University Of Arizona

NSF Awards · FY 2026 · 2026-10

Artificial Intelligence (AI) serves as a strategic engine for economic growth, national security, and global competitiveness. This project establishes a Research Experiences for Undergraduates (REU) site, AI Research for Intelligent Systems and Efficiency (ARISE), in the Electrical and Computer Engineering Department at the University of Arizona. The project recruits and trains eight undergraduate students each summer, engaging them in immersive research at the intersection of AI applications, efficient algorithms, and hardware system optimization. The overarching goal is to address real-world engineering challenges through sustainable and effective computational solutions. The project’s novelties are its focus on the "full-stack" nature of AI integrating the systems and hardware architecture with high-level AI applications and algorithms and a collaborative mentoring approach that provides participants with multidisciplinary expertise. The project's broader significance and importance are the development of a skilled workforce in national priority areas; the creation of educational demos and course materials for the public; and the fostering of technology transfer to industry partners to strengthen the domestic technology ecosystem. The ARISE REU site organizes research into three complementary thrusts: AI Applications, Efficient Algorithms, and Hardware Systems. Each project is co-mentored by faculty paired from different thrusts to facilitate interdisciplinary collaboration across the computing stack. Research projects focus on the frontiers of the field, exposing students to state-of-the-art AI algorithms, efficient training and inference techniques, and distributed learning. The technical approach emphasizes application-guided optimization, high-performance computing, reconfigurable systems, and hardware-software co-design to solve real-world engineering tasks, including autonomous agents, design automation, healthcare, and defense applications. Participants gain expertise in energy-efficient AI through intensive bootcamps, research seminars, and systematic training in research methodologies. Structured mentoring and professional development activities prepare participants for diverse careers in the AI infrastructure and semiconductor sectors. The project shares outcomes through technical publications, demonstrations, and the integration of research findings into university curricula and industry-university cooperative research centers. This work strengthens the national capacity for AI innovation by preparing an interdisciplinary cohort of researchers to solve the efficiency bottlenecks of next-generation AI computing and systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 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.

NIH Research Projects · FY 2026 · 2026-06

Abstract: Decades of research on transplantation have made it clear that the immunological barriers to engraftment are substantial. The goal of this proposal is to generate transgenic mouse models that will allow standardized and orthogonal testing of pathways important for overcoming transplantation barriers, and through this work, instruct efforts in regenerative medicine. We will generate mice that, upon Cre recombinase expression, will express modules of genes that either 1) suppress complement deposition and membrane attack complex formation; 2) express single chain peptide-β2m-MHC trimers to engage inhibitory receptors on natural killer cells; 3) express CD47 to engage the inhibitory receptor Sirpα. These animals can be used as donors for future transplantation experiments to genetically define allogeneic barriers and instruct similar edits in stem cells for regenerative medicine.

NIH Research Projects · FY 2026 · 2026-06

The endoplasmic reticulum (ER) is the synthesis site of many membrane and secreted proteins, amounting to as much as 40% of all protein synthesis. Robust heart function depends on ER proteins, such as ion channels and hormones, emphasizing the importance of ER protein homeostasis, i.e. ER proteostasis in the heart. ER proteostasis comprises the balance of ER protein synthesis, folding, trafficking and ER associated degradation (ERAD) of damaged and misfolded proteins. Little is known about ER proteostasis in atrial myocytes, where we posit it to be essential for production of important peptide hormones in the classical ER secretory pathway. ER proteostasis is maintained mainly by the ER unfolded protein response (UPR). We previously showed that the aspect of the UPR mediated by the ER-localized transcription factor, ATF6, which detects and is activated by increased demand for ER protein folding, is required for adaptive growth of ventricular myocytes during exercise and pressure overload. In contrast to ventricular myocytes, atrial myocytes are endocrine cells that use the classical ER secretory pathway, comprised of the ER, Golgi and secretory granules, to synthesize, process, store and, upon the appropriate stimulus, secrete hormones, such as the blood pressure (BP) lowering peptide, atrial natriuretic peptide (ANP). Consistent with the potential importance of ATF6 in atrial myocytes, we found that atria express considerably more ATF6 than ventricles, perhaps to maintain robust hormone production. This concept is supported by our finding that atrial myocyte ATF6 was upregulated by conditions that stimulate ANP synthesis and secretion and that ATF6 deletion decreased ANP most likely due to the loss of ATF6-fortified ER secretory pathway vitality. These preliminary results support the hypothesis that ATF6 is required for expression of “service” proteins that reside in the ER secretory pathway, such as chaperones, disulfide isomerases, ERAD components, that maintain a robust ER secretory pathway proteostasis needed for efficient production of “client” proteins that are made in, and routed through the ER secretory pathway. Our specific aims will examine the effects of atrial myocyte specific ATF6 loss- and gain-of-function in mice on 1- atrial myocyte ANP expression, folding, secretion and ANP-based cardiovascular physiology, in vivo; 2- the atrial ER proteome and ER secretome; and 3- renewal of the atrial proteome. We believe these studies are significant because they will reveal new information about the nature of ATF6 and the UPR in atrial myocyte ER secretory pathway proteostasis in a mouse model that mimics the dietary sodium intake that contributes to hypertension and cardiovascular disease in humans. We believe that the innovation of the studies lies in the novelty of the techniques as applied to the problem, including atrial myocyte specific gene targeting, a first-of-its-kine, in vivo ERAD assay, and complementary proteomics approaches. Taken together these novel technologies place us in a strong position to examine what we believe to be critical functions for ATF6 in the atrial myocyte ER secretory pathway which may impact a wide range of atrial functions.

