University Of Washington

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT Next-generation sequencing (NGS) has become increasingly integral to the practice of clinical oncology, where its ability to scalably examine hundreds to thousands of targets now routinely enables identification of prognostic and therapeutically actionable markers that support the practice of precision medicine. There are many applications for which it would be useful to detect and quantitate genetic and epigenetic cancer-associated variants at ultra-low levels (<1 in 10,000 or more), such as identifying drug-resistance mutations in tumors, detecting residual cancer cells after therapy, or early cancer detection. Nevertheless, standard NGS technologies are hampered by a relatively high error rate (~1 in 100bp), below which true biological variation cannot be distinguished from noise. Various methods have been proposed to bypass this issue by allowing error correction of NGS sequence reads, but such techniques are limited by technical and economic considerations, and have consequently seen little uptake in clinical use due to issues of cost-effectiveness, scalability, and practicality. Separately, there remains an unmet need for approaches that are able to sensitively assess both cancer-specific mutations and methylation profiles in a specimen using a single library preparation and sequencing run. We have recently developed a new experimental paradigm that addresses the limitations currently presented by error corrected sequencing techniques: we join the two strands of DNA from an initial template fragment into a single, covalently linked molecule. Error correction of the duplex can be performed by comparing separate reads from the two linked strands, thereby eliminating the need for redundant sequencing of template molecules. This provides robust error correction with scalability, cost-effectiveness, efficiency, and quantitative precision, and is compatible with low-to-mid output short read sequencing platforms (ie, Illumina) that are already in widespread clinical use. The current proposal will expand functionality of our approach to simultaneously detect DNA methylation patterns and cancer-associated mutations with ultrasensitivity. In our first Aim, we will develop methods and supportive bioinformatic analysis pipelines in support of this technology, and will characterize the cardinal performance metrics of the approach using reference material. In our second Aim, we will apply our technique to detect disease-associated mutations and methylation markers that identify residual tumor cells after leukemia therapy, the key prognostic variable predicting relapse. In the third Aim, we will identify cancer-associated methylation and mutation signatures in cell-free DNA from prostate cancer patients to improve cancer detection and monitoring. This work will provide information and deliverables having immediate, direct, and transformative benefit to cancer patients by improving the quality of oncology sequencing assays while imbuing them with enhanced diagnostic capabilities for the ultrasensitive detection of cancer associated mutations and epigenetic changes through the use of a single assay.

NSF Awards · FY 2025 · 2025-09

Modern data centers rely on large-scale memory systems to support everything from search engines to scientific computing. However, the increasing demand for memory leads to significant resource and energy use, particularly when older hardware is discarded before its useful life ends. This project addresses that challenge by developing MemWise, an intelligent system that efficiently manages memory across a range of hardware types. The project’s novelties are a programmable memory controller that coordinates how data is moved and stored across different generations of memory; a technique for reusing older memory modules alongside newer ones to reduce cost; and new fault-tolerant methods to ensure reliability even as memory components age. The project's broader significance and importance are in demonstrating that thoughtful system design can extend hardware lifespans and reduce waste, without sacrificing performance. MemWise is tested in a range of real-world environments, including public cloud services, private infrastructures, and direct-to-hardware computing setups, highlighting its adaptability and impact across the data center ecosystem. Specifically, the project designs a tiered memory architecture connected via a flexible hardware interface called Compute Express Link (CXL). A programmable controller dynamically moves data among memory types based on usage patterns, age, and reliability. To guide decision-making, the research introduces a new metric that jointly considers system efficiency and application performance. This allows both software developers and system tools to make informed trade-offs in real time. The controller also supports automatic data migration and transparent fault handling across unreliable memory units, which is essential for mixed-hardware environments. By co-designing hardware and software and validating ideas with real prototypes, the project advances the state of memory system research and offers practical strategies for improving the long-term efficiency of computing infrastructure. Expected impacts include lower operational costs, reduced waste, and broader educational opportunities for students involved in the research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This Pathways to Enable Open-Source Ecosystems (POSE) project enables automation in laboratory science workflows. Laboratory automation increases the precision and efficiency of science experiments, enabling the collection of large data sets and automating feedback loops. However, scientific experiments are highly varied and require deep domain expertise and specialized equipment. There are no one-size fits all approach to establishing a self-driving lab. This project addresses this opportunity by advancing RepLab, an Open-Source Ecosystem (OSE) for open-source laboratory automation. RepLab will support scientists in adopting automation in their workflows to increase their efficiency and accelerate their progress while training a generation of scientists with new skills. This ecosystem will vet open-source laboratory automation technologies by testing, benchmarking, and validating their performance and reliability. This solution will lower the barrier to replicating the technologies by conducting design for distributed production and supply chain assessments. Enabling automation in laboratory science workflows will foster U.S. innovation and accelerate technology development and translation by supporting industry and small businesses in establishing competitive science workflows. This POSE project establishes an OSE for laboratory automation. The team will validate and support open-source infrastructure that underpins self-driving labs, enabling scientists to close the loop on their experiments and accelerate their progress supported by automation and computing-enabled discovery. The OSE increases user trust in vetted open-source hardware and control software, and encourages scientists to incorporate optimization in their approaches. Users will be able to replicate laboratory automation infrastructure across different sites. As ecosystems for open-source hardware are also less well-established than their open-source software counterparts, this solution will improve collaboration and co-creation mechanisms and may become valuable to others facing similar challenges stemming from distributed manufacturing, trustworthy hardware, and quality control. RepLab will support onboarding by developing extensive documentation and training materials for scientists from various domains seeking to automate their laboratory processes. In support of these efforts, the OSE will convene several avenues for community engagement, including online and in-person workshops. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Abstract: Over 30% of patients who develop acute hypoxemic respiratory failure (AHRF) and require ventilator support will die in the hospital. Treatment for AHRF remains extremely limited, as nearly every clinical trial in the last 20 years has failed to demonstrate improvements in mortality. A major obstacle for these trials is that most patients improve with existing care; this dramatically limits our ability to detect benefits for the remaining patients with persistent HRF, who are the ones most at-risk for death and in-need of new therapies. Currently, there are no accurate ways to distinguish patients with persistent HRF early, when trial enrollment and intervention is critical. The goal of this project is to develop robust models to identify patients at high risk for persistent HRF early, by using innovative opportunities in data science and machine learning to capture complex data sources (text and imaging) and accurately predict risk. The project will also allow Dr. Neha Sathe, an early career investigator and Pulmonary & Critical Care physician, to gain expertise in state-of-the- art methods to develop, evaluate, and improve multisource prediction models in real-world settings. In Aim 1, Dr. Sathe will develop and validate models that identify patients at high risk for persistent HRF, by integrating retrospective data from electronic health records, chest radiograph reports, and banked blood at two medical centers. In Aim 2, she will evaluate and explain these models in a new prospective cohort, to develop strategies and infrastructure for deploying and monitoring these models in future work. In Aim 3, she will develop novel methods to analyze sources of data with high potential to improve prediction of persistent HRF (chest radiograph images and tracheal aspirates, which are readily collected but under-leveraged). This work will yield models that improve the ability of trials to identify effective therapeutics for high-risk patients, minimize exposure to potentially costly or toxic therapeutics in patients likely to resolve, and provide significant insights to advance precision medicine. This work will also yield research infrastructure that can be adapted to rigorously develop and test predictive models for other clinical problems in critical care. To achieve these aims, Dr. Sathe will have complementary mentorship across the thriving ecosystem at the University of Washington for translational AHRF research, medical data science, and informatics. Altogether, this proposal aligns with NIH strategic objectives to leverage new opportunities in data science, and will support Dr. Sathe's long-term goal of understanding how to best use these opportunities to individualize and improve the care of patients with AHRF.

