Ohio State University

NIH Research Projects · FY 2025 · 2025-09

Health disparities are preventable and identifying their social determinants and underlying mechanisms is of high scientific priority. A 2020 National Academies of Sciences report articulated a clear national agenda to target health disparities among the sexual minority (SM) population, and there is a renewed focus on research to address disparities among racial and ethnic minority individuals. Critically, data on biological mechanisms linking stress and health in the SM population are limited. We will assess inflammation via serum interleukin(IL)-6 and C-reactive protein (CRP). Low-grade, chronic inflammation is implicated in age-associated frailty, morbidity, mortality, and accelerated epigenetic aging.36 Epigenetic age, is a highly novel and robust predictor of multiple diseases, longevity, and all-cause mortality. Examining epigenetic clocks through DNA methylation offers a cutting-edge approach allowing us to answer critical questions: Does SM stress accelerate aging? Can stigma and discrimination propel molecular aging and shorten health span (the length of time that a person is healthy—not just alive)? Capitalizing on a time-sensitive, unparalleled opportunity to add biological data collection to the National Couples’ Health and Time Study (NCHAT) to create additional data for the NCHAT Stress Biology Study (NCHAT-BIO), we aim to fill these empirical gaps. Funded by NIH, NCHAT is the only longitudinal, probability, population-representative study (N = 3,642, ages 22-67, 50% women) with representation of heterosexual (55%), sexual minority (45%), non-Hispanic white (62%), and racial and ethnic minority (38%) coupled adults with comprehensive psychosocial measurement. This project advances innovative health research by combining survey data on stressor exposures, psychological health, and health behaviors with biological data from an estimated 2000 of NCHAT’s respondents. Aim 1: Delineate differences in epigenetic aging and inflammation by sexual orientation and test interaction effects of sex and race/ethnicity. Aim 2: Determine effects of sexual minority stressors on epigenetic aging and inflammation. Aim 3: Identify modifiable factors linking sexual orientation and sexual minority stressors with epigenetic aging and inflammation. Sexual minorities face significantly greater risk for chronic health conditions than heterosexuals. The biological underpinnings of these group differences have been minimally studied representing an unacceptable gap in knowledge. This study offers the remarkable opportunity to examine epigenetic aging and inflammation in a large, US-representative sexual minority and heterosexual population, permitting crucial tests of mechanisms linking stress and health, and interaction effects of race/ethnicity and sex. This research promises to uncover modifiable treatment targets to address health disparities. The resultant assays will be publicly available to other researchers via ICPSR offering an unrivaled resource for future researchers. By leveraging NCHAT to create NCHAT-BIO, this is a singular and time urgent opportunity.

NSF Awards · FY 2025 · 2025-09

How proteins interact inside plant cells plays a vital role in how plants grow, adapt, and defend themselves against pests and environmental stresses. However, current tools to study these interactions often rely on non-native systems or invasive methods that can disrupt natural cellular processes. This project will develop a novel, non-invasive protein delivery system to deliver proteins directly into native plant tissues. The new system uses a small, engineered protein called MTD4, which can carry functional proteins into plant cells without disruptive delivery systems and much more efficiently than previous methods. This innovation enables researchers to observe the activity of proteins in real time within live, physiologically intact plant cells, offering a clearer picture of how plant responses are regulated. The approach is fast, flexible, and applicable to a variety of plant species, making it a powerful tool for functional genomics. Development of the system will generate proteomics data from protein-protein interactions that can be used for further functional analysis by the plant community. The project involves strong training and outreach components, providing hands-on research opportunities for graduate students, postdocs, and undergraduates. Through a partnership with Ohio’s Farm Science Review, students will engage with farmers and the public to communicate scientific discoveries and real-world agricultural challenges through storytelling and creative media. This project will enhance our understanding of plant genomes, foster interdisciplinary education, and support the development of resilient, high-performing crops for a sustainable future. This project aims to develop an intracellular protein delivery (ICPD) system to enable study of proteins delivered into plant cells in a physiologically relevant context, by leveraging a highly active membrane translocation domain, MTD4. Current delivery systems, including heterologous expression systems, protoplast transfection, and agroinfiltration are limited by the lack of cellular context, slow protein expression, and/or unintended stress responses. Preliminary studies demonstrate that MTD4 is capable of rapidly delivering a variety of soluble proteins directly into the cytosol of plant cells, with minimal damage to the plant. This project aims to: (1) optimize the ICPD system in tomato and Arabidopsis, and (2) develop ICPD for delivering cargo proteins into various subcellular organelles. This system delivers proteins into plant cells via foliar spray, enabling nuanced analyses of protein function and interactions in real time. By preserving cellular integrity and context, ICPD represents a significant advancement in functional genomics tools for plants. Beyond basic discovery, ICPD has biotechnology potential in high-throughput screening applications and synthetic biology approaches. The project provides robust training and outreach activities designed to foster the next generation of plant scientists and communicators strengthening capacity in plant biology and agricultural biotechnology. Project data resources will be accessible through long-term public repositories. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project examines how people understand and organize information when they speak, especially how sentence structure changes to highlight either shared knowledge or new information in conversation. While past research has focused on languages with fixed word order, this study looks at languages where the grammar of sentences (especially word order) is much more dependent on conversational context and other information. It also examines how grammar, word meanings, and context work together and are understood by the brain. By including languages with different types of grammars, the project advances psycholinguistic research to better reflect how people learn and use language in real life, and how they process information in conversation. The methodology relies on the collection of cognitive data, including eye gaze data that measures where people look while listening to, speaking, and reading in their language. The data test how conversational context can make sentences easier, or harder, to process. Findings regarding how cognitive factors interact with real-world knowledge support the development of better artificial intelligence (AI) models, including large language models that are less efficient at incorporating contextual information. Other benefits to society include advances in biotechnology through use-based application of neuroscience technologies and improved AI translation software that more seamlessly relates information between languages. This project investigates the interface between syntax and information structure, focusing on how given information, new information, and emphasized information are variably cued by the grammar of a language and, in turn, variably processed in the brain. Specifically, the project compares languages that are configurational (conveying information with specific words or intonation within a relatively fixed syntactic order) with those that are less-configurational (conveying information with variable syntactic order and changes to morphology). The proposed study addresses two key research questions (1) what psycholinguistic reflexes are associated with marking focus (new/emphasized information) in typologically distinct languages, and (2) how information structure governs syntactic production, particularly in languages with less-configurational grammar. The study targets languages with discourse-sensitive syntax to broaden empirical coverage. Through experimental approaches and robust statistical modeling of eye gaze data, the project determines cross-linguistic patterns in information structure processing and production, contributing to theoretical models of the morphosyntax-discourse interface. Additionally, the project adapts psycholinguistic methodologies to ecological factors, and directly probes the ways that language is sensitive to the context in which it is used. 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