NIH Research Projects · FY 2026 · 2026-06

Cryptococcus neoformans and Cryptococcus gattii are major fungal pathogens that cause severe infections, including cryptococcal meningitis, particularly in immunocompromised individuals. The World Health Organization (WHO) has identified C. neoformans as one of the top four highest critical priority fungal pathogens due to its substantial disease burden, increasing drug resistance, and the lack of effective therapies. Even with available treatments, the mortality rate for cryptococcal meningitis often exceeds 50%, highlighting the urgent need for new therapeutic strategies. Dihydroorotate dehydrogenase (DHODH), a pivotal enzyme in the de novo pyrimidine biosynthesis pathway, is a promising antifungal target due to its essential role in fungal survival. The success of olorofim, a selective DHODH inhibitor effective against Aspergillus fumigatus, underscores the therapeutic potential of targeting DHODH. However, olorofim is ineffective against C. neoformans and C. gattii, likely due to structural and sequence differences in their DHODHs. Importantly, studies have shown that pyrimidine biosynthesis in C. neoformans is vital for survival, as serum and cellular pyrimidine levels are insufficient to bypass DHODH inhibition. This makes CneDHODH an attractive and actionable target for antifungal drug development. This proposal focuses on developing selective inhibitors of CneDHODH to address the critical therapeutic gap for cryptococcal infections. High-throughput screening (HTS) will be carried out to identify inhibitors from large compound libraries. Identified hits will be validated for potency, selectivity against human DHODH, and antifungal activity against C. neoformans and other clinically relevant pathogens. Iterative medicinal chemistry will optimize lead compounds to improve their pharmacokinetics, potency, and selectivity. Structural studies, including co-crystallization and molecular modeling, will provide detailed insights into inhibitor binding, enabling rational drug design. Finally, optimized compounds will be tested in animal models of C. neoformans infection to evaluate their in vivo efficacy, pharmacokinetics, and safety. The development of CneDHODH inhibitors will address a critical unmet need for effective antifungal therapies for cryptococcal infections while extending the applicability of DHODH as a drug target to fungal pathogens beyond A. fumigatus. This work has the potential to transform the treatment of invasive fungal infections caused by Cryptococcus spp., significantly improving outcomes for vulnerable patients. By leveraging innovative screening approaches, structural insights, and medicinal chemistry, this project aims to deliver novel antifungal agents that could save lives and reduce the global burden of cryptococcal infections.

NIH Research Projects · FY 2026 · 2026-06

Quiescence and senescence are two distinct cellular dormancy states that play critical roles in tissue homeostasis, regeneration, and aging. Despite advances, the molecular mechanisms governing the heterogeneous quiescence depth and its transition to senescence remain unclear. Our long-term goal is to develop strategies to manipulate cellular dormancy states to treat diseases linked to disrupted dormancy- proliferation balance. The objective of this application is to elucidate the transcriptomic and epigenetic paths controlling quiescence depth and its transition into senescence by modulating the Rb-E2F bistable switch threshold. We hypothesize that distinct gene expression and chromatin accessibility changes lead to progressively deeper quiescence by increasing the Rb-E2F switch threshold and delaying E2F target gene induction, and eventually to senescence transition when the switch threshold becomes insurmountable under physiological conditions. We will test our hypothesis by pursuing two specific aims: 1. Identify single-cell transcriptomic and epigenetic paths controlling quiescence depth by modulating the Rb- E2F switch and its effectors by combining innovative single-cell multiomics profiling, computational modeling, and detailed experimental validation. 2. Determine the molecular mechanisms underlying the quiescence-to-senescence transition by measuring and modeling transcriptomic and epigenetic differences between bifurcated quiescent and senescent cells along the transition paths and detailed experimental validation. Our approach is innovative because it integrates cutting-edge single-cell multiomics, advanced computational modeling, and rigorous experimentation to dissect the heterogeneous paths and mechanisms underlying quiescence depth regulation and senescence transition at an unprecedented resolution. The proposed research is significant because it is expected to provide a unified framework linking the Rb-E2F switch threshold to the continuum of cellular dormancy states, advancing our understanding of their molecular basis and interconnections, and informing the development of novel therapeutic strategies to restore tissue homeostasis and function in aging and disease.