NSF Awards · FY 2025 · 2025-09

Current ocean sensing platforms are constrained by high costs, limited scalability, and insufficient data resolution—particularly for capturing submesoscale ocean dynamics that are vital for understanding ocean mixing and climate. This project proposes the development of CALANOID, a modular, low-cost, air-deployable ocean profiling float system, designed to enable dense, distributed in-situ observations. This project fosters collaboration between industry and academia and supports workforce development through hands-on engineering and research opportunities. The open-standard design ensures extensibility and accessibility for the broader scientific community, contributing to open science and equitable participation in global ocean research efforts. The project advances the state of the art in ocean sensing technology through the design and validation of a modular, scalable profiling platform that addresses critical gaps in spatial and temporal resolution of submesoscale measurements. Key innovations include a compact, piston-cylinder buoyancy engine designed for precise vertical profiling, and the NanoCTD, a miniaturized, and a solid-state CTD sensor. The integration of low-cost MEMS-based temperature and pressure sensors further enhances data quality while maintaining affordability. The system is designed to meet stringent performance targets, including 400-meter pressure ratings and two-month operational endurance, and will be validated through rigorous lab and field testing, including air-drop and calibration trials. 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

The goal of this Midcareer Investigator Award in Patient-Oriented Research is to enhance the ability of Dr. Claire Creutzfeldt to train, mentor, and support the career development of clinician-scientists focused on developing and testing interventions to improve the quality of life, quality of care and quality of communication for patients with severe stroke and their families. Three quarters of all strokes occur in persons aged 65 years and older. Recent advances in acute stroke treatment have led to reduced morbidity and mortality, but also to an increased number of survivors, who, along with their family members, are faced with long-term challenges and unique, unmet palliative care needs including steep declines in quality of life, receipt of care that does not reflect patients’ values and preferences, and high rates of caregiver burden. In addition, a variety of social, economic, and regional characteristics appear to predict differences in who is more or less likely to receive life-sustaining treatment and access specialists in stroke or palliative care. Although the scope of the problems is well documented, there are few evidence-based interventions to improve patient outcomes. Solving this problem will require the collaboration of scientists with expertise in prognosis, communication and shared decision-making; aging research; and social determinants of health research. Dr. Creutzfeldt is extremely well positioned to lead these efforts. She is a midcareer investigator with a mature, NIH-funded program of research on neuropalliative care with a focus on stroke. She has an extensive track record of successful mentoring in patient-oriented research. She has assembled a team of senior scientists and collaborators to accomplish the proposed research and mentoring plan. She will increase her skills as a mentor and expand her scientific expertise by participating in targeted career development activities in mentoring, social determinants of health research, and communication and decision science. The scientific goal of this proposal is for Dr. Creutzfeldt and her mentees to use the existing data and infrastructure of her ongoing R01s to develop new lines of research exploring palliative care needs specific to severe stroke (Aim 1), and adapt known palliative care interventions and develop new ones specific to severe stroke to improve quality of life, enhance the receipt of goal-concordant care, and reduce caregiver burden after stroke (Aim 2). Aim 3 focuses on extending the reach of neuropalliative care interventions to ensure generalizability all people facing severe stroke. Together, these scientific aims and career development activities will expand Dr. Creutzfeldt’s ability to train the next generation of patient-oriented researchers in developing interventions to improve the quality of care and quality of life for patients with severe stroke and their family care partners.

