn’t directly reduce local food waste. Micro-pantries are small, decentralized, unattended, shared food pantries and fridges that allow local residents and businesses to donate food within their own neighborhoods. Despite their growing popularity, little is known about how much food is distributed through micro-pantries, how many individuals use them, and the safety and quality of food supplied. Consequently, HROs are reluctant to leverage this vast network of satellite micro-pantries, and local health departments have valid food safety concerns. Through a Stage 1 planning grant, we mapped 275 existing micro-pantries in the greater Seattle area, developed and tested a low-cost modular sensing platform to provide reliable data on micro-pantry usage and food safety, and conducted user engagement research. Through a first deployment, we estimated more than 4 million pounds of food being distributed in a year through the entire micro-pantry network in the study area of Seattle. While many different types of stakeholders use micro-pantries—including owners, donors, and recipients—they lack a system for centralized information-sharing. The current project aims to assess at scale whether a cyber-physical network of connected shared micro-pantries can complement HROs as a reliable and efficient system for hyper-local food redistribution while allowing for localized reduction of food waste. Our multi-disciplinary team of experts in urban logistics, supply chain, wireless sensor technology, food safety, and public health will collaborate with a national recycling company, public jurisdictions, and HROs to (1) deploy at scale modular, low-cost, wireless sensor platforms to gather and communicate data on micro-pantry usage and food safety conditions; (2) create a centralized information and communication system for micro-pantries donors and recipients; and (3) assess the potential to improve the safety and quality of food donated through micro-pantries, working with the state health department to establish best practices. This research and action project will advance knowledge of how civic-engaged research on sensor and information-sharing technologies can be rapidly designed and piloted to support hyper-local food redistribution, reduce inefficiencies and food waste, and enhance community access to safe, high-quality food. This research will provide a valuable, first-of-its-kind, formal study of micro-pantries as a potential solution to address food security and food waste at the neighborhood level, seeking to close gaps in traditional food rescue and distribution. 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

Environmental issues like wildfires can serve as effective science learning contexts to promote scientific literacy and citizenship. This project will partner with teachers, teacher educators, and disciplinary experts in data science, fire ecology, public health, and environmental communication to co-design a data-driven, justice-oriented, and issue-based unit on wildfires. In the unit, student will engage in various data practices to gain insights into the issue of wildfires and how it affects their lives and communities. They will create data stories targeting specific stakeholders as a culminating activity. This project will contribute to the field by examining how data science can be meaningfully incorporated into K-12 science education to prepare informed and responsible citizens. It will directly impact approximately 15 middle school teachers and 1500 middle school students in Northwestern Nevada, including students from low socioeconomic status backgrounds and underrepresented groups. This project seeks to theorize how learners can leverage disciplinary knowledge and practices in environmental and data science as a foundation for making data-informed actions towards a more just and sustainable society. The construct of environmental science data literacy will be developed to include three interconnected components: 1) understanding environmental science and/or data science ideas and practice, 2) identifying areas of own expertise within environmental science and/or data science, and 3) using environmental science and/or data science as a foundation for change. Accordingly, student learning outcomes examined in this project include proficiency in science and data practices, identity formation, and agency development. The project will also investigate teacher professional learning during curriculum co-design and enactments through a cultural-historical activity theory lens. This project is funded by the Discovery Research preK-12 program (DRK-12) that seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models, and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. 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 will combine large flow-cytometry datasets with novel machine learning models to reveal the geographical distribution of phytoplankton and show how the environment shapes these patterns. Neural network methods for flow-cytometry data analysis will be applied to data from over 100 cruises across the Pacific and Atlantic Oceans. The project will develop computationally efficient mixture of neural network models, a generative model framework for changepoint detection, and spatially dependent convolutional neural networks. These methods will make oceanographic data analysis more automatic and efficient while also allowing for model-based rediscovery of ocean provinces as well as predictive mapping of ocean microbe populations and traits. The proposed methodology will advance AI and statistics, data science, and oceanography while also being useful across a broad range of disciplines that deal with complex high-dimensional dependent data such as environmental science, ecology, agriculture, epidemiology, and econometrics. The methodology will also be useful for various data science industries that handle high-dimensional mixture data or flow cytometry. Public-use software packages will be created. The project will develop computationally efficient neural network models that automatically classify cell level data with environmental covariates. This will streamline the analysis and reveal biological responses to changing environments. Generative neural networks will be used for changepoint detection. Latent variables will identify shifts in phytoplankton communities and help redefine ecological ocean provinces. Finally, convolutional neural networks will be applied to density regression and spatial interpolation of flow cytometry data. This predicts complete cytogram “images” extending data value beyond cruise tracks, to help create global phytoplankton biogeographies. 