University Of Florida

NSF Awards · FY 2025 · 2025-07

Our oceans play a vital role in everything from coastal safety and national security to the economy and public health. Yet, large parts of the ocean remain difficult to explore and monitor. This project is focused on developing smarter and more reliable tools to help us better understand and navigate these vast underwater environments. Using new advances in robotics and artificial intelligence, the research team is working to improve small, cost-effective underwater robots that can travel long distances and operate on their own, even in areas where GPS and human control are unavailable. These mobile sensor platforms will be able to collect valuable information about the ocean in real time, adapt to changing conditions, and work together as a team — all without needing constant guidance. The project not only advances technology but also plays an important role in educating the next generation of scientists and engineers. The ability to monitor and survey oceans persistently and cost effectively on a large scale is of great significance to coastal safety, homeland security, national economy and public health. The proposed research addresses critical issues in transforming modern computing technologies for solving pressing problems of marine science. Recent decades marked a phenomenal transformation in our ocean exploration and perception approaches due to the progress in robotic computing platforms such as autonomous underwater vehicles (AUV). This research aims at enhancing the resilience and versatility of cyber-physical systems (CPS), consisting of mobile sensor platforms and ocean dynamics simulation, as our gateway to better explore and understand the harsh underwater environments. This project proposes research that will improve the long-term autonomy and intelligence of cost-effective mobile robots in previously under-explored ocean regions. Using Artificial Intelligence (AI) and Machine Learning (ML) techniques will enable mobile sensor networks with intelligent distributed sensing capabilities while ensuring their scalability and survivability within highly unpredictable dynamical environments. The developed strategies allow AUVs to localize themselves much more accurately in the oceans when GPS is not available. This project will also have a direct aim at training the next generation of engineers and computers scientists with expertise in naval architecture and marine sciences. In addition to the technical advances in CPS and AI used for ocean sampling, distributed sensing, and networking anticipated above, this project provides an application focus that will be of interest to researchers and students working in electrical, mechanical, and naval engineering, as well as computer, ocean, and biological sciences. 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 · 2025-07

Modern microelectronic computer chips include considerable low-level software critical to the system functionality. This software must be ready when the system is shipped to customers, and it is difficult to modify post deployment. One approach to ensure that the low-level software works as intended on the hardware is to produce a pre-production version of the chip for software/hardware testing, in a method referred to as "post-silicon validation". Post-silicon software validation is a highly complex and expensive activity requiring significant upfront planning and accounting for significant validation cost. Unfortunately, there has been little research in post-silicon software validation; existing research focuses primarily on functional and security validation of the underlying hardware. The project addresses this crucial problem via a comprehensive foundational paradigm and tool suite to streamline post-silicon software validation. The project’s key novelties include a unique architecture for observing hardware-software interaction in a silicon platform, methods to generate appropriate test inputs for exercising these interactions, and an objective metric to identify the quality of validation. The project’s broader impacts and significance include a pathway to derive high assurance in correctness of modern microelectronics systems that include tightly interacting hardware and software components, as well as creation of hands-on training modules to enable awareness in the problem for undergraduate and high-school students. The technical insight of the project is that a comprehensive post-silicon validation methodology requires cooperation of three components: an architecture for recording and transporting system events providing observability of the system internals during execution, a test generation methodology that is observability-aware, and a new coverage metric that accounts for the test scenarios being exercised and events being observed. The project realizes this insight through cooperative application of a novel architecture for collecting and synchronizing hardware-software events and a design automation flow that integrates this architecture with test generation and coverage calculation. The methodology targets validation of open-source System-on-Chip designs as well as emergent commercial 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 · 2025-07

Advancements in continuous health monitoring and bioelectronic medicine, such as vagus nerve stimulation for treatment-resistant depression, have the potential to reduce healthcare costs and minimize the side effects of conventional treatments. These applications rely on continuous communication between resource-constrained wearable and implantable sensors that form an interconnected system known as the Internet of Bodies (IoB). Current wireless technologies depend on inefficient coil-based or transduction-based energy transfer, along with electromagnetic communication which consume significant power. On the contrary, this project explores a transformative approach that leverages the human body as a conductive medium for both power transfer and communication, significantly improving system-level efficiency. Novel methods enhance data rates through custom modulation schemes and support multiple simultaneous devices via innovative multiple-access mechanisms. The research also establishes fundamental limits of tissue-coupled power transfer based on device design, electrode placement, and frequency of operation, with an aim of creating more effective and minimally invasive medical implants for improved quality of life and compliance. The project also integrates educational initiatives, developing hands-on learning modules in hardware design for K-12, undergraduate, and graduate students. Collaborative workshops with science museums and local schools strengthen the workforce pipeline in hardware design for biomedical engineering. The project’s technical approach entails establishing the foundational principles for high-speed, energy-efficient, and self-powered medical IoB (MED-IoB) systems by utilizing the human body as a conductive medium for both power transfer and communication. Moving beyond conventional electro-quasistatic techniques, this study explores tissue resonance properties to enhance power transfer efficiency (PTE) during harvesting, and energy-efficiency during all other operations, facilitated by ultra-low-volume (<0.05 cubic mm) and energy-efficient (~1 pJ/bit) integrated circuits. A key focus is on characterizing the alignment insensitivity and efficiency of tissue-coupled signal transfer as a function of device design, electrode placement, operating frequency, and tissue properties. The research includes the design and validation of system-on-chip implementations that integrate sensing, processing, and tissue-coupled transceivers, demonstrating improved PTE and energy-efficiency through in-vitro and ex-vivo experiments. Custom synchronization and hybrid (CDMA+FDMA) multiple-access mechanisms enable robust, multi-device networks for MED-IoB applications. The project’s outcomes are aimed at improving bio-physical circuit models and optimizing architectures for next-generation medical electronics. Beyond technical advancements, this work emphasizes educational innovation through collaborations with the university’s Center for Precollegiate Training and a local science museum. The development of modular hardware and heuristic teaching materials enhances experiential learning in hardware design, ensuring better accessibility to engineering education across all levels of the STEM workforce pipeline. 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 · 2025-07