Approximately 85% of the matter in the universe is thought to be dark matter. The presence of this mysterious component is well documented in large galaxies, but the amounts of dark matter in galaxies much smaller than the Milky Way are poorly understood. With a new dataset of 100,000 dwarf galaxies, this program will measure their composition and proportion of dark matter, stars, and gas. The principal investigators will also use advanced computer models and simulations to explain the most important physical properties of these dwarf galaxies. This program will develop summer research internships, seminars, and mentoring schemes to increase the retention rates of undergraduate students majoring in physics and astronomy in the States of California, New Jersey, and Ohio. This program will also support research and training opportunities for undergraduate and graduate students in astrophysics. This program will characterize the baryon and dark matter content of massive dwarf galaxies with a united and novel theory and observational approach. This program is made possible by the Merian survey, which is identifying 100,000 well-characterized massive dwarf galaxies. This program will match the internal kinematics of galaxies with their halo mass, using HI line shapes and weak-lensing masses. The principal investigators will use simulations, which successfully reproduce the HI properties of dwarf galaxies, to determine if Cold Dark Matter (CDM) or self-interacting dark matter (SIDM) is a better match to the kinematics and halo properties of massive dwarfs. Using the DESI Y1 data set, the PIs will study how environmental effects impact dwarf properties and study to what degree this impacts weak lensing measurements. 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

Non-Technical Abstract: This project will study a special kind of material called a liquid crystal (LC). It flows like a liquid but has some internal structure and is already used in everyday items like phone and TV screens. In this research, scientists will place LCs on tiny, patterned surfaces and gently move drops of water or other fluids across them. As the fluid moves, it changes how the LC molecules line up, and the LC can “remember” the direction the fluid moved, even after the motion stops. This could lead to new ways to store and use information without electronics, such as fluid-based memory that works like a read-only chip. It could also help track how bacteria or cells move on surfaces or detect tiny changes in fluid flow. The project will also give college and high school students hands-on lab experience, helping them build skills for future careers in science, technology, and manufacturing. Technical Abstract: This project will investigate how confinement and interfacial shear induce polar order and reconfigurable dipole lattices in apolar nematic LCs. Unlike ferroelectric systems that rely on intrinsic molecular dipoles, nematic LCs are typically nonpolar and symmetric, with no preferred direction. This research investigates how symmetry breaking through geometric confinement and fluid interfaces can lead to the emergence of stable elastic dipoles, i.e., pairs formed between topological point defects and fixed geometric anchors. These dipoles exhibit discrete orientations that can be switched by fluid motion, forming two-dimensional dipole lattices with programmable states. The work will combine experiment and theory. This project will fabricate patterned microstructures, introduce nematic LCs, and apply immiscible fluid droplets to induce reorientation. Observations will be made using polarized light microscopy. Theoretically, the project will use Landau–de Gennes Q-tensor modeling and hydrodynamic simulations to predict defect configurations and dynamic responses under shear. Three main research thrusts will be pursued: (1) establishing control over defect orientation through confinement and flow, (2) developing predictive models of defect behavior in various geometries and LC mesophases, and (3) exploring multistate memory behavior and flow detection applications. The outcomes will expand understanding of defect-driven order in soft materials, demonstrate how polar textures can be induced in apolar systems, and provide a basis for novel technologies such as fluid-responsive sensors and passive, read-only memory elements based on liquid materials. The project will also help train students in experimental and computational techniques relevant to future scientific and engineering careers. 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 Co-occurring parental substance use disorder and child maltreatment are key risk factors associated with health inequities and disparities among children and families in rural areas. Importantly, rural communities face geographic, economic, and social barriers that limit their access to high-quality healthcare resources and services, exacerbating health disparities. Ohio Sobriety, Treatment, and Reducing Trauma (Ohio START) is a family-centered child welfare service delivery intervention that builds on cross-system collaboration with behavioral health service organizations to provide timely access to behavioral health services for families involved with the child welfare system. Ohio START also supports parents’ journey to recovery through peer mentors with lived experience in substance use recovery and previous child welfare involvement. Ohio START, while promising, has not been rigorously studied to determine how it modifies parents’ spatial and social environments to reduce parental substance use and improve child health outcomes in rural communities. It is also unclear how parents experiencing economic disadvantage change their spatial and social environments in rural communities where there are fewer service resources. These are important questions that, if answered, could enhance interventions to better serve socioeconomically disadvantaged families in rural communities. Capitalizing on unique opportunity to leverage the ongoing Ohio START initiative and access child welfare– involved families in rural areas, we seek to collect novel, longitudinal activity space and social networks data from 400 families engaging in Ohio START. The study has three specific aims: (1) To determine whether the proximity and availability of behavioral health providers in child welfare workers’ referral networks from parental activity spaces predicts intermediate and long-term family health outcomes. (2) To examine how Ohio START family peer mentoring services affect attributes of parental activity spaces and social networks and family resilience over time, and how these changes are associated with long-term family health outcomes. (3) To test whether rurality moderates the effects of activity spaces, behavioral health services referral networks, and social networks on intermediate and long-term family health outcomes. The study aims directly address one of the NICHD’s research priorities “Understanding Social Determinants of Health and Developmentally Informed Strategies to Mitigate Health Disparities.” The proposed project will produce valuable knowledge that can be used to modify START and other family-level intervention efforts to mitigate risks and maximize protection in parents’ spatial and social networks to enhance family resilience, prevent child maltreatment, and promote child well-being in socioeconomically disadvantaged rural communities.