NSF Awards · FY 2025 · 2025-09

Submesoscale density fronts at the ocean’s surface are just hundreds of meters to tens of kilometers in width, however they are thought to have a substantial impact on the exchange of heat and other properties in and near the mixed layer, contribute to across-scale energy transfers, and consequently play a role in modulating chemical and biological processes in the surface ocean. This study will use existing shipboard observations of surface temperature and salinity along with satellite imagery to study global patterns in submesoscale variability. This research has the potential to expand our knowledge of the dynamics, seasonality, and impact of submesoscale density fronts to encompass the global scale. This is currently an underdeveloped area of research since the majority of observational studies of oceanic submesoscale fronts focus on individual fronts or specific regions. Improving our global understanding of submesoscale fronts may inspire future observational process studies in new regions of the world, motivate future theory and modeling studies that focus on currently under-studied regimes, and give rise to improved submesoscale parameterizations. The research component will support a graduate student for 5 years. The educational component of this CAREER award will introduce 9 undergraduate art students to scientific research. The art students will be fully involved in the science-based activities, and will be advised by both scientists and artists throughout their science-inspired summer projects. This will help train the next generation of science communicators while introducing a total of about 45 undergraduate science students to the importance of creativity in science. Despite the indication from modeling studies that submesoscale frontal dynamics have a global-scale impact, the majority of observational work has focused on individual fronts or specific regions of the world ocean. This gap is due to the inherit challenges in collecting and interpreting multiscale measurements that include length scales spanning both the frontal and global scale. To fill this gap, a new method was recently developed by the lead investigator of this project that uses a global ship-based thermosaliniograph dataset and high resolution satellite sea surface temperature data to detect submesoscale density fronts, determine their widths, and calculate their cross-front horizontal buoyancy gradients. This project uses this frontal detection method, combined with additional global datasets, to target three open research areas: A) The submesoscale plays a critical role in the seasonal evolution of mixed layer depth and, subsequently, air-sea exchange. This study will explore the global seasonal cycle of submesoscale frontal dynamics, determining how and why the seasonal cycle varies regionally. B) The local mesoscale velocity field and wind stress is known to impact submesoscale dynamics. This project will investigate if the mesoscale vorticity, mesoscale strain rate, and wind stress modulates submesoscale dynamics globally in a way that is consistent with existing theories. C) The conversion of potential energy stored in the wintertime mixed layer to kinetic energy in submesoscale fronts and eddies is critical for cross-scale energy exchange during springtime restratification. This project plans to quantify this energy conversion rate globally and seasonally. These results will then be placed within the broader cross-scale energetic context. 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: The overall premise of this research is that copper (Cu) status is a previously under-recognized and potentially modifiable risk factor in cirrhosis, the most advanced stage of chronic liver disease. In the United States, chronic liver disease affects 4.5 million people and accounts for over 41,000 deaths per year. Cirrhosis requires high rates of healthcare utilization compared to other diseases with an annual cost of approximately $21 billion. Malnutrition is one of the few modifiable factors that have been associated with poor prognosis. Current guidelines in nutritional intervention focus on protein and calorie intake, and give little consideration to trace elements, which have wide ranging physiological effects. Cu deficiency in the absence of liver disease compromises Cu dependent enzyme functions which can cause iron overload, tissue fibrosis and susceptibility to infections – pathologies also observed in cirrhosis. Our recent large cohort study identified Cu deficient cirrhosis patients, as defined by low serum Cu concentrations, had higher infection rates and a 3.4-fold increased risk of death compared to patients with normal Cu levels. Our preliminary findings and the well- established importance of Cu in human health raise several important questions: Does reduced circulating Cu, the standard definition of Cu deficiency in the general population, similarly reflect a deficiency state in cirrhosis? Is the higher infection and mortality risk observed among patients with low serum Cu mediated by Cu dependent enzymes and immune cells? Is reduced circulating Cu a secondary response in cirrhosis, therefore should be “left alone,” or should patients receive Cu supplementation in order to improve functional Cu store and its associated physiological functions? In an attempt to answer these questions, we designed a pilot randomized, placebo-controlled, crossover trial to determine the effect of Cu supplementation on Cu dependent biochemical changes, patient safety and patient reported outcomes.

NIH Research Projects · FY 2026 · 2025-09

Abstract. This HEAL project will employ computational, machine learning (ML) and Artificial Intelligence (AI) tools to accelerate the discovery of vaccines and antibodies against highly toxic ultrapotent synthetic opioids (UPSO). United States, Canada, and Europe have registered dramatic increases in fatal drug poisoning due to the widespread availability of fentanyl, fentanyl analogs, emerging compounds of the nitazene class, and their mixtures. Because of their potency, ease of illicit synthesis, and widespread availability, UPSO are fueling the ongoing overdose crisis. Beyond their impact on individuals with Opioid Use Disorder (OUD), these compounds could be involved in mass casualty incidents (MCI) or deliberately deployed in chemical attacks. Current FDA- approved countermeasures consist of opioid receptor antagonists, which may not always be sufficient to reverse overdoses involving UPSO or drug mixtures containing UPSO. To address this public health threat, our team has developed vaccines and monoclonal antibodies (mAb) against a series of UPSO. Vaccine-elicited antibodies and mAbs selectively bind the target drug in serum, reduce distribution of the unbound (free) drug to the brain, and prevent or reverse drug-induced effects. Based on their pre-clinical profile, anti-UPSO vaccines and mAbs have the potential to counteract overdose toxicity and poisoning in both civilian and defense scenarios. Due to their selectivity, vaccines and mAbs could be combined with existing treatments to increase survival. Because of the speed with which new and emerging UPSO can enter the drug supply, new tools to optimize and streamline the process of vaccine and antibody discovery are needed to rapidly address these threats by accelerating their translation into the product development space. To address this challenge, this project will employ state-of-the- art structure- and computational-guided platforms and AI/ML tools to accelerate discovery of vaccines and mAb against emerging UPSOs such as nitazenes. Specifically, AIM1 will focus on discovery of novel conjugate vaccines against nitazenes paired with computational methods to identify correlates of vaccine efficacy, AIM2 will focus on isolation of mAb against nitazenes and other UPSO accelerated by structure-informed design, and AIM3 will focus on evaluation of vaccines and mAb in vivo in rats challenged with nitazenes, fentanyl analogs, and their mixtures. Completion of this research proposal will lead to development and validation of computational methods to rapidly isolate vaccines and mAb against novel and rising UPSO, and other chemicals of concern.