The behavior of cyber-physical systems (CPS) powered by Artificial Intelligence (AI) is becoming increasingly impactful, complex, and potentially dangerous. Therefore, it is critically important to monitor the safety of the deployed AI-enabled CPS. However, this monitoring can be disrupted and rendered ineffective due to anomalous data that arises due to open environments and sophisticated AI behaviors. How much do these anomalies degrade the safety of the CPS? To answer this question, this project will devise an approach to computing the predictive confidence in the safety of a CPS experiencing one or several anomalies. This approach will be validated on small-scale autonomous racing cars and autonomous underwater vehicles. If successful, this research will result in superior safety monitoring capabilities and, thus, support continued adoption and deployment of advanced AI in automated CPS of great societal importance. Despite recent advances in anomaly detection, there is little connection between anomaly severity and safety violations in a CPS, beyond vague statistical correlation. This project seeks to close this gap by designing a general methodology to compute safety confidence in AI-enabled CPS undergoing anomalous behavior. The key insight is that the state-of-the-art anomaly measures can be aligned with the typical CPS components to inform online safety prediction. Leveraging this insight, this project will develop a collection of modular, meaningful, and safety-relevant anomaly scores for perception, dynamics, and control components of a typical closed-loop CPS. These scores will then be used to inject uncertainty into safety monitoring using symbolic functions that provide formal guarantees of calibrated prediction of safety confidence. As a result, this methodology promises to make autonomous CPS aware of how anomalies impact their safety. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-07

Project summary This application is for funds to purchase the AB SCIEX QTRAP 7500 LC-MS/MS. This mass spectrometer has important performance features that distinguish it from other instruments available on campus and from other instruments available on the market and will enable us to perform high-end experiments for small molecule quantitation. The QTRAP 7500 offers state-of-the-art technology for quantitation, identification, and structural analysis of small molecules. The main features we require include an electrospray ionization source for small sample amounts obtained from dissected tissues, extracellular vesicles or tissue culture; the APCI (Atmospheric pressure chemical ionization) source as an alternative ionization source to optimize MS/MS spectra for certain molecules; an ultra-high pressure liquid chromatography (uHPLC) interface for tandem mass spectrometry (MS/MS), the ability to perform in two operating modes (triple quadrupole and linear trap), and to switch quickly between them, a fast scan rate, the ability to perform quantitative measures of small molecules using selected reaction monitoring (SRM) and multiple reaction monitoring (MRM), and the ability to identify and quantify bioactive lipids. The QTRAP 7500 excels in each of these areas and is the instrument chosen. We will be able to solve challenging analytical problems and identify and quantify small molecules, such as lipids, hormones and environmental contaminants and their metabolites related to disease. Most of the users of the instrument are already collaborating in projects, thus this will help solidify their scientific interactions. Twelve investigators with major research projects, eleven with NIH RO1 support and one with NIOSH support, and six investigators for five minor research projects require the sensitivity and high throughput ability of the QTRAP 7500 mass spectrometer to answer important questions. We have developed preliminary data for many of the projects. Of the major projects, three involve analysis of environmental contaminants, pesticides, or opioids in small tissue samples (Vulpe, Denslow and Bolser), eight involve analysis of bioactive lipids (Baker, Alli, Vulpe, Denslow, Swale, Vite, Moore, and Wise) and four require analysis of hormones (Baker, Reznikov, Yue and Jones). The minor users fall within these same general methods but also require analysis of PFAS in fish or of harmful algal bloom toxins in cells in tissue culture. Many of the projects, but in particular the projects requiring lipidomics analyses, will require multiple reaction monitoring (MRM). Both Electrospray (ESI) and Atmospheric Pressure Chemical Ionization (APCI) will be required for the projects. These projects will result in better understanding of fundamental processes that lead to the potential treatment of diseases in humans.

NIH Research Projects · FY 2025 · 2025-07

Background. Cellular senescence is a process in which cells become irreversibly cell cycle arrested and resistant to apoptosis, but still metabolically active with a pro-inflammatory secretome, named senescence- associated secretory phenotype (SASP). Diabetes (DM) complications have been related to premature age- independent accumulation of senescent cells in tissues. Previous evidence suggests that harmful effects of DM on periodontal tissues are partly due to a hyper-inflammatory phenotype, though the full mechanisms behind this process remain undefined. Thus, further investigation into the cellular and molecular mechanisms linking DM to periodontal disease pathogenesis is needed. Our main hypothesis is that healthy and diseased periodontal tissues in subjects with type 2 DM (T2DM) present altered expression of markers of senescence, when compared to those of normoglycemic individuals. Hence, T2DM-induced premature senescence may represent a mechanism by which the periodontal tissues of subjects with T2DM is injured. This proposal will use two specific aims to address this hypothesis. Aim 1. To compare the expression of cellular senescence markers in healthy and diseased gingival biopsies between young/middle-aged individuals with T2DM and those who are normoglycemic, and to investigate whether the combination of T2DM and periodontitis impacts cellular senescence markers beyond their individual effects. Gingival tissues will be collected from young/middle-aged subjects according to the following groups: 1- without periodontitis or DM, 2- without periodontitis but with T2DM, 3- with periodontitis but without DM, and 4- with both periodontitis and T2DM. SA-β-Gal and lipofuscin staining will be used to grossly quantify the amount of senescence within the tissues. In order to quantify the expression of makers of senescence within the tissues, Luminex® technology and QuantiGene Plex assays will assess the mRNA levels of p16INK4a, p14ARF, p53, p21, Rb, HMGB1, H2AX, P53BP1, LMNB1, HP1α, ATR, ATM, Chk1, Chk2, BCL-2, BCL-XL, and HIF-1α. Moreover, immunohistochemistry and immunofluorescence will be used to probe for protein levels of p16INK4a, p14ARF, p53, p21, H2A.X, P53BP1, laminin B1, HP1α, ATR, ATM, BCL- 2, and HIF-1α. In order to characterize and quantify the mRNA and protein levels of SASP factors within the biopsies, two-pronged Luminex® technology approaches will be taken, the QuantiGene Plex and ProcartaPlex assays. Aim 2. To correlate markers of senescence with the local levels of inflammatory mediators. This analysis will be coupled with that performed in Aim 1 to take a systems biology approach to correlate markers of senescence with the levels of various immunoinflammatory mediators by the Luminex® technology and 80-Plex ProcartaPlex Panel. Latent Class Analysis (LCA) will identify phenotypes of senescence within each experimental group and describe the features of each phenotype identified. Within each LCA defined phenotype, GO analysis will be performed using DAVID database to determine whether T2DM, periodontitis and inflammatory microenvironment affects genes or proteins associated with senescence.

