Ohio State University

NSF Awards · FY 2026 · 2026-09

Plants must constantly balance growth and reproduction with the need to survive environmental stress. Disease, heat, and physical challenges from their surroundings can all threaten plant fitness, yet plants cannot move to escape these conditions. Plant cells cope with these challenges using dynamic, subcellular compartments called biomolecular condensates. Unlike membrane-bound organelles, these compartments form by temporarily concentrating specific molecules together without surrounding physical membranes, allowing cells to rapidly reorganize gene activity, gene products, and stress responses. Although much is known about how condensates form, far less is known about how they control whole-organism traits like growth, resilience, and survival. This project will study a newly discovered class of condensates in the model plant Arabidopsis that contain proteins in the Guanylate-Binding-Protein Like (GBPL) GTPase family. The team will determine how these condensates help plants respond to environmental challenges such as infection, physical stress, and high temperature. Understanding the fundamentals of how living cells use these dynamic molecular assemblies to coordinate complex behaviors serves the national interest, laying a foundation for biotechnology strategies to improve crop resilience under increasingly variable environmental conditions, benefiting agriculture, food security, and economic stability. In addition, the project will train undergraduate and graduate students in interdisciplinary science spanning plant biology, biophysics, genomics, and artificial intelligence (AI), while sharing data, tools, and educational activities with the broader research and public communities. The deformable, viscoelastic materials within cells must remain dynamic, yet organized, to sustain life. Biomolecular condensates are non-equilibrium assemblies of proteins and nucleic acids, formed through liquid–liquid phase separation and related phase transitions, and there are prominent examples of them that regulate transcription, RNA processing, signaling, and development by concentrating specific factors without membranes. Despite major advances in understanding physical principles of condensate formation, how condensates control physiological outputs in multicellular organisms remains unclear. This CAREER project studies a newly discovered class of condensates formed by GBPL proteins in Arabidopsis thaliana under diverse environmental and physiological challenges. The project will determine how GBPL condensates integrate stress-associated signals, reorganize activities in the nucleus, and coordinate adaptive responses across distinct cellular and developmental contexts. It advances biotechnology by revealing condensate-based mechanisms that could be harnessed to engineer crops with improved resilience to environmental stress, while also generating genetic, biochemical, imaging, and computational resources for the broader plant science community. It advances AI through machine-learning-guided discovery of new GBPL-associated factors and predictive analysis of sequence features, interaction networks, and condensate behavior. By integrating AI, genetics, genomics, biochemical reconstitution, and quantitative live-cell imaging, this project will establish mechanistic principles for condensate-mediated signaling in multicellular plants while embedding interdisciplinary training across undergraduate and graduate levels. 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 2026 · 2026-09

Plasma-assisted combustion is a new technology that can improve combustion efficiency. It can also enable use of difficult-to-burn fuels such as ammonia. However, predicting the performance of plasma-assisted combustion requires models that take into account how plasma interacts with surfaces. This project will use a combination of modeling and experiments to identify the fundamental physics driving interactions of plasma with surfaces. The research outcomes will be critical tools that better simulate plasma-assisted combustion and the ignition of various fuels. These results will apply broadly to a variety of applications in aerospace, energy, and advanced manufacturing. The project will train graduate and undergraduate students, providing them with expertise in advanced laser diagnostics and computational modeling. The goal of this project is to quantify the effective secondary electron emission coefficient under conditions relevant to plasma-assisted combustion, including high surface temperatures and nitrogen-oxygen mixtures. Current simulations use coefficient values that are significantly smaller than those inferred from experiments, creating a discrepancy likely driven by excited metastable species. To resolve this, the project will utilize a coupled experimental and computational approach. The experiments will employ novel techniques to measure current-voltage characteristics and isolate the critical cathode sheath voltage, while simultaneously measuring metastable and radical species using advanced laser diagnostics. Concurrently, the computations will develop a new hybrid fluid-kinetic framework that integrates a nonlocal kinetic sheath model. This framework will utilize a thermodynamically consistent theoretical framework for electron heating due to vibrationally excited states that accurately accounts for the additional heating when the vibrational temperature is higher than the gas temperature. Furthermore, a semiclassical theory for heavy-particle kinetics will be implemented to determine more accurately the rates of the reactions involving electronically excited states. By matching simulated characteristics to experimental measurements, the project will derive and validate the first physics-based correlations for secondary electron emission in a combustor environment. 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 2026 · 2026-08

Human-aided movement of insects across and between continents has increased rapidly with globalization. This has resulted in the introduction of highly damaging, non-native insects that impact ecosystem health, natural resource management, and human well-being. Given the multifaceted and catastrophic effects of biological invasions, substantial efforts have been devoted to understanding the factors that enable a species to arrive and establish in a new location. Changes over time in the invasion patterns of bark and wood boring insects have been particularly well studied – this group of insects is closely associated with international trade due to their presence in wood packaging materials (e.g., crates, pallets, dunnage) and includes species capable of causing widespread forest mortality. It remains unclear, however, how the arrival of bark and wood boring insects at finer time scales (e.g., season to season) affects their ability to invade. The goals of this work are to determine the role that seasonality plays in shaping the arrival of bark and wood boring insects, evaluate how arrival timing influences their ability to establish, and, ultimately, strengthen biosecurity measures to protect agriculture, urban, and rural forests from insect damage. Broader impacts include the dissemination of educational materials to the public to broaden awareness of invasive species, increased participation in STEM via a high school internship, and new curriculum for enrollees in an online plant health management program. Increased invasion rates by insects over the past several decades have been partly attributed to the growing use of wood packaging materials, a ubiquitous component of containerized cargo that functions as the main mechanism of arrival for bark and wood-boring species. Whereas the annual to decadal changes in invasion patterns by bark and woodboring insects have been well studied, the timing of intercontinental species movement at finer temporal scales (e.g., season to season) has received comparably little attention. This research will evaluate broad patterns in bark and wood borer arrival by season, leverage historical data on selected species to explore factors that shape arrival success, and quantify the probability of establishment for selected species given their arrival at various time points throughout the year. The following hypotheses will be tested using laboratory, field, greenhouse, and modeling studies: (1) arrival of bark and wood boring insects occurs year-round but shows a pronounced decrease in spring and summer, (2) rates of insect development drive the timing (e.g., month) of arrival, a pattern further structured by linkages among arrival location, departure location, and time of transit, and (3) the likelihood of establishment in the recipient ecosystem will be significantly elevated during short windows of opportunity in spring. Testing these hypotheses will elucidate how the risk of arrival and establishment by bark and wood boring beetles changes throughout the year and further clarify the role of seasonality in insect invasions. 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 2026 · 2026-07