NSF Awards · FY 2026 · 2026-06

Label-free biomedical optical imaging (LBI) is a technology used to study tissues by measuring interactions between tissue and light. This CAREER project will link biological activity to the signals detected by LBI. The goal is to determine how biological changes, such as changes in how genes are expressed, affect the way light interacts with tissue. The research will create new artificial intelligence (AI) methods to map the relationship between gene activity and LBI. The results could lead to better tools for diagnosing disease, studying tissue health, and improving pathology. In addition, the project includes a strong educational plan to prepare future leaders in bioengineering and AI. New learning activities will be created for students and the public that combine biology and AI. These activities will be designed to improve public understanding of AI and prepare students for modern science and technology careers. Overall, this project supports national interests by advancing leadership in biotechnology, optics, and AI. A major knowledge gap exists in understanding how high-level biological changes influence interaction between light and tissue. The goal of this CAREER project is to establish a clear, measurable relationship between changes in gene networks and contrast observed in LBI. The research will measure tissue-wide gene expression patterns and determine how these patterns influence optical properties such as tissue fluorescence. First, the project will define how alterations in gene networks correspond to changes in LBI contrast across tissues. Second, new LBI image features and feature extraction algorithms will be developed to better represent transcriptomic signatures. Third, the relationship between gene expression and LBI will be incorporated into novel AI models to digitally analyze and classify tissues without chemical assays. This framework will help improve interpretation of optical imaging data and develop novel applications of AI in biotechnology. The project will advance biomedical imaging and AI by enabling rapid, non-destructive estimation of gene network activity and by establishing a general framework that can be extended to other optical methods and biological systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY The ability for cells to detect and respond to metabolic cues is critical to maintaining homeostasis, and perturbations in the sensing mechanisms that respond to oscillations in metabolic flux are the root cause of many diseases, including sepsis, autoimmunity, cancer, and diabetes. Protein post-translational modifications (PTMs) serve as these sensing mechanisms, facilitating alterations in enzyme function and/or localization. As a result, alterations in PTMs often drive disease, removing the breaks on homeostatic metabolic signaling to serve the nutritional needs of the diseased cell. Currently, we have a fundamental gap in our understanding of the composition, abundance, and enzymatic control of PTMs and how they are altered in both health and disease. My lab is dedicated to unraveling fundamental questions in PTM biology. To accomplish this, we developed sensitive LC-MS/MS-based methods to identify and quantify global changes in PTMs across a broad spectrum of biological samples. Using this approach, we identified lactoylLys as a novel PTM that dictates glycolytic flux and inflammatory signaling. These PTMs are driven through non-enzymatic reactions from acylglutathione intermediates that are controlled by glyoxalase 2 (GLO2). In cells lacking GLO2, these PTMs are significantly elevated, altering cell fate. Expanding on the success of the previous funding period, our primary goal for this renewal is to further define the role of GLO2 in the homeostatic regulation of Lys acylations. My research program is dedicated to understanding three fundamental questions: 1) What is the substrate profile for GLO2 and how does this dictate Lys acylation? We will determine the acylglutathione substrates for GLO2 using in vitro-based enzyme kinetic assays. In addition, we will determine the regulation of these substrates in cells and elucidate the primary transport mechanism into the mitochondria. Lastly, we will quantify the PTM profile across subcellular compartments in both wild-type and GLO2 knockout cells. 2) Is the glyoxalase cycle a metabolic liability in glycolysis-reliant disease states? Using informed decisions from our cell inventory of glyoxalase function, we will generate a panel of GLO1 and GLO2 knockout cell lines. Cell growth will be monitored on an Incucycte Live-Cell Analysis system to quantify proliferation and metabolic regulation. We will also evaluate Glo1 as a therapeutic target to reduce inflammatory signaling. 3) Is our view of the histone code too small? To date, we are capable of quantifying >100 PTMs using our robust LC-MS/MS-based assay. Using this approach, we will quantify the histone PTM profile in tissues collected from wild-type and Glo2 knockout mice. We will also screen eraser enzymes for their full spectrum of substrates in cells. Our primary goal is to re-define our understanding of how PTMs are regulated and how these processes go awry in disease. This project will address a fundamental gap in our basic understanding of how cell metabolism and PTMs are regulated. Due to the far-reaching implications of this project in the context of disease, this research program continues to be an ideal fit for the R35 MIRA Award.

NIH Research Projects · FY 2026 · 2026-06

Toxoplasma gondii is a common intracellular parasite that chronically and asymptomatically infects neurons in the central nervous system (CNS) of humans and rodents. The asymptomatic nature of this infection is notable as most microbes that enter CNS cause devastating disease. At the same time, T. gondii’s tropism for and persistence in the CNS underlies the symptomatic and potentially lethal disease seen in the immunocompromised and rarely, the immune competent. This neuronal persistence has traditionally been thought to arise from an inability of neurons to mount IFN-γ induced, cell-intrinsic, anti-parasitic responses. However, using an innovative Cre-based system that permanently marks CNS cells injected with T. gondii protein, over the last decade, we have deter- mined that: 1) T. gondii predominantly and extensively interacts with neurons during in vivo infections; 2) that >90% of these interactions do not result in a persistent infection; 3) that human and murine neurons generate IFN- γ dependent, cell-intrinsic anti-T. gondii responses; 4) that neuronal clearance of parasites is more effective in vivo than in vitro; and 5) that >80% of these T. gondii injected neurons are destined to die within several weeks. These findings are significant because they question long-held ideas about the capabilities of neurons to mount anti- parasitic defenses and challenge the concept that neurons tolerate persistent infections because these long-lived cells must be preserved at almost any cost. Collectively, these findings strongly suggest that we have a substantial gap in our understanding of the role neuron-T. gondii interactions play in determining the establishment and outcome of CNS infection. The goal of this proposal is to address this gap by beginning to develop a mechanistic understanding of how neurons counter T. gondii while avoiding immune pathology. Here we will define the IFN-γ de- pendent, anti-parasitic mechanism utilized by human neurons (Aim 1); determine the IFN-γ independent mechanisms that enhance neuronal clearance of intracellular parasites in vivo (Aim 2); and identify how and why neurons injected with T. gondii protein die (Aim 3). The outcomes of these studies represent essential steps for developing a nuanced understanding of neuron immune responses and identifying new therapeutic targets for ultimately developing curative treatments that address the CNS phase of disease.