NIH Research Projects · FY 2025 · 2025-09

Abstract: Dysfunctional mechanisms of the mitochondrial redox-sensitive signaling response to exercise in aged skeletal muscle. Individual: This career development award application details training and scientific research for Matthew D. Campbell, Ph.D. a skeletal muscle and mitochondrial biologist in the Radiology department at the University of Washington, Seattle. His short-term goals are to take his background in mitochondria, redox biology, and contraction dynamics in aged muscle and expand them with tools evaluating tissue metabolism and mitochondrial protein interactions using novel 3D engineered tissue and in vivo mouse models. His long-term goals are to form an independent research laboratory focused on the mechanisms of muscle contraction/relaxation dynamics that interact with metabolic function in muscle. Research: Aging in skeletal muscle is a complex pathology involving redox signaling, metabolic deficits, oxidative damage, neuromuscular junction defects, and protein expression and interaction changes. Previous work by Dr. Campbell has shown that restoring basal redox state improves mitochondrial and skeletal muscle function. This proposal builds on his previous work to establish that dysfunctional redox-sensitive signaling alters substrate utilization following exercise (Aim 1), impacts beta oxidation and TCA cycle flux (Aim 2), and mitochondrial protein interactions (Aim 3). Career Development Plan: The proposed work in this application would support Dr. Campbell’s short-term and long-term goals by enhancing his current expertise in muscle and mitochondrial function with work focused on developing novel human induced pluripotent stem cell lines, assays of in vivo whole tissue metabolism, dissecting mechanisms of redox signaling, and generating/analyzing large datasets of metabolomics and peptide/protein interactions. His professional development activities will include technical training, formal coursework, guest lecturing on mitochondrial metabolism, presentation of his research at local and national conferences, grant writing and review, routinely scheduled lab meetings and individual meetings with his mentors. Research Environment: The University of Washington offers an outstanding environment for the training in muscle research and aging and includes the Northwest Metabolomics Research Center, Center for Translational Muscle Research, and a Nathan Shock Centers of Excellence in the Basic Biology of Aging. Dr. Campbell’s mentorship team includes senior faculty members that are well-known and respected for their work in 3D engineered tissue, metabolism, and mitochondrial protein interactions.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Alzheimer’s disease (AD) is a prevalent chronic neurodegenerative disorder, projected to affect between 11 to 16 million individuals in the US by 2050. Neurodegeneration in AD initiates with the formation of amyloid beta (A) plaques decades before observable symptoms. This latent, pre-clinical phase represents a crucial "window of opportunity" when new A lowering therapies can slow the disease progression and mitigate its clinical impacts. However, existing biomarkers of preclinical AD, such as cerebrospinal fluid (CSF), plasma and positron- emission tomography (PET), face significant limitations. This underscores an urgent need for readily available and noninvasive biomarkers capable of detecting AD at its latent, preclinical, stage when disease-modifying therapies are most effective. Resting state functional MRI (rs-fMRI) is gaining recognition for its potential to provide non- invasive biomarkers for AD pathology, particularly through the assessment of alterations in functional connectivity (FC) dynamics and networks related to A deposition. However, current computational approaches for analyzing rs-FC networks and their dynamics have notable limitations, including the use of ad-hoc or black-box analytical methods, over looking heterogeneity in rs-fMRI data and dynamic FC across subjects and populations, and limited sample sizes, impacting the applicability, generalizability, and replicability of existing biomarkers and their ability to reveal underlying disease mechanisms. This project aims to rectify the deficiencies in current computational methods and is grounded in the fundamental hypothesis that improved biomarkers for early detection of AD and related dementias (ADRD) can be obtained by leveraging the dynamics of rs-fMRI data and FC, while accounting for heterogeneity across subjects and studies. To achieve this goal, the first aim develops a new model to comprehensively capture the dynamics of FC and offer robust biomarkers for AD pathology by assessing the accumulation of 𝐴. To account for individual level heterogeneity, the second aim develops a multivariate dynamic models featuring both local and global structures to incorporate heterogeneous brain connectivity among different brain states in different individuals. The third aim then develops effective techniques for detecting alterations in brain connectivity networks, while accounting for subject-level heterogeneity through random effects, helping uncover underlying processes of ADRD initiation and progression. Finally, to address the limited sample sizes in individual studies, the fourth aim proposes an innovative transfer learning framework that leverages the inherent similarities among multiple datasets to improve the reliability of connectome-based ADRD biomarkers. Upon evaluation and validation, the above methods will be implemented as efficient, open-source software tools in form of R-packages and python libraries, accompanied with extensive documentations, illustrative examples, and interactive visualization capabilities, to maximize the adoption of the proposed methods by the broader community.

NSF Awards · FY 2025 · 2025-09

The project develops tools to read expanded genetic alphabets that contain bases other than A, T, G, and C, and studies how natural biological systems interact with these unnatural DNA letters. This research contributes to transformative applications in nucleic acid biotechnology, and has the potential to improve diagnostic assays, lead to the discovery of novel therapeutics, and enhance biomanufacturing techniques. The project integrates these research activities with robust educational objectives: preparing a globally competitive workforce through workshops for industry professionals, fostering public engagement with hands-on community activities, and developing online resources for data science education. Graduate and undergraduate students participating in the project will gain valuable skills and mentorship experience, contributing to STEM workforce development. This research addresses key challenges in expanded genetic alphabets, focusing on three objectives. First, it aims to improve generalizability (and accuracy) of next generation sequencing for unnatural base pairs through deep learning. Second, it develops single-context sequencing models that enable high accuracy measurements of polymerase replication fidelity for unnatural bases. With this new methodology, the project measures polymerases replication fidelity of various polymerase for these unnatural bases in various model in vitro systems - such as PCR and LAMP. Lastly, the project investigates the biocompatibility of unnatural base pairs in a microbial host by examining metabolic processes, replication fidelity, error repair pathways, and host responses. These approaches promise to bridge key technological gaps and knowledge gaps in expanded genetic alphabets, helping bridge this area of research towards transformative applications in biotechnology. 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