High-quality data are increasingly central to modern machine learning and artificial intelligence, enabling advances in scientific discovery, automated decision-making, and emerging AI technologies. Yet there often lack transparent and reliable mechanisms to appropriately credit and compensate those who contribute data used to train AI systems. This project will develop statistical and machine-learning methods for measuring the value of data in AI model training and data-driven decision systems. The work addresses fundamental challenges in data valuation, including robustness to strategic manipulation, computational scalability for large-scale learning systems, and principled uncertainty quantification in assigning value to data contributions. The outcomes of this project will support transparent, fair, and sustainable AI data ecosystems while improving incentives for sharing high-quality and socially beneficial data. The project will also support graduate and undergraduate training, development of educational materials, public dissemination of results, and open-source software for the broader AI and data science communities. The research will develop statistical foundations for scalable and robust Shapley-value-based data valuation in modern machine learning through three integrated directions. First, it will develop priority-aware valuation rules that incorporate precedence relationships and priority weights, enabling originality, provenance, and individual risk considerations to be incorporated within a unified axiomatic framework for AI data attribution. Second, it will study the statistical and computational limits of approximating Shapley values and related semi-values in high-dimensional and large-scale learning settings, with the goal of designing efficient estimation and approximation algorithms for contemporary AI models. Third, it will develop a population-level theory of data value through Shapley density, a continuous analogue of finite-sample data valuation, establish convergence guarantees, and provide methods for accelerated computation and principled uncertainty quantification. Together, these contributions are expected to advance the statistical and algorithmic foundations of AI data valuation, enable scalable and trustworthy assessment of training data contributions in machine learning systems, and support fair and robust data-sharing ecosystems for future AI applications. 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 2026 · 2026-07

Extracellular vesicles naturally occur in biological fluids. These vesicles and their cargo are a promising source of biomarkers for personalized healthcare, including early cancer detection, health monitoring, and infectious disease. However, current methods for isolating and analyzing these particles are derived from cellular analysis methods, such as size-based sorting, biological targeting, or density-based isolation. For vesicles, these methods are often costly, time consuming, and produce inconsistent results. A key limitation is the poor understanding of vesicle mechanical properties, such as stiffness. Due to their small size and low concentration in biofluids, it is difficult to evaluate mechanical responses such as deformation during analysis. As a result, these mechanical properties are difficult to measure. Their small size also makes it difficult to directly observe individual vesicles using conventional optical microscopy. In this project, new approaches will be developed to quantify the mechanical properties of extracellular vesicles. This work will enable faster and more reliable manipulation of these particles. The project will support applications such as biomarker discovery. The methods in this project combine artificial intelligence–enabled models with state-of-the-art electron microscopy and super-resolution fluorescence microscopy. Together, these tools will provide meaningful measurements of mechanical properties. Additionally, this project will train personnel in advanced biomedical techniques. The trained workforce will support broader impact goals to sustain American leadership in biotechnology. This project will establish a physics-informed artificial intelligence assisted computational framework by utilizing high-resolution experimental imaging data to quantify the mechanical properties of extracellular vesicles. A structural representation based on implicit functions will be developed to capture vesicle geometry and deformation. A reduced-order mechanical model will relate observed shape changes to underlying material properties such as elasticity. Machine learning methods will analyze large imaging datasets, generated through this work along with using previously published data to identify deformation patterns thereby enabling inverse estimation of mechanical parameters for heterogenous vesicle populations and biological fluids. The expected outcomes include robust, high resolution imaging data sets, new algorithms, and methods for extracting mechanical information from images, reliable models for vesicle deformation, and a potentially transformative foundation for mechanobiology by linking nanoscale mechanical properties to biological function. 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 2026 · 2026-07

Tensors are fundamental data structures underpinning applications in scientific/engineering computing and machine learning. High-dimensional sparse tensors contain many zero elements and thus require specialized data representations and optimized algorithms. Robust support for high-performance sparse tensor computations is currently lacking, for example, for the challenging sparse tensor operator graphs arising in quantum chemistry. Both within a single machine and for distributed execution across multiple machines, there is a pressing need for software infrastructure that accelerates software development and the scale/performance of distributed scientific computations on sparse tensors. This project will build such an infrastructure to help scientists, especially in the fields of Quantum Chemistry and Machine Learning, to achieve (1) high performance, (2) reduced effort for software development, and (3) performance portability for distributed sparse tensor computations. The project makes contributions along multiple directions: (1) Multi-Level Intermediate Representation (MLIR) integration: integration with the popular MLIR compiler infrastructure, to enhance sustainability and dissemination; (2) data structures and algorithms for sparse tensors: novel hash-based data representations for sparse tensors, together with corresponding efficient parallel and adaptive algorithms for tensor operators; (3) optimizations for tensor operator graphs: new operator fusion optimizations for graphs of tensor operators, to reduce memory and increase performance; (4) distributed tensors: data structures and efficient operations to enable high-productivity development of distributed sparse tensor algorithms, together with compiler support to automatically generate implementations of distributed sparse tensor operators with minimized data movement costs; (5) engagement with domain scientists to achieve and sustain infrastructure impact. 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 2026 · 2026-06

