University Of Florida

NSF Awards · FY 2025 · 2025-10

This project is a collaborative effort that brings together expertise in formal methods, machine learning, computer-aided design, and fabrication of in-memory computing systems. The main goal of the project is to create formal methods that can synthesize neural networks in the memory of the computer and also prove their correctness. The project pursues tasks that include the verification of neural networks accelerated using analog in-memory computing (IMC) and the synthesis of hybrid analog-digital IMC for neural networks using formal methods and machine learning. The project demonstrates these innovations using in-field fabrication of IMC systems. The effort creates new algorithms for enabling the deployment of robust AI models on emerging in-memory hardware technologies that may be more prone to errors than traditional CMOS technologies. The project would also allow the training of neural networks with reduced power consumption. This is particularly important given the larger adoption of AI and the need to train more and more powerful neural networks. The endeavor enables several other contributions to the research community, including enhancing the reliability of neural networks on in-memory circuits, increasing diversity in computer engineering and computer science, and fostering interdisciplinary collaboration across formal methods, machine learning, and hardware design. The project focuses on advancing formal methods to tackle real-world challenges encountered in emerging in-memory computing systems. By leveraging recent innovations in machine learning and formal methods, the project synthesizes crossbars for neural nets using decision diagrams, neural nets, and reinforcement learning. It verifies bidirectional digital IMC circuits before demonstrating such in-memory computing systems through fabrication. This effort expands our understanding of the capabilities and limitations of in-memory computing systems and creates innovations in fields such as in-memory computing, formal methods, and artificial intelligence. 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

Data-driven machine learning (ML) components have been deployed in multiple cyber-physical systems, from sensing and perception to planning and control. However, the reliability and safety of such ML-based applications remain the most challenging and significant concern for the industry, users, and regulators. Rigorous effort has been made to develop formal methods for ML-based application certification. Most research focuses on qualitative verification of the safety and robustness of neural networks and neural network control systems. There is a lack of methods that can quantitatively verify the temporal properties of ML-based applications, which has been a problem of keen interest for industrial companies in the automotive industry, as quantitative verification results, e.g., probability of collision, provide richer information for better decision-making and planning of autonomous systems under sensing, perception and actuating uncertainties. This project proposes to continue collaborations with industrial partners to develop a new quantitative verification approach for temporal properties of learning-enabled cyber-physical systems (Le-CPS). The project's novelties are the development of new ProbStar Temporal Logic (PSTL) for specifying complex temporal behaviors of Le-CPS and new qualitative and quantitative verification algorithms for verifying Le-CPS temporal properties. The project's impact is supporting transitioning advanced verification technologies into practice via developing a user-friendly interface and improving documentation, benchmarks, evaluation, and engagement with the broader community. The first research objective is to develop the first qualitative and quantitative verification approach for Le-CPS at the system level based on ProbStar reachability. The exact verification scheme provides the precise probability of a safety probability being satisfied, while the approximate scheme obtains the estimated lower and upper bounds of this satisfaction probability. Notably, the exact verification scheme can also construct and visualize the complete set of counterexamples. The second research objective is to develop ProbStar Temporal Logic (PSTL), a formalism enabling quantitative verification of temporal properties of Le-CPS. To construct ProbStar traces, the investigator will develop depth-first-search Prob=Star reachability algorithms for Le-CPS. Finally, the investigator team will develop a new quantitative verification algorithm for temporal properties by transforming a PSTL formula into an abstract disjunctive normal form (DNF) and realizing it on ProbStar traces. To facilitate the adoption of new verification techniques into real robotic applications, the project will develop a user-friendly interface and Robotic Operating System (ROS) integration interface, which supports ROS message collecting, generating verification and monitoring ROS nodes, and creating modeling ROS nodes. The project team will evaluate the efficiency of the new verification algorithms and tool on well-known benchmarks such as advanced emergency braking systems, learning-enabled adaptive cruise control systems, and real learning-enabled F1Tenth testbed. 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 project investigates market-driven spectrum access and management approaches that leverage artificial intelligence (AI) to enhance radio spectrum allocation, sensing, and market optimization. The work facilitates evolution from current radio spectrum management strategies to more dynamic and efficient methods, thereby increasing the overall utility and efficiency of the radio spectrum which is a key resource for all sectors of modern society. The new approaches investigated in this project rely on private sector band managers, who dynamically allocate spectrum resources while ensuring compliance and mitigating interference. Band managers must contend with strategic behavior by market participants, who for example may share incomplete or incorrect information, and must handle attacks by adversarial users. This research addresses these challenges by developing AI-driven mechanisms that balance efficiency, security, and stability. Deployment of the new market-driven approaches could significantly enhance the spectrum available to and hence the capacity of next-generation wireless communication systems and other spectrum dependent systems. This research comprises three integrated thrusts considering different aspects of a future robust AI-powered market-driven spectrum system. Thrust 1 develops new learning-based spectrum allocation mechanisms that integrate multi-armed bandits with auction strategies to optimize spectrum sharing among strategic users. Thrust 2 focuses on securing spectrum sensing by detecting adversarial manipulations using novel decision-boundary-based techniques. Thrust 3 ensures market stability through adaptive monetary policies optimized via safe reinforcement learning. By integrating these components, the project creates fundamental understanding of how to establish an AI-driven spectrum market that is resilient to both environmental and strategic uncertainties. 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