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 Screening, Brief Intervention, and Referral to Treatment (SBIRT) is an evidence-based comprehensive approach that can 1) quickly screen individuals for at-risk alcohol and other substance use and mental health issues, 2) utilize in-the-moment delivery of brief (i.e., 5-10 minute) intervention with motivational interviewing techniques and 3) when needed, provide referral to treatment. Regarding alcohol-impaired driving, SBIRT has been shown to be efficacious in reducing self-reports of drinking and alcohol-impaired driving, reports of alcohol-related vehicle crashes, and increasing referrals to treatment. Given the success of SBIRT for alcohol- impaired driving it may also be impactful for polysubstance-impaired driving. However, to date there has been limited evaluations of SBIRT regarding its effects for polysubstance-impaired driving. Despite the potential of SBIRT to reduce polysubstance-impaired driving, reported barriers include competing priorities for staff to provide SBIRT, staff turnover, and availability of treatment options. Brief motivational interventions (BMI) have evidence for reducing alcohol and cannabis use and consequences among young adults with additional previous support for reducing impaired driving. Web-based personalized feedback intervention (PFI) adapted from these in-person BMI may also be effective in reducing impaired driving and would address limitations within the SBIRT framework. Our team is contracted to roll out SBIRT training with 200 facilitators between Jan 2025-Sept 2025, which will include updated material specific to alcohol- and polysubstance-impaired driving. The proposed project would leverage this newly trained network and provide a web-PFI (SBIRT+PFI) to their young adult clients who screen at moderate or moderate to high risk or have any reports of impaired driving. Consented young adults would be randomized to receive the PFI or waitlist control and be assessed at enrollment and 3 months later. We also will collect data from the SBIRT facilitators to garner information on sustainability and barriers of implementing SBIRT. Thus, the objectives of this project will be to 1) examine the changes in polysubstance-impaired driving among young adults among those who receive the expanded SBIRT+PFI, and 2) using both qualitative and quantitative approaches, document real-word successes and difficulties in sustaining SBIRT models including: a) barriers to implementing with fidelity and b) percentage of staff turnover. Findings of the project can help inform decisionmakers and policymakers on the value of SBIRT and SBIRT+PFI for polysubstance-impaired driving as well as considerations for continued sustainability.

NIH Research Projects · FY 2025 · 2025-09

Abstract National Center of Biotechnology (NCBI) and International Nucleotide Sequence Database Collaboration (INSDC) databases have been cornerstones of public sharing of pathogen genomic data for basic science and epidemiological investigations. To ensure the integrity of sequence databases, all sequences must be validated and curated prior to deposition into GenBank. A major bottleneck in the rapid sharing of viral sequencing data is the requirement for inclusion of gene and/or protein annotations along with curation of sequences prior to deposition to NCBI GenBank. Annotations are critical for cross-referencing other NCBI databases, while curation is required to ensure the accuracy and usability of the databases. However, correctly annotating and performing appropriate quality control can be challenging to non-specialist submitters. Notably, this limitation is restricted to viral sequences, as prokaryotic and eukaryotic genome annotation and quality control has been automated via the NCBI's Prokaryotic and Eukaryotic Genome Annotation Pipelines. Recently, NCBI has created an open-source viral annotation tool called VADR (Viral Annotation DefineR). VADR validates and annotates viral sequences using RefSeq-based models. VADR is currently limited to a select number of human viruses, including SARS-CoV-2, influenza virus, monkeypox virus, norovirus, dengue virus, and respiratory syncytial virus. The implementation of VADR has reduced manual reviews by NCBI indexers by >95% for these viruses, illustrating its critical role in prescreening submitted viral sequences. However, for most viruses relevant to clinical infectious diseases and public health, there is no way to rapidly submit unannotated consensus sequences to open databases. Here, we propose to accelerate the public sharing of viral sequences by building, validating, and implementing sustainable, open-source VADR models for human viruses relevant to public health. Specifically, we will build, validate, and implement appropriate open-source models for VADR for respiratory viruses, viruses associated with vaccine preventable diseases, and hepatitis viruses, taking advantage of the moderate numbers of viral sequences available for these viruses. Our group has significant knowledge of viral genome quality control, annotation, and submission, including their associated challenges as currently constructed in NCBI. Using viral genome data from other sequencing projects, we will validate the accuracy and sustainability of our newly generated VADR models on sequences not yet present in NCBI GenBank. Working together with NCBI, we will deploy these open-source models to automate analysis for GenBank by the end of the grant period. The proposed work will both ease submission and increase open genomic data for viruses of public health importance. This work will and ensure that we are ready for increasing amounts of routine public health sequencing and help overall preparedness for the next pandemic. 1

NIH Research Projects · FY 2025 · 2025-09