Symmetry is a fundamental tool to understand physical and mathematical systems. Classically, symmetries of a system are captured by a collection of invertible self-maps called a group. Collections of certain quantum mathematical objects naturally form higher categories, and thus they admit richer collections of non-invertible or quantum symmetries. Examples of such quantum mathematical objects include algebras of quantum observables and quantum spin systems, which are mathematical models of pieces of matter, where atomic degrees of freedom are represented by local Hilbert spaces. Topologically ordered quantum spin systems give local quantum error correction codes for quantum computation. The main goal of this project is to use techniques from operator algebras and higher category theory to study the quantum symmetries of topologically ordered quantum spin systems. This project also funds research training for both graduate and undergraduate students. This project has three main focuses. First, the PI will develop the operator algebra approach to topologically ordered quantum spin systems, using nets of von Neumann algebras to study superselection sectors. The PI will also make connections between superselection theory for topological order and the quantum information theoretic boundary algebra approach to topological order. Second, the PI will make rigorous connections between higher category theory and topologically ordered quantum spin systems by both developing new higher dimensional quantum spin systems using higher fusion category input data, and by showing topologically ordered quantum spin systems themselves naturally form higher categories. Third, the PI will develop the theory of fermionic and anyonic operator algebras to better study topologically ordered phases of matter which arise in nature. 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 2026 · 2026-06

The conference Young Geometric Group Theory XIV will take place at the Korea Institute for Advanced Study in Seoul, South Korea, from June 8 to June 12, 2026. Young Geometric Group Theory XIV will bring together early career mathematicians working in geometric group theory and related areas to exchange ideas, present research, and build collaborations. The conference will include mini courses by leading researchers, research talks, poster sessions, and discussion groups designed to encourage interaction among participants at different career stages. By supporting the participation of twenty early career researchers based in the United States, the project will help ensure that promising young mathematicians can take part in this international exchange. The conference is particularly significant because it will be the first edition of the series held in Asia, expanding connections between research communities in Asia, Europe, and the United States. Broader impacts include strengthening the global mathematical community, supporting the professional development of early career researchers, and fostering long-term international collaborations. The project supports travel and local participation costs for twenty United States-based graduate students and early career researchers to attend the conference. The program is designed to promote intellectual exchange through a combination of mini courses delivered by senior experts, research talks by participants, informal discussion sessions, lightning talks, and a poster session. These activities create opportunities for early career researchers to present their work, receive feedback, and develop collaborations with peers and senior mathematicians. The selection of speakers and topics is intended to expose participants to several central directions in geometric group theory, including hyperbolic groups, mapping class groups, and related geometric and dynamical structures. The project will help integrate emerging researchers into the international research community and encourage future collaborations across institutions and continents. Conference webpage: https://sites.google.com/view/yggt2026/ 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 2026 · 2026-06

PROJECT SUMMARY Chronic or non-healing wounds affecting over 2.5% of the U.S. population, present significant medical challenges, escalating the risk of infection, tissue degradation, and potential amputation. Macrophages, crucial for combating infections and controlling inflammation, play a key role in maintaining tissue homeostasis and promoting wound healing. Recent studies highlight the crucial role of metabolic reprogramming in macrophages responding to inflammation. Metabolic shifts impact the availability of essential metabolites that serve as cofactors or substrates for epigenetic enzymes, thereby influencing genome accessibility and regulating macrophage differentiation and function. Despite this, the specific nexus between metabolic rewiring and epigenetic modification governing the reparative function of macrophages within both inflamed and injured tissues remains largely unexplored. We recently discovered that a key component of the serine synthesis pathway, phosphoserine aminotransferase 1 (PSAT1), is an essential metabolic checkpoint in macrophages for anti-inflammatory (M2) activity to control tissue inflammation. Our preliminary data reveal that PSAT1's metabolic role in serine generation within M2 macrophages is integral for mitochondrial heme synthesis, respiratory activity, and the regulation of intracellular levels of α-ketoglutarate (α-KG), a crucial metabolite in the mitochondrial TCA cycle and a substrate for histone demethylation. As a result, the tri-methylation of lysine 27 on histone H3 (H3K27me3), a repressive histone mark associated with transcriptional silencing, is increased in the absence of PSAT1, significantly impairing macrophage anti-inflammatory function. These exciting data support a novel and provocative hypothesis that the metabolic reprogramming of serine synthesis, regulated by PSAT1, can influence the mitochondrial homeostasis and epigenetic accessibility of reparative M2 macrophages in the context of tissue injury and inflammation. In this proposed project, I will advance our interesting findings and scrutinize the hypothesis rigorously with the following two directions: 1) Elucidate the role of PSAT1 in macrophage mitochondrial homeostasis and reparative activity. 2) Investigate the interplay between PSAT1 metabolism and epigenetic modification in reparative macrophages. I will employ incisive transcriptomics, metabolomics, and immunological approaches to achieve these goals. The results will answer a fundamental and long-standing question surrounding the role of metabolic rewiring in macrophages for wound healing and tissue resolution. Moreover, this project will break new ground, providing crucial insights on the much-needed crosstalk between metabolism and epigenetics.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY The overall goal of this project is the comprehensive structural and dynamic characterization of the highly flexible human K-Ras oncoprotein and its interactions with protein-binding partners and small-molecule ligands in both its normal and dysregulated states by the combined use of state-of-the-art experimental and computational methods. K-Ras is well-known to be exceptionally susceptible to carcinogenic mutations in certain key amino-acid positions, which through a cascade of protein signaling processes dysregulate cell proliferation. K-Ras is directly associated with about 25% of all human cancers. As a GTPase, K-Ras is a molecular switch with wild-type K-Ras being in its on-state when bound to GTP and turning to the off-state through catalytic hydrolysis of GTP to GDP. In oncogenic mutants, such as G12C, G12D, and G12V, K-Ras is perpetually locked into active signaling of the Ras-Raf-MEK-ERK pathway leading up to malignancy. This project builds on recent breakthroughs in the applicant’s lab overcoming two key obstacles that have severely impaired past efforts to understand K-Ras and its interactions with its protein partners and potential drug ligands: they made the functionally critical Switch I and Switch II regions of K-Ras fully visible and assigned by NMR and overcame the need to work with non-hydrolyzable GTP-analogs instead of native GTP as an integral K-Ras ligand. Leveraging these advances, it is proposed to comprehensively investigate the structural-dynamics ensembles of K-Ras in the presence and absence of its functionally critical protein binding partners and small-molecule ligands by NMR and computational modeling. This entails the full characterization of the modes of interactions of K-Ras wild-type vs. G12 mutants and GTP- vs. GDP-bound with the small-molecule drug candidate MRTX1133, protein GTPase activating protein (GAP), and the RBD and CRD domains of the downstream signaling protein-kinase B-Raf. The wealth of quantitative NMR data at atomic resolution will give novel information essential for our understanding of the driving forces underlying K-Ras and its function in health and disease. These data will provide powerful synergies with computational approaches, such as AlphaFold and extended molecular dynamics computer simulations for obtaining a realistic, experimentally validated in silico description of K-Ras behavior in the presence of its binding partners for wild-type and the mutants. Validated conformational ensembles will be subsequently mined for allosteric effects and used for virtual ligand screening including cryptic pockets uncovered during this process. Due to its highly dynamic nature, the fully quantitative atomic-level structural-dynamic model of K-Ras and its binding partners is likely to be directly beneficial enabling the discovery of key molecular determinants of K- Ras cancer biology and guiding the design of new therapeutic strategies to silence mutationally activated K-Ras.