NSF Awards · FY 2025 · 2025-09

This award is made in response to Dear Colleague Letter 24-130, as part of the ECosystem for Leading Innovation in Plasma Science and Engineering (ECLIPSE) interdisciplinary program. Clean drinking water is very important, but it is beooming harder to find and keep clean because of pollution from factories and other industries. One type of dangerous pollution is from chemicals called PFAS (short for Per- and Polyfluoroalkyl Substances). These chemicals are made of carbon and fluorine, and even tiny amounts in drinking water can be harmful to people’s health. Because PFAS does not break down easily, they’re sometimes called "forever chemicals." However, scientists have discovered that plasma — a special state of matter made up of energetic gases — might help remove PFAS from water. When plasma touches water that has PFAS in it, it can break the PFAS into smaller and less harmful parts. In some cases, it might even destroy the PFAS completely. This happens because the plasma creates high-energy electrons, light, and ions (charged particles) that react with the PFAS at the surface of the water. The goal of this project is to better understand how plasma interacts with polluted water and how these reactions can be controlled to clean water more effectively. The project results should enable the design of better systems to remove PFAS and other pollutants from water. This project will also help train new scientists and engineers through a program called the US Low Temperature Plasma Summer School, which supports education and training in this technology. The investigation of plasma-water interactions leading to PFAS destruction will be a collaborative experimental and computational effort. The experiments will employ advanced laser diagnostics to measure electric fields and densities of reactant (and product) species in the gas phase and in the near water surface layers. The Electric Field Induced Second Harmonic generation measurements will quantify the effect of energetic electrons on surfaced-enhanced PFAS decomposition by the plasma. The PFAS decomposition and conversion will be monitored in situ, by surface Vibrational Sum Frequency Generation. Individual product species will be identified using high-resolution near-IR Tunable Diode Laser Absorption Spectroscopy. These measurements will be compared with the modeling predictions, to validate the kinetic model and infer the rates of the underlying kinetic processes. The computations will investigate the kinetic processes occurring at the interface of the atmospheric pressure plasma and the water surface, with an emphasis on acceleration of electrons and ions into the water surface. New algorithms to compute distributions of electron and ion energies onto the water surface will be implemented, and reaction mechanisms developed. Following their validation, the models will provide insights to quantities that are difficult to measure experimentally, such as the spectrum of plasma produced vacuum-ultra-violet fluxes onto the water. Although focused on remediation of PFAS from drinking water, the outcome of these investigations will provide guidance to a wide range of plasma-liquid interactions relevant to medicine, chemical conversion and materials synthesis. This project is supported by 1) Process Systems, Reaction Engineering and Molecular Thermodynamics program, 2) Environmental Engineering program and 3) Plasma Physics program, in response to Dear Colleague Letter 24-130, as part of the ECosystem for Leading Innovation in Plasma Science and Engineering (ECLIPSE) interdisciplinary program. 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

Developing photocatalysts that can convert carbon dioxide to a beneficial carbon feedstock has become an important research subject in chemistry. Photocatalysts use sunlight to transform raw materials into valuable products with minimal impact on ecosystems. In this project, Professor Psaras McGrier at The Ohio State University (OSU) is preparing metal-free photocatalysts using an advanced class of crystalline porous polymers. These photocatalysts can be used to convert carbon dioxide to chemical fuels and value-added products using visible light. Professor McGrier is utilizing several education and outreach programs at OSU to engage students from the local Columbus (Ohio) City School District in STEM activities. These activities include participation in the Breakfast of Science Champions (BoSC), an OSU program that allows middle school students from these local schools the opportunity to visit and learn more about various cutting edge research projects. Professor McGrier is also co-directing and mentoring students from the OSU Chemistry and Biochemistry (CBC) Post-Baccalaureate Bridge Program, which offers significant program assistance to post-baccalaureate students to help prepare them for a Ph.D. In this project funded by the Chemical Catalysis program of the Chemistry Division, Professor Psaras McGrier at The Ohio State University (OSU) is developing metal-free donor-acceptor and organic hydride-based covalent organic frameworks (COFs) that can initiate the photochemical reduction of carbon dioxide using visible light. The goal is to obtain metal-free COF-based photocatalysts that can convert carbon dioxide to chemical fuels and value-added products in a selective manner. Such investigations will help supplement the carbon cycle and reduce environmental concerns in a more sustainable manner. This proposal includes several education and outreach programs to help increase the participation of young students in STEM fields. These activities include participation in the Breakfast of Science Champions (BoSC), an OSU STEM program that allows middle school students from Columbus City Schools the opportunity to visit and learn more about various cutting edge research projects. Professor McGrier will also co-direct and mentor students from the OSU Chemistry and Biochemistry (CBC) Post-Baccalaureate Bridge Program, which offers significant program assistance to post-baccalaureate students to help prepare them for a Ph.D. 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 Ohio State University and Texas Tech University are collaborating on an EDU Core Research project to identify factors that affect participation in STEM education and the workforce. This project will specifically address access to STEM education and the workforce by examining student debt and its effects on participation in STEM majors. Students who major in STEM fields frequently earn more than their non-STEM counterparts, and STEM is critical for the economy and for addressing today's needs and opportunities. However, the up-front costs of college and the challenges of STEM curricula can be formidable, often leading students to make choices that may lead to increased debt loads. This project undertakes quantitative and qualitative analyses of how student debt and STEM majoring affect each other and jointly shape educational, graduation, and post-graduation outcomes, including decisions to continue in a STEM major, time to graduation, income, debt, and financial burdens. This project addresses causality concerns by testing two key hypotheses. The first hypothesis suggests that college costs and resulting educational debt have become important drivers of student decisions. The second hypothesis proposes that student debt is an important determinant of student behavior, including major choice, major switching, and degree completion. The project's quantitative analysis is using unique, population-level administrative data from the State of Ohio to conduct causal analyses, investigate differences across socioeconomic and demographic groups, study differences by type of public institution, and ask whether and how these relationships have changed as students have taken on higher debt burdens. This work is informed by a complementary qualitative analysis of interviews with students at public universities to understand their subjective decision-making experiences related to debt, STEM majors, and STEM careers. Taken together, the work will greatly advance understanding of the causal and subjective mechanisms shaping the size of the nation's future STEM workforce. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. 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