Tuberculosis (TB) in pregnancy increases risk of adverse perinatal outcomes, including preeclampsia, preterm birth, low birthweight, and neonatal death. Pregnant women living with HIV (WLHIV) in high burden settings are at elevated risk for TB, despite high antiretroviral therapy (ART) uptake. Isoniazid (INH) as TB preventive therapy (TPT) is routinely offered to pregnant WLHIV at high risk of TB in the US and globally. Safety studies of INH for TB prevention in pregnancy show inconsistent results and call for more robust evaluations of perinatal outcomes following prenatal INH use. To date, no studies have assessed perinatal and infant growth outcomes in the context of quantified cumulative maternal and infant in-utero INH exposure. Further, limited safety data exist for INH use in pregnant WLHIV on newer dolutegravir (DTG)-based ART regimens, which may interact differently with INH. Acquiring safety data from programmatic INH initiation across gestational ages, including 1st trimester, could inform prenatal INH policies for WLHIV on DTG-ART. We currently quantify DTG exposure via drug measurements in breastmilk and hair in an ongoing cohort (n=480) of WLHIV on DTG-ART enrolled in pregnancy and followed with their infants to 36 months to evaluate safety outcomes in Kenya—a setting where there is high uptake of programmatic INH during antenatal care by WLHIV (PrIMA-DTG, P01HD107669 Project 1). PrIMA- DTG built a maternal/infant specimen repository to assess other drug exposures, including INH. Among WLHIV enrolled in PrIMA-DTG, 60% initiated INH during pregnancy, of whom 40% initiated in the 1st trimester and 53% in the 2nd trimester. Using methods to measure HIV drug levels in hair that our team refined in maternal/infant pairs, we will quantify INH exposure in WLHIV receiving INH for TPT in pregnancy and their infants using existing hair samples from the well-characterized PrIMA-DTG cohort (Aim 1) and assess if extent of INH exposure is associated with perinatal and infant outcomes (Aim 2). Our overall goal is to use objective measures to provide comprehensive safety data on INH exposure for pregnant WLHIV on commonly used DTG-ART and their infants. This will be the first large-scale study to directly quantify cumulative maternal INH exposure and fetal transfer in the context of DTG-ART. By leveraging our expertise in perinatal ART safety and TB, high prenatal INH use among WLHIV in Kenya, and an existing maternal/infant specimen repository designed for studies like the one proposed, we are uniquely positioned to execute the proposed aims. This study is intentionally designed to address existing gaps in the safety data of prenatal INH use among WLHIV on DTG-ART. Our findings will inform TB prevention policies and clinical practice in the US and globally by providing INH safety data across gestational ages with objective exposure measures.

NIH Research Projects · FY 2025 · 2025-09

The ability to differentiate Stem Cells (SC) into functional pancreatic islets highlights new opportunities to address the tissue shortage for cell replacement therapies in diabetes. However, a barrier to the broad applicability of this approach across the human population is the variable efficiency of current protocols in controlling lineage biases and functional maturation of different SC lines, thereby yielding heterogeneous islet cell preparations containing variable proportions of endocrine and immature cell types. In this collaborative project, we propose to broaden the therapeutic potential of SC-based treatments by developing a new epigenetic approach to enhance the efficiency of islet tissue derivation from SCs. Our innovative approach will generate SC-derived pancreatic islets by activating, in a stepwise manner, key islet cell developmental and survival programs using Artificial Intelligence (AI)-designed mini-proteins (EpiBinders) capable of enforcing epigenetic regulation of select islets’ gene networks. In preliminary proof-of-principle studies, using newly developed reporter SC lines allowing live-monitoring of fate choices during differentiation, we provide evidence that transient targeting of an (AI)-designed mini-binder of the Polycomb Repressive Complex 2 (PRC2) fused to dCas9 (EBdCas9) to PDX1 and NGN3 promoters significantly accelerates the differentiation of SC lines into glucose-responsive islet -cells, increasing the yield and homogeneity of islet tissue from multiple SC clones. Building on these findings, our goals are to develop a pipeline of functionally validated AI-designed EpiBinders for epigenetic programming of SCs into islet tissue, establish protocols for EpiBinders’ delivery at select stages of SC differentiation, and validate their specificity and efficiency as drivers of islet cell development and function in multiple SC lines, both in vitro and in vivo in cell transplantation models. We anticipate that the new tools, knowledge, and resources developed through this interdisciplinary effort will significantly broaden the applicability of SC-based therapies for diabetes and for a wide range of degenerative diseases, while establishing impactful platforms that advance discovery and enable future innovation.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT Atrial fibrillation (AF) is a common arrhythmia associated with serious consequences. Recent evidence suggests that much of the morbidity attributed to AF may be caused by underlying abnormalities in the structure and function of the left atrium, known as atrial cardiopathy. Among individuals free of AF at baseline, measures of atrial cardiopathy are independently associated with incident cardiovascular disease. Additionally, atrial cardiopathy is a potent prognostic marker in higher-risk patients with established cardiovascular disease. However, our limited understanding of the molecular mechanisms that link atrial cardiopathy with clinical disease is a major obstacle to developing new therapies. We propose to use proteomics and genomic methods in diverse primary and secondary prevention populations to improve our understanding of the etiology of atrial cardiopathy-related cardiovascular diseases, and to identify novel proteins that may be causal risk factors for these complications. The primary prevention study population includes participants of four NHLBI-funded prospective cohort studies who met criteria for atrial cardiopathy and were free of clinically recognized cardiovascular disease. In these cohorts, we will leverage existing data on proteomics, atrial cardiopathy, and potential confounding factors at the same study visit, and adjudicated cardiovascular events during follow-up. The secondary prevention study population includes participants of the