NIH Research Projects · FY 2026 · 2026-06

Project Summary/Abstract: Estrogen receptor-positive (ER+) breast cancer (BC) poses a significant clinical challenge due to a substantial subset of invasive ER+ tumors (>30%) that progress to incurable metastatic states. Invasion in the primary tumor microenvironment (TME) is increasingly recognized for its pivotal role in BC progression. Notably, extracellular matrix stiffness within the TME drives tumor invasion. Recent clinical studies have shown that invasive ER+ tumors are surprisingly stiffer than ER- tumors. We found that adaptation to high matrix stiffness (stiff condition- ing) enhances ER+ BC cells' ability to sense and respond to stiffness, significantly impacting durotactic invasion; conversely, soft-conditioning suppresses durotactic invasion in ER+ BC cells. Differences between soft and stiff conditioned ER- BC cells in durotactic invasion are less significant. Our goal is to determine how two crucial TME components—matrix stiffness and estrogen (E2)—act as critical regulators of ER+ BC invasion. We propose that the response of ER+ BC cells to E2 is linked to their matrix stiffness acclimation, i.e., mechanohormonal Condi- tioning. The mechanisms underlying the heightened sensitivity of ER+ BC cells to stiffness remain unknown. Our clinical data show that ER+ BC patients with tumors exhibiting stiff-conditioning signatures have poor outcomes, highlighting the significance of studying the TME's mechanical properties in ER+ BC. The ER transcriptional target, EVL, is crucial for strengthening focal adhesions (FAs) and enriching suppressive cortical actin bundles (SCABs), indicating its dual role in cell adhesion and cortical contractility. EVL appears in distinct protein com- plexes in stiff- versus soft-conditioned cells, suggesting different functional roles based on mechanical condition- ing. Moreover, stiff-conditioning of ER+ BC cells induced a fibrotic response in vivo, significantly less pronounced in tumors derived from soft-conditioned cells. This suggests a feedback loop between stiff conditioning and in- creased fibrosis, leading to greater stiffness. We hypothesize that STIFF+E2 mechanohormonal conditioning of ER+ BC cells promotes durotactic invasion through EVL-mediated focal adhesion strengthening, while SOFT+E2 conditioning suppresses durotactic invasion through EVL-mediated cortical actin bundles generated under weak adhesion; and that these EVL-mediated invasive phenotypes are amplified in the primary tumor by promoting a fibrotic response in the TME. We will investigate this hypothesis through two specific aims: SA1, determine how the balance between traction force and cortical tension establishes a durotactic invasive phenotype; and SA2, determine how mechanohormonally conditioned cells interact with the TME. We are using several in vitro models of ER+ BC to study 2D and 3D cell motility, durotactic invasion, and force measurement and an ER+ Mammary INtra-Ductal (MIND) xenograft models, together with intravital imaging (multiphoton and optical coherence elas- tography) to visualize changes in the mechanical TME in the primary tumor. This work will uncover how mecha- nohormonal conditioning by matrix stiffness and estrogen in the TME drive durotactic invasion in ER+ BC cells and remodel the TME, further amplifying mechanohormonal conditioning.

NSF Awards · FY 2026 · 2026-06

The goal of project MESA (Multidisciplinary Environment for Scientific Advancement) is to build a shared, open-source platform in which scientific data from many fields are automatically described, organized, and connected, so any researcher can find and use them in minutes to hours instead of weeks to months. At its core, metadata-enabled scientific agents read each new dataset, attach descriptions drawn from community-curated standards, and recommend how it can be combined with related information from other disciplines. It will enable researchers spanning diverse disciplines, such as astronomy, biology, environment, public health, and computer science to expedite their discoveries and engage in seamless cross-disciplinary collaboration. The project will be developed and tested with established NSF-supported synthesis centers in environmental data science (ESIIL) and molecular and cellular science (NCEMS), and in AI in agriculture (AIIRA), as well as the international Event Horizon Telescope (EHT) Collaboration. The MESA outreach model ensures that benefits reach real use cases across the range of institutions with diverse levels of research activity and that graduate students, postdoctoral researchers, and professional staff are trained in the design and use of trustworthy, agentic AI for science. MESA will be implemented as a federated, cloud-native data-mesh and data lakehouse coupled to an agentic AI layer operating as an integrated data system and service. There are three coordinated technical objectives: agentic AI-powered metadata generation; cross-domain data integration linking datasets through interoperable APIs, shared ontologies, and policy-driven governance; and agentic orchestration managing workflows and translating user intent into reproducible research objects. MESA will operate on public NSF cyberinfrastructure accessed through ACCESS-CI (Texas Advanced Computing Center, Open Storage Network, CyVerse, and Jetstream-2 Cloud) and the integrated Rule-Oriented Data System (iRODS) from the Renaissance Computing Institute (RENCI). The two-year prototype effort includes automated metadata generation for astronomy simulations, life-science data repositories, multi-omics integration, precision agriculture, sensor networks, and environmental data synthesis to demonstrate platform capability. 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

PROJECT SUMMARY / ABSTRACT Human skin is ubiquitously exposed to solar ultraviolet (UV) radiation, other environmental toxicants, and combinations thereof. Solar radiation is a potent environmental human carcinogen. Cutaneous exposure to solar ultraviolet (UV) radiation is a causative factor in skin photocarcinogenesis, and nonmelanoma skin cancer (NMSC) is the most common malignancy in the United States with increasing incidence, a public health burden of considerable magnitude. In search of relevant environmental co-exposures that might modulate skin stress responses to solar UV, we have focused recently on skin chlorination, a largely understudied environmental exposure in the context of drinking water and recreational freshwater disinfection operative on a global scale. Our unpublished research has identified a novel chlorination product, chloro-cis-urocanic acid (Cl-cis-UCA), detectable in human skin upon co-exposure to solar UV and HOCl. Trans-urocanic acid (trans-UCA) is a major filaggrin-derived skin chromophore undergoing photoisomerization upon exposure to solar UV causing the formation of the bio-active mediator cis-urocanic acid (cis-UCA) in human skin. Even though a critical role of cis- UCA in the modulation of skin photodamage, photoimmunosuppression, and photocarcinogenesis is firmly established, occurrence and function of the novel UCA-derivative Cl-cis-UCA remain undefined. In this exploratory R21 project entitled ‘Endogenous HOCl-derived chloramines in skin inflammation and carcinogenesis’ we propose investigations focusing on this novel, heretofore unrecognized skin chloramine component (Cl-cis-UCA), formed uniquely upon co-exposure to solar UV and environmental chlorination stress. Our goal of investigating its cutaneous occurrence and potential role in skin photodamage, photoimmunosuppression, and photocarcinogenesis is pursued as follows: Aim #1 explores occurrence and function of Cl-cis-UCA in organotypic human epidermal reconstructs exposed to the isolated and combined action of solar UV and environmental chlorination stress. The analytical method pursued here is based on non- invasive tape stripping followed by Cl-cis-UCA detection amenable to rapid human translation. Aim #2 explores the role of Cl-cis-UCA exposure in established murine models of solar UV-induced photodamage (sunburn and inflammation), systemic photoimmunosuppression, and photocarcinogenesis. Given the ubiquitous nature of solar UV and chlorination stress co-exposure experienced by human skin worldwide, examining formation and function of the novel skin chlorination photoproduct Cl-cis-UCA in relevant skin models of photodamage and photocarcinogenesis addresses an urgent need to define the role of this intermediate in human skin originating from a ubiquitous environmental co-exposure. Based on our novel analytical approach and functional studies, Cl-cis-UCA might be recognized to play a role as a novel exposure biomarker, molecular target, and mediator of cutaneous responses relevant to human skin.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY/ABSTRACT Per- and polyfluoroalkyl substances (PFAS) comprise a class of several thousand compounds that present a major public health problem worldwide. The 5th National Conference on Per- and Polyfluoroalkyl Substances, will be held in Tucson, AZ, a region affected by PFAS drinking water contamination, to grow the impact of the national conference through facilitating PFAS information sharing, collaborative research, and remediation across the U.S. This meeting uniquely convenes scientists, government officials, environmental advocates, affected community members, Tribal Nations, journalists, utility managers, and attorneys to support and better inform the priorities of PFAS sectors. The 5th PFAS conference will utilize the new geographic location to cultivate novel cross-sector collaborations to help prevent future PFAS contamination and exposure-related health risks and leverage an enhanced diversity of perspectives to more effectively prevent future exposure and protect human health. By moving the 5th meeting location to Tucson, AZ, another PFAS-affected community in a state that has become a leader in PFAS testing and action, we will expand the access, impact, and participation from affected communities at this conference. This proposed meeting will bring together all involved groups to examine the complex set of social, scientific, political, and environmental health issues associated with PFAS exposure. The planning committee will utilize principles from funds of knowledge and ensure that the conference is designed around multi-directional environmental health dialogue.