NIH Research Projects · FY 2025 · 2025-07

Summary Neurodegenerative diseases (NDs) are expected to double in the next two decades. This will result in an extraordinary global burden on healthcare budgets and families. Most neurodegenerative diseases are idiopathic and thought to arise from multi-factorial insults, many modifiable, rather than a single cause. Even in largely genetic-based NDs, their emergence and severity are influenced by modifiable factors, most of which are common across NDs. A major barrier preventing effective therapy development, or concrete public heath prevention-based directives is the lack of efficient integration of research findings across these multiple complex factors. In fact, the current biomedical research model, naturally siloed to deliver knowledge depth at the expense of integration, sometimes hinders the broader multi-disciplinary study of complex syndromic diseases including neurodegenerative diseases. To address this gap, we propose to expand the current format of an existing annual conference, the International Research Conference in Neurodegenerative Diseases to now include programmatic aspects that foster interdisciplinary exchange of ideas and the active participation of non- neurodegeneration investigators studying risk-associated aspects of relevance to neurodegenerative disease. This unique opportunity can be achieved by immersing the existing IRCND meeting into the University of Florida College of Medicine environment with nationally and internationally renowned research centers and institutes in neurodegeneration (CTRND, Center for Neurogenetics, & Fixel Institute) and, more importantly, leading centers spearheading research into physiological systems and mechanisms linked to neurodegenerative diseases. These include but are not limited to aging (UF center for cognitive aging & translational research), sensory loss (Center for Smell & taste), respiratory control (Breathe Center), and environmental/occupational risk factors such as infection (Sepsis Research Center) and traumatic brain injury (BRAIN center). Additionally, supported by a $70m investment from NVIDIA, UF provides a unique opportunity to harness the rapidly growing artificial Intelligence (AI)-focused biomedical research infrastructure to further foster knowledge expansion and collaborations that can lead to a better integration of these complex multifactorial traits and neurodegenerative disease mechanisms. The goal is to help “de-silo” neurodegenerative disease research by spurring interdisciplinary collaborative efforts that will nourish science, create “integrative” neurodegeneration new scientists, and more effectively drive new therapy development.

NIH Research Projects · FY 2025 · 2025-07

ABSTRACT Friedreich's ataxia (FRDA), the most prevalent inherited ataxia, causes debilitating neurodegeneration and cardiac issues, typically leading to mortality by age 35. Current treatments, including the FDA-approved omaveloxolone, offer limited efficacy and present notable side effects. We propose an innovative approach with QTE therapy, targeting the underlying molecular mechanisms of FRDA. Our initial studies have shown that QTE therapy modulates important FRDA target protein levels, improves cardiac function, mitigates iron overload, and significantly increases survival rates in FRDAkd mice, outperforming existing treatments. In Aim 1 of this project, we focus on optimizing the QTE dosage and formulation through Physiologically-Based Pharmacokinetic (PBPK) modeling, assessing drug synergy, bioavailability, and blood-brain barrier penetration. In Aim 2, we will evaluate the optimized dosage’s efficacy in reversing behavioral and pathological deficits in FRDAkd mice, using comprehensive neurological and cardiac assessments. We aim to demonstrate significant improvements in cardiac function and neurological health, surpassing current treatment benchmarks. Our team is well-equipped to transition QTE therapy from preclinical research to clinical trials. In this project, we will focus on dosage and formulation optimization through PBPK modeling and conduct comprehensive efficacy testing in FRDAkd mice. For the next phase, we will collaborate with regulatory strategy experts, drug manufacturing specialists, formulation and packaging experts, and clinical trial professionals. This will guide us toward IND submission and the initiation of clinical trials. Our commercial strategy will be developed in partnership with a patient advocacy group to leverage orphan drug benefits, promote community engagement, and establish alliances with firms experienced in rare diseases. This approach is designed to streamline the market entry and distribution of QTE therapy, offering a novel, effective, and safer treatment alternative for FRDA patients.

NIH Research Projects · FY 2026 · 2025-07

Modified Project Summary/Abstract Section This proposal aims to examine differences in oropharyngeal cancer (OPC) outcomes among Medicaid-enrolled adults, focusing on the effects of Medicaid dental coverage policies and dental care utilization. We hypothesize that Medicaid beneficiaries with OPCs in states offering preventive dental coverage, as well as those who regularly visit the dentist, will be more likely to be diagnosed at an early stage and have better survival rates. The study will use a combination of clinical, surveillance, and administrative data. First, we will characterize differences in early-stage diagnosis and 5-year survival rates for OPCs based on race-ethnicity, sex, and rurality, using the SEER-Medicaid dataset, a nationally representative cancer registry linked with Medicaid enrollment data from 1999-2019. Next, we will assess the impact of state Medicaid dental coverage policies on OPC outcomes, using a quasi-experimental difference-in-difference design. This analysis will determine whether Medicaid beneficiaries in states with dental benefits are more likely to be diagnosed early and have better survival outcomes compared to those in states without such coverage. We will also explore whether these benefits reduce health disparities in OPC outcomes. Finally, we will investigate how dental care utilization patterns—regular preventive care, sporadic acute care, or no care—affect stage at diagnosis and survival rates, using data from the OneFlorida+ clinical research network, which integrates electronic health and dental records, Medicaid claims, and cancer registry data. This analysis will provide insights into how dental care patterns influence OPC outcomes among Medicaid enrollees. Our study is the first to comprehensively examine the role of Medicaid dental policies and utilization in OPC outcomes, using linked datasets from clinical, surveillance, and Medicaid claims sources. The findings will inform policy decisions regarding the provision of preventive dental services in Medicaid to improve cancer outcomes for vulnerable populations. This research aligns with the NIDCR's strategic priority to advance early detection of oral cancers and supports the Healthy People 2030 goal of increasing early-stage cancer diagnoses. The knowledge gained will contribute to effective healthcare policies and interventions for populations disproportionately affected by OPC.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY/ABSTRACT Severe immune dysregulation occurs in rare patients with loss-of-function mutations in the gene encoding the ubiquitin ligase Itch. 14 patients have been identified, presenting with chronic lung disease, various forms of organ-specific autoimmune diseases, and high levels of autoantibodies. One patient was successfully treated with a bone marrow transplant, showing that disease is caused by malfunctioning immune cells. Murine studies have enabled mechanistic dissection of Itch function in T cells, but these roles do not adequately explain why patients develop autoimmunity, particularly autoantibodies. Recent evidence shows that Itch directly restrains B cells to limit antibody production. In activated B cells, Itch prevents aberrantly high levels of the master metabolic regulator, mechanistic Target of Rapamycin Complex 1 (mTORC1) activity, a hallmark of lymphocytes in patients with more common autoimmune diseases such as lupus. Despite the evidence that Itch functions within B cells to restrain mTORC1 activity and antibody responses, where Itch functions within the mTORC1 activation pathway is unknown. These mechanisms are germane to understanding formation of autoimmunity in Itch deficient patients. The objective of this proposal is to determine how Itch limits aberrant mTORC1 activity in B cells. Our central hypothesis is that Itch regulates mTORC1 nutrient sensing to limit mitochondrial activity. Using primary mouse B cells and patient-derived lympoblastoid cell lines that are Itch-deficient or Itch-sufficient, we will 1) define the nutrient-sensing mTORC1 activation pathways regulated by Itch in B cells, and 2) determine how Itch inhibits purine nucleotide-mediated mTORC1 activity. This work will determine if Itch exerts regulatory control over mTORC1, providing a new target for the design of therapies that can treat rare patients with Itch mutations and those with mTORC1-associated autoimmune diseases.