Lithium-ion batteries are among the most widely used energy storage systems powering modern electronics, from smartphones to electric vehicles. However, their production depends on limited and unsustainably sourced transition metals like lithium and cobalt, which pose long-term challenges for cost and environmental impact. This research project aims to develop new organic electrode materials as sustainable alternatives for energy storage in batteries. These materials are composed of earth-abundant elements such as carbon, nitrogen, oxygen, and sulfur, offering a more economical and environmentally friendly path to renewable energy solutions. In addition to advancing sustainable battery technology, the project will provide hands-on training for graduate students in artificial intelligence, organic synthesis, and electrochemistry. The educational aims include developing a laboratory exercise for an undergraduate general chemistry course that will introduce students to connections between electrochemistry and environmental water quality testing and a mentoring program for these students which connects them with peer tutors and includes an outreach program to engage middle school students in Columbus, Ohio through interactive science activities. Organic electrode materials represent a promising and sustainable alternative to transition metal–based cathodes and anodes in lithium-ion batteries. However, the practical implementation of organic electrode materials remains limited due to their high synthetic costs, sloped or multi-step voltage profiles, and poor electronic conductivity. Currently, the discovery of these materials relies heavily on slow, trial-and-error experimental screening. This project aims to accelerate organic materials development through the development of SPARKLE (Symbolic Predictive Algorithm for Recognizing Key Molecular Elements), an interpretable, multi-objective machine learning tool. In Aim 1, the research will develop a multi-objective predictive model within the SPARKLE framework that evaluates candidate organic electrode materials based on solubility, specific energy, and synthetic accessibility. Aim 2 will extend SPARKLE's application to non-aqueous battery systems, including lithium-ion and magnesium-ion batteries. Finally, in Aim 3, the project will develop a new SPARKLE module capable of predicting organic material electrode performance under low conductive carbon loading conditions, with the goal of designing electrode materials that require less than 10% carbon additive while retaining high specific capacity. This systematic study will ultimately reveal unintuitive design principles for new organic electrode materials and enable more focused research efforts, replacing extensive trial-and-error screening. 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 strong interaction is responsible for the binding of protons and neutrons into atomic nuclei. Improved quantitative understanding of how this happens is essential not only for fundamental nuclear research at US experimental facilities but for progress in astrophysics, for experiments on the nature of neutrinos, and for applications to energy and homeland security. The era of precision calculations of nuclear structure and reactions is underway, enabled in part by research findings and products from past NSF grants to the PI. New activities use machine learning and quantum computing tools and will include extending the range and capabilities of statistical methods for assessing theoretical uncertainties and for physics discovery, improving the extraction of information from experiment that minimally depends on model assumptions, and developing and testing a novel approach to systematically describing the full nuclear landscape. The training received by undergraduates and graduate students in carrying out these activities contributes directly to the building of a skilled scientific workforce. The mix of analytical and numerical computation the students and postdocs must employ is excellent preparation for both academic and industrial careers that is validated by the strong track record of past members of this group. Activities are being pursued in three categories: statistical methods for effective field theory (EFT) uncertainty quantification, development and application of machinery for process-independent quantities, plus explorations of nuclear renormalization group (RG) for quantum computers, and path integral formulations for finite density nuclear systems and for artificial neural networks (ANNs). Extensions of reduced basis emulators will enable the use of Bayesian methods for uncertainty quantification of nuclear interactions, few- and many-body systems, and electroweak probes. This project will extend the RG perspective, which exploits scale and scheme dependence in nuclear reactions. Projects range from further treatments of short-range-correlation physics relevant for JLab experiments, to the novel treatments of knock-out and other reactions for FRIB and astrophysics, to the implementation of the similarity RG as a quantum computing algorithm. Finally, path-integral-based methods will be employed on two fronts: in the background-field formalism to advance toward the goal of EFT for nuclear DFT and to provide guidance and tools for improving implementations of neural networks for nuclear applications. These projects all contribute to the goal of microscopic, model-independent calculations of nuclei; they will impact forefront problems in low-energy nuclear physics and multi-messenger astrophysics as outlined in the 2023 Long Range Plan for Nuclear Science. This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments. 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

Biological oscillators, the rhythmic and regular increases and decreases of gene expression or protein activity, drive many dynamic molecular and morphological processes. Probably the most familiar is the circadian clock, but there are also ultradian clocks that operate on the order of minutes and/or hours. Examples of ultradian clocks are those that regulate periodic root branching in Arabidopsis, molting cycle in C. elegans, timing of mitosis to tightly coordinate cell proliferation and tissue morphogenesis, stem cell maintenance, bulk protein degradation and re-synthesis in proliferating mammalian cells, and tissue patterning during somitogenesis, a fundamental vertebrate developmental process that we have studied for many years. Somitogenesis is the anterior to posterior sequential segmentation of the vertebrate embryonic mesoderm into blocks of tissue called somites, which later give rise to axial skeletal muscle, vertebrae, and dermis This rhythmic and regular process is controlled by a molecular oscillator called the segmentation clock which operates in the unsegmented presomitic mesoderm (PSM) and is characterized by rapid cycles of mRNA and protein expression with a species-specific periodicity. Like many cell-autonomous oscillatory networks, the segmentation clock is regulated by a self-sustaining negative feedback loop, where a core oscillatory factor, a transcriptional repressor, inhibits expression of downstream oscillatory genes, including itself. For segmentation clock oscillations to persist, the transcript and protein molecules of clock genes must be short- lived. In all vertebrates examined to date, transcriptional repressors of the Hes/her gene family are considered the evolutionarily conserved pacemakers of the segmentation clock. For proper somite formation, rates of each oscillatory step, from transcription to translation to decay, must be regulated to ensure correct somite size and number. Computational modeling and experimental perturbations have shown that transcriptional and translational time delays (the amount of time from transcription or translation initiation to the emergence of a mature mRNA or protein, respectively) and degradation rates of both transcript and protein are parameters having the largest influence on clock period and evidence from in vivo studies assessing the impact of timing of mRNA and protein processing and decay mirrors modeling predictions. Transcriptional regulation alone is not sufficient to produce genetic oscillations and we are focusing on understanding the post-transcriptional regulatory mechanisms that promote proper oscillatory expression. The two key questions that we address in this proposal are: (1) What are the key targets of the core Hes/Her oscillators that carry out oscillatory output? and (2) What are the post-transcriptional regulatory mechanisms driving rapid decay of oscillatory gene transcripts. Answering these questions will address how oscillatory gene expression regulates pattern and fate.