NIH Research Projects · FY 2026 · 2026-06

Project Summary Temperature influences the metabolic rate, changes membrane fluidity and impacts neural activity. Animals including humans therefore developed intricate strategies to limit fluctuations of body- temperature. These include behavioral strategies, such as seeking places of comfortable temperature, which are shared among all animals as well as autonomous means of body-temperature regulation in mammals and birds. Temperature dysregulation on the other hand impacts processes as diverse as sleep and immune function. Recent research revealed how vertebrates sense temperature and how this information is relayed to the brain. Furthermore, cell-types that control aspects of autonomous and behavioral thermoregulation have been identified in conserved brain structures such as the preoptic area. However, our understanding of behavioral thermoregulation is very limited. We do not understand how the behavioral thermal preference is set and adjusted according to physiological needs. We also do not know how vertebrates including humans process temperature stimuli to seek out comfortable temperatures. This lack of fundamental knowledge makes it difficult to understand how temperature dysregulation might arise. To fill this critical gap in our knowledge, we will use the vertebrate larval zebrafish as an animal model. Zebrafish are transparent and we can monitor activity throughout the entire brain using functional calcium imaging. Together with mathematical tools this allows us to predict how the preferred temperature influences thermosensory processing, allowing the animal to thermoregulate. By comparing hot and cold avoidance behaviors together with neural activity we will understand how animals adjust behavior based on whether they move towards their preference or away from it. This is a critical component of seeking out a preferred temperature. By directly modulating the preferred temperature through inflammatory signals, we will test how the brain encodes and modulates the preference an animal seeks. Using ablations to manipulate circuit activity we will test our models and gain mechanistic insight into the establishment of the preference. This research will yield fundamental principles of how a vertebrate brain sets and modulates the thermal preference. This knowledge will be fundamental in understanding homeostatic dysregulation.

NIH Research Projects · FY 2026 · 2026-06

The incidence of Alzheimer’s disease (AD) in the USA is expected to increase from 7.2 million to 13.8 million by 2060, highlighting the urgent need to discover new treatments. Madagascar is known for its unique plant biota and hosts about 12,000 plant species, of which 80% are endemic. Malagasy flora has provided clinically used bioactive compounds such as the anticancer drug vincristine. Madagascar is now among the top priority conservation sites in the world since the forest is deteriorating due to urban expansion, pollution, bushfires, and poverty. The overall goals of this proposal are (1) to assist in conserving the unique plants of Madagascar by developing a model program for bioprospection and conservation, (2) to identify plants from Madagascar that contain phytochemicals that can be used against Alzheimer’s disease (AD), and (3) to promote scientific training and capacity development in the island. This project will focus on three main objectives involving medicinal plant inventory and conservation, exploration/discovery, and training and capacity building. Plants have been used in traditional medicines for central nervous system (CNS)-related conditions and many are known to produce antioxidant compounds. We aim to perform an ethnobotanical survey and develop conservation approaches for plants that are being used in traditional medicine for brain-related diseases including dementia and contain neuroprotective constituents from among the rich biodiversity of flora in Madagascar. Oxidative stress can impair the functions and activities of the CNS and has been shown to contribute to the onset and progression of many neurodegenerative conditions including AD. Our previous studies have shown that that activation of NRF2, which is an antioxidant regulatory transcription factor, elicits neuroprotection both in vitro and in vivo, can attenuate decreases in dendritic arborization and synaptic density, reduces oxidative stress, and improves mitochondrial function in hippocampal neurons isolated from AD mice as well as in the brains of AD mice treated with NRF2 activators. These data suggest that identification of NRF2-activating compounds could be a useful approach to discovering novel AD therapeutic compounds. Based on the structures of previously isolated bioactive compounds and their plant families of origin, we hypothesize that plant species with a high level of endemicity from the Malagasy flora will produce many previously unknown NRF2-activating compounds. The project will assist aging populations developing AD diseases by not only identifying and standardizing plants that have already been used in traditional medicine but also discovering new chemical scaffolds for discovering AD drugs.

NSF Awards · FY 2026 · 2026-06

Humans cannot pay attention to everything equally because the brain has limited processing capacity. Instead, we constantly make rapid, dynamic decisions about what to attend to, both in the external environment and in our internal thoughts and goals. In daily life, we also encounter distractions (both external and internal), which further tax our attentional decisions. Deciding what information to prioritize also drives what we end up perceiving and remembering. The current work aims to understand what happens when attention is disrupted and what the consequences are for perception and memory. The work has far-reaching potential applications, ranging from maximizing human intelligence and decision-making in rapidly shifting situations to development of more naturalistic artificial intelligence and wearable biotechnology. A critical part of developing useful tools that can effectively aid human dynamic decision-making in the real world ((e.g., AI-powered smart glasses) involves incorporating knowledge of how dynamic decisions about attention can focus or distort our perceptions, memory, actions, and experience. A combination of behavioral experiments, neuromodulation, and computational analyses are used to investigate how dynamic decisions about spatial attention and distraction affect ongoing perception and memory processing. The work evaluates the idea that spatial distractions not only disrupt filters gating external spatial attention but also disrupt nonspatial filters that gate working memory encoding and internal cognitive control. The team explores whether spatial disruption of external attention is necessary to induce visual working memory intrusions, whether external-to-internal filter disruption is modality-specific, and whether intrusions can be avoided through learning. Complementary disruptions of internal attention (abrupt changes deciding what remembered items are prioritized), explores the consequences of dynamic attentional decisions on feature perception and memory. 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 2026 · 2026-06