Deep learning-based perception and control are increasingly popular in recent autonomous systems. Unfortunately, deep learning is vulnerable to various changes in the visual environment, known as visual shifts, which threaten the system’s performance and safety. This project aims to ensure the safety and performance of vision-based autonomous systems subject to visual shifts, including sun glares and seasonal changes such as snow-covered terrain. We will build specialized modeling, training, and adaptation techniques to overcome visual shifts based on the core idea that visual uncertainty should be treated by balancing informativeness with conservatism. This balance can be achieved by focusing on the most surprising visual phenomena to make safe choices in unforeseen circumstances. The intellectual merit of this project includes developing theories and algorithms at the intersection of formal verification and information theory that will endow vision-based autonomous systems with high-performance behaviors and previously unavailable theoretical guarantees. The broader impacts of this project include making vision-based autonomy safer and more reliable, particularly in the automotive sector, as well as transitioning insights gained from the project to practice by collaborating with researchers in the auto industry, publicly releasing our data and code, and providing university students with hands-on experience with safe perception and control. This project will develop an end-to-end methodology that leverages information-theoretic and statistical techniques in modeling, analysis, training, control, and adaptation. At the foundation of the proposed methodology is a robust framework for probabilistic verification and control synthesis, which will provide conservative models and safety estimates under latent shifts. Building on these models is a neuro-symbolic training process that bridges the gap between visual perception, safety, and control. Finally, to protect the system from overreacting to unseen shifts, this project will develop an online adaptation framework based on quick change detection and perception/control switching. The proposed methodology will be validated on a variety of autonomous systems from different domains, including physical experiments of small-scale autonomous racing. This research is expected to provide tight complementary connections between information-theoretic learning and formal techniques for safe control. 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

Modern cloud-services rely on virtual machines due to their high efficiency and flexibility. Alongside regular services, current new offerings from cloud-service providers allow exploiting under-utilized virtual machines at a fraction of the original cost. Such computing resources, however, are highly heterogeneous and elastic. Heterogeneity means that virtual machines can have different computational speeds and storage constraints. Elasticity means that these virtual machines can be preempted under short notice (on the order of minutes) if a high-priority job appears; on the other hand, new virtual machines may be available over time to compensate for any shortage of computing resources. Such behavior can result in computational failure or significantly increase computing time. In response to the challenges of both heterogeneity and elasticity in cloud systems, this project will formulate new heterogeneous elastic computing frameworks, aiming for optimal and implementable solutions. The results of this project can lead to sizable economic benefits. The project's educational activities are designed to integrate research into teaching, and include mentoring both graduate and undergraduate students, alongside outreach programs to undergraduate and K-12 students with the goal of fostering interest in transformational computing technologies. Another highlight of this project is the newly developed YouTube channel by the investigator. Motivated by practical measurements and constraints, this project formulates new heterogeneous elastic computing frameworks with both coded and uncoded storage placements. Then, it develops novel methodologies using combinatorial and information-theoretic tools to establish fundamental tradeoffs for such systems. Using these theoretical tools, the project designs low-complexity algorithms for real applications and evaluates them on Amazon Elastic Compute Cloud (Amazon EC2) in order to show significant gains of the proposed approaches compared to the state-of-the-art solutions. The project is structured around research topics: (1) heterogeneous coded storage elastic computing; (2) heterogeneous uncoded storage elastic computing; (3) secure uncoded storage elastic computing from user’s perspective and (4) heterogeneous elastic computing: convert theory to practice. 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