This proposal for an NIDDK Research Education Program has an overarching goal to further the development of individuals into future surgeon-scientists engaged in urology research careers. The planned Program reflects the commitment of the University of Washington Department of Urology to the current and future research workforce needs in Urology. This Program will capitalize on unparalleled resources including a committed Department, a robust research environment, dedicated and experienced faculty, data and research support resources, and a structured curriculum. These resources include research mentors who are practicing urologists, near-peer mentors who are current residents and fellows to act as career development mentors, and a biostatistician to facilitate maintaining the timeline for the Program by working with the participants on research study analyses. This Program leverages the success of the Department’s past mentored research experiences to refine and expand the scope and breadth of research opportunities in areas of urologic disease from earliest prenatal development to the commonest aging associated problems. The objective of this application is to train medical students in benign clinical and health services research, seek out residency positions with dedicated research experiences, all with the long-term goal to support the career development of individuals into urology research careers and to create novel research solutions to benign urological conditions with high public health impact. To achieve this objective, program participants will pursue the following Specific Aims: 1) To gain exposure to methodologies for conducting contemporary clinical and health services research in benign urologic diseases; 2) To conduct a hypothesis-driven research project through which they will develop sophisticated skills in applying clinical and health services research methods; 3) To gain skills in analysis of study data and presentation of research results through manuscripts, scientific presentations, and patient-centered research dissemination strategies; and 4) To prepare trainees for careers in academic urology through dedicated mentorship that includes career and leadership development. Supported by the mentoring and resources of this program, the overarching goal of this NIDDK Research Education Program is to ignite these participants to initiate and retain an excitement for urological research and carry it with them throughout their entire careers.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT Bacterial biofilms are microbial communities with aggregates of bacterial cells enclosed in an extracellular matrix. Biofilms present a significant healthcare challenge because of their prevalence in infectious diseases and resistance against treatment. Second messengers, which are critical intracellular signaling molecules that are produced in response to environmental stimuli, play a key role in biofilms. The regulation of these signaling pathways is a fundamental question in microbiology and biofilm research. In particular, the second messenger molecules cyclic diguanylate monophosphate (c-di-GMP) and cyclic adenosine monophosphate (cAMP) play critical roles in biofilm formation in many bacterial species. The opportunistic pathogen Pseudomonas aeruginosa is a model species for studying biofilms. My postdoctoral work has developed a tricolor reporter system to simultaneously study c-di-GMP and cAMP signaling at the single cell level in P. aeruginosa. Using this system, I found that c-di-GMP and cAMP signaling are activated under distinct surface conditions during the initial stages of biofilm formation, when bacteria attach and respond to a surface. This proposal aims to elucidate the molecular coordination between c-di-GMP and cAMP signaling and their roles in later stages of biofilm formation. Aim 1 will characterize how cAMP influences the enzymatic activity of c-di-GMP cyclase and phosphodiesterase, identify the enzyme targeted by cAMP, and investigate the role of an unknown protein PA3413. Aim 2 will examine how c-di-GMP impacts the three key proteins controlling cAMP signaling and employ an unbiased screen to identify additional genetic factors involved. Aim 3 will elucidate the coordination of c-di- GMP and cAMP signaling during biofilm maturation and dispersion, particularly under host-related environmental conditions. These aims will reveal the regulatory mechanisms between these two critical second messengers, deepen our understanding of phenotypic heterogeneity within biofilm communities, and are expected to facilitate future research into the broader regulatory networks of second messengers. This proposal also includes a career development plan for me to successfully transition to an independent faculty position. I will leverage the guidance of highly experienced mentor and co-mentors during the K99 mentored phase, gaining training in 1) second messenger signaling, 2) biofilm cultivation and dispersion, and 3) live cell confocal microscopy. I will also enhance my skills in mentoring, lab management, and scientific communication. In transition to the independent R00 phase, I will apply these skills and use the data generated from the proposed studies to position myself for a successful R01 grant prior to the end of this award.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT Kidney diseases, including chronic kidney disease (CKD), acute kidney injury, and glomerulonephritis, impact nearly 850 million people worldwide, leading to significant morbidity, mortality, and rising healthcare costs. While spatial transcriptomics has offered valuable insights by mapping gene expression within specific kidney niches, a comprehensive understanding of kidney disease requires spatial multiomics approaches that can detect both RNA and protein at single-cell or subcellular resolution. However, current spatial omics technologies struggle to analyze the kidney's complex structure and diverse cell types, and integrating RNA and protein assays at high resolution remains both technically challenging and costly. The overall goal of this project is to address these limitations by optimizing Pixel-seqV2, a submicron-resolution spatial transcriptomics assay, and developing ProteoPixel-seq for integrated co-profiling of transcriptomes and proteomes in human kidney disease. Building on Pixel-seqV1, which uses 1-µm-resolution "polony gels", as capture DNA arrays for spatial RNA sequencing, we will enhance its resolution to 0.6 µm through a scalable "polony gel stamping" method, reducing the array fabrication cost by over 100-fold. In Aim 1, Pixel-seqV2 will be optimized for spatial transcriptomics of the kidney at single-cell resolution. Sub-Aim 1A focuses on integrating 0.6-µm-resolution polony gels and optimizing assay conditions to improve RNA capture sensitivity and spatial resolution, while Sub-Aim 1B involves applying the optimized assay to both mouse and human kidney samples, refining cell segmentation algorithms to accurately delineate complex cell boundaries and detect rare, pathologically relevant cell populations. In Aim 2, ProteoPixel- seq will be developed for integrated spatial co-profiling of transcriptomes and proteomes. Sub-Aim 2A aims to expand Pixel-seqV2 with high-plex proteomic analysis using DNA-tagged antibodies, optimizing tissue assay conditions for simultaneous RNA and protein detection. In Sub-Aim 2B, we will apply ProteoPixel-seq to CKD patient biopsy samples, focusing on interactions between kidney stroma and infiltrating immune cells to reveal molecular pathways involved in disease progression and identify potential therapeutic targets. This project will yield catalytic tools for spatial multiomics tissue mapping, offering the first demonstration of integrated spatial transcriptomics and proteomics in kidney disease, significantly enhancing our understanding of kidney pathology and supporting the development of novel diagnostics and therapeutics.