ARCADIA trial, all of whom experienced a recent cryptogenic stroke and had evidence of atrial cardiopathy. This trial randomized patients to aspirin or apixaban and conducted adjudication of recurrent stroke events during follow up. In ARCADIA, we will conduct new proteomic profiling to measure levels of ~5,400 proteins using pre-randomization blood samples. The primary outcomes for this study are AF, ischemic stroke, and heart failure. We will conduct longitudinal analyses of individual proteins and study outcomes separately in these primary and secondary prevention study populations, which include over 30% Black participants. Genomic data will be used to select strong genetic determinants for Mendelian randomization experiments, which will evaluate whether significant protein associations with study outcomes are likely to be causal. For key proteins, we will conduct mass spectrometry validation and develop targeted immunoassays to facilitate additional clinical research. The rigorous approach described in this application will help to accelerate the discovery of novel etiologic factors and potential therapeutic targets for cardiovascular disease prevention.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Melioidosis is a tropical infection caused by inoculation, inhalation, or ingestion of the Gram-negative soil saprophyte and Tier 1 select agent Burkholderia pseudomallei (Bps). The overall melioidosis mortality rate exceeds 40% in endemic areas of southeast Asia such as northeastern Thailand (despite appropriate treatment), and modeling indicates that 165,000 cases of human melioidosis occur annually worldwide. For decades, diagnosis of melioidosis has required culture of Bps from a clinical specimen. This may take several days, delaying appropriate treatment, and in many resource-limited settings the necessary microbiology facilities for bacterial culture and identification are not available. Few suitable non-culture-based diagnostics exist. Performing a case-control analysis nested within a prospective, single-center cohort study of patients hospitalized with infection (Ubon-Sepsis study), we have developed an eight-protein signature in plasma that, in preliminary studies, has high accuracy differentiating melioidosis from other causes of infection. These results suggest that measuring a limited number of circulating proteins during initial presentation has significant diagnostic potential for melioidosis. Our central hypothesis is that this proteomic signature is a novel and accurate diagnostic tool in identifying patients with melioidosis. To test this potentially high impact hypothesis, we will leverage our singular expertise in melioidosis proteomics, human immunology, and access to independent Thai and Australian cohorts of melioidosis patients in the following specific aims: 1) Externally validate the diagnostic accuracy of the eight-protein aptamer-based signature for the detection of melioidosis in two geographically independent prospective studies. 2) Confirm the diagnostic accuracy of the aptamer- derived proteomic classifier for melioidosis using orthogonal immunoassays. If our hypotheses are proven, the use of blood proteins as a melioidosis diagnostic could significantly enhance our present approaches to identifying melioidosis. Subsequent development of this tool for clinical use could have a beneficial impact on the burden of this often-lethal infectious disease.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT Over the past 5 years, NICHD has supported in-depth studies of CHEU through an R61/R33 mechanism in 5 programs. We propose to amplify the impact of the prior awards by collaborating in a multi-country application focused on CHEU research that involves prior R61/R33 awardees conducting studies in high HIV prevalence settings (Botswana, Kenya, and Zimbabwe). Our U19 application entitled “Researching Interventions and Implementation Strategies to Evaluate the Health and Development of Children Affected by HIV”, has the following Aims: To build a U19 structure that includes 34 Research Projects and 3 Cores as follows: Project 1 (Neuroanatomic pathways): Using existing cohorts, to compare brain structure in CHEU and CHU at 5-10 years and characterize longitudinal brain structural changes in Botswana and Kenya using low-field and high-field MRI; and determine associations between brain structure and neurodevelopment. Comparator groups include children and adolescents unexposed to HIV (CHU) and PrEP-exposed CHU. Project 2 (Intervention): Determine the impact of a combination intervention (which includes psychosocial support through problem-solving therapy [Friendship Bench]; counseling on optimal infant and young child feeding; child play/early learning to spur interaction and language/motor skills) on neurodevelopment in CHEU and CHU in an individual RCT across 3 countries. Project 3 (Implementation): To determine validity and feasibility of using a risk-score to identify and refer CHEU at most risk for neurodevelopmental delay; evaluate acceptability, feasibility and cost of Project 2 interventions; and to model cost-effectiveness and impact of risk-based screening/referral and interventions among CHEU and CHU. To implement a shared Scientific Administrative Core (SAC) to oversee scientific directions and implementation of the Research Projects and administrative, fiscal, and communication for the U19. To implement a shared Data Management and Analysis Core (DMAC) to oversee data management, standardization, quality control and analysis and to build capacity for in-country data analysis. To implement a shared Dissemination and Engagement Core (DEC) to develop materials and engage stakeholders at community, county/local, national, and global (WHO, UNICEF) levels in RISE U19 findings.