The objective of this research project is to establish and operate a research network for enhancing airport resilience. It brings together experts from academia, industry, and government to develop a novel framework for analyzing airport resilience to natural hazards and operational disruptions. The network aims to quantify the resilience of airport operations, synthesize best practices for recovery, and explore the integration of emerging technologies to simulate and optimize airport responses to external shocks. Through an interdisciplinary approach, the network intends to addresses complex resilience challenges in the aviation sector, enabling data-driven decision-making processes, anticipating evolving threats, thereby advancing both the science and practice of infrastructure resilience. Airports are highly complex systems with the primary function of maintaining flight schedules while ensuring safety, sustainability, and economic viability. The research network looks to produce conceptual models informed by resilience science to help airport managers identify critical functions across interconnected sub-systems, quantify the impacts of disruptions, evaluate resilience against both known and emerging threats, and optimize resource allocation for performance improvements. This goal intends to be achieved through three key objectives: 1) evaluating how resilience indicators translate into airport operational impacts and vary across different types of disruption; 2) identifying challenges related to data access, management, privacy, security, and interoperability while exploring pathways to improve data integration; and 3) proposing new paradigms for infrastructure resilience research by applying resilience science concepts to airport operations. Using Dallas Fort Worth International Airport as testbed, the project seeks to address significant gaps in resilience measurement and evaluation and generate robust and generalizable knowledge for transportation systems. Most importantly, it looks to forge a self-sustaining, collaborative platform for expert engineers, scientists, and practitioners to share insights that will improve airport resilience strategies, inform broader efforts in standardizing aviation resilience metrics, and set the foundation for long-term competitiveness of US transportation sector. 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 project aims to serve the national interest by building an understanding of how generative artificial intelligence (GenAI) can be effectively utilized in undergraduate computer science education. GenAI technologies hold promise for providing personalized computer science learning at scale. However, the proliferation and the rapid advances of these technologies make it difficult to determine how to most effectively design GenAI-based instruction in undergraduate computer science courses. This Level I Engaged Student Learning project will use systematic review methods to explore, document, and synthesize how GenAI technologies have been used in undergraduate computer science contexts. The significance of this work includes advancing understanding of when and in what contexts GenAI facilitates student learning, enabling evidence-based decision-making regarding when and how to utilize these technologies. Project goals include (a) critically examining how faculty are designing GenAI-based pedagogy and the resulting impacts on student learning, and (b) quantifying the effects of GenAI-based instruction as compared to other methods and documenting the factors that moderate these effects. A primary contribution of this project will be the development of a novel evidence-based design framework with concrete and actionable steps for integrating GenAI in undergraduate computer science education. Outcomes of this research can help answer questions such as, to what extent generative artificial intelligence can improve learning and what specific skills or pedagogical approaches can benefit the most from these technologies. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Radiation induced lymphopenia (RIL) occurs in approximately one-half of cancer patients receiving external beam radiotherapy and this post-therapy condition has been shown to greatly reduce five-year survival rates. It is theorized that RIL is induced by direct lymphocyte cell kill, suggesting that assessment of the radiation dose to lymphocytes during radiotherapy is a necessary requirement for their consideration as a dose avoidance structure. In order to facilitate the computation of radiation dose to circulating lymphocytes, their supporting structures must be modeled in patient anatomic models used in tissue dosimetry. Currently, there exists no intra- organ blood vasculature nor lymphatic vasculature nor specialized lymphoid tissue structures within the International Commission on Radiological Protection’s (ICRP) reference computational phantoms – commonly used as a digital model of medical patients. Additionally, there exists major inaccuracies in the inter-organ whole- body blood vessels of these reference phantoms, and no modeling of microscale vasculature of blood or lymph has been yet undertaken to account for their large portion blood and lymph within the human body. Additionally, in applications of radiopharmaceutical therapy, blood self-dose, which is substantial for short-range radiations such as alpha particles and lower-energy electrons, emitted by many common radiopharmaceuticals, cannot be entirely quantified due to the lack of intra-organ vasculature in the ICRP phantoms. Dose to lymphatic vessels, additionally can be computed for the first time in RPT applications with these models in place. Therefore, this project hypothesizes that the development of a comprehensive model of blood and lymphatic vessels and corresponding lymphoid tissues will facilitate the computation of blood and lymphocyte radiation dose for the first time and will subsequently allow for consideration of circulating and non-circulating lymphocytes as organs-at- risk in radiotherapy treatment planning. The proposed project will be achieved by the completion of four Specific Aims: Aim 1: develop whole-body adult mesh models with intra-organ and inter-organ vasculature. Aim 2: develop corresponding lymphatic tissue and vessel models. Aim 3: develop organ specific microscale blood and lymph vasculature models. Aim 4: compute organ radionuclide S values for radiopharmaceutical therapy and lymphocyte dosimetry for external beam radiotherapy. Completion of these aims will greatly improve the computational models used as tools in reference medical dosimetry for a new assessment of dose to blood, as well as set the stage for adaptation of these models via deformable registration to expand to patient-specific modeling and personalized dosimetry.