NIH Research Projects · FY 2025 · 2025-07

PROJECT ABSTRACT This NIDCR Dual Dentist Scientist Pathway to Independence Award (K99/R00) for Tumader Khouja BDS MPH PhD, will establish Dr. Khouja as an independent oral health services researcher developing and implementing evidence-based interventions that integrate dental care into medical settings utilizing both qualitative and quantitative research methods. This long-term goal will be achieved via a 5-year training and research plan that will launch Dr. Khouja’s independent program of research and academic career and support the NIDCR’s mission to increase and maintain a strong cohort of new and talented independent dual degree dentist scientists. The career goals of this program are: (1) training in intervention sciences [and biomedical informatics]; (2) gain expertise in qualitative methods to inform, design, and evaluate theoretically based behavioral interventions, and (3) professional development as an independent researcher. These career goals will be achieved via formal coursework, trainings, national conferences, mentorship, and research experience. The overall objective of this proposal is to understand the barriers and facilitators to non-traumatic dental condition (NTDC) management in the emergency department (ED) through quantitative and qualitative methods. The first aim will determine the national variation in NTDC prescribing in the ED and subsequent ED/urgent care revisits and hospitalizations within 30-days of an index ED visit. Using national electronic health records and integrated claims datasets and a random effects model, we will identify factors associated with prescribing for NTDC and variation at the patient, provider, hospital and state levels. The second aim identifies ED providers’ perceived barriers and facilitators to the management of NTDC in the ED. Using individual in depth interviews, ED providers (physicians, advanced practice providers) will identify the facilitators and barriers to management and prescribing for NTDC in the ED. The third aim will pilot and refine a multifaceted approach for NTDC prescribing in the ED and assess the acceptability and feasibility of the implementation of this strategy in ED settings. We will develop a 2-level interventional strategy [using human centered design methods] that will aid ED providers in NTDC prescribing and refine it based on our findings from the previous aims and field experience. As we test the intervention in an ED, we will use [EHR data from the University of Pittsburgh Medical Center (UPMC) to] evaluate appropriate treatment and clinician experience with ED use for NTDC before and during the intervention. Further trials will test the effectiveness of the intervention in U- and/or R-level proposals. An outstanding interprofessional team comprised of a public health dentist, pharmacist, ED physician, behavioral psychologist, biostatistician [and biomedical infromatician] will provide mentorship to ensure the success of this project. The long-term goal of this program of research is to develop a generalizable and sustainable intervention to improve NTDC management in ED and other medical settings. [This work supports priorities of the U.S. Surgeon General, NIH Director, and NIDCR to integrate oral health and general health through collaborative alliances and creating a diverse pipeline of clinician oral health researchers.]

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY/ABSTRACT Cancer Cachexia (CC) presents a profound challenge to cancer survival, affecting roughly 80% of cancer patients and contributing to a substantial proportion of cancer-related fatalities. Among its manifestations, skeletal muscle weakness and wasting stand as a pivotal concern, impacting both the quality of life and survival rates of afflicted individuals. The goal of this F32 application is to pursue research training in the newly emerging area of muscle circadian clock disruption in cancer The circadian clock mechanism in skeletal muscle regulates a rhythmic daily program of gene expression (i.e. clock output genes) contributing to muscle homeostasis, while muscle clock disarrangements are implicated in several models of muscle atrophy. My preliminary investigations unveiled transcriptomic shifts in core clock genes within skeletal muscle in various pre-clinical CC models, suggesting a potential role for the clock in cancer-induced muscle detriments. These observations are supported by compelling unpublished data from collaborative research between the Esser and Judge laboratories, showing disruptive alterations in the muscle circadian clock and clock output genes in pancreatic cancer-bearing mice. These combined findings provide the rationale for this F32 proposal. This proposal stands out for its scientific novelty, being the first to delineate the muscle clock profile and assess its impact as a modifier in the development and progression of CC. My overarching hypothesis is that the muscle clock serves as a CC modifier and loss of clock function will lead to accelerated and aggravate CC-induced muscle impairments. The outcomes of this proposal will 1) define the cachexia stage at which the muscle clock is disrupted, interrogate clock disruptions and its relationship with cachexia manifestations in a muscle type-specific fashion, and 2) provide an extensive assessment of both behavioral and muscle-specific changes in CC modulated by the muscle clock mechanism.

NIH Research Projects · FY 2025 · 2025-07

ABSTRACT Extracellular vesicles (EVs) have been an emerging approach for delivering genome editing therapeutics. Due to their superior biocompatibility and low immunogenicity compared to lipid nanoparticles (LNPs) and viral vectors, EVs have been in increased needs for developing gene therapy. Despite great promise for clinical translation, standardization of EV isolation and cargo loading at the scale has been challenging, which substantially hindered the clinical translation of EV-based genome editing technologies. Transfection of EVs for genome editor loading is a key step in functional therapeutic development. However, current transfection approaches suffer from low transfection rate, and low scalability and volume processing throughput. In this project, we aim to optimize and evaluate our recently developed high-throughput EV loading platform with rigorous quality characterization on EVs and genome editor payload, which will provide essential data on CMC, Critical Quality Attributes (CQAs), and toxicology of EV based CRISPR gene therapy for IND submission. The Aim 1 will focus on optimization of high-throughput EV loading platform (µDES) with genome editors for GMP manufacturing. The Aim 2 will optimize ExoQuality approach as the IND-enabling assessment to establish the Critical Quality Attributes (CQAs) of EV based genome editing therapeutic products. The Aim 3 will focus on the pharmacological and toxicological evaluation on developed CRISPR RNP EVs with modality in treating hearing loss for IND submission. The project will lead to the scalable GMP manufacturing procedures for EV based genome editing products and establish acceptable CQA criteria and CMC data to accelerate EV based genome editing product for IND submissions. The outcome will significantly impact on accelerating the translation of novel, safe, and effective therapeutic genome editing strategies to first-in-human clinical trials.