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Next-generaƟon biobanks will contain sequence data for millions of individuals. This project will develop genotype phasing methods for these immense data sets. Genotype phasing esƟmates an individual's haplotypes, which are the two sequences of alleles that are inherited from the parents. Genotype phasing is necessary in order to perform powerful haplotype-based analyses. This project will develop computaƟonal methods that substanƟally reduce the cost of genotype phasing, so that it is possible to phase large biobanks for an acceptable cost. In addiƟon, the project will develop methods that allow marker filtering, sample filtering, and haplotype-based analyses to be performed directly on highly compressed data. This will remove the need to decompress phased genomic data prior to data filtering or haplotype-based analyses. This project will develop two methods that significantly improve genotype phase accuracy. The first method will idenƟfy difficult-to-phase heterozygous genotypes at run Ɵme and apply a special phasing algorithm to these heterozygotes. The second method will increase the number of geneƟc markers available for haplotype-based analyses by allowing less stringent quality control filters to be applied without harming genotype phase accuracy. Finally, the project will develop an open-source pipeline for phasing All of Us Research Program sequence data, and it will apply this pipeline to phase each sequence data release. The phased sequence data will be a shared resource that enables researchers to perform powerful haplotype-based analyses of All of Us genomic data, which will increase the power to detect genomic variants that influence heritable traits and diseases.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Differentiation and tissue growth are both dependent upon faithful adherence to programs that encompass lineage hierarchy, spatial location, and integration of signaling information. By deciphering these programs, biologists have increasingly been able to generate diverse model tissues from cell lines such as immortalized keratinocytes. This has allowed scientists to study processes that were previously confined to the human body such as wound healing in interfollicular epidermis. Despite the fidelity of progenitor cells to the programs that govern growth, it has been well documented that individual clones expand to different degrees and have immensely variable contributions to tissues. In the context of homeostasis, it is poorly understood which signaling pathways, lineage trajectories, and spatial microenvironments drive clonal dominance. The methods (optical lineage tracing and RNA sequencing) and model systems (mice) often used to study clonal expansion provide an incomplete picture of this phenomenon in human skin development. To address these shortcomings, our lab has recently developed two prime-editing based molecular recording platforms that record the precise order in which cells experience biological events, termed ENGRAM (ENhancer-driven Genomic Recording of transcriptional Activity in Multiplex) and DNA Typewriter. With their combined application, the lineage and signaling history of a cell may subsequently be read out via high-throughput sequencing. In this proposal, I aim to further develop and deploy these molecular recording systems systems in a well characterized human model of skin development, an immortalized keratinocyte-derived epidermal organotypic model, to study epidermal fate specification and clonal competition. Through polony-indexed library-sequencing (Pixel-seq), a high-resolution spatial transcriptomics method, I will generate maps of clonal growth with corresponding measurements of each clone’s signaling and lineage history in the context of its spatial microenvironment. I will utilize these data to answer fundamental questions about the drivers of clonal competition in homeostatic epidermis, and will in the process generate immortalized keratinocyte lines for downstream use in organoid and disease models.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY The exposome refers to the complete array of non-genetic factors that an individual experiences during their lifetime. Accurately measuring the chemical exposome, which may provide novel insights into environmental determinants of chronic disease, presents significant analytical challenges. Complex interactions between co-exposures can occur but are difficult to quantitatively predict. These mixture effects are likely due to the heterogeneous nature of common environmental chemicals, which represent thousands of classes and span vast concentration ranges in human blood. Additionally, inter-individual variation in xenobiotic metabolism can lead to different toxicological outcomes within an exposed population. High-resolution mass spectrometry (HRMS) has emerged as a powerful technique for broadly profiling xenobiotics and their transformation products in complex matrices. Despite recent advances in HRMS-based exposome analysis, however, there is a paucity of MS reference data for common environmental chemicals and their metabolites. There also remains a crucial need to harmonize measurements of exposome molecules across different laboratories and instrumental platforms. To address these challenges, we propose the development of large- scale, multidimensional reference databases dedicated to common environmental chemicals in the ToxCast library. These databases, which are essential for MS-based compound identification workflows, leverage ion mobility-mass spectrometry (IM-MS) to enhance selectivity and provide additional structural information. Gas-phase analyte ions in IM-MS are rapidly separated by size and shape using a dynamic electric field (traveling wave IM; TWIM) with a neutral buffer gas (e.g., N2). Ions with smaller rotationally averaged surface area, or collision cross section (CCS), exhibit greater mobility within the drift tube than less structurally compact gas-phase ions. IM-MS consequently enables three-dimensional separation when coupled to polarity-based LC separation and mass-based MS. The high inter-day and inter-laboratory reproducibility of TWIMCCSN2 measurements across diverse analyte classes, complemented by its compatibility with high- throughput analytical workflows, makes IM-MS ideal for confidently identifying exposome molecules in complex mixtures. Therefore, Specific Aim 1 will utilize LC-IM-MS/MS to create a multidimensional reference database that incorporates accurate mass-to-charge (m/z), CCS, retention time, and MS/MS fragmentation spectra for the ~4500 ToxCast compounds. To expand the scope of human exposome research to xenobiotic transformation products, Specific Aim 2 will focus on elucidating the major phase I/II metabolic pathways of the ToxCast compounds by human liver. The proposed research will generate crucial reference data for confidently identifying exposome molecules and their metabolites in complex matrices.