NIH Research Projects · FY 2025 · 2025-09

Project Summary This supplement will enable the continued progress of the parent project to develop an AAV gene therapy product for the treatment of mucopolysaccharidosis type IIIB, a disease for which there is no current treatment. Specifically, this supplement performs the process and analytical development to optimize production methods and release criteria for tcm8AAV-coNAGLU as a drug substance prior to cGMP manufacture of the drug product. We will transition production to a suspension platform in order to scale up production volume consistent with the needs of a commercial product. This portion of the aims of the parent project were originally to be performed by the NIH BPN biologics contract development and manufacturing organization (CDMO) during the first and second year of the project, however, changes at NIH have prevented contracting with a CDMO to begin this process. We have the capacity to perform non-GMP virus manufacturing runs to optimize production methods and release criteria; develop master and working cell banks; develop reagent lists and protocols for production; and establish process and analytical methods development supporting characterization of tcm8AAV-coNAGLU (+/-CpG dep) drug substance. We did not have this capacity when the parent grant was submitted and did not budget for this or propose to do these functions at our institution but to have them done by NIH through the CDMO in accordance with the aims of the grant. In order to reach the milestones of the parent project, the process and analytical methods for scale up of tcm8AAVco-NAGLU production must be completed by year 3 in order to allow GMP grade manufacture during year 3 of sufficient quantity of drug substance for GLP toxicology starting in year 3. In cooperation and agreement with the lead development team participants from NIH and the NIH consultants, a statement of work for the planned CDMO contract was assembled. The University of Florida Powell Gene Therapy Center has created a Process and Analytical Development Group and hired scientists with industry experience in commercial AAV vector production to allow us to perform the project specific Process and Analytical Development at UF and at considerably less cost than is anticipated from CDMO bids. Specific aim 1: Perform non-GMP virus manufacturing runs to optimize production methods and release criteria; develop master and working cell banks; Develop reagent lists and protocols for production. Specific Aim 2: Establish process and analytical methods development supporting characterization of tcmAAV8-coNAGLU/CpGdepcoNAGLU drug substance (DS) and drug product (DP).

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT College student heavy drinking is a longstanding national public health concern and a top priority of the NIAAA. Nearly 1 in 3 college students engage in heavy drinking (4+/5+ drinks for women/men) and about 1 in 8 students engage in high-intensity drinking (8+/10+ drinks for women/men). Heavy drinking is associated with experiencing alcohol-related problems such as drinking and driving, sexual assault, injury, and even death. One critical predictor of heavy drinking is impaired control over alcohol, which consists of drinking for longer or consuming more drinks than one had planned, with a specific focus on violations of self-prescribed drinking limits. Impaired control is a critical prospective predictor of alcohol-related problems, a consistent cross- sectional predictor of heavy drinking, and an early indicator of alcohol use disorder later in life. The event-level nature of this construct suggests impaired control may be a novel target for momentary interventions to reduce heavy drinking among college students. However, few studies have assessed impaired control at the event- level, and no studies have explicitly examined violations of self-prescribed drinking limits (i.e., self-reported limits relative to actual number of drinks consumed or time spent drinking). Further, no prior studies have examined the event-level impacts of impaired control on acute alcohol outcomes (i.e., heavy drinking and alcohol-related problems), or the situational factors that could be targeted via intervention to reduce both impaired control and its potentially harmful effects. In this F31, the applicant, together with an excellent training team, will 1) assess direct relations between impaired control (i.e., whether and by how much one violates limits set before drinking on a given day) and alcohol outcomes (i.e., drink quantity; alcohol-related problems), controlling for between-person differences (e.g., baseline drinking) and 2) test situational (e.g., perceived behavioral control; drinking context) moderators of the relationships between impaired control and alcohol outcomes. The proposed study involves primary data collection of drinking behaviors and related constructs (i.e., perceived behavioral control, drinking context) using a 28-day ecological momentary assessment protocol. Results from the proposed study will enhance our understanding of event-level associations between impaired control over alcohol and alcohol outcomes and inform the development, delivery, and implementation of brief and momentary behavioral interventions targeting heavy drinking among college students. In addition to the study’s potential impact on the field and public health, the proposed project and training plan will prepare the applicant (Ms. Kalina) for a competitive post-doc and independent career as a scientist at a research-intensive university.