NSF Awards · FY 2025 · 2025-09

Xiaosong Li of the University of Washington is supported by the Chemical Theory, Models, and Computational Methods program in the Division of Chemistry to develop advanced computational methods for understanding quantum phenomena driven by magnetic fields. These magnetic-field-induced processes are fundamental to breakthroughs in chemical transformations, quantum materials, and quantum information science, with broad applications that can benefit society. Xiaosong Li will simulate microscopic quantum dynamics on experimentally relevant time scales, enabling deeper insight into how magnetic fields control molecular and electronic behavior. The new methods will provide a foundation for the rational design of next-generation quantum technologies and energy-efficient materials. In addition to advancing scientific knowledge, this research supports interdisciplinary education and training at the interface of inorganic, physical, theoretical, and materials chemistry. Undergraduate and graduate students involved in the project will gain hands-on experience in computational science and high-performance software development, equipping them with essential skills for careers in science, engineering, and education. Xiaosong Li will establish a rigorous first-principles framework for modeling time-resolved magnetic circular dichroism (MCD) spectroscopy. At its core is the development of a relativistic, time-dependent multiconfigurational approach capable of simulating MCD spectral evolution on attosecond to femtosecond timescales under finite magnetic fields. Complementary nuclear gradient techniques will be implemented to enable geometry optimization and excited-state characterization within relativistic multireference calculations. Together, these methods will allow for the simulation and interpretation of femtosecond-to-picosecond time-resolved MCD spectra, facilitating detailed structural and electronic analysis of complex molecular systems. A central application of this framework will focus on elucidating the covalency and electronic structure of rare-earth-doped magnetic complexes by tracking their evolving MCD signatures. These advances will offer new insights into excited-state dynamics and catalytic processes that are critical to chemical reactivity, energy conversion, and quantum materials design. All computational methods developed through this work will be integrated into Chronus Quantum, an open-source software platform. Broad dissemination of these capabilities will provide the scientific community with advanced tools for simulating magnetic and time-resolved spectroscopies, promoting reproducibility, enabling interdisciplinary collaboration, supporting education and training, and accelerating discovery in quantum chemistry, materials science, and condensed matter physics. 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 AND ABSTRACT This F32 research proposal will evaluate the axonal length-dependent pathology of Charcot-Marie-Tooth disease, type 2 (CMT2) using a human stem cell-based model. CMT is the most common inherited peripheral neuropathy in humans and is characterized by distal muscle weakness caused by motor and sensory neuron dysfunction. CMT2 is a genetically dominant axonal form, with mutations in eight aminoacyl-tRNA synthetases (ARS) identified as causative. Interestingly, while ARS mutants share similar pathological features, studies have yet to identify a common gain- or loss-of-function underpinning disease etiology. Furthermore, it remains to be determined why these mutations impact neurons in an axonal length-dependent manner. The only known shared function of ARS is protein translation, in which ARS conjugate amino acids with their cognate tRNAs. However, previous research has established CMT2 mutations do not consistently impact enzymatic function. Interestingly, a recent study discovered that a CMT2 GARS mutation resulted in aberrant interaction of GARS with stress granule protein G3BP1. Further, G3BP1 and other granule proteins have been recently identified as components of naturally occurring RNA transport granules. Thus, I hypothesize that GARS is regularly transported along axons in RNA granules, and CMT2 mutations impact transport of granules, reducing the amount of ARS and other species reaching axon terminals in an axonal length-dependent manner, altering axonal transcriptomes and proteomes. To test this hypothesis, I will first examine the interaction of ARS proteins with various granule protein molecules in disease-relevant human induced pluripotent stem cell-derived motor neurons. I will employ co-immunoprecipitation and imaging analyses to confirm ARS proteins, in particular GARS, associate with RNA granules (Aim 1). I will next evaluate the impact of a GARS mutation on length-dependent transport and local translation in motor neurons. Utilizing microfluidic chambers, I will culture GARS mutants and isogenic control motor neurons in chambers with increasing lengths, thus controlling the length of the axonal projections. I will then evaluate axonal transport and local protein synthesis deficits in the mutant neurons (Aim 2). The data generated from these studies will include axonal-specific transcriptomic and proteomic profiles for both normal and mutant motor neurons. This will provide the field with an in-depth analysis of the local changes occurring in motor neurons, giving new insight into CMT2 pathology, identification of novel targets for therapies, and a greater understanding of the mechanisms underpinning axonal protein synthesis. Further, these studies, and future work building off this project, will expand our understanding of neuronal development and evolution. The PI of the project will train with well-established, highly regarded professors at the University of Washington in the fields of neuroscience, stem cell biology, and bioengineering, and the training plan will build strong professional skills – publications, communication, mentoring – to foster growth into a well-rounded independent research scientist.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT/SUMMARY Understanding the role of sex steroids like estrogens and androgens within the brain is crucial for elucidating their impact on various brain functions, including mood, cognition, and reproductive behavior. However, our current understanding of how these steroids modulate neuronal physiology and behavior in vivo remains limited, largely due to the absence of technologies capable of monitoring these hormones in real time within the brain. The goal of this project is to bridge this gap by developing a suite of genetically encoded fluorescent biosensors for steroid hormones. These biosensors will enable cell-specific monitoring of steroid hormone levels in awake and behaving animals, providing unprecedented insights into the dynamic roles of neuroactive steroids derived from endocrine tissues or synthesized within the brain. Our central goal is to engineer green fluorescent biosensors for estrogens and androgens based on their nuclear receptors. By incorporating fluorescent reporters into receptor regions that change conformation upon ligand binding, we aim to create sensors with enhanced ligand affinity and signal strength through structure-guided engineering and high-throughput screening. Additionally, we plan to develop far-red shifted sensors for multiplexed applications, extending our innovative approach to a broader spectrum of steroid hormones. Our project is innovative because it employs structure-guided design and a high-throughput engineering platform to rapidly screen thousands of protein variants. This approach significantly accelerates the development of biosensors capable of real-time steroid hormone monitoring in vivo. Furthermore, by applying these sensors to study the rapid effects of steroid signaling on neural activity in the hypothalamus of behaving mice, we aim to dissect the temporal dynamics of steroid action beyond their genomic effects. This research is significant because it addresses a critical gap in our ability to study steroid hormones in live animals. By providing tools for real-time monitoring of steroid hormone signaling within genetically specified brain cell types, we pave the way for a deeper understanding of how steroids influence brain circuit function and behavior. The successful completion of this project will not only advance our knowledge of steroid hormone dynamics but also offer new avenues for therapeutic intervention in steroid-related disorders.