NSF Awards · FY 2025 · 2025-09

This project investigates which conditions have the greatest influence on life expectancy in the United States. With U.S. life expectancy declining, and billions spent each year on programs aimed at improving longevity, there is a pressing need for clear, actionable research that identifies which investments are most effective. The project develops a process to track these conditions across levels of administration and uses advanced data methods, including artificial intelligence (AI), to determine which combinations of factors are most strongly tied to longer lives. Findings from this work help identify where policies are falling short and offer a foundation for improving how public funds are allocated. In doing so, this research supports the broader goal of strengthening public resilience—an essential element of national well-being and security. This Mid-Career Advancement (MCA) project has three main goals. The first is to gather and standardize data on conditions that influence life expectancy, document how each is measured, and assess the strength of the evidence linking each measure to average life expectancy. Second, this project applies innovative modeling techniques, including use of AI, to assess how combinations of conditions and governments explain variation in life expectancy across populations. Third, building on the information gathered while achieving the first two goals, the project team creates a framework for a database designed to address the characteristics most strongly associated with life expectancy. To accomplish these objectives, umbrella reviews (i.e., reviews of reviews) are conducted of 1,164 studies examining the relationship between life expectancy and conditions in the United States. Using standardized protocols for determining the ‘weight of evidence,’ scores are assigned to each measure based on how strongly it is linked to life expectancy. Analysis methods then are used to harmonize and integrate these measures with life expectancy data. 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

Families play a crucial role in shaping health and well-being across the entire lifespan, with family ties taking on increased importance in later life. Given the changing demography and economics of aging families and the implications of these changes for health and well-being, an integrated, interdisciplinary approach is urgently needed. The Center for Aging Families (CAF) ignites scientific inquiry on the centrality of contemporary and complex aging families as deeply influential on aging health. To illuminate how rapid shifts in family relationships across the life course link to population health and health disparities of aging adults, CAF brings together an interdisciplinary community of scholars of all stages from Bowling Green State University (BGSU), Ohio State University (OSU), and Purdue University (Purdue). CAF affiliates are widely recognized in the field as leaders in family change, population health, and aging. The CAF leadership team is composed of four senior scholars with extensive administrative experience: Susan Brown (BGSU), Sarah Hayford (OSU), Hui Liu (Purdue), and Rin Reczek (OSU). CAF will leverage existing individual-level collaborations to build infrastructure that fosters deep and meaningful connections and generates high-quality and high-impact science. The impact of CAF is transformative, beginning with Phase 1’s strategic strengthening of connections between our three institutions, followed by Phase 2’s expansion of the CAF network to institutions nationwide and globally, broadening its reach and influence. CAF is primed to make significant advancements by seeding new and innovative lines of research in family aging research via pilot funding and grant-writing training and mentorship (Program Development Core); translation and dissemination of research findings (Communication and Dissemination Core); and an integrated research infrastructure (Administration and Research Support Core). CAF research will focus on three overarching research themes that are central to aging family life: 1) the changing demography of aging families, with the focus on the demographic changes of intimate, procreative, and family of origin ties among aging populations; 2) aging family health, with a focus on how these broader demographic shifts matter for health, health behavior, cognition and healthcare of older adults; and 3) health disparities in family aging, leveraging the NIA’s health disparities framework to integrate approaches that address disparities in family changes and their health impacts across contextual and behavioral influences. The research infrastructure provided by CAF will stimulate cutting-edge research that aligns with NIA priority topic areas for population-based social science aging research. Establishing and supporting the Center for Aging Families (CAF) will have a transformative impact on the field of aging family research.