NIH Research Projects · FY 2025 · 2025-09

Menopause is a period experienced by women between the ages of 45-60. As the population of women with HIV (WWH) ages, it's crucial to understand the relationship between HIV and menopause. Compared to women without HIV (WWOH), WWH often experience higher rates of behavioral health issues, including substance use and mental health distress, which may affect menopause onset and symptoms. Hormone replacement therapy (HRT) is a common treatment for menopause, but research on the interaction with mental health effects is mixed. Some studies report a reduction in mental health disorders, such as depression, with HRT use, while others indicate an increased risk of depression. HRT is a behavioral intervention, requiring initiation and adherence, yet little research has explored how behavioral health (i.e., substance use, mental health, and HRT) influences menopausal symptoms in both WWH and WWOH. Using syndemic theory, this study aims to identify syndemic phenotypes of behavioral health issues and HRT use in WWH and WWOH aged 35-70, from premenopause to perimenopause, using latent class analysis. We will examine how these behavioral health syndemics relate to common perimenopausal symptoms, stratified by HRT use and HIV status. To achieve the goals of this study, we will utilize the Women’s Interagency HIV Study (WIHS, N= 4,000) to analyze syndemic changes and symptom profiles. Additionally, we will conduct qualitative interviews with WWH who are experiencing menopause to understand the impact of behavioral health syndemics during this hormonal shift in real time (N=25). This Kirschstein NRSA Predoctoral Fellowship (PA-23-272) proposal seeks to conduct a multiphase sequential explanatory mixed-methods study to understand psychosocial changes during menopause, the role of HIV, and the syndemic impact on symptoms. This proposal responds to the NIH Office of Research on Women's Health (ORWH) Notice of Special Interest (NOT-OD-24-079) and the National Institutes of Aging (NIA) interest in psychosocial changes during and associated with menopause . A benefit of this award is training to establish the foundation of a successful career in epidemiologic research and intervention science such as 1) learning strengths, limitations, and implications of using longitudinal public health data to measure substance use and health outcomes; (2) understand the rationale for using different statistical methods to answer longitudinal research questions with public health data; and (3) leading collection and analysis of qualitative data as part of a mixed-methods study, in addition to developing skills such as manuscript production and giving presentations.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT A well-studied model of respiratory motor plasticity is phrenic long-term facilitation (pLTF), a prolonged increase in phrenic motor output following moderate acute intermittent hypoxia (mAIH). Over 2 decades, we developed a nuanced understanding of intra-cellular mechanisms giving rise to pLTF, and factors regulating its expression in the rest/light phase. With mAIH, serotonin-driven pLTF is constrained by modest adenosine 2A (A2A) receptor activation. When extracellular adenosine levels are high, greater A2A receptor activation initiates an alternate adenosine-dependent mechanism of plasticity. Although the serotonin driving plasticity arises from raphe serotonergic neurons, the cellular source of adenosine was unknown. Thus, in the first 4 years of this grant, we asked the question: what is the source of hypoxia-evoked adenosine? We developed an inter-cellular model whereby phrenic motor neuron to microglia signaling evokes spinal adenosine formation and regulates pLTF. During hypoxia, phrenic motor neurons release Fractalkine (Fkn) and activate receptors on the lone CNS cell- type expressing Fkn receptors—microglia. Microglia respond by converting extracellular ATP to adenosine, restraining serotonin-driven pLTF. These experiments were performed during the daily rest/light phase; our discovery that mAIH delivered in the active (vs rest) phase profoundly impacts the mechanism driving pLTF due to diurnal shifts in basal adenosine levels lead us to realize we must update our model to account for rest/active phase differences. Two fundamental hypotheses guide this proposal: 1) shifts in microglial state across the rest/active cycle alter phrenic motor neuron/microglia interactions and, thus, mAIH-induced pLTF; and 2) microglial (and pLTF) responses to pro-inflammatory stimuli reverse in the active/dark phase. Using a multi-disciplinary approach, we will interrogate a revised inter-cellular model by testing 5 hypotheses: 1) Phrenic motor neuron-microglia fractalkine signaling inhibits pLTF less in the active/dark phase; even though 2) Fkn receptor activation evokes greater active phase spinal adenosine formation; 3) Microglial expression of key molecules for adenosine formation increases in the active (dark) phase; 4) Unlike the rest/light phase, mild systemic inflammation has minimal impact, or even enhances active phase pLTF; and 5) Due to differences in microglial reactivity, ventilatory LTF exhibits sexual dimorphism during the rest (not active) phase. We know little concerning the impact of “biological clocks” in any aspect of ventilatory control, let alone respiratory plasticity. Thus, we will expand on our discovery that time-of-day has powerful effects on AIH-induced pLTF by considering the complex rest/active phase effects on phrenic motor neuron/microglia interactions. Regardless of outcome, these studies will greatly advance our understanding of diurnal rhythms in respiratory plasticity and accelerate ongoing efforts to develop AIH as a therapeutic modality to treat devastating human clinical disorders that compromise breathing, threatening life itself. Greater understanding of diurnal rhythms is sorely needed since nocturnal rodents and diurnal humans are typically studied in opposite phases of the rest/active cycle.

NSF Awards · FY 2025 · 2025-09

This award supports fundamental research into mathematics that will enable a better understanding of the connection between neural rhythms and cognitive function. The research represents a necessary first step in the development of new treatment options for brain disorders caused by abnormal brain rhythms including schizophrenia, autism spectrum disorder, attention deficit hyperactivity disorder, and Parkinson's disease, promoting science and advancing national prosperity. Results of the project will contribute to the understanding of the biophysical mechanisms that support short-term memory, enabling a better understanding of how patients with disorders such as Alzheimer’s disease lose their ability to remember where they are. The award will support graduate students and researchers at the interface of neuroscience, mathematics, and engineering. The research plan includes two outreach efforts to encourage high school students to pursue careers in engineering and mathematics: 1) Week-long, hands-on research experiences with activities coordinated by the University of Tennessee, and 2) National mathematical modeling contests for regional high school and undergraduate students at the University of Florida. This project will advance the mathematical understanding of emergent behaviors in populations of coupled neural oscillators. Due to the size and dynamical complexity of most neural systems, model order reduction is often an imperative first step for mathematical analysis. However, the underlying assumptions required for the implementation of current approaches (e.g., weak coupling, symmetry, repetitive firing with a steady frequency) result in idealized phenomenological models that often give an incomplete and/or incorrect picture of the mechanisms governing the aggregate behavior. This fundamental limitation leads to gaps in our general understanding of how individual neurons organize to produce brain rhythms that ultimately support essential cognitive functions. The project aims to fill this gap in knowledge, significantly advancing coupled oscillator theory in neuroscientific contexts to accommodate strongly coupled neural networks, neural bursters, systems with nonnegligible heterogeneity, and higher order N-body interactions. Successful completion of this project will yield comprehensive, interpretable, mechanistic, and tractable techniques for the analysis of coupled oscillators, facilitating a deeper understanding of the mechanisms governing synchronization, rhythmic activity, and information processing in the brain. These techniques will be integrated with archival microelectrode data from rat entorhinal cortex to investigate the dynamical mechanisms that govern theta phase precession, a phenomenon that allows an animal to keep track of its precise location in space. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