This project studies how subsets of symmetry groups grow when their elements are multiplied together. In many familiar settings, combining two sets makes them much larger, but in some cases the resulting product set remains unexpectedly small, revealing hidden algebraic and geometric structure. Understanding this phenomenon is a central problem in additive combinatorics and has strong connections with harmonic analysis, geometry, and number theory. The project will develop new principles for detecting and describing this hidden structure in noncommutative settings. By advancing fundamental knowledge about symmetry, growth, and structure, the project will strengthen core research in the mathematical sciences and deepen links among several major areas of pure mathematics. It will also support the training of graduate students and junior researchers, promote collaboration across fields, and broaden participation through mentoring, workshops, and public mathematical outreach. The project investigates direct and inverse problems for product sets in locally compact nonabelian groups. Its main goals are to obtain sharp measure-doubling estimates and corresponding inverse and stability theorems, with four interconnected directions: rearrangement inequalities and inverse theorems in Lie groups, with applications to endpoint forms of the Kunze–Stein phenomenon; minimal doubling for sets of arbitrary measure in compact semi-simple Lie groups, toward a continuous analogue of Babai’s conjecture; inverse and stability results for the nonabelian Brunn-Minkowski inequality in noncompact locally compact groups; and applications to Elekes-Szabó-type incidence and counting problems. The methods combine additive-combinatorial and probabilistic arguments with harmonic analysis, model-theoretic ultraproduct and limit methods, quotient and fiber-structure analysis, and the structure theory of locally compact and Lie groups. Expected outcomes include new sharp growth inequalities, structural classifications of near-extremizers, and new bridges among additive combinatorics, geometry, harmonic analysis, and model theory. 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 2026 · 2026-06

PROJECT SUMMARY Although the plasma membrane of animal cells demonstrates considerable structural and functional plasticity, it is also susceptible to mechanical disruption and perforation by various enzymes, chemicals, and pore-forming proteins. To counteract this vulnerability, evolution has equipped cells with robust repair mechanisms that rapidly reseal the membrane, preventing cell death and restoring homeostasis. It is well established that calcium influx through membrane disruptions serves as a key signal to activate repair. However, the downstream molecular mechanisms responsible for executing this process remain incompletely understood. To address this gap, we investigated one of the most potent membrane-damaging agents: bacterial pore-forming toxins known as cholesterol-dependent cytolysins (CDCs). These toxins are major virulence factors of Gram- positive pathogens, including Listeria monocytogenes, Streptococcus pneumoniae, Streptococcus pyogenes, Clostridium perfringens, and Bacillus anthracis, which cause severe diseases worldwide. Our research focuses on the repair of cells injured by listeriolysin O (LLO) and pneumolysin (PLY), the CDCs respectively produced by L. monocytogenes and S. pneumoniae. We discovered that septins play a critical role in repairing cells injured by LLO and PLY, and by mechanical wounding. This finding supports that septin-mediated repair mechanisms are relevant to infections and other diseases characterized by excessive cell injury, abnormal, or impaired plasma membrane repair. Septins are a conserved family of multifunctional cytoskeletal proteins that bind phospholipids and F-actin, playing key roles in shaping and regulating the plasma membrane. We will assess the role of septins across multiple cell lines and primary cells, including epithelial cells, macrophages, and cardiomyocytes, in response to different forms of plasma membrane injury, such as mechanical disruption and CDC-induced perforation. In Aim 1, we will investigate the spatiotemporal dynamics of the newly identified septin repair domains, from injury to resealing. We will define the spatiotemporal relationships between septin repair domains and other known repair pathways, and if septins function as scaffolds to recruit and organize repair effectors in space and time. Using super-resolution and FRET microscopy, we will establish the molecular architectures of the septin repair domains. To elucidate the mechanisms by which septins mediate plasma membrane repair, we will test several, non-mutually exclusive, repair mechanisms in Aim 2. Additionally, we will employ an unbiased proteomic approach to define the septin interactome during plasma membrane repair. Finally, we will define the role of the identified repair pathways in host cell invasion by L. monocytogenes and pathogenesis using a murine model of infection. This work will provide fundamental insights into the mechanisms of plasma membrane repair, with broad implications for understanding physiological and pathological processes and developing therapeutic strategies against infections and other diseases involving excessive plasma membrane damage or dysregulated repair.

NIH Research Projects · FY 2026 · 2026-06

Doxorubicin is a highly effective chemotherapeutic agent used for treating a wide range of malignancies, including breast cancer and pediatric cancers. However, its clinical utility is often limited by life-threatening cardiotoxicity, which can lead to irreversible heart failure. While clinical risk factors such as cumulative dose and age are well-established, individual genetic susceptibility plays a critical role in doxorubicin-induced cardiotoxicity (DIC). Currently, predicting which patients will develop DIC remains challenging. The longterm goal of this project is to identify genetic determinants of DIC to enable precise risk stratification and cardioprotection. During the K99 phase, the Principal Investigator (Pl) successfully established a highthroughput CRISPR interference/activation (CRISPRi/a) screening platform in human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). In the R00 independent phase, the Pl will transition from gene-level perturbation to the characterization of specific genetic variants or single nucleotide polymorphisms (SNPs). Specifically, Aim 3 (R00 Phase) will utilize Prime Editing 7 (PE?), a precise genome editing technology, to install a library of 116 clinically implicated genetic variants into human iPSCCMs. This "in vitro GWAS" approach will quantify the functional impact of each variant on cardiomyocyte survival under doxorubicin stress. Top candidate variants will be further validated using high-throughput 3D engineered heart tissues (EHTs) to assess physiological contractility and automated live-cell imaging to determine cytotoxicity kinetics. Finally, these functional biological scores will be integrated with clinical GWAS data to generate Integrated Risk Scores. This research will bridge the gap between clinical genetics and functional biology, establishing a foundation for the personalized prediction and prevention of chemotherapy-induced heart failure.