NIH Research Projects · FY 2025 · 2025-09

Summary Mosquitoes rely on a combination of sensory cues to locate their hosts, including olfactory, heat, and visual stimuli. Previous studies have shown that olfactory cues play a crucial role in mosquito behavior, but emerging evidence suggests that visual cues are also important, particularly in the final stages of host localization. While significant research has focused on the role of olfaction in mosquito host-seeking behavior, the contribution of vision remains less understood. This proposed research seeks to address this gap by investigating our working hypothesis that bimodal visual-olfactory stimuli elicit more robust behavioral and neural responses from mosquitoes – eventually leading to altered blood-feeding choices. We will test several hypotheses regarding the role of visual and olfactory integration in mosquito behavior and neural processing. Specific Aim 1 investigates the multimodal learning and memory of visual and olfactory stimuli, including host-related cues, in the Yellow Fever Mosquito, Aedes aegypti. This aim will utilize an aversive conditioning paradigm with two testing assays - a two-choice T- maze and a free-flight blood-feeding choice assay, in addition to neurogenetic sensory knockout lines to characterize the interplay between olfactory and visual inputs in mosquito responses. We will determine how aversive learning affects mosquito attraction towards human host odorants and visual wavelengths (1a) and will test the hypothesis that olfaction is the primary driver of learning performance, with visual stimuli enhancing those responses (1b). Specific Aim 2 will investigate the encoding of learned and unlearned host-related visual and olfactory stimuli in the A. aegypti mushroom body (MB), an crucial sensory learning and integration center in the insect brain. Using 2-photon calcium imaging, we will record neural activity in the mushroom body Kenyon cells and dopaminergic neurons in response to olfactory, visual, and bimodal stimuli. This aim will test the hypotheses that KCs exhibit increased activity in response to host-related cues (2a) and that PPL1 dopaminergic neurons will increase their activity following aversive associative conditioning (2b). This comprehensive examination of visual-olfactory sensory encoding in a vector species will significantly advance our understanding of the neural bases of mosquito learning and its implications for host selection. Additionally, the completion of the proposed aims will provide the first evidence of visual-spectral learning in mosquitoes and the first recordings from the mosquito MB. Carrying out these experiments will provide me with training in calcium imaging, neurogenetic techniques, and data analytical approaches. We are confident that the collaborative environment at the University of Washington, along with my mentor's experience in training successful scientists, puts me in an ideal position to achieve these aims and gain the necessary training for my future career.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT Floods account for the majority of global climate change-attributable deaths in the 21st century and are predicted to increase substantially as the climate continues to rapidly change. As such, the health toll of floods stands to increase and will disproportionately affect the most vulnerable populations – particularly older adults. Older adults face heightened flood vulnerability due to disproportionate residence in floodplains, frailty, diminished hearing and vision, pre-existing chronic conditions, reliance on electricity-dependent medical equipment, and dependence on healthcare access. Thus, identifying policies to safeguard older adults' health during and following floods is paramount. In the US, the Federal Emergency Management Agency (FEMA) is tasked with prevention and mitigation of the effects of climate disasters. Two programs that are particularly important in the relief efforts from flood events are FEMA's National Flood Insurance Program (NFIP) and the Individuals and Households Program (IHP). These programs provide resources and services to individuals before, during, and after climate disasters – including major floods. As the rate of floods dramatically increases, understanding the ways in which floods affect mortality and these programs stand to buffer those effects is paramount. A critical research gap remains about (1) the effects of these FEMA programs on health, and (2) how long major floods may affect health beyond the initial days. This project will examine the effect of these programs on all-cause mortality among older adults, and will estimate both the short- and long-term effects of floods on all-cause, cardiorespiratory, and injury- related mortality at various time lags. Our overarching hypothesis is that floods affect health for up to a year following a major flood and that programs such as NFIP and IHP mitigate these effects. To test this hypothesis, we aim to (1) estimate the effect of NFIP availability on ZCTA-level mortality among Medicare enrollees after major floods and use an agent-based model to simulate NFIP mortality benefits, (2) estimate the effect of FEMA IHP application approval rate on mortality among Medicare enrollees after major flood exposure and use an agent-based model to simulate IHP mortality benefits, and (3) assess the relationship between exposure to major floods and all-cause, cardiorespiratory, and injury-related mortality at the county level in the short- and long-term. The proposed research includes a high-quality training plan that includes academic and scientific support for the success of the applicant in demographic methods, epidemiologic methods, causal inference and quasi-experimental design, climate epidemiology, Bayesian methods, agent-based modeling, and professional development. All of the proposed research will be supported by an interdisciplinary team and environment at the University of Washington. Collectively, the project will provide evidence that can inform future climate adaptation and FEMA policies to reduce preventable flood-attributable mortality.