NIH Research Projects · FY 2026 · 2026-05

Abstract Sphingolipids play significant roles in mediating inflammation in disease. Patients with inflammatory bowel disease (IBD) and colitis associated cancer (CAC) demonstrate elevated expression of various sphingolipid metabolism enzymes. Acid ceramidase (AC) is highly expressed in disease and deacylates ceramide generating the product for the generation of the bioactive lipid sphingosine 1-phosphate (S1P). Samples from patients with IBD and colorectal cancer, and animal models of colitis, have identified tissue infiltrating macrophages to be a likely source of elevated AC expression; however, the role of AC and immune cell sphingolipid metabolism in IBD remains understudied. The goal of this proposal aims to elucidate the role of myeloid cell derived AC in the progression of IBD and determine the role of AC in immune cell recruitment in IBD. Several studies have implicated sphingolipid metabolism in IBD and CAC using genetic manipulation or pharmacologic inhibitors in mouse models. Many of these studies have examined total body knockouts; however, the contribution of immune cell specific sphingolipid metabolism has not been thoroughly investigated. Our lab has demonstrated that loss of AC in myeloid cells protected mice from acute colitis using dextran sulfate sodium (DSS). The preliminary data presented in this proposal have extended these studies assessing myeloid specific loss of AC in a model of chronic colitis utilizing IL-10 deficient mice. The loss of AC protected from colonic inflammation, increased ceramide content, and reduced innate immune cell infiltrate. Furthermore, loss of AC in myeloid cells altered adaptive immune cell populations in the colon and peripheral tissues, potentially indicating a role for AC in cell- to-cell communication. Our findings have led us to hypothesize that loss of AC in myeloid cells impairs inflammatory signaling, and immune cell recruitment or maturation to alleviate chronic colitis. We therefore propose the following specific aims: Specific Aim 1: Determine the mechanism by which loss of AC protects from chronic colitis. Specific Aim 2: Determine the role of AC in T cell maturation and immune cell recruitment in colitis.

NSF Awards · FY 2026 · 2026-05

This project is funded through the NSF Translation to Practice (TTP) program, which supports efforts to translate research discoveries into practical tools that benefit communities, industry, and society. For the TTP program, teams advance research results toward real-world deployment and adoption. Lightweight, high-quality mirrors are essential for many technologies that benefit society, from cameras on airplanes and satellites to defense systems and scientific instruments. However, creating advanced mirrors with the complex shapes that are needed for highest performance is extremely difficult and expensive. This research team develops an innovative new manufacturing process called ultrafast laser stress figuring (ULSF) that makes these advanced mirrors much lighter, cheaper, and easier to produce. This technology bolsters the optics industry. Beyond economic benefits, the research team trains the next generation of engineers and innovators and demonstrates how scientific discoveries become real-world products. The core innovation is a mirror figuring technique, ULSF, that uses ultrafast lasers to create precise stress patterns inside glass mirrors, deterministically shaping the mirror to nanometer accuracy. The research team develops an open-source software tool to help optical engineers design systems using ULSF mirrors and demonstrates unmounted freeform ULSF mirrors that meet diffraction-limited optical accuracy. They also create mounted ULSF mirrors that maintain optical performance under operational loads and build a complete benchtop optical system with mounted ULSF mirrors demonstrating high-quality imaging performance. In partnership with the Massachusetts Institute of Technology (MIT) Lincoln Laboratory, the result of this project is mountable, cost-effective ultralightweight mirrors that optical engineers can readily integrate into their designs. 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

PROJECT SUMMARY The National Summer Undergraduate Research Project (NSURP) was created as an eight-week virtual summer research program in the microbial sciences that matches underrepresented minorities in STEM undergraduate students nationwide with faculty mentors worldwide to address the barriers presented by COVID-19 cancelations. NSURP mentees conduct research from any location with an internet connection, allowing students to learn how to conduct impactful research by forming and testing hypotheses. As individuals returned to in-person programs, NSURP found a population unable to travel, independent of COVID-19, due to financial, familial, geography, and health constraints. As we see an increased demand from these individuals, post-pandemic, who can’t travel to in-person opportunities, we seek to expand our ability to serve these undergraduates with research opportunities in our program and provide them with year-long mentoring and professional development. NSURP seeks to apply its virtual research model to human health-related topics, such as the basic and applied disease research conducted within the purview of the NIAID, both in microbiology and immunology. Implementing NSURP within the NIAID infrastructure will create more opportunities for URM scientists and ultimately aid in developing a diverse biomedical research workforce. The central premise of NSURP is to meet undergraduates where they are, facilitate exposure and pursuit of a scientific opportunity, and give them the tools and confidence to continue in their scientific careers. To maximize the contribution of NSURP within the NIAID mission, we will 1) provide virtual biomedical-specific summer research opportunities within the NIAID mission for minoritized undergraduates, 2) facilitate synergistic virtual scientific and career multi-level mentoring relationships, and 3) assess program impact by evaluating educational outcomes of NSURP participants and conducting longitudinal studies on the effects of culturally-responsive virtual mentorship training.