NIH Research Projects · FY 2026 · 2026-06

PROJECT ABSTRACT The overarching goal of the Neuro-RISE (Training in Interdisciplinary Neurotrauma Research, Innovation, and Scientific Excellence) program is to train the next generation of neurotrauma scientists to become leaders in advancing research that improves the lives of people with spinal cord injury (SCI) and traumatic brain injury (TBI). Trainees will be equipped to develop and translate novel therapeutic, rehabilitative, behavioral, and technological strategies that reduce disability and promote health, independence, and quality of life across the lifespan. This goal will be met by taking advantage of an exceptionally rich institutional environment that offers myriad resources and a complementary and collaborative team of 17 primary and 16 secondary mentors committed to training and career development. The program is further enriched by the inclusion of 16 lived-experience mentors—eight individuals with SCI and eight with TBI—with varied backgrounds and injury experiences. Predoctoral trainees will benefit from 2 years of interdisciplinary training that carefully melds four core levels of scientific inquiry: (i) adaptation and plasticity, (ii) rehabilitation diagnostics and interventions, (iii) novel devices and technologies, and (iv) chronic symptom management. Training will occur in one of two areas of concentrated expertise: (i) SCI or (ii) TBI. Predoctoral trainees will be selected from a highly competitive national pool admitted to the Neuroscience, Psychology, or Health and Rehabilitation Science Graduate programs. The training team is truly interdisciplinary. Each trainee’s primary mentor and Translational Mentoring Team will be drawn from expert faculty spanning the Colleges of Medicine (COM), Education and Human Ecology (CEHE), Food, Agricultural and Environmental Sciences (CFAES), and Engineering (COE). Neuro-RISE also offers a distinctly immersive and human-centered training experience that integrates people with lived experience of SCI or TBI into each trainee’s research project—bringing real-world insights directly into scientific inquiry. The program promotes bench-to-bedside and bedside-to-bench translation by preparing trainees to apply mechanistic discoveries to clinical care and interpret clinical phenomena through rigorous scientific inquiry. Trainees will participate in a curated curriculum that includes commercialization and team science training, advanced methods seminars tailored to neurotrauma research, and a community-engaged research workshop co-led by individuals with SCI and TBI. With intentional focus on research ethics, scientific rigor, and impact-driven scholarship, Neuro-RISE prepares emerging scientists not only to publish, but to lead multidisciplinary teams, secure competitive funding, and transform care for people with neurotrauma across the lifespan. Neuro-RISE will recruit two predoctoral trainees in year one, then grow to admit four annually thereafter. As a top-tier NIH-funded academic medical center, The Ohio State University is uniquely positioned to train translational neurotrauma researchers poised to lead future innovations.

NSF Awards · FY 2026 · 2026-06

This project introduces an innovative method to build custom, wireless medical implants inside the body using a robotic probe that enters through a small 'keyhole' incision and prints and assembles complex functional devices without the trauma associated with traditional, large-incision surgery. This method not only reduces recovery times and surgical risks but also broadens the accessibility and potential applications of wireless implants, benefiting a wider range of patients. This innovative technology promises to revolutionize medical fields such as cardiac care, lung surgery, and biomedical sensing, offering new solutions for monitoring and treatment. Beyond its immediate medical impact, the project aims to inspire and educate students by integrating hands-on engineering concepts into educational modules, fostering interdisciplinary learning, and expanding career opportunities in both healthcare and manufacturing. The new approach will bridge the gap between clinical practice and advanced engineering, redefine the roles of surgeons and medical device manufacturers, and ultimately enhance the quality of life and healthcare outcomes for patients. The research of this project aims to advance the field of intracorporeal (i.e., inside the body) three-dimensional (3D) printing by developing new dielectric materials and optimizing fabrication processes for wireless implantable electromagnetic (EM) components. Building on previous success with conductive biocompatible materials, the research will focus on characterizing and combining these materials with novel dielectrics to achieve sub-millimeter printing resolution at body temperature. The main research tasks include defining manufacturing techniques for multi-layer EM structures, embedding non-printable circuit elements, and understanding how manufacturing fidelity affects EM performance. The project team will formulate design guidelines for EM components that are no longer constrained by miniaturization, enabling the creation of implantable antenna arrays and advanced signal control structures previously unattainable due to size limitations. The project will employ both theoretical and numerical analyses to uncover relationships between material properties, design strategies, and device performance, aiming to exceed current EM metrics. By integrating these innovations into simulations and practical demonstrations, the research will contribute significantly to the knowledge base, paving the way for safer, more effective, and versatile wireless implants that can be manufactured directly inside the body. 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 2026 · 2026-05