NSF Awards · FY 2025 · 2025-09

Web agents powered by large language models (LLMs) could usher in a new era of intelligent automation, completing complex tasks across websites and transforming how people interact with the web. Yet today’s agents rely mainly on prompting off-the-shelf LLMs, limiting their ability to adapt to specific sites, achieve high reliability, and operate cost-effectively. This project proposes an alternative, modular paradigm in which LLMs are orchestrated with lightweight, task-specific models to enhance adaptability, reliability, and safety while reducing inference costs. The work aims to boost productivity, lower barriers to web access for all users, and train the next generation of scientists through new courses and outreach activities. This project identifies several major gaps in the existing literature, including evaluation, planning, grounding, safety, and continual learning. These research gaps prevent web agents from quickly adapting to new websites, continually learning on the job, and achieving high reliability. This project will pursue four coordinated research thrusts to bridge these gaps: (1) a new public benchmark of complex, realistic web tasks for measuring progress and guiding innovation, (2) a world model for the web to enable model-based planning—the agent can simulate the effects of different actions before acting, (3) a specialized visual grounding model that maps agent plans to precise actions on websites to improve agent reliability, and (4) a safety control plane as a safeguard to support continual learning. Together, these advances will lay the foundation for the next generation of modular, general-purpose, and safety-aware web agents. 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 award supports the convening of a workshop and extended mentoring opportunities and collaborations in the area of scientific proposal development. The workshop enhances rigor in research design, scientific discovery, and scientific writing for the public with the goal of increasing opportunities for innovation for early-career scientists nationwide. Broader impacts of the research include enhancing scientific proposal writing curricula at institutions across the nation and contributing to supporting early career scientists in developing project ideas and project proposals in key areas of social and behavioral science. It specifically supports the development of novel project ideas with translational impact in all of NSF’s scientific priority areas. The workshop and related mentoring activities expand the development of an innovative social scientific workforce for a variety of sectors in the nation’s economy. The workshop involves participants from a wide range of scientific disciplines to enhance opportunities for multidisciplinary collaborative exchange and to generate and share best practices in scientific discovery and pluralistic engagement in 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 / Abstract The mechanistic underpinnings of diseases involving the exocrine pancreas are poorly understood. Chronic pancreatitis is often accompanied by inflammation of the pancreas, irreversible fibrosis, and destruction of the pancreatic parenchyma resulting in abdominal pain, malnutrition, exocrine pancreatic insufficiency, diabetes, and, in some cases, pancreas cancer. Our Clinical Center (Ohio Kentucky Pancreatitis Research Center (OKPRC)) is interested in continuing our efforts to continue leading and supporting the research infrastructure of the Chronic Pancreatitis Clinical Research Consortium to conduct longitudinal clinical research to address the many knowledge gaps related to chronic pancreatitis. We propose a series of hypothesis driven studies to: 1) validate the use of a novel biomarker for diagnosis of chronic pancreatitis, 2) understand nutritional and metabolic alterations in chronic pancreatitis, and 3) advance clinical trials for chronic pancreatitis. First, we aim to complete a phase 2 (validation) biomarker study of circulating NGAL for diagnosis of chronic pancreatitis, considering applications to both rule-in and rule-out the disease. In regard to nutritional alterations, we will conduct novel analyses of dietary patterns in participants with chronic pancreatitis to identify associations with disease-related complications and identify potential opportunities for a dietary intervention. We will extend initial analyses regarding the use of a pancreatic hormone (pancreatic polypeptide) as a diagnostic marker of diabetes in the setting of chronic pancreatitis. We will also evaluate for genetic risk in chronic pancreatitis for the development of osteopathy. Lastly, we describe two clinical trials that are in progress to illustrate the strengths of our collaborative team, and opportunities to improve the efficiency of future clinical trials. Altogether, we aim to contribute to the collaborative environment of the Consortium to accomplish these (and other) studies to improve our ability to diagnose and treat chronic pancreatitis.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Law enforcement training and job duties can expose law enforcement cadets (LECs) to repetitive head impacts (RHIs). During the training academy, LECs complete weeks of subject control technique training, including defensive tactics training and boxing. These are designed to test the LEC’s critical thinking, train acute stress management, and teach proper use of force under pressure. However, these RHIs have the potential to result in mild traumatic brain injuries (mTBIs), which can lead to behavioral changes, impaired cognitive performance, and alterations to neural structure and function. These types of training drills cannot be eliminated, despite their potential for RHIs, as having defensive skills is a critical component of law enforcement job performance. The goal of this project is to inform modifications in training schedules and injury mitigation strategies within the training academy, and ultimately safeguard operational capability and career longevity of LEOs by the following specific aims. Aim 1. To determine if male sex is a risk factor for higher HAE exposure in LECs. Aim 2. To determine if greater neck strength is a protective factor for lower HAE exposure in LECs. Aim 3. To determine if better visual-motor function is a protective factor for lower HAE exposure in LECs. I will be successful in completing the proposed research plan with the support and mentorship I will receive from my mentorship team. Dr. Caccese (sponsor) will provide expertise in RHI monitoring and concussion research, enhancing my understanding of best practices in this field. Dr. Jeffrey Wing (co-sponsor) will provide statistical analysis and interpretation expertise, to further my understanding and application of concepts learned in didactic statistical coursework. Dr. Di Stasi (co-sponsor), an excellent clinician-scientist with a successful track record in securing research funding, will assist in both my clinical and scientific development to attain my long-term goal of being a leading clinician-scientist in the concussion research field. She will provide guidance in how to translate the results from this proposed research to intervention studies as I begin my career. Dr. Onate (co-sponsor) has expertise in clinical treatment of tactical athletes and research, supporting the foundation of the proposed training and research plan. He will also provide constructive feedback and education on my mentorship and leadership skills. This training and research plan will be conducted at The Ohio State University (OSU), a world-class research organization and a multifunctional hub of clinical and basic science research. I will have access to the several centers under the umbrella of OSU, including the School of Health and Rehabilitation Sciences, the Sport and Tactical Athlete Injury Resiliency Science Lab, the Chronic Brain Injury Program, and the Human Performance Collaborative. As a result of my mentor’s participation in the above centers, I will have the support that is integral to both my training and research plan, and my career development as a productive, independent clinician-scientist.

NIH Research Projects · FY 2025 · 2025-09

Project Summary A major challenge faced by People with Visual Impairment (PVI) is wayfinding outside. Existing solutions, such as TappyGuide, leverage a user’s smartphone to generate directions to a destination. However, a barrier is that the followed path may not be accessible because sidewalk features such as potholes, uncontrolled crossing, interrupted sidewalks, and objects are not known in advance. Our goal is to develop a mobile live, local, outside map framework called the VisionWay, customized for PVI-user as a wayfinding tool to analyze, before starting to walk, the accessibility of candidate paths and choose one to the destination. To address this challenge, barrier, and achieve our goal, we assembled a team of men, women, trainees, and members who have lived experience with vision impairment (VI). Our strengths include engineering, computer science, human factor engineering, mixed methods research, implementation science, clinicians including low vision and orientation and mobility specialists (OMS), as well as team members and participants with VI. We designed aims for R61 technology development and feasibility, and R33 prototype expansion. Specifically, in R61, Aim 1: we will study how to measure and detect PVI Accessibility on Sidewalks. R61, Aim 2 we will examine PVI-centered modality for adoption of multimodal path presentation, path scores, and descriptions across various navigation scenario complexities. In R33, Aim 3 we will generalize, integrate, and test the VisionWay framework. Finally, in R33, Aim 4 we will plan for implementation with dissemination and maximize sustainability of this PVI-centered accessible wayfinding map. Upon the successful completion of these aims, our goal is to deliver the VisionWay framework that include multimodal vibro-audio feedback, and live updates of dynamic data that inform about accessibility of pedestrian paths. The expected outcomes include dissemination and sustainability plans for the long-term growth of the proposed VisionWay map deliverable.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT Precision genome editing presents opportunities for treatment of disease and for use in biotechnology. Tyrosine family site-specific DNA recombinases (Y-SSRs), such as Cre, are uniquely positioned as chemical biology tools for genetic engineering because they are capable of excision, integration, and inversion of DNA sequences without requiring exogenous co-factors. A major focus in tyrosine recombinase research is the retargeting of these enzymes to new DNA sites, primarily using substrate-linked directed evolution (SLiDE). Although successful in many regards, SLiDE has two substantial drawbacks that limit its use: limited capacity for negative selection can lead to promiscuous recombinases, and lack of selection tunability limits the ability to select for highly active recombinases. Off-target activities could have serious negative consequences in the human genome, limiting the use of evolved recombinases as probes or eventual therapeutics against human disease. Rigorous characterization of the activity of evolved recombinases towards off-target sites and the development of methods to lower or eliminate these off-target activities is a high-priority enabling technology. Likewise, development of a directed evolution scheme that can select for high recombination efficiency would enable the production of recombinase libraries with higher average efficiency in less time. Here, we aim to address both specificity and efficiency of recombination during evolution by developing novel SLiDE methodology. First, to combat promiscuity in evolved recombinases, we will develop a SLiDE method that leverages simultaneous positive selection at a desired target site and negative selection against a library of off- target sequences. We will use this method to evolve novel recombinases for activity against loxHTLV, the target of the previously-engineered RecHTLV. The activity of novel recombinases with the off-target library will then be quantified and compared to that of wild-type Cre and RecHTLV to observe differences in their promiscuity profiles. Second, to enable selection for higher efficiency recombinases, we will establish a bacterial system by introducing toxic “kill switches” into evolution vectors that are temperature controlled. Recombinase-expressing E. coli that have been transformed with these vectors will select for enzymes that can excise the toxic gene from all plasmid copies before timed activation of the kill switch. We will validate this system with existing recombinases and then evolve a new recombinase for activity on loxHTLV to compare to the activity and evolutionary timeline of the previously evolved RecHTLV. We hypothesize that this novel selection for recombination efficiency will rapidly produce an enzyme with improved efficiency in fewer rounds of evolution. Development of these selection systems will accelerate the development of specific, efficient evolved recombinases, streamlining the timeline for development of novel tools and potential therapeutics.