NSF Awards · FY 2025 · 2025-09

Frontier AI models have pushed the boundaries of machine learning and artificial intelligence research and sparked transformative technological innovation in many US industries. These large-scale AI models are able to process and generate text, image, audio, and video, and currently require massive amounts of data and computing. This Mathematical Foundations of Artificial Intelligence (MFAI) project aims to uncover the mathematical principles that explain when and why these highly advanced AI models are so effective, and to overcome the fundamental limits of brute-force scale presently employed to surpass human expert intelligence in benchmarks. The project looks to advance the capabilities of AI models to conduct inference in new situations in which there is no training data, and to perform complex reasoning and problem-solving tasks. This research seeks to ensure that the US remains the global leader in AI, advancing economic prosperity, national security, and global competitiveness. This project aims to rigorously characterize the mathematical frontiers of generative AI models, including state-of-the-art large language models (LLMs), by developing new theoretical frameworks and modeling principles rooted in machine learning, probability theory, variational analysis, mathematical statistics, and information theory. The research will investigate how frontier AI models achieve remarkable performance despite fundamental theoretical barriers and will identify the key mathematical quantities that drive their generalization abilities. The project looks to develop new mathematical analyses of diffusion-based generative AI models, design novel data strategies for AI models used towards zero-shot inference, and discover scaling laws enabling models to achieve compute-optimal accuracy tradeoffs for inference and generation. This award is jointly funded by the Directorate for Mathematics and Physical Sciences, Division Of Mathematical Sciences; Directorate for Engineering, Division of Civil, Mechanical, & Manufacturing Innovation, and Directorate for Computer & Information Science & Engineering, Division of Computing and Communication Foundations and Division of Information & Intelligent Systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The final stages in the evolution of the most massive stars play a crucial role in astrophysics, injecting energy and enriched metals into the interstellar medium, and producing compact objects – neutron stars, black holes, and the wide range of transient phenomena associated with them. The predictive power of theoretical models for massive stars, however, is severely limited by the large uncertainties associated with this regime of stellar evolution. The project will constrain some of the most uncertain processes in massive stellar evolution, such as the efficiency of mass loss and the impact of supernova kicks. This project will develop new tools that can maximize the science extracted from the wealth of data on resolved stellar populations and object catalogs that will be available in the coming decade with the advent of facilities, like the Vera C. Rubin Observatory. The project will also broaden the impact of the research through a mixture of new and well-established outreach projects designed to foster engagement by educators and students in science. The spatially resolved stellar populations in Local Group galaxies will be used to place unprecedented constraints on the evolution of the most massive stars. To this end, the investigators will precisely measure the formation efficiencies and evolutionary timescales (i.e., the delay time distributions, or DTDs), for the main outcomes of massive stellar evolution: Wolf-Rayet and Intermediate Mass Stripped Stars, Blue, Yellow and Red Supergiants, X-ray Binaries, and Supernova Remnants. The investigators will model each of these DTDs using the state-of-the-art population synthesis code COSMIC. A systematic comparison between measured DTDs and COSMIC predictions will therefore constrain some of the most uncertain processes in massive stellar evolution, such as the efficiency of mass loss, the effects of the common envelope phase, and the impact of supernova kicks. These constraints will provide a new level of detail in our ability to trace and quantify the energetics and enrichment from the progenitors of many astrophysical transients and gravitational wave sources. 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 Mass spectrometry (MS) stands as a cornerstone of analytical techniques used across various fields of biological and chemical sciences. Its ability to provide detailed molecular information makes it indispensable for both basic and applied research. Miniaturized mass spectrometers offer significant benefits in areas relevant to NIGMS priorities by enabling rapid, on-site biochemical analysis and diagnostics in resource-limited or decentralized environments, which can advance precision medicine, public health, and fundamental biomedical research. Their portability allows for real-time monitoring of disease biomarkers, therapeutic drug levels, and metabolic states directly at the point of care, facilitating timely interventions. While miniaturized MS systems excel in portability and real-time analysis, they typically lack the versatility and thorough sample separation of conventional systems, limiting their applicability to a narrower range of sample types. Integrated sample preparation strategies that improve the matrix tolerance, accuracy, precision, and versatility of miniaturized MS instruments are crucial to address the growing need for decentralized diagnostic tools in both clinical and field environments. Microfluidics offer precise control, reduced sample volumes, and improved reproducibility, and are a promising platform for automated sample preparation. However, current microfluidic approaches have complexity and cost issues. 3D- printed capillaric microfluidics present a promising solution. They allow rapid prototyping and eliminate the need for complex instrumentation, making them accessible to non-experts and diverse scientific fields. The overarching goal of this ESI MIRA grant is to develop capillaric microfluidics for integrated sample preparation in mini-MS systems. The research will focus on three broad research areas that align with NIGMS's mission of advancing basic biological processes and translational applications, namely: (1) integrating capillaric microfluidics with mini-MS cartridges and ionization systems, (2) automating dried reagent rehydration and addition of internal standards and derivatization agents, and (3) implementing solid phase extraction and assay validation with clinical samples. The detection of antiretroviral drugs and a comparison with traditional MS will be used as a proof-of-concept to demonstrate accuracy and precision of the developed technologies. This project aims to revolutionize sample preparation in mini-MS, making it more accurate, precise, and versatile, and ultimately benefiting scientific research and diagnostics. The PI has a strong background in bioengineering and has made key contributions to the design, 3D-printing, and applications of capillaric microfluidics. This project has the potential to uncover new long-term directions and innovations both in the development of microfluidic technologies and their application to a wide a variety of fields.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT Transplant is a valuable treatment option for many people with kidney failure, but in order to receive a kidney, patients must first be referred to a transplant center and complete a process of physical and psychosocial evaluation. Nephrologists and dialysis organizations are increasingly incentivized to refer greater numbers of patients with kidney failure to transplant centers in an effort to improve equitable access to this treatment. Such efforts can also be expected to increase the number and clinical complexity of patients engaging in the evaluation process. However, many patients will not ultimately receive a kidney and the evaluation process itself can be demanding, opaque, and lengthy and can take an emotional and psychosocial toll for patients and families. As more patients are drawn into this care process, multiple national stakeholder groups have emphasized the importance of minimizing burdens and improving patient experience. However, efforts to improve this patient experience are hampered by a lack of validated measures. In recent years, leading professional societies, regulators, and patient communities have called for the development of patient-reported outcome measures of the pre-transplant process. The aim of the proposed work is to develop and establish the content validity of a Kidney Transplant Evaluation Patient Reported Experience Measure (KTE-PREM). In Phase 1 (concept elicitation), we will perform a structured literature review, analyze existing qualitative interview transcripts, and conduct focus groups with patients who were referred to a transplant center, their family members, and clinicians who care for these patients (including primary nephrologists, social workers, and transplant team members). Patients will be recruited from Northwest Kidney Centers (a large non-profit dialysis organization based in Washington state which typically refers patients to three regional transplant centers) and the University of Washington. These sequential steps will result in a comprehensive set of concepts potentially relevant to a KTE-PREM. In Phase 2 (concept prioritization), we will identify items most relevant to patients to be included in a KTE-PREM by conducting a national survey among patients receiving care at two large non-profit dialysis organizations (Northwest Kidney Centers and Dialysis Clinic, Inc) who were referred to a kidney transplant center. In Phase 3 (instrument construction and refinement), we will design a draft KTE-PREM and iteratively refine the instrument through cognitive interviews with a local group of patients who were referred to a kidney transplant center. The proposed work directly extends from Dr. Butler’s recent NIH/NIDDK supported research (K23DK129777), which has identified a need and opportunities to improve person centricity of the kidney transplant evaluation process. Following development of the KTE-PREM, additional funding will be sought for validation. We anticipate that this validated instrument will have applications in clinical, research, and policy work intended to measure and improve patient experience in the kidney transplant evaluation.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT Sexually transmitted infections (STIs) disproportionately affect cisgender adolescent girls and young women (AGYW) who often experience STI complications, including infertility, chronic pelvic pain, and increased risk for HIV acquisition and peripartum morbidity. Approximately one in four AGYW in East Africa have Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), or syphilis. In Kenya, HIV and STIs comprise a syndemic with 40% of new HIV infections occurring among AGYW. Yet, research to address STIs lags behind HIV in this priority population and no primary STI prevention tools are currently available to cisgender women beyond condoms. In Kenya, 40% of women access contraception without interfacing with facilities, including at retail pharmacies, and are missed by facility-based HIV services like pre-exposure prophylaxis (PrEP). In our ongoing work among AGYW seeking contraception at 20 pharmacies in Kisumu, Kenya (NCT05467306); all AGYW offered STI testing accepted, 29% had CT or NG, and 70% accepted expedited partner therapy (EPT) and report no social harms. Among AGYW seeking emergency contraception, only 3% previously used HIV post-exposure prophylaxis (PEP), highlighting an opportunity to offer HIV PEP to AGYW via pharmacies. These preliminary data support that strategies to address STIs and promote HIV PEP/PrEP use for AGYW would be ‘high-yield’ in pharmacies. Qualitative data suggest that STI testing motivates health promoting behaviors, even when STI results are negative. To date, no studies evaluate if serial STI testing promotes PrEP persistence. ‘Event-driven’ doxycycline PEP (doxy-PEP) for CT, NG, and syphilis found no protective benefit for Kenyan women accessing PrEP at facilities, likely due to low adherence. AGYW more frequently access emergency contraception at pharmacies compared to facilities; thus, ‘event-driven’ strategies, like HIV PEP (“PEP-in-Pocket”) or doxy-PEP, may have higher use in pharmacies. We propose a RCT in Kisumu, Kenya–a region with 20% HIV prevalence–to test co- offering HIV PEP/PrEP and STI testing with and without doxy-PEP in pharmacies and prospectively assess HIV PEP/PrEP use and persistence, and STI incidence among AGYW (n=720). Aim 1 will conduct a 3-arm RCT among AGYW seeking contraception at pharmacies to compare HIV PEP/PrEP delivery with: 1) serial STI testing and doxy-PEP vs. 2) serial STI testing alone vs. 3) no serial STI testing or doxy-PEP. Aim 2 will assess implementation outcomes to inform scale up of integrating HIV PEP/PrEP, STI testing, EPT, and doxy-PEP into pharmacies using operational data, interviews, and surveys. Aim 3 will estimate cost and cost-effectiveness by incorporating time-and-motion data and micro-costing. We hypothesize that expanding HIV and STI prevention options to include HIV PEP, STI testing, EPT, and doxy-PEP in pharmacies will be cost-effective and improve HIV and STI outcomes in AGYW, a population disproportionately affected by STIs and HIV. Our study is designed to inform pharmacy delivery of HIV PEP/PrEP, STI testing, EPT, and doxy-PEP and provide evidence to inform policy and WHO guidelines for STI/HIV prevention among AGYW.