NIH Research Projects · FY 2026 · 2026-05

Optical microscopy is a cornerstone of biomedical research, enabling detailed visualization of biological structures and processes. However, traditional refractive microscopes are inherently limited by chromatic aberrations, group delay dispersion, and narrow spectral ranges, while existing reflective designs suffer from central obscuration, reducing contrast and efficiency. These limitations restrict the ability to perform high-resolution, broadband, and multimodal imaging, hindering advancements in biomedical research. To address these challenges, we propose the development of an ultra-broadband microscope based on an innovative obscuration-free, off-axis freeform reflective optical architecture. This system is designed to achieve diffraction-limited performance across an ultra-broadband spectral range — from the ultraviolet (UV) to the infrared (IR) — while eliminating chromatic aberrations and minimizing optical dispersion. By leveraging freeform optics and an unobscured reflective design, this technology will provide superior contrast, higher optical efficiency, and enhanced imaging capabilities beyond the limitations of conventional refractive or reflective microscopes. A key innovation of this project lies in the development of off-axis freeform reflective configurations and novel optomechanical integration strategies to create a compact, ultra-broadband-compatible microscope. This project will focus on three key objectives: (1) Design and optimization of a compact, high- performance off-axis reflective microscope with diffraction-limited imaging across a wide spectral range. (2) Prototype fabrication and assembly, advancing high-precision diamond-turning techniques to ensure superior optical quality and robust system integration. (3) Performance validation through rigorous experimental testing across UV, visible, and infrared spectra, benchmarking the prototype against state-of-the- art commercial refractive and reflective microscopes. The anticipated outcome is a transformative microscope platform that enables high-contrast, ultra- broadband imaging with an unprecedented working spectrum. This technology will establish a new paradigm for high-performance optical microscopy, unlocking new opportunities in biomedical research and expanding imaging capabilities across multiple disciplines. This proposal directly aligns with the objectives of NIGMS NOFO (PAR-25-203) by advancing a demonstrated proof-of-concept ultra-broadband microscope into a fully functional prototype with broad applicability in biomedical and biological sciences research. By overcoming fundamental optical constraints, this microscope has the potential to transform biomedical imaging and biological research, facilitating new discoveries and expanding the frontiers of optical microscopy.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY (See instructions): The long-term goal of this project is to characterize the transcriptional regulation underlying development and understand function of a newly discovered intestinal cell type called best4+ cells, that is depleted in inflammatory bowel disease patients, It is however unresolved whether best4+ cell loss causes disease or whether disease kills best4+ cells, Improper differentiation of intestinal cells often leads to loss of specialized functions that manifest in disease, Since it is not well understood how best4+ cells contribute to intestinal homeostasis and how their dysfunction leads to disease, this proposal also focuses on understanding the function of best4+ cells, The function of best4+ cells remain largely unknown in any organism; however, previous single-cell profiling predicted some physiological roles such as pH sensing, electrolyte homeostasis, and regulation of satiety, Since best4+ cells are absent from mouse models, experiments will ablate best4+ cells in the zebrafish intestine and study the response of other intestinal cell types using single-cell RNA sequencing as well as measure disease hallmarks in the intestine (pH changes, epithelial barrier loss, inflammation etc,), Furthermore, factors predicted from single-cell RNAseq studies will be perturbed to understand the roles of each of these genes in regulating overall best4+ cell function, To test the alternate hypothesis whether best4+ cell loss is a symptom of disease rather than cause, this proposal will also induce inflammation in the zebrafish intestine in the presence and absence of best4+ cells to characterize how various intestinal cells including best4+ cells respond to changes in the intestinal environment and what aspects of the overall intestinal response to disease is regulated by best4+ cells.

NSF Awards · FY 2026 · 2026-04

Qubits, or quantum bits, are the fundamental units of quantum computers and they can be built from a variety of technologies, e.g., neutral atoms, ions, superconducting circuits, photons. Irrespective of the technology used to build them, they are fragile and can retain their coherence (to preserve the stored information) only for a limited time, which typically ranges from microseconds to seconds. Besides, when the qubits are manipulated through external forces to perform useful computation, the application of these forces can also be faulty, leading to many errors in the computational process. The field of quantum error correction and fault tolerance tries to protect information from qubit decoherence and faulty processes by introducing a controlled amount of redundancy in the way the information is stored. A classical analogy would be to repeat, or clone, each bit of information so that if less than half of the repetitions are corrupted, then the majority vote will still recover the original bit. Since quantum mechanics forbids the cloning of qubits, the research community has developed very sophisticated codes and decoders to store and manipulate information in the presence of noise and faults. These codes work by checking parities of information stored in the qubits, which necessitates interactions between qubits that are far apart. But in many hardware technologies, engineering such long-range interactions is very challenging, so it is imperative to develop scalable methods to address this challenge. This project will develop such a scalable solution by networking multiple small quantum processors and using network-provided entanglement to circumvent long-range interactions. These networked quantum computers can then solve outstanding challenges in digital security, drug discovery, materials design, and other applications with a major positive impact on society. The research will advance knowledge by investigating both fundamental and translational aspects of a networked architecture for optimal quantum low-density parity-check (QLDPC) codes. The project will create new synergies between quantum computing and networking, e.g., through error correction-based entanglement purification protocols. The expected intellectual contributions of this project include: (1) network geometries needed to implement these codes; (2) a fundamental understanding of the challenges when performing encoded computation and error correction in a networked setting; (3) protocols to purify (potentially encoded) entangled states that enable these networked operations on the QLDPC codes under investigation; (4) detailed code-level and network-level simulations using tools such as Stim to evaluate the architecture; and (5) a detailed comparison of the proposed approach with the standard monolithic architecture for fault tolerant quantum computing. The project’s Education Plan will provide early exposure to linear algebra and its application in quantum research for STEM education. Undergraduate students will be mentored by Ph.D. students for a mutually beneficial experience. These activities integrate well with the investigator’s work for the NSF Engineering Research Center for Quantum Networks and the Arizona Quantum Initiative, whose mechanisms can help scale the proposed education activities. The early exposure to linear algebra is also broadly beneficial for students interested in other fields such as artificial intelligence and data science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-04