NIH Research Projects · FY 2026 · 2025-07

PROJECT SUMMARY Anxiety is a top unmet need in people with Parkinson's disease (PD) associated with high levels of distress, caregiver burden, lower quality of life, and disruption of usual activities. Diagnosis and treatment of anxiety in PD is complicated due to its association with dopamine fluctuations, comorbidity with depression, and lack of evidence-based treatments. The result is that anxiety often goes undiagnosed and untreated, or is treated with inappropriate, potentially dangerous medications. The goal of the proposed study is to understand the brain networks involved in anxiety, test how they are affected by dopamine fluctuations resulting from the treatment of PD motor symptoms with dopaminergic agents, and determine if anxiety and depression have dissociable neural networks in PD. The study aims to achieve these goals using detailed psychiatric and neuropsychological assessments as well as high-resolution functional magnetic resonance imaging (fMRI) methods in PD patients with anxiety, PD patients without anxiety or other psychiatric conditions, and healthy controls. A targeted fMRI activation task will be employed designed to tax medial temporal lobe networks including the amygdala. High-resolution resting- state fMRI will be used to assess connectivity between amygdala and local and whole brain cortical networks. Completing these assessments both during the on and off-dopamine medication state will further help determine whether anxiety and underlying amygdala networks are altered by fluctuations in dopamine levels. Together the proposed studies will provide key insights into the role of the amygdala, its subregions, and its networks in the experience of anxiety in PD. The results will determine whether anxiety and depression are dissociable conditions in PD subserved by distinct amygdala subregions and networks in PD. Finally, this work could additionally provide a target for the development of new therapeutic interventions for anxiety in PD.

NSF Awards · FY 2025 · 2025-07

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professor Chenjie Zeng of the University of Florida will develop generalizable methodologies for the synthesis of nanosized, atomically precise clusters of semiconductors. Semiconductor nanomaterials, such as quantum dots, have useful properties for electronic and photonic applications. The ability to synthesize semiconductor nanoclusters with atomic-level control over their sizes, shapes, surfaces, and compositions would enable understanding of their structure-property relationship and provide insights into the developing of technologically important materials. The interdisciplinary nature of the project will provide comprehensive research training for graduate and undergraduate students, equipping them with the integrated knowledge and skills to address complex challenges in nanochemistry and nanotechnology. Additionally, outreach activities will introduce semiconductor nanomaterials to the public and K-12 students, fostering broader interest in nanoscience. The Zeng research group aims to develop generalizable methodologies that are based on cation exchange reaction for enabling atomic-level control of the structures and properties of II-VI semiconductor nanoclusters and expanding the library of atomically precise nanoclusters. Specifically, Aim 1 will develop an approach for controlling the surface structures of II-VI clusters by tailoring metal-ligand complexes; Aim 2 will focus on tuning the core sizes and shapes of II-VI clusters by adjusting metal-chalcogenide template clusters; and Aim 3 will explore pathways for homovalent cation exchange to achieve atomically defined heterostructures. The expanded library may serve as a model system for atomic-level understanding of the structures of semiconductor nanoclusters, the collaborative roles of various ligands in passivating nanocluster surface, and the detailed mechanisms underlying cation exchange reactions. Access to this library of II-VI nanoclusters may also offer a robust testing ground for high-level theoretical calculations, enabling the development of consistent models to describe their electronic structures. The fundamental understanding may facilitate the rational control of semiconductor nanocluster properties for emerging optical, electronic, and spin-based 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.

NIH Research Projects · FY 2025 · 2025-07

Project Summary Midbrain expressing dopamine transporter (DAT), located in the ventral tegmental area (VTA) and substantia nigra pars compacta (SNpc), are involved in psychostimulant use disorders, psychiatric diseases, movement disorders, and HIV-associated neurocognitive disorder. Cocaine, a highly addictive psychostimulant, blocks DAT-mediated dopamine (DA) uptake, increasing extracellular DA levels in the CNS. Key to this proposal, activation or inhibition of DA neurons impacts peripheral immunity. For example, I have shown that peripheral immunity is dysregulated when DA neurons are lesioned, suggesting that midbrain DA can influence peripheral immunity, but the midbrain-to-peripheral circuitry is poorly understood. My pilot data suggest that midbrain DA neurons send projections to the dorsal vagal complex (DVC) and make synapses within the DVC. Neurons in the DVC express D1-like and D2-like dopamine receptors and change their activity in response to dopamine. The DVC projects to multiple organs, including the spleen via the celiac ganglion. My pilot data support the connection between midbrain-DVC-spleen, and that chemogenetic stimulation of midbrain DA neurons alters splenic immunity. I have also shown that immune cells express DAT, where in immune cells DAT activation is immune suppressive, highlighting distinct roles for DAT activity on peripheral immune cells versus DAT activity in the brain. Mice with global DAT deletion (DAT KO) exhibit heightened peripheral inflammatory responses to immune challenges, supporting an immune suppressive role for DAT in peripheral immunity. To study CNS DAT activity on peripheral immune cells independent of DAT's effects on peripheral immune cells, I generated mice chimeras with selective DAT deletion in the peripheral immune system (while keeping CNS DAT intact). Preliminary experiment in these chimeric mice shows immune cells DAT deletion causes distinct immunological changes compared to global DAT KO. My pilot data support the hypothesis that midbrain DA neuron activation affects peripheral immune responses via DA release in the DVC, regulating a midbrain-DVC-spleen circuit independent of immune cell DAT activity. In Aim 1, I will use viral tracing approaches to investigate whether midbrain DA neurons project to and make synapses within the DVC, whether these projections extend to the spleen in a multi-synaptic mechanism, and whether activation or inhibition of these neurons modulate splenic immunity. In Aim 2, I will investigate whether systemic cocaine versus locally applied cocaine in the CNS differentially affect splenic immunity. In Aim 3, I will test the hypothesis that DAT on peripheral immune cells modulates their immune response to immune stimulation. The results will provide novel insights into how DAT- expressing neurons influence the immune system and the independent role of DAT in immune cells, providing strong conceptual and methodological training under Drs. Khoshbouei and Okun, and under a mentoring team comprised of established professors, who are experts in the proposed approaches and are accomplished mentors, helping me to complete the proposed research and achieve career development goals of this proposal.