An award is made to the University of Florida (UF) to install a helium liquefaction system in the Advanced Magnetic Resonance Imaging and Spectroscopy (AMRIS) Facility. AMRIS supports an internationally recognized user program as part of the NSF-funded National High Magnetic Field Laboratory with state-of-the-art, high-homogeneity superconducting magnets that rely on a stable, uninterrupted supply of liquid helium. AMRIS annually supports the scientific research programs of ~400 users, particularly scientists in the areas of developmental biology, physiology, chemical biology, biomaterials characterization, gas separations, and structural biochemistry. AMRIS also supports human resource development in instrumentation science by training students and postdoctoral researchers in radiofrequency, cryogenic, and superconducting technologies, areas that are of critical importance to maintaining and enhancing the nation's competitive edge in science and technology. The new liquefaction system will improve the ability to preserve helium, a critical, limited resource, and ensure a reliable supply of liquid helium to support the operation of superconducting magnets within AMRIS. Magnetic Resonance Imaging (MRI), In Vivo Spectroscopy (MRS), and Nuclear Magnetic Resonance Spectroscopy (NMR) are widely used analytical tools in Biology, Chemistry, and Materials Research that rely on superconducting magnets to study structure, dynamics, mechanisms, physical processes, and biological function over many length and time scales. MRI can uniquely characterize tissue structures in living organisms noninvasively. MRS can distinguish and localize individual metabolites and measure metabolic flux to elaborate chemically rich maps of organisms as they develop and differentiate. NMR is a fundamental tool for determining chemical structures, quantifying the relative abundance of different molecular species, monitoring molecular conversions, characterizing protein structures and dynamics, and measuring molecular level transport processes in complex materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

An award is made to the University of North Carolina at Chapel Hill, the University of Florida, and Smith College, to develop a large-scale network to study the abundance and seasonality of moths across Eastern USA. This project will combine counts of caterpillars (juvenile moths), generated by the citizen-science project Caterpillar Count!, with counts of adult moths that will be captured by automated, non-lethal traps. Moths and caterpillars are one of the most important insect groups because they eat plants such as crops and forest trees and also serve as food for wildlife. This work will generate resources necessary to share seasonal abundance data with other scientists and the general public. Completion of this project will contribute to an effective contemporary workforce by involving students and creating educational materials. This project will also contribute to elevating scientific literacy in the general public by recruiting citizen-scientists and by organizing moth observation events during National Moth Week. Moth Monitoring 2.0 will be the first large-scale monitoring network designed to integrate abundance and phenology data across both larval and adult life stages. This network will generate data through standardized sampling protocols that will be instrumental for understanding broad-scale abundance patterns of an ecologically important insect group across a large region. This project will result in the development of new hardware and software solutions for automated monitoring, machine-learning based identification, data submission, storage, and visualization, and will create a new repository for the sharing of biological material. The work will expand the existing network of Caterpillars Count! monitoring sites, and add new functionality for monitoring by the general public. All of these data will be freely available to researchers seeking to address questions about how disturbances, including land use and other global change drivers, are impacting insect declines and ecosystem health. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY A family history (FH) of alcohol use disorder (AUD) increases one’s personal risk of AUD by 3-4 fold. Given the large proportion of the burden of AUD endured by these individuals and their families, the development of impactful prevention and treatment strategies should consider focused strategies for those individuals. A compelling approach to accomplish this goal is to augment brain resilience mechanisms to compensate for underlying vulnerabilities, which would have greatest efficacy in those at highest risk for poor outcomes. With this objective in mind, we previously identified candidate neurocognitive mechanisms of resilience to familial risk for AUD by comparing FH-positive young adults with versus those without AUD relative to controls. Using this resilience-centered framework, a well-powered, data-driven analysis identified self-reported attentional ability as the factor that most robustly predicted AUD resilience. We validated this finding in a large, independent sample, demonstrating that at-risk young adults with FH that do not binge drink report heightened attentional ability, not only relative to those that binge drink, but even relative to low-risk, non-binge-drinking young adults, supporting the notion that attentional processes support resilient outcomes to AUD and binge drinking. We now seek to advance these cross-sectional, self-report associations in a rigorous, systematic investigation to identify targetable cognitive processes and brain regions that promote AUD-related resilience. Currently, the mechanisms linking attentional ability to AUD resilience remain undetermined. The proposed studies seek to establish how attentional ability protects against the development of alcohol use disorder in at-risk individuals by combining both observational and experimental methods. In Aim 1, we will examine the role of facets of attention measured with fMRI, EEG, and behavior on alcohol use trajectories in 200 emerging adults, including those at risk (FH-positive) and those not at risk (FH-negative) for AUD. The neural correlates of the relationship between attentional ability and future alcohol use will be examined with computational analyses of fMRI and EEG data acquired during attention tasks. In Aim 2, we will test whether fMRI and EEG markers of attention attenuate neural responses to pictorial alcohol cues as a mechanism of AUD resilience. In these two longitudinal aims, we predict that greater “top-down” neurobehavioral indicators of attentional ability (Aim 1) will promote reduced “bottom up” cue reactivity (Aim 2), resulting in shallower alcohol use trajectories in FH-positive (vs. FH-negative) individuals. Aim 3 will include a subsample of 50 “non-resilient” subjects reporting problematic alcohol use. We will test causal relationships by examining effects of boosting attentional ability with methylphenidate on neural cue reactivity (as a proxy for addiction) in a placebo-controlled, within-subjects study. Successful completion of this project will substantially increase our understanding of the neuroscience of resilience related to addiction. This knowledge will support the development of treatment and preventive strategies that could significantly mitigate alcohol-related problems in the millions of individuals in the US with familial risk.