NSF Awards · FY 2025 · 2025-09

Stratospheric water vapor plays an important role in regulating Earth’s surface temperature and the concentration of stratospheric ozone, which protects life from harmful ultraviolet radiation. In the tropics, the coldest point in the atmosphere — the tropical cold point tropopause (CPT) — acts as a critical gateway that controls how much water vapor enters the stratosphere. Even small changes in CPT temperature can lead to significant impacts on climate, weather, and stratospheric ozone levels. This project aims to improve scientific understanding of the processes that shape the temperature of the CPT. Because changes in CPT temperature can influence tropical rainfall, cyclones, and convection, the results of this study are also relevant to understanding regional weather extremes. In addition, the project will support the Ph.D. research of two graduate students, providing valuable training for the next generation of atmospheric scientists. The goal of this project is to better understand the physical and dynamical processes that govern year-to-year changes in tropical CPT temperature. The investigators hypothesize that tropical CPT temperature responds to warming in the tropical troposphere through two competing mechanisms: radiative heating and dynamic cooling. These effects largely cancel out on interannual time scales, though not necessarily on seasonal or decadal scales. A second hypothesis addresses an unexplained relationship between tropical CPT and stratospheric temperatures, which may be related to the Brewer-Dobson Circulation (BDC), the deep overturning motion of the stratosphere. To test these hypotheses, the research team will analyze satellite observations, including high-vertical-resolution GPS radio occultation (GPS-RO) temperature data, modern atmospheric reanalyses, and simulations from chemistry-Earth System and radiative transfer models. This integrated approach will improve understanding of tropical CPT variability and help to resolve conflicting evidence in current Earth system research regarding its drivers. 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

Ensuring the reliable and economical production and delivery of electric power is a critical national objective with broad economic and national security implications. Power system modeling plays a key role in enabling these objectives by allowing operators and regulators to accurately simulate how a power grid behaves under different conditions, to design and test control strategies for various grid components, and to predict the impact of changes to the power grid structure, among other tasks. Within this context, data assimilation (DA) and model calibration (MC) tasks, which involve the combination of observations and numerical models, are critical to ensure that predictions from power system models are accurate and useful. This project will have a direct impact on a wide range of fields where “computer models” are used, including atmospheric sciences, oceanography, ecology, astronomy and engineering, among many others. This project aims to develop machine learning tools for data assimilation and model calibration aimed to situations in which the behavior of the underlying system can be described through physical laws encoded in (systems of) ordinary differential equations (ODE), differential algebraic equations (DAE) or partial differential equations (PDE). The techniques are based on Bayesian non-parametric regression methods, where the structure of prior is derived from the system of differential equations describing the underlying system. The main expected outcome of this project is a novel set of tools for DA and MC tasks that: (1) are applicable across a broad spectrum of ODE/DAE/PDE-based systems; (2) allow for proper uncertainty quantification; (3) are endowed with rigorous theoretical guarantees, (4) can be efficiently implemented in practical settings. Hence, the project will expand the toolbox available to scientists and engineers that operate and regulate the U.S. power grid, enhancing the reliability of critical national infrastructure. 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 Latino adults face a higher risk of Alzheimer’s disease and related dementias (ADRD) than non-Latino White adults. The number of cases is expected to rise sharply in the coming decades, with Latinos facing the largest increase among all racial and ethnic groups. This growth will intensify caregiving demands on Latino families, who already face challenges managing behavioral and psychological symptoms of dementia (BPSD) and often lack culturally appropriate professional support. STAR-C is an evidence-based behavioral intervention that equips caregivers with strategies for managing BPSD. It was originally delivered in-person by a coach but was later adapted to a virtual format with e-learning modules and phone check-ins with a coach. However, STAR-C was not developed with consideration of Latino cultural values, beliefs, and practices. As a result, the content lacks cultural and linguistic relevance for Latino caregivers. Latino caregivers’ engagement with the STAR-C virtual intervention also remains underexplored since prior testing included mostly non-Latino White participants. To enhance access for Latino caregivers, it is crucial to address cultural and linguistic relevance, time constraints, and the need for personalized support. Artificial intelligence (AI) offers a promising solution by providing culturally and linguistically tailored on-demand support. We propose to integrate an AI-driven virtual assistant with chatbot functionality into STAR-C. This virtual assistant will provide real-time support between coach check-ins and reminders to engage with e-learning modules that have been culturally adapted. This Stage I study has three specific aims. Aim 1 is to develop and integrate an AI-driven virtual assistant in the STAR-C intervention. We will follow our recent approach for creating a Large Language Model (LLM)-based chatbot. We will refine the model with input from STAR-C coaches, selecting the best-performing version. We will conduct usability testing with Latino caregivers to further optimize the virtual assistant based on their feedback. Aim 2 is to evaluate fidelity and caregiver acceptance of the AI-enhanced STAR-C intervention. Fifty Latino caregivers will participate in a 6-month intervention involving e-learning modules, phone check-ins with a human STAR-C coach, and on-demand support from the virtual assistant. The study will assess fidelity of intervention delivery and caregivers’ perceived usefulness, ease of use, behavioral intention, and actual use of the intervention. Aim 3 is to assess Latino caregivers’ attitudes toward using the AI-enhanced STAR-C intervention. Qualitative interviews with the 50 participants will assess their perceived benefits, challenges, and emotional responses to the AI-enhanced STAR-C intervention. These qualitative findings will be integrated with quantitative data from Aim 2 to provide a comprehensive understanding of Latino caregiver acceptability of the AI-enhanced and culturally adapted STAR-C intervention. The findings of this Stage I study will lay the groundwork for advancing along the NIH Stage Model to develop a scalable and effective intervention that improves health outcomes for Latino caregivers and their family members with ADRD.