NSF Awards · FY 2025 · 2025-07

Today’s high-performance computing (HPC) applications produce vast volumes of data for post-analysis, presenting a major storage and I/O burden for HPC systems. To significantly reduce this burden, researchers have explored to use lossy compression techniques. While lossy compression can effectively reduce the size of data, it also introduces errors to the compressed data that often lead to incorrect computation results. As a result, scientists hesitate to use lossy compression in their scientific research. Thus, there is a critical need to develop an effective method to identify compression strategies which minimize error impact for a diversity of programs. This project aims to develop a systematic approach that helps scientists automatically select a lossy compression algorithm with the lowest error impact based their HPC programs and target compression ratios. It also integrates educational and outreach activities including student training and development of new curriculum on trustworthy data reduction and dependable HPC systems. Modeling compression error propagation in HPC programs is challenging because existing lossy compressors are developed with distinct principles that generate largely different compression errors on diverse HPC data. This project includes four key thrusts: (1) developing an accurate and efficient fault injection infrastructure that integrates with the fault models of commonly used lossy compression algorithms; (2) designing a fine-grained approach to characterize error propagation in HPC programs through program analysis and deposition based on the data dependencies and life cycle of compressed data; (3) developing a predictive model using machine learning techniques to select a compression strategy that minimizes the error impact on a given program and compression ratio; and (4) integrating the technique with domain-specific error impact metrics in real-world HPC applications and demonstrates the effectiveness of the technique by selecting compression strategies that give low error impact for the same ratios. Not only this project has an enormous positive impact on HPC cyberinfrastructure, but it also helps redefine the optimization of lossy compression techniques with emphasis on both efficiency and error 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 2025 · 2025-07

The Industry-University Cooperative Research Center (IUCRC) for Accessible Healthcare through AI-Augmented Decisions (AHeAD) will develop trustworthy and usable AI technologies, so quality care is accessible by all populations. AHeAD is a multi-university research partnership between UL Lafayette (lead), Tulane, University of Florida and Georgia Tech. The center’s research will create validated AI-enabled systems, quality assurance frameworks, and best practices that enable healthcare organizations to offer quality care for all, reducing healthcare gaps while saving costs. By training the next-generation AI workforce and releasing open-source AI models, the center will drive innovation, create new jobs, and grow the American economy. AHeaD's goal is to develop trustworthy AI technologies that improve healthcare access and outcomes for all populations. Research focuses on creating privacy-preserving, interoperable, explainable and resource-efficient AI models for healthcare. The center's multidisciplinary program includes AI/ML, data science, systems engineering, and health sciences, supported by computational infrastructure and real-world health data through industry partnerships. Research will advance trustworthy AI, privacy-aware data integration, behavioral context modeling, and human-AI integration. The center will foster workforce development through student training and industry collaborations, building a skilled talent pool to accelerate healthcare AI translation from research to practice. The University of Florida’s team focuses on creating AI tools to improve trustworthiness and interpretability of wearables and smart instrumentation. They also build connections between the AI engineers and clinicians to better support workforce development. AHeAD will address critical national healthcare challenges and advance U.S. competitiveness in AI-enabled healthcare. The Center creates a rich environment for training next-generation professionals through integrating industry-relevant AI applications into curriculum development and providing direct experience solving healthcare challenges with real-world data. The Center will create and maintain standardized healthcare datasets, publish open-source software and research outputs, and advance technologies with broad healthcare applications. This multifaceted approach promises to improve the health of millions of Americans while generating substantial cost savings for both government and industry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY/ABSTRACT Carpometacarpal osteoarthritis (CMC OA) is a leading cause of global disability, causing significant functional limitations and pain, as well as decreased quality of life. Current treatment options fail to adequately address disease impact, offering limited pain relief, only temporary functional stability, and/or debilitating constraints to the physiologic thumb joint. There is a need to develop novel therapies based on the biomechanical and somatosensory factors that influence CMC OA disease progression. CMC OA is a multifaceted disease with heterogeneous symptom presentation. There is limited research appropriately evaluating biomechanics and pain in CMC OA, despite their combined influence on disease impact. Current biomechanics and pain research allows for robust analysis of functional limitations and somatosensory deficits. A comprehensive evaluation of structure, function, and pain in CMC OA is needed to fully understand disease impact and properly address diagnostic and treatment concerns. The proposed research will provide an innovative evaluation of function and pain in CMC OA by (1) determining a direct relationship through movement- evoked pain (MEP) analysis and (2) utilizing advanced machine learning algorithms to identify the complex relationships in biomechanical and somatosensory data, define patient phenotypes, and predict symptoms. Implementing kinematic, kinetic, and muscle activity analysis with MEP will innovatively relate robust biomechanics research with pain for CMC OA (Aim 1). The results from this aim will identify compensatory movement patterns that alleviate pain, thereby informing alternative and effective therapies. Implementing machine learning algorithms to evaluate complex, multifaceted clinical data will allow for the evaluation of heterogeneous symptomology unique to CMC OA (Aim 2 and 3). Utilizing probabilistic cluster analysis and explainable AI (XAI) will increase transparency and confidence in the interpretation of clinical data by identifying the variables driving model prediction. These variables will be used to distinguish patient phenotypes and understand the mechanisms underlying disease progression. The results from these aims will inform current diagnostic standards for CMC OA and lay the groundwork for predictive prognostic models. Overall, the aims proposed in this research work harmoniously to provide a holistic evaluation of CMC OA disease impact. The results of this research will advance scientific knowledge of the factors that influence disease: structure, function, and pain. This F30 fellowship will provide the applicant with an exceptional foundation in biomechanics and advanced data science techniques. The skills developed through the applicant's training plan will prepare her for a career developing personalized rehabilitation strategies as a Physical Medicine and Rehabilitation (PM&R) physician-scientist.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY Non-Communicable Diseases (NCDs), including cardiovascular disease, hypertension, central obesity, type 2 diabetes mellitus and respiratory disease, are responsible for 80% of adult deaths annually, and are responsible for having the greatest impact on health adjusted life expectancy and quality of life. Fetal growth restriction (FGR; estimated fetal weight <10th percentile), which occurs in up to 10% of pregnancies, is associated with increased risk of developing NCDs later in life. This is potentially because FGR results in developmental programming of fetal tissues and organs in order to adapt to the adverse conditions resulting in FGR, which persist into adulthood but ultimately predispose physiological and metabolic dysfunction. We have developed the use of a polymer-based, nanoparticle that facilitates non-viral gene delivery specifically to the placenta. Our placenta-specific, nanoparticle gene therapy is capable of increasing expression of human insulin-like growth factor 1 (hIGF1) in multiple animal and human placenta models. Importantly, our nanoparticle gene therapy is proven to be safe to both mother and fetus. We have consistently demonstrated that treatment increases placental glucose and amino acid transporter, and growth factor expression in diverse models of FGR (surgically-induced, genomic manipulation, maternal nutrient restriction (MNR)) because IGF1 is central to most mechanisms responsible for FGR associated with placental dysfunction, and a major regulator of placental and fetal growth and development. This proposal aims to 1) determine the impact of placental nanoparticle gene therapy treatment on developmental programming in fetal liver and kidney in late pregnancy, in the proven guinea pig MNR model of FGR, 2) Identify the mechanisms by which manipulating placenta signaling with nanoparticle gene therapy affect communication with fetal liver and kidney cells in human cell culture models, and 3) investigate the long-term impact of placenta-specific nanoparticle gene therapy on offspring liver and kidney physiology and metabolic health. Preliminary investigations confirm that placenta-specific, nanoparticle gene therapy increased fetal weight in preexisting FGR using the guinea pig MNR model. Furthermore, short-term placenta-specific nanoparticle gene therapy normalizes changes associated with FGR in fetal liver gene expression and kidney collagen deposition, hereby establishing a model in which further investigations into developmental programming of fetal organs can be investigated. This proposal is innovative and significant as it utilizes a nanoparticle technology currently being trialed in the treatment of cancer, but in the setting of reproductive medicine, thus generating knowledge that will inform clinical innovation in order to set the foundation for a healthy pregnancy and lifelong wellness.