NSF Awards · FY 2025 · 2025-09

This I-Corps project is based on the development of a software system to enable intelligent and autonomous robots. Currently, industries such as construction, shipbuilding, aerospace, and energy often face inefficiencies, costly rework, and skilled labor shortages resulting in billions of dollars in annual losses and project delays. This technology introduces a mobile robotic platform equipped with laser-based augmented reality and artificial intelligence (AI)-driven task planning that may be used to transform digital blueprints into actionable on-site guidance. Workers may be able to follow millimeter-accurate projections rendered directly onto physical surfaces, reducing errors and eliminating the need for manual plan interpretation. The solution has the potential to provide substantial labor savings, faster project delivery, and improved safety compliance. Commercially, the system provides value across a range of industries by automating critical workflows and enabling less experienced workers with the ability to perform complex tasks with confidence. This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of a robotic AI agent designed to support precision layout, task execution, and in-situ inspection in dynamic, unstructured field environments. The system integrates four core innovations: a laser-ink spatial augmented reality (AR) module for millimeter-accurate, building information model (BIM)-aligned task and work-plan projection; a mobile robotic platform equipped with simultaneous localization and mapping (SLAM)-based autonomous navigation and adaptive projection control; a transformer-based Vision–Language–Action (VLA) model capable of grounding natural language commands, multimodal perception, and structured BIM data into context-aware action policies; and a closed-loop digital twin framework that enables real-time synchronization between physical execution and virtual design. The VLA model is trained on domain-specific multimodal datasets, enabling it to semantically interpret and execute construction-relevant tasks beyond the capabilities of general-purpose large language models. The goal is to enhance task accuracy, reduce execution errors, and embed continuous quality assurance and quality control into field operations. This technology has the potential to provide savings on labor, faster project delivery, and improved safety compliance in a wide range of industries including construction, shipbuilding, aerospace, and energy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Project Summary The overall objective of this project is to evaluate the potential of magnetic resonance (MR) measures of bioenergetics and microvascular function to track disease progression and treatment in dystrophic muscle. Duchenne muscular dystrophy (DMD) is characterized by progressive muscle weakness, fatigue, deteriorating functional capabilities, loss of independence, and early death. Muscles in individuals with DMD are deficient in dystrophin, which is accompanied by a lack of sarcolemma-localized neuronal nitric oxide synthase mu (nNOSμ). Gene therapies aimed at delivering micro-dystrophin genes are emerging as viable therapeutic options in DMD; however, a number of questions remain. Currently, the significance of the nNOSμ domain of micro-dystrophin is not clear. It has been proposed that nNOSμ plays an important role in preventing fatigue and maintaining blood flow during and following activity. We have previously shown that muscle energetic status is altered, mitochondria function compromised, and microvascular responses are impaired in DMD compared to unaffected controls using 31phosphorus-magnetic resonance spectroscopy (31P-MRS) and magnetic resonance imaging (MRI). In this project, we propose to evaluate the effects of current and emerging approaches of gene delivery of micro-dystrophin with and without the nNOSμ domain when combined with exercise in dystrophic mouse models using ultrahigh field MR (Aim 1). Furthermore, we will determine the responsiveness of energetic and microvascular function measures to disease progression directly in individuals with DMD by performing a natural history longitudinal study over three years; this aim will also include a subgroup who are treated with Elevidys, a recently FDA-approved gene therapy for DMD (Aim 2). In addition, to evaluate the role of nNOSμ on muscle energetic status, in vivo oxidative capacity, and microvascular function, we will compare participants with Becker muscular dystrophy (BMD) who have dystrophin mutations with or without the nNOSμ domain (Aim 3). Collectively, we hypothesize that MR measures of bioenergetics and microvascular function will be responsive to disease progression and will be effective in monitoring improvements with treatments targeting restoring dystrophin with sarcolemma localized nNOSμ in dystrophic muscle. We anticipate that our findings will lead to the validation of noninvasive quantitative techniques that can be implemented to evaluate potential interventions targeted at improving mitochondria and vascular function in muscular dystrophies and other neuromuscular diseases.