NIH Research Projects · FY 2025 · 2025-09

Project Summary/Abstract The objective of this proposal is to develop a Natural Killer (NK) cell-based immunotherapy for the treatment of pediatric solid tumors. The use of chimeric antigen receptors (CAR) to augment T cell responses towards cancers has been successful and translated to the clinic for CD19+ hematological malignancies. Conversely, the use of CAR T cells for the treatment of solid tumors has been largely unsuccessful; highlighting the need for alternate strategies and methodologies. Unfortunately, CAR T cell therapies are fraught with catastrophic and fatal adverse events. The use of NK cells for CAR-based therapies is still in its infancy compared to T cells; part of this is because they are understudied. NK cells possess a panoply of germline encoded receptors that help dictate how NK cells react to target cells. These receptors allow NK cells to decide which cells to kill or not, with the addition of powerful signaling from CARs NK cells can still decide which cells to target. Furthermore, NK cells from an unrelated donor may impart the most potent anti-tumor activity and selecting the correct pairing will be critical to CAR NK cell clinical success. Thus, my overarching hypothesis is that NK cells will be better suited than T cells to eradicate malignant cells and preserve healthy cells reducing the risk of adverse effects. This hypothesis will be evaluated in two interrelated research aims. Aim 1 explores the novel orientation and organization of monomeric CAR NK cells with synapse augmentation, Aim 2 is focused on defining a donor selection algorithm for CAR NK cell products. I will pivot from the standard dimeric CAR designs that lead to dysregulated signaling and cellular dysfunction to an optimized monomeric design that has a stable off state reducing aberrant signaling. Donor variability is a critical factor when determining optimal cell source for cell therapies and, to date, the clinical selection parameters for NK cells, I believe, hobbles their full potential. I will utilize in silico protein modeling with functional testing to define a new selection method for donor cell sources. The proposal and will be initially performed at St. Jude Children’s Research Hospital, a state-of-the-art NCI- designated comprehensive cancer center. At the conclusion of the K22 award I will have delineated novel modification CAR designs and a universal donor bank selection algorithm for CAR NK cells. This will allow for the progression of CAR NK cell therapies to translate into the clinic and embodies the goal of the NCI “…to advance scientific knowledge and help all people live healthier, longer lives.”

NSF Awards · FY 2025 · 2025-09

Stars, like our Sun, spin more slowly as they get older. This happens because they lose energy through winds and magnetic fields. But scientists have found that certain stars—called K dwarfs—don’t slow down the way we expect. These stars seem to stay active and spin faster than they should, even as they age. This project will help us understand why. The researchers will build new computer models to test how energy and magnetism move inside these stars. They will also use large telescopes to directly measure the magnetic fields of K dwarfs that behave in surprising ways. Understanding how stars age is important for many areas of science, including learning the ages of planets around other stars. This project also supports science education in Hawaii by helping teachers bring real astronomy into their classrooms. By combining new models, detailed observations, and outreach, this project will promote scientific progress and expand opportunities for diverse learners to explore space science. The investigators aim to address the unexpected rotational evolution and persistent magnetic activity observed in K dwarf stars (~3900–5300 K). The project will combine novel internal angular momentum (AM) transport models—including hydrodynamic, gravity wave-driven, and magnetic processes—with modern magnetic braking laws implemented in the Yale Rotating Stellar Evolution Code (YREC). These models will be tested against a wide array of observational constraints, including open cluster rotation sequences, field star rotation periods, magnetic activity proxies, light-element depletion trends, and newly acquired spectropolarimetric data. The observational component will include direct measurements of magnetic field strength and topology in anomalous K dwarfs using PEPSI (LBT), ESPaDOnS (CFHT), and SPIRou (CFHT), focusing on stars in the Hyades cluster. The overarching goals are to improve theoretical understanding of rotational evolution in low-mass stars and to enable more accurate use of gyrochronology in stellar age determinations. The project also supports the development of new astronomy-based curricular tools through the TeachAstro program for middle and high school STEM teachers in Hawaii. 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