SUMMARY We propose to define lipid alterations in the pancreas during type 1 diabetes (T1D) using high-resolution, multimodal imaging. Using human tissue from nondiabetic and T1D donors in the Vanderbilt Pancreas Biorepository, we will apply an integrated imaging pipeline—combining imaging mass spectrometry, liquid chromatography mass spectrometry, Xenium spatial transcriptomics, and multiplexed immunofluorescence—to spatially map lipid species alongside RNA and protein markers. These spatial datasets will be co-registered and analyzed to identify cell-type- and niche-specific lipid changes in T1D. Single-nucleus RNA-seq data from PanKbase will be used to predict cell types and transcriptional niches, enabling integrative mining of lipid signatures. All data will be developed in collaboration with PanKbase and formatted for ingestion into this public resource, directly supporting the type 1 diabetes research community and NIDDK’s strategic goals in molecular tissue atlasing.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY/ABSTRACT X-linked hypophosphatemia (XLH) is caused by inactivating mutations in the PHEX (phosphate-regulating gene with homology to endopeptidases on the X chromosome) gene, resulting in di sturbances in systemic and local mineral metabolism, increased levels of circulating fibroblast growth factor 23 (FGF23), and hypophosphatemic rickets. In XLH, pulp necrosis and dental abscesses are prevalent at all ages and risk of periodontitis increases with age. These contribute to decreased oral and systemic health. While mechanisms underlying dental disorders in XLH have been assumed to result from mineralization defects, no studies have directly investigated or demonstrated this connection. Conventional therapy for XLH includes daily oral phosphorus and vitamin D or the use of inactivating FGF23 antibody (FGF23Ab; burosumab) reduces excess FGF23. Dental health outcomes have been unclear, with both preclinical studies and human case studies reporting conflicting results of worse outcomes, no improvement, or some improvement, compared to conventional therapy. Burosumab only addresses heightened FGF23 levels and does not correct local mineral metabolism changes or other primary or secondary effects from PHEX loss-of-function. PHEX is expressed by a wide range of cells and mutations alter other signaling pathways that contribute to non-skeletal defects observed in XLH; no current XLH treatment options address these alterations. Recently, it was discovered that pediatric and adult patients with XLH were reported to have a persistent state of inflammation, but it remains unknown how this is associated with dental and periodontal manifestations of XLH. There is a lack of information on effects of XLH on immune cells (monocytes, neutrophils, T-cells, and B-cells) and cytokine profiling. Treatment effects on immune cells and cytokines were never evaluated to understand how these interventions modulate immunological status associated with PHEX mutations. XLH patients experience very high rates of pulp infection, necrosis, abscess, and tooth loss, and current treatments do not fully prevent this. Similarly, these patients experience high rates of periodontal disease later in life that is only partially reduced by conventional therapy. Local changes in immune response and microbiome have never been investigated. Based on the preliminary data summarized above, we hypothesize that dental and periodontal infections in XLH result, in part, from altered immune response. This hypothesis will be tested by 3 Specific Aims: (1) Define altered immune and inflammatory profile in the Hyp mouse model of XLH; (2) Identify immune mechanisms contributing to increased dental pulp infections in XLH and (3) Investigate immune mechanisms associated with periodontal disease in XLH. Successful completion of this proposal will provide critical new insights into how XLH affects immune function and the associated dental and periodontal manifestations. This can make a difference in therapeutic approaches for XLH that could improve oral health and quality of life.

NIH Research Projects · FY 2026 · 2026-05

Progressive multiple sclerosis (PMS) is a leading cause of neurological disability and remains a critical unmet clinical need, particularly among older adults. In contrast to relapsing-remitting MS (RRMS), which typically begins in young adulthood, PMS emerges more commonly in middle age and is characterized by gradual, irreversible neurological decline driven by chronic, compartmentalized inflammation and neurodegeneration within the central nervous system (CNS). With over half of individuals with MS in the United States now between ages 55 and 64, elucidating the mechanisms that underlie disease progression is essential for improving diagnosis, monitoring, and treatment. Current immunotherapies that effectively target peripheral immune pathways in RRMS offer limited benefit in PMS. In particular, broadly depleting B cell therapies such as anti-CD20 antibodies poorly penetrate the CNS, confer modest efficacy in PMS, and increase infectious risk— particularly concerning in the older PMS population. These challenges highlight the need for therapies that more precisely target CNS-resident immune mechanisms. Emerging evidence, including the recent success of a CNS-penetrant Bruton’s tyrosine kinase inhibitor in non-relapsing PMS, implicates CNS-resident B cells—especially those within meningeal ectopic lymphoid structures—in PMS pathogenesis. However, the identity, antigen specificity, and pathogenic functions of these CNS- compartmentalized B cell subsets remain poorly understood. Our preliminary studies have unexpectedly identified a novel population of clonally expanded, innate-like Fcrl5⁺IgM⁺ B cells—termed Baci cells—in the meninges of middle-aged mice with chronic experimental autoimmune encephalomyelitis (EAE), an animal model that recapitulates key features of PMS. Notably, a hyperexpanded Baci clone produces IgM antibodies reactive to phosphatidylcholine, a major myelin lipid, suggesting potential autoreactivity. We hypothesize that Baci cells contribute to chronic neuroinflammation, cortical demyelination, and disease progression via natural autoreactive IgM antibody production, antigen presentation to autoreactive T cells, and pro-inflammatory cytokine secretion. Aim 1 will determine the role of Baci cells in CNS-compartmentalized inflammation by selectively depleting them using a novel Fcrl5-Cre transgenic mouse during chronic EAE, and evaluating their role in the reactivation of encephalitogenic T cells. Aim 2 will define the antigen specificity and pathogenic potential of natural IgM antibodies produced by clonally expanded Baci cells in the CNS. Aim 3 will investigate the presence of a human analog of Baci cells in cerebrospinal fluid from individuals with MS and examine its correlation with progressive disease phenotype and extent of neurological disability. This proposal advances a novel concept in PMS pathogenesis by uncovering and functionally characterizing an unconventional age- associated, innate-like B cell subset. Using innovative genetic tools and mechanistic approaches, it aims to define how Baci cells drive progression, paving the way for safer, more targeted therapies.