NSF Awards · FY 2025 · 2025-09

This project develops powerful new tools for understanding today’s most complex data, leveraging cutting-edge artificial intelligence (AI) techniques to help data analysts across diverse fields make informed, automated decisions. Modern science increasingly depends on massive and intricate datasets. Yet many of these datasets are messy, heterogeneous, and too large or irregular for traditional methods to manage effectively. This research introduces novel approaches to analyze such data -- methods that are fast, flexible, explainable, and AI-powered. These tools help scientists and decision makers identify patterns, quantify uncertainty, and make better data-driven decisions. In parallel, the project advances public education in data science and mathematics by creating learning opportunities for high school and college students and by bringing cutting-edge ideas into classrooms and community events. In this way, the project invests in the next generation of talent and underscores the role of AI-enhanced statistical reasoning in solving urgent challenges across science, health, and industry. Technically, the project develops a unified framework for graph-based statistical and machine learning inference in two fundamental classes of complex data: manifold data, which exhibit hidden geometric structures, and mixture data, which arise from overlapping subpopulations. Three core research aims guide the effort. The first develops methods to estimate causal effects in structured data using stochastic nearest-neighbor graphs. The second advances tools to measure statistical dependence and conditional independence using sophisticated graph-based statistics. The third addresses inference in heterogeneous mixture models, with applications to brain single-cell data related to autism spectrum disorder. The project integrates state-of-the-art concepts from statistics, including distribution-free inference, optimal transport, and generative modeling, all of which are central components of today’s AI toolkit. Collaborations with partners from medical and high tech industries help ensure that the resulting methods translate into tools for researchers and professionals working at the frontiers of data science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Each year, more than 250,000 older adults in the United States are transferred from the emergency department (ED) to another hospital for care, yet more than a quarter do not require specialized resources or procedures once transferred. It is unknown whether these potentially avoidable transfers positively impact patients or simply expose them to the risks of transfer without clear benefit as data on outcomes after interfacility transfer of older adults is lacking. Acutely ill or injured elderly patients may be particularly vulnerable to separation from their social support systems and are at increased risk for adverse events such as delirium and falls. Even justifiable transfers may not be concordant with patient preferences. This knowledge gap, combined with a lack of understanding of the experiences and specific challenges faced by older adults during transfer, limits physicians’ ability to provide fully informed, patient-centered care when considering whether to transfer an elderly patient. The overall objective of this proposal is to quantitatively and qualitatively advance our understanding of interfacility transfers of older adults and lay the groundwork for development of an intervention to optimize transfer decisions. This project proposes the following specific aims: 1) To characterize outcomes of older adults who experience interfacility transfer; 2) To explore older adult and care partner experiences with transfer; and 3) To identify factors influencing transfer decisions among key decision-makers. To accomplish Aim 1, we will quantify hospital outcomes and adverse events among transferred older adults, including clinically relevant subgroups of older adults, using statewide administrative claims data. For Aims 2 and 3, we will conduct interviews with patients, care partners, and other key individuals involved in the transfer process. This proposal is closely aligned with the “National Institute on Aging: Strategic Directions for Research, 2020-2025;” this investigation will be used to develop strategies to improve the interaction of older adults with the health system. Specifically, this work will translate to improvements in patient-centered care delivery for older adults potentially needing transfer. This GEMSSTAR project, professional development plan, and mentorship by accomplished clinician- researchers with complementary skill sets will help the PI acquire relevant skills to become a physician- scientist with expertise intersecting aging, emergency medicine, and patient-centered outcomes research. The findings will also serve as preliminary evidence to support a future Beeson career development award application to develop interventions to help older adults and their providers successfully navigate interfacility transfer decisions.