Accurate atmosphere-ocean-sea ice model simulations of the Antarctic region are becoming increasingly necessary, particularly since 2016 when Southern Hemisphere sea ice reached its lowest extent since the start of the satellite era. This project will advance atmosphere-ocean-sea ice modeling in the Southern Ocean by enhancing the representation of atmospheric cloud physics and heat exchange between the ocean and sea ice in state-of-the-science models. Model development will incorporate recent observations of high latitude clouds and aerosols, leading to improvements that are critical for weather forecasting on subseasonal to seasonal timescales. These advancements will be subsequently incorporated into a high-resolution, fully coupled regional model of the atmosphere, ocean, and sea ice for the Southern Ocean, to be used to simulate the strong year-to-year variability of sea ice conditions observed in recent decades. It is crucial to enhance our understanding of this variability because it influences global weather patterns, helps reveal the underlying mechanisms driving both gradual and abrupt sea ice changes, ultimately improving the accuracy of both local and global numerical models. This project will advance the simulation of polar environments in two key ways. First, it addresses biases caused by parameterization of cloud microphysics (specifically supercooled liquid water clouds) and ocean-sea ice heat exchange in numerical models of the Southern Ocean. Reducing these biases will enable more realistic simulations of the marine environment, refining and improving operational weather forecasting and Earth system models for the region. Second, the analysis of the coupled model simulations will deepen understanding of physical processes governing the Antarctic sea ice, atmosphere, and upper ocean variability, including contextualization of the abrupt shift in Antarctic sea ice state since 2016. The project will provide training for early career researchers, building valuable skills that are high in demand across several sectors of both academia and industry. The knowledge generated will be shared through open webinars designed to train new investigators on the deployment of the fully coupled model developed as part of this project. 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

In recent years, the ambitious concept of Digital Twin (DT), with the aspiration of creating a virtual representation of a complex asset or process, has received enormous interest from industry leaders, engineers, policymakers, and scientists. The DT promises to digitally replicate the high-dimensional, multi-modal, and dynamic physical systems and assist decision-making with reliable predictions. For example, in aerospace applications, a DT that can accurately simulate flight operational conditions based on data and feedback from the “flying twin” has great potential to improve aerial vehicles' safety, reliability, and control, especially in extreme service conditions. However, the development of DTs has been largely ad hoc, and generalizable mathematical foundations are poorly understood. The investigators aim to develop Artificial Intelligence (AI) solutions in terms of heterogeneous transfer and personalized federated learning to solve mathematical and computing challenges in developing a DT for Unmanned Aerial Vehicles (UAVs). An interdisciplinary approach is taken that combines expertise in Statistics, Aerospace Engineering, and Computer Science to develop a holistic statistical and computational framework that is useful for the development of DTs generally, even beyond aerospace applications. The team will undertake educational outreach programs in collaboration with university partners. The team will also teach and mentor middle school and high school students in a free summer camp to explore data science and analytics. In addition, the team will teach and train graduate and undergraduate students in interdisciplinary data science and engineering. Finally, to enable rapid translation of the proposed research to digital twins in academic and industry environments, the research team will create R and Python packages and libraries. The project will develop (1) a flexible heterogeneous transfer learning approach that allows for unknown, possibly nonlinear functional relationships between the response and the features and allows for differing covariates or control conditions and (2) a personalized federated learning approach that accommodates diverse computational resources and storage capacities of local devices with applications to (3) solve challenges in developing DT in UAVs. The project will innovate in all three thrusts of this project. First, while the potential of DTs to improve the safety and reliability of their flying twins has been long recognized, there are very few principled approaches toward developing a DT with uncertainty quantification. The project contributes to understanding the mathematical challenges and building a mathematical foundation for DTs of UAVs. Second, while several methods exist for knowledge transfer between domains, most approaches assume the same set of features between a target domain and a source domain. Moreover, they do not provide rigorous uncertainty quantification. The project will develop a heterogeneous transfer learning framework with statistical guarantees. Third, while federated learning (FL) has emerged as an important method for training DTs with distributed data across multiple edge devices, the main assumption of such methods is that agents or edge devices learn a model with the same parameters. However, in DT applications, agents may have different computational and storage resources. This project responds to the challenge by developing FL algorithms where the learning tasks are personalized to the agents' resource constraints. 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

Advances in audio processing technologies like speech enhancement, audio compression, and hearing aids rely on automatic methods to evaluate whether processed audio sounds good to listeners. However, current computational approaches for assessing audio quality fail to match human perception, leading to systems that optimize for mathematical metrics rather than what actually sounds good to human ears. When engineers develop these technologies, existing evaluation metrics often disagree with human judgments, resulting in audio processing algorithms that may improve technical measurements while actually degrading the listening experience. This research will create computational tools that can automatically evaluate audio quality without requiring human listeners for every assessment, while maintaining strong agreement with human perception. The project develops new artificial intelligence models that learn to assess audio quality the way humans do, using novel machine learning training architectures and methodologies applied to human perceptual judgments across speech, music, and environmental sounds. These advancements will improve quality assessment for recorded speech, with direct applications in speech analysis and synthesis. This will ultimately lead to improvements in human language technologies such as speech enhancement, speaker extraction, and assistive hearing technologies which directly rely on perceptual quality assessments for improvements. They will also have a broader impact to audio technologies used in telecommunications, entertainment, medical devices, and consumer electronics by ensuring that automated systems optimize for genuine improvements during human listening experiences. The project will also support graduate student training in machine learning and audio processing, contributing to workforce development in these critical technical areas. The technical approach builds on co-training architectures that simultaneously optimize full-reference, no-reference, and non-matching reference quality assessment models using shared embedding networks. Full-reference methods evaluate a degraded signal by comparing it to a clean reference version, while no-reference methods make the evaluation without regard to a clean version by modeling the statistics of clean audio. Recently introduced non-matching reference models provide an alternative that mitigates some limitations of both approaches by comparing a signal to a clean reference recording that contains different content. By co-training full-reference, no-reference, and non-matching reference architectures, the learned networks condition each other during training, leading to more robust models that correlate better with human perception. The research will also develop novel multi-task learning strategies that train across multiple objective quality measures (e.g., perceptual evaluation of speech quality (PESQ), signal-to-noise ratio (SNR), and scale-invariant signal-to-distortion ratio (SI-SDR)) while incorporating diverse subjective data including Mean Opinion Scores and pairwise comparisons. Analysis of the trained models will probe what acoustic, perceptual, and environmental information is captured in different layers of the learned representations. The project will validate these universal models through extensive evaluation on downstream audio processing tasks including speech synthesis, enhancement, speaker extraction, and music source separation. By creating loss functions and evaluation metrics that correlate strongly with human perception across diverse audio types, this research will enable the next generation of audio technologies that truly optimize for human auditory experience. 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.