NIH Research Projects · FY 2026 · 2026-05

Project Summary/Abstract Psychological stress is an established contributor to bone and tooth loss and impaired bone growth. In the US, more than 50 million people currently experience bone loss while a similar number is afflicted by anxiety (40 million) and/or depressive disorders (16 million). Bone and tooth loss resulting from psychological stress has been observed in all age populations. A murine model of stress, repeated social defeat (RSD), recapitulates key physiological, immunological, and behavioral alterations in humans exposed to psychosocial stress such as bullying and loss of social status. RSD activates the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system to create a state in which primed, pro-inflammatory monocytes traffic from the bone marrow to the brain to generate neuroinflammation and anxiety-like behavior. In addition, RSD rapidly induces bone loss through increased activity of osteoclasts, and bone growth plate reduction. However, the precise mechanisms by which RSD influences bone have not been identified. Therefore, the overall goal of this project is to exploit a rodent model of psychological stress to better explore the relationships between immunological processes, mental and bone health. This will be accomplished in three Specific Aims. Aim 1 will examine the kinetics of bone loss and growth plate reduction in adolescent male and female mice following a period of RSD. Aim 2 will investigate a central role for osteoclast activation and chemokine (CXCL12) signaling in RSD-induced monocyte mobilization. Aim 3 will investigate mechanisms of RSD-induced growth plate reduction. Outcomes of this project will help achieve the long-term goal of developing more specific interventions to treat psychological stress-related disorders of skeletal physiology.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY Vision restoration after corneal damage may require a transplant, which currently relies on human donor corneas, of which there is a shortage worldwide. To address this limitation, I propose to develop a partial thickness acellular corneal graft. A functional graft must have similar optical and mechanical properties to the native cornea. I will evaluate combinations of patterns and bioink materials including methacrylated gelatin (GelMA) and methacrylated silk fibroin derived materials (SilMA). In particular, I will investigate the effects of 1) hydrogel and crosslinker composition, 2) fibril alignment due to nozzle diameter, and 3) resulting scaffold optical, mechanical, structural, and biological properties. In Aim 1, I will evaluate the effect of the bioprinting process parameters including bioink viscosity and nozzle diameter on the structural, mechanical, rheological, and optical scaffold properties. Sample microstructure will be evaluated using scanning electron microscopy (SEM). Blends of GelMA with SilMA with photo-crosslinker will be evaluated with different ratios of bioink ingredients and varied photo-crosslinking times. In addition to rheological characterization of the bioinks, bioprinted corneal scaffolds will be evaluated by rheology as well as compression and tensile testing. Suture retention testing will be used to compare the printed scaffolds with molded materials and the natural cornea. Optical properties including optical transparency and refractive index will be evaluated using a spectrophotometer and a refractometer, respectively. In Aim 2, in vitro cytotoxicity will be evaluated with human corneal epithelial cells. Epithelial cell migration and fibrosis will also be evaluated in vitro. Since this scaffold is designed to replace only the anterior part of the corneal structure with an intact endothelium, I will test the epithelialization ability of the printed scaffolds using human corneal epithelial cells. Fibroblasts will be cultured on the bioprinted scaffolds to quantify markers of fibrosis. Successful completion of this project can serve as a basis to create partial thickness corneal grafts. This project will also provide the basis for further evaluations of different materials requiring specific optical and/or mechanical properties.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY Fetal Alcohol Spectrum Disorders (FASD) represent a major public health concern, significantly contributing to intellectual disabilities with a prevalence rate of 1-5% in the United States and an estimated annual economic burden of $5 billion. Despite its substantial impact, there is currently no cure for FASD. Existing research, including our own, indicates that various brain cells and regions exhibit differential vulnerability to alcohol-induced injury, with underlying mechanisms remaining poorly understood. For instance, neurons are particularly susceptible to alcohol stress, often undergoing acute apoptosis, and those that survive exhibit impaired functionality. Our primary goal is to elucidate the brain region- and cell-specific mechanisms underlying alcohol- induced developmental neurotoxicity (AIDN) and develop cell-specific precision interventions. By addressing the following critical questions, we aim to uncover novel targets for intervention: 1) Why are neurons more vulnerable to alcohol-induced injury? What neuron-specific signaling pathways contribute to this vulnerability? What are the brain region- and cell-specific targets for neuroprotection? To achieve these objectives, we will employ cutting- edge, multidisciplinary approaches including virus-based cell-specific gene modification, spatial transcriptomics, various imaging systems (e.g., multiphoton imaging), and various behavioral tests. Our research will utilize complementary models, including human induced pluripotent stem cell-derived 3D mini brains and mouse models, to investigate the mechanisms of AIDN at molecular, cellular, tissue, and animal levels. Additionally, we will assess the neuroprotective effects of neuron-specific and mitochondria-targeted interventions in AIDN. This study aims to provide a comprehensive understanding of the mechanisms driving alcohol-induced cognitive and behavioral impairments. By mapping acute and long-term brain region- and cell-type specific gene expression profiles following developmental alcohol exposure, our research offers promising strategies for early, brain cell type-targeted precision interventions and treatments for FASD patients. Moreover, the inclusion of human stem cell-based mini brain models will enhance the translational potential of our findings, potentially offering insights applicable to other neurodevelopmental disorders.

NIH Research Projects · FY 2026 · 2026-05

ABSTRACT Air pollution is responsible for approximately 9 million premature deaths and more than $4 trillion in annual economic losses. Ozone (O3), a criteria air pollutant, contributes to this mortality by causing lung inflammation and injury, which is augmented in susceptible populations. One such susceptible population are invididuals who have obesity. This is significant given that >10% of the global population, including 42% of the U.S. population, is obese (BMI>30), thereby representing a large population that can exhibit enhanced O3 sensitivity. A hallmark of obesity is increased circulating n-6 polyunsaturated fatty acids (PUFA). One of the n-6 PUFAs known to be elevated in obesity is linoleic acid (LA). LA is highly abundant in the western obesogenic diet but largely understudied in the context of lung inflammation. Once ingested, LA can be metabolized into hydroxyoctadecadienoic acids which are further metabolized by enzymes including soluble epoxide hydrolase (sEH) into distinct OXLAMs (oxidized linoleic acid metabolites) that drive inflammation. EpOMEs and DiHOMEs are sEH driven OXLAMs that have been shown to both drive and dampen tissue inflammation. Presently, it is unknown if high LA intake in an obesogenic diet drives increased OXLAM production, including EpOMEs and DiHOMEs, and if this augments O3-induced lung inflammation. Based on strong preliminary data, we hypothesize that decreasing dietary consumption of LA will reduce lung LA and subsequent production of EpOMEs and DiHOMEs, which will mitigate obesity-exacerbated lung inflammation and injury following O3 exposure. This hypothesis is conceptually innovative as it defines how dietary LA intake in obesity drives susceptibility to O3 and offers a precision nutrition strategy to mitigate this impact in a vulnerable population. The approach to test this hypothesis will rely on mouse models and human bronchoalveolar lavage and plasma samples from individuals with a differential body mass indexes (BMI). In Aim 1, we will establish that decreased dietary LA consumption mitigates obesity-induced augmentation of O3 lung inflammation by decreasing lung LA accumulation and production of OXLAMs. In Aim 2, we will dissect the contribution of individual sEH driven OXLAMs on O3-induced pulmonary inflammation. Impact: This study will open an entirely new avenue of air pollution research, focused on how dietary LA intake in obesity leads to generation of pulmonary OXLAMs and drives inflammation following O3 exposure. The results of this proposal could support future targeted dietary interventions in obesity that could mitigate O3-induced pulmonary inflammation and injury and more broadly impact other types of lung injury. Thus, these studies will directly pave the way for innovative precision nutrition approaches in susceptible individuals that will combat the adverse health effects of air pollution.