NIH Research Projects · FY 2025 · 2025-09

Modified Project Summary/Abstract Section In this application for a Mid-Career Investigator Award (K24), the candidate, Dr. Ali Zarrinpar, presents a 5-year plan that seeks to support and protect the time devoted to patient-oriented research (POR) and to serving as a research mentor for junior clinical investigators pursuing POR research, such as clinical residents and junior clinical faculty. It will further allow him to continue his career development, conduct and mentor research in transplant immunobiology and immunometabolism. Consistent with the goal of supporting Dr. Zarrinpar’s career trajectory, the research aims of the project proposed will supplement his current work and allow him to extend the impact of his research program in the areas of immunosuppression and immunometabolism. The research goal of the proposal is to integrate methodology learned from model systems with human samples to learn how to use metabolic modulation to affect the immune response to ischemia-reperfusion injury, a major source of graft injury during organ transplantation. The mentoring goal focuses on continued training of undergraduate students, medical students, graduate students in biochemistry and molecular biology and biomedical engineering, dual-degree (MD/PhD) students, surgery residents, postdoctoral fellows, and early-stage faculty to conduct POR in transplant immunobiology and immunometabolism. Dr. Zarrinpar will provide training in POR methods, data analysis, data interpretation, manuscript preparation, grantsmanship, research ethics, and professional development. All of this will take place within the context of Dr. Zarrinpar’s research laboratory, which includes weekly lab meetings and one-on-one sessions with trainees. In addition, Dr. Zarrinpar provides lectures and seminars through multiple training programs at the University of Florida. His own training goal consists of coursework and training experiences in areas that are important to his career development as an investigator and mentor, particularly in the topics of immunobiology, quantitative methods, and leadership skills. These areas of training and professional development have been selected in synergy with the research aims and ongoing funded-projects in his laboratory with the overarching goal of broadening the impact of his research program and those of his mentees. In summary, the support of the Mid-Career Investigator Award (K24) will allow Dr. Zarrinpar to devote his efforts to patient-oriented research and research mentoring in transplant immunobiology and immunometabolism.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT In the past decade, there has been an explosion in availability of real-world data (RWD). With RWD’s vast potential, there is a need for scientists with the training to lead multidisciplinary teams to generate insights from such data and translate them into population health improvements. In August 2022, I was awarded my first R01, which seeks to determine the impact of sleep apnea treatment on the risk for cognitive decline and Alzheimer’s disease and related dementias (ADRD). While I have been fortunate to achieve “research independence,” I need leadership training to catapult into a mid-career scientist with a broader NIH-funded research program. The purpose of this proposed K02 award is to support my transition into a multiple R01-funded researcher and establish leadership in using RWD to advance sleep health and Alzheimer’s disease prevention both locally at the University of Florida (UF) and nationally. In pursuit of this goal, I have designed a 5-year comprehensive leadership development program to provide the needed training and resources to develop key skills needed for the next stage of my career: a) obtaining a better understanding of RWD methodologies, b) gaining leadership skills for managing interdisciplinary research teams and collaborations, and c) establishing a Sleep Research Collaboratory at UF focused on sleep, Alzheimer’s disease, and RWD. The planned leadership training is specifically designed to capitalize on the extensive RWD resources available to us at the University of Florida, including an extensive database of both public and private healthcare claims, and notably OneFlorida+, a large PCORI-funded research network that includes a data trust of electronic health records from almost 19 million patients in the Southeast. Leveraging resources and collaborations at UF, I will conduct research to generate preliminary data supporting two R01 submissions to understand the impact of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) on cognitive outcomes in patients with sleep apnea. Recent research has indicated GLP- 1 RAs as a promising treatment for sleep apnea, making it crucial to explore their effects on cognition. The first proposal will be a competing renewal of my R01 focusing on understanding if treating OSA with GLP-1 RAs can slow cognitive decline and, ultimately, ADRD. A second new R01 will propose to conduct an emulated clinical trial in OneFlorida+ to investigate the impact of GLP-1 RAs on the treatment of OSA and cascading cognitive outcomes. The leadership training I propose, alongside the collaborations I will build for these R01 submissions, will directly contribute to developing a research community at UF focused on sleep research and ADRD using RWD. The leadership activities proposed herein will provide me with an excellent opportunity to transition my current R01 into a larger NIH-funded program that leverages UF