NIH Research Projects · FY 2026 · 2025-07

One of the challenges of the post-genomic era is to elucidate the function of the encoded proteomes. Even in widely studied model organisms such as Escherichia coli K12 or fission yeast, around 30% of the genes have yet to be linked to a function but this number rises to 50-70% in most non-model organisms. Since 1995, I have pioneered the use comparative genomic methods combined with experimental validations to identify the function of unknowns. This body of work has led to identifying the function of over 65 gene families resulting in over 160 publications since I started my independent group in 2004. The MIRA R35 mechanism will allow me to fully take advantage of the exploratory type of research driven by comparative genomic approaches that have often led our research program into unforeseen directions to discover novel enzymes and pathways, some conserved from bacteria to humans. Our research currently focuses mainly on the synthesis and function of complex tRNA and DNA modifications and the study of pyridoxal phosphate homeostasis. Two projects were developed in more detail in the proposal. The first focuses on the identification of missing tRNA modification genes including the transporters for the Queuosine (Q) micronutrient and the second on the role of orphan PLP binding enzyme families (YggS and YdcR). The importance of tRNA modifications in health and humans is currently emerging and to fully understand their roles, it is critical to identify all the corresponding genes at least in the major model organisms. One of the major gaps in knowledge that we will tackle is the missing Q precursor transporters in eukaryotes. This modification of the wobble base is important for translation accuracy and its absence can lead to memory defects in mammals. Eukaryotes salvage Q or its precursor base queuine (q). Identifying the missing Q/q transporter will allow us to better understand how this micronutrient is salvaged from the food and microbiota. YggS is a member of a very conserved protein family. Our group was the first to link this family to vitamin B6 homeostasis in E. coli and humans. This finding has now been confirmed in many other organisms by other groups. Mutations in the human homolog PLPBP lead to an inherited treatable form of vitamin B6-dependent early-onset epileptic encephalopathy. However, the exact molecular function of this protein family is still unknown. Recent findings from our laboratory on the E. coli YggS revealed an RNA binding role and genetic interactions with the predicted regulator PLP-binding regulator YdcR and the pyridoxal kinase PdxY, which will be further characterized as a novel window into the function of this enigmatic protein family.