Project Summary/Abstract Psoriatic arthritis is a chronic and progressive inflammatory arthritis closely linked with psoriasis. Blockade of the IL-17 signaling pathway shows impressive efficacy and excellent safety for treatment of psoriasis but less so for PsA. Tissue penetrance limits the bioavailability of monoclonal antibodies to the joint and fibrocartilaginous enthesis and IL-17A signaling is diversified by local immune cells in tissues rendering IL-17A inhibition less effective. A higher dosage poses the risks of serious infections and certain types of cancer as all IL-17 inhibitors are immunosuppressive therefore to improve clinical outcomes in PsA more selective targeting is required. Herein, we have developed a novel animal model of IL-17A gene transfer where mice develop joint and skin inflammation associated with enthesitis, fingernail psoriasis and onycholysis hallmarks of PsA pathology. We will interrogate our murine model to define the early cellular and molecular pathways that dictate IL-17A-induced pathologies using state-of-the-art transgenic mice and molecular tools. Our work will uncover the pathogenic mechanisms of IL-17A and uncover novel molecular targets that can be exploited for therapeutic intervention and directly benefit PsA patients worldwide.

NSF Awards · FY 2026 · 2026-01

The project aims to advance the Findable Accessible Interoperable Reusable (FAIR) principles within the Earth and Space Science sample data ecosystem by promoting the adoption of persistent identifiers infrastructure (PID) across the geoscience community. The project plans to achieve the goal by engaging with four types of key stakeholders: researchers, sample repositories, data repositories, and scholarly publishers, through community engagement, education, training, and coordination. Scientific research across many disciplines relies on material samples as a basic element for reference, study, and experimentation. Given the high cost of collecting and curating these samples, it is essential to maximize research investments by ensuring samples and related data are open and FAIR. This can be done by a) assigning persistent identifiers to samples; b) persistently associating samples with their resulting research products; and c) citing samples in research literature. The infrastructure to support these actions is fragmented and incomplete, but uptake of best practices is growing. To advance adoption of open science practices in sample-based research, the project will engage four key stakeholders in the sample data ecosystem: (1) researchers, (2) sample repositories, (3) data repositories, and (4) scholarly publishers. For each community, the team will conduct an analysis of the current adoption of relevant open science practices such as usage of sample persistent identifiers (PIDs) and sample citation. Informed by this work, the group will organize these communities to begin realizing the vision of a functioning sample data ecosystem, where samples and their research products are fully described, persistently linked, and FAIR. This award by the Office of Advanced Cyberinfrastructure in the Directorate for Computer and Information Science and Engineering is jointly supported by the Directorate for Geosciences. 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 · 2025-11

Viruses are the most abundant biological entities on Earth, infecting all major categories of life. To study viruses, the viral genomics community has generated enormous amounts of scientific data through advanced gene sequencing and other techniques. Exploring and extracting scientific insights from data of this size is challenging and requires accessible training that can teach the research community how to use the advanced computational tools and infrastructure needed for research. At the same time, the rapid development of novel algorithms and Artificial Intelligence-driven methods for analyzing this data outpaces current training opportunities, both for scientists who use computational infrastructure and for the research computing professionals who design and maintain these systems. A critical gap exists in equipping both groups with practical skills to leverage shared, high-performance computing systems and integrate reproducible scientific processes into the nation's advanced research computing frameworks. By helping scientists use cutting-edge, AI-driven tools to understand viruses better, this project promotes the progress of scientific understanding of viruses, their interactions with their hosts, and their effects on biological systems. The broader impacts of this research support public health, environmental understanding, and national preparedness. This work also builds a stronger research community by making training accessible to a broad range of scientists and students. The iVirus Cyberinfrastructure (CI) Training Initiative will develop six modular, self-paced, online training resources to enable effective use and development of scalable pipelines in NSF-supported CI ecosystems. The training modules will be designed around the principles of Findable, Accessible, Interoperable, and Reusable (FAIR) software, emphasizing interactive learning, reproducibility, evolvability, and sustainable design. The modules will be open-source, portable across CI platforms, and designed to meet the diverse needs of researchers and developers working in viral ecology. Training content will be developed through a unified pipeline that includes (1) interactive instructional design, (2) modular training components, (3) expert input and curated test datasets, and (4) community engagement and dissemination. A key feature of this project is the integration of hands-on viral genomics (viromics) CI training into the annual Ohio State University Viromics Workshop, where materials will be piloted and refined through participant feedback. Together, these resources will help close the skills and knowledge gaps in viral ecology CI training, enabling a broad range of researchers and developers to accelerate discovery and innovation using NSF-supported computational resources. 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 · 2025-11