NIH Research Projects · FY 2025 · 2025-09

Project summary This proposal responds to RFA-DK-18-510, which requests applications for Research Sites to continue in the Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN). In this application, the University of Washington (UW) seeks support for a Research Site to participate in efforts to unravel the clinically confounding symptoms of the lower urinary tract. This proposal was developed following the guidelines stipulated by the National Institute of Diabetes, Digestive and Kidney diseases (NIDDK). The aim of the proposed UW Research Site, in conjunction with the Data Coordination Center and LURN Research Sites, is to develop and validate symptom-based instruments to measure lower urinary tract symptoms both in men and women, and to better define the subtypes of persons with lower urinary tract symptoms. The key responsibilities of the Research Site will be to adopt the study protocols developed by the LURN investigators, validate the developed Patient Reported Outcome (PRO) measures, recruit study participants, and conduct extensive characterization (subtyping) of them.

NSF Awards · FY 2025 · 2025-09

When the ocean loses heat to the atmosphere, a small but important difference in temperature occurs between the ocean surface and the water about 1 mm below the surface. This temperature difference of 0.2 to 0.5 °C plays a significant role in how much carbon dioxide is absorbed by the ocean. The only way to measure the temperature right at the surface, dubbed the “skin” temperature, is with an infrared radiometer that measures the ocean surface radiation. Because the ocean surface reflects some infrared radiation, a correction for the sky radiation reflected from the ocean surface into the sensor is required. Standard infrared measurements of the sea surface use a wavelength band for which the atmosphere is transparent in the infrared. Using this waveband, an additional measurement of the downwelling radiation from the sky is necessary because of the large difference of sky temperature between clear and cloudy conditions. This research implements a simplified technique using a special infrared wavelength band that significantly reduces the difference of sky temperature between clear and cloudy conditions and thus eliminates the need to make the sky measurement. The waveband is in a semi-transparent region, which results in the sky radiance coming from the water vapor in the atmosphere from 3 km above the sensor. The awardees have shown that the sky radiance in this band can be modeled using the air temperature and relative humidity in the vicinity of the sensor. The result is that a sky radiance measurement is not necessary. This also reduces the complexity of the calibration method by using one internal reference target rather than two. These simplifications make possible routine measurements of infrared sea surface temperatures from buoys and uncrewed surface vehicles. An early career scientist and an undergraduate student will be actively involved in research and gain hands-on experience through mentorship programs . Over the past five years, the Principal Investigator (PI) has developed an instrument called IRISS-OPT (InfraRed Instrument for Sea Surface temperature-Optimal Band) for measuring ocean surface skin temperature. IRISS-OPT combines a simplified one-point in situ calibration with the so-called optimal band technique, which eliminates the need for a sky radiance measurement. IRISS has demonstrated an accuracy comparable to the proven instrument known as ROSR (Remote Ocean Surface Radiometer) from a research vessel. Modeled profiles of air temperature and water vapor are used to replace the sky radiance measurement. The combination of the simplified calibration and elimination of the sky measurement using IRISS-OPT now makes it practical for deployment on Uncrewed Surface Vehicles (USVs) and buoys. This research will transition the current design to a stand-alone version with internal power and recording and optional external power and output dubbed IRISS-USV. Two existing IRISS-OPT units will be upgraded for use on buoys. Two new units with a reduced form factor to fit on the saildrone Uncrewed Surface Vehicles will be designed and fabricated. The units will be validated at sea on a research vessel and buoys. The research to develop a stand-alone version of the IRISS sensor to measure the ocean surface skin temperature is timely, innovative, and transformative. The combined simplification of a single ambient temperature calibration and no sky measurement will significantly increase the practicality and accessibility of ocean surface skin temperature measurements. The activity is transformative because it will, for the first time, make it possible to measure skin temperature from USVs and buoys with an accuracy comparable to proven ship-based instruments. 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.