NIH Research Projects · FY 2025 · 2025-07

Enter the text here that is the new abstract information for your application. This section must be no longer than 30 lines of text. The University of Florida Genetics Training Program (UF-GTP) is a partnership between the University of Florida Genetics Institute (UFGI) and the University of Florida College of Medicine Graduate Program in Biomedical Sciences with the rationale to combine the strengths of the two programs to provide superior training in genetics and genomics while also providing access to the superior artificial intelligence infrastructure at UF. UFGI administers the Genetics & Genomics (G&G) Ph.D. program which is a university-wide program that provides doctoral level training in genetics and genomics with access to 7 colleges and 51 departments. The Graduate Program in Biomedical Sciences is administered by the College of Medicine and offers a Ph.D. program with a concentration in genetics that provides access to the array of clinical and basic science departments within this college. The UF-GTP will take advantage of the existing recruiting and academic training frameworks provided by these graduate programs and the recent expansion in junior and senior research training faculty across campus. The overarching goal of the program is to develop and expand a highly talented pool of professional researchers with the skills to pursue successful careers in the biomedical workforce. The UF-GTP Program proposes a series of high impact training experiences designed to enhance existing predoctoral training activities and broadly impact all students in the participating programs. Key elements include core courses within a flexible and customizable curriculum; monthly research in progress seminars; participation in the annual UF Genetics Symposium, an annual orientation providing a forum for feedback; training in rigor and reproducibility, training in scientific communication skills, and a continuing commitment to exceptional scientific training. Benchmarks for success will be an increasingly accomplished cohort of UF-GTP Trainees and Faculty Trainers, the highest level of retention for all trainees, a high level of research productivity, and successful transition into biomedical research careers that fully capitalize on their doctoral training. Critical evaluation of these benchmarks will be achieved through the implementation of a quantitative assessment plan.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY Anxiety is a top unmet need in people with Parkinson's disease (PD) associated with high levels of distress, caregiver burden, lower quality of life, and disruption of usual activities. Diagnosis and treatment of anxiety in PD is complicated due to its association with dopamine fluctuations, comorbidity with depression, and lack of evidence-based treatments. The result is that anxiety often goes undiagnosed and untreated, or is treated with inappropriate, potentially dangerous medications. The goal of the proposed study is to understand the brain networks involved in anxiety, test how they are affected by dopamine fluctuations resulting from the treatment of PD motor symptoms with dopaminergic agents, and determine if anxiety and depression have dissociable neural networks in PD. The study aims to achieve these goals using detailed psychiatric and neuropsychological assessments as well as high-resolution functional magnetic resonance imaging (fMRI) methods in PD patients with anxiety, PD patients without anxiety or other psychiatric conditions, and healthy controls. A targeted fMRI activation task will be employed designed to tax medial temporal lobe networks including the amygdala. High-resolution resting- state fMRI will be used to assess connectivity between amygdala and local and whole brain cortical networks. Completing these assessments both during the on and off-dopamine medication state will further help determine whether anxiety and underlying amygdala networks are altered by fluctuations in dopamine levels. Together the proposed studies will provide key insights into the role of the amygdala, its subregions, and its networks in the experience of anxiety in PD. The results will determine whether anxiety and depression are dissociable conditions in PD subserved by distinct amygdala subregions and networks in PD. Finally, this work could additionally provide a target for the development of new therapeutic interventions for anxiety in PD.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY Non-Hodgkin lymphomas are a common cause of cancer-related death in people living with HIV (PLWH). Remarkably, despite advances in chemotherapy and antiretroviral therapy, HIV-infected individuals with Epstein- Barr virus (EBV)+ diffuse large B cell lymphoma (DLBCL) suffer from worse overall survival compared to those with EBV- DLBCL. Thus, new therapies need to focus on exploiting EBV, an untapped vulnerability. EBV is acquired orally and can cause infectious mononucleosis. While EBV infects epithelial cells and B cells, it remains latent in B cells. The presence of latent EBV in each cancer cell can be exploited for oncolytic therapies wherein EBV is forced into the lytic/replicative phase followed by genomic incorporation of a nucleoside analog to block DNA replication, resulting in death of the cancer cell. However, the success of oncolytic approaches depends on the fraction of cells in a tumor that is responsive (typically 20-50%) to lytic triggers of EBV. Therefore, understanding the mechanisms underlying lytic trigger susceptibility versus refractoriness and using such knowledge to increase the responsive fraction is key to the success of oncolytic therapies. We seek to identify core upstream mechanisms that disrupt silencing of the EBV gene BZLF1 to initiate expression of the latent-to-lytic switch protein ZEBRA regardless of the specific lytic trigger or cell background. Our studies show that KAP1, a prominent cellular corepressor, is recruited by the DNA-binding protein SZF1 (also a host protein) to epigenetically silence multiple EBV lytic genes (including BZLF1), thereby promoting the refractory state. We have also learned that several lytic triggers in a variety of latent cell backgrounds transcriptionally activate cellular TXNIP, a key trigger of the diabetes-related NLRP3 inflammasome, to activate caspase-1, resulting in KAP1 depletion (but not total loss), and thereby, derepressing BZLF1. This discovery, in cultured B cells and EBV+ lymphoma cells from transplant recipients, allows us to predict and isolate single ‘pro- lytic’ cells in which EBV is poised to enter the lytic phase. Using this novel tool to study events that are upstream of the EBV lytic switch, we will test the hypothesis that core factors drive TXNIP which decreases the stability of the epigenetic repressor KAP1 thereby increasing EBV’s susceptibility to lytic activation, regardless of the trigger. In AIM 1, we will build a unified model of the core factors p300 and MondoA that transcriptionally activate TXNIP to turn the EBV lytic cycle on in pro-lytic cells. In AIM 2, we will elucidate the mechanism and consequences of destabilizing viral genome-bound KAP1 on the lytic phase and consequently, influencing B cell transformation. Many African children with endemic Burkitt lymphoma (eBL) that commonly affects the jaw and other facial bones die each year due to limited access to the highly specialized chemotherapy needed to treat such cancers. Beyond PLWH who have EBV+DLBCL, children with eBL could also benefit from oncolytic therapies as these therapies may be more readily accessible.

NSF Awards · FY 2025 · 2025-07

In the world of high-performance computing (HPC), the growing complexity and shrinking size of hardware components make systems more vulnerable to "soft errors"— temporary glitches that can disrupt calculations. Traditionally, these issues were managed through hardware-based solutions like redundancy, but these approaches consume significant energy, a major concern for modern processors. This project addresses the challenge of making HPC systems more resilient to soft errors without the high energy costs of traditional methods. It focuses on identifying and protecting the most vulnerable parts of a program — the specific states where errors are most likely to cause problems. By doing this efficiently, the project aims to ensure that programs can continue to function correctly even when errors occur. The broader benefits of this project include advancing the field of reliable computing, promoting energy-efficient technologies, and supporting education by making cutting-edge resilience techniques accessible to software developers and classrooms. Ultimately, this work contributes to the creation of more robust and efficient computing systems that can handle the increasing demands of modern technology, benefiting industries, education, and society as a whole. This project aims to address the increasing vulnerability of HPC systems to transient hardware faults, or soft errors, which are exacerbated by larger system scales, advanced technology scaling, and lower operating voltages. Traditional hardware-only solutions such as dual modular redundancy are becoming less viable due to their high energy consumption, making it essential for future HPC applications to tolerate such faults. The project focuses on developing a compiler-directed framework that rapidly and accurately models error propagation, identifying and protecting only the most vulnerable program states to minimize performance and energy overheads. The project involves integrating static program analysis, dynamic input fuzzing, program invariants, redundancy, and compiler code transformations to create an efficient protection strategy. By automating the process of hardening programs to meet specific reliability targets, the investigator aims to advance the field of reliable computing, reducing the barriers to implementing resilience techniques in HPC systems, and contributing to the development of energy-efficient, fault-tolerant software. This project is jointly funded by the Software and Hardware Foundations Program, the Office of Advanced Cyberinfrastructure, and the Established Program to Stimulate Competitive Research (EPSCoR) Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.