Changing environmental conditions are dramatically transforming Earth’s drylands, which cover nearly half of the planet and support the livelihoods of 3 billion people through farming and food production. As these regions face increasing drought and more extreme weather, it is crucial to understand how the tiniest organisms in soil—microbes—cope with stress. These microbes are responsible for recycling nutrients like nitrogen, which plants need to grow. This project focuses on how microbial communities in desert soils continue performing these essential roles, even as their environment becomes more difficult to survive in. The research will examine soils in the Sonoran Desert over several years to explore how microbes adapt and reorganize in response to environmental stress. The team will use advanced genetic tools to study these tiny organisms—revealing who they are, what they do, and how they respond to environmental stress—alongside carefully designed laboratory experiments to detect early warning signs that soil ecosystems may be approaching collapse. In addition to advancing understanding of how life persists in extreme environments, the project will deliver practical tools for monitoring soil health. These tools aim to support decision-making for land managers, farmers, and environmental agencies working in dry regions. Educational resources will be developed, including an interactive public art installation that invites people to explore the hidden world of soil microbes through gameplay. The project will also provide research training opportunities for students and foster collaboration with agricultural extension professionals, ensuring the work benefits both scientific progress and real-world land stewardship. This project investigates molecular mechanisms underlying microbial community resilience in arid ecosystems using Thermoproteota as a model system for understanding organism-mediated stability. The research combines five years of temporal multi-omics analysis (2021-2025) with controlled mesocosm experiments to test three nested hypotheses spanning community, population, and molecular scales. The team will integrate metagenomics, metatranscriptomics, and metabolomics with Bio-orthogonal Non-Canonical Amino Acid Tagging (BONCAT) to distinguish active from dormant populations and track protein synthesis during stress responses. Controlled manipulation experiments will simulate intensified monsoon cycles to identify critical thresholds where adaptation mechanisms fail, particularly focusing on Thermoproteota’s unique stress tolerance strategies including manganese-based catalases and protein repair systems. Advanced statistical modeling using Structural Equation Models and Mixed Effects Models will connect molecular adaptations to ecosystem-level nitrogen cycling stability. The research will develop a hierarchical framework of resilience indicators spanning rapid molecular responses to slower community restructuring patterns. Key innovations include dual amino acid BONCAT protocols for soil systems, integration of activity measurements with genome-resolved multi-omics, and development of predictive models linking individual adaptations to community stability. The experimental design employs 272 archived samples and 243 controlled mesocosm samples to validate indicators across temporal scales and stress intensities, ultimately producing quantitative tools for monitoring ecosystem resilience and predicting critical transition points in arid systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This NSF CAREER project aims to develop foundational mathematical tools to address emerging challenges in distributed and uncertain systems, such as those in energy infrastructure, machine learning, and wireless communication. Despite recent advances in networked systems, current models and algorithms lack provable performance guarantees for a broad class of critical problems. These include (i) Generalized Nash games, where agents compete over shared resources; (ii) Bilevel optimization with constraints at both decision levels; and (iii) Saddle point problems with coupling constraints. Existing methods are not equipped to handle the complex, interdependent decision spaces or the uncertainty and decentralization that characterize these large-scale systems. This project addresses these gaps through a unified lens of Generalized Quasi-Variational Inequalities (GQVI), a powerful yet underdeveloped framework for capturing interdependent decisions under constraints. By improving solution methods for GQVI, the project will contribute to more reliable, efficient, and scalable decision-making tools for real-world applications. The educational plan includes engaging undergraduates in hands-on research experiences and training them to lead outreach activities in middle and high schools, featuring Python coding exercises and modules on optimization. Additionally, a virtual tour with faculty presentations and lab visits will simulate in-person field trips, aiming to inspire interest in STEM and improve engineering retention. The proposed research builds a unified theoretical and algorithmic framework for solving deterministic, stochastic, and distributed GQVI problems. The project is organized around three interconnected thrusts. First, it develops novel algorithms using operator relaxation techniques to manage the interdependencies that arise in constraint structures, providing provable convergence guarantees that overcome limitations of existing methods. Second, it reformulates nonlinear constrained GQVI problems as fixed-point problems and designs new parametric primal-dual algorithms tailored to this structure, enabling efficient and scalable solutions. Third, it addresses the complexities introduced by uncertainty and decentralization through the development of consensus-based and asynchronous methods, alongside advanced variance reduction strategies for solving large-scale stochastic and distributed settings. These innovations will deliver the first non-asymptotic convergence guarantees for GQVI problems, with broad impacts in game theory, bilevel programming, power systems, and machine learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Next Generation (NextG) networks aim to create an immersive multi-sensory user experience by supporting new technologies such as integrated precision sensing, artificial intelligence/machine learning (AI/ML) optimizations for automated network management, and a virtualizable architecture running on commodity hardware. Prominent in NextG networks is a new service-centric model, in which infrastructure providers virtualize the network into logically isolated network slices, running services with different demands. This flexible architecture is driven by the deployment of powerful ML algorithms that manage resources at fine and longer timescales. However, the security of decision-making algorithms is under-explored. This project aims to fill this research gap by investigating the security and verifiability of resource allocation for NextG networks. The project’s novelties are in exploring new threats emerging from the automated nature of decision-making and devising robust, secure, and verifiable resource management solutions. The project's broader significance and importance are in improving the availability and self-healing capabilities of the nation's wireless infrastructure and the safety-critical applications it supports. Moreover, the project strengthens the US workforce by providing training opportunities in the critical areas of cybersecurity, AI/ML, and communications. The research agenda is organized in three interrelated thrusts. The first thrust establishes a comprehensive threat model against reinforcement learning-based methods, which are commonly employed for resource allocation, and studies the impact of attacks. Guided by the insights gained from exploring the attack surface in Thrust 1, the second thrust designs robust resource allocation methods for operating in uncertain and maliciously distorted radio environments. A multi-pronged defense, which limits information leakage from the wireless medium while developing attack-resilient allocation methods through adversarial training and robust reward function design, is investigated. The third thrust focuses on verifying that allocation policies adhere to the service-level agreements between stakeholders by building proofs of service to validate the resource transactions that take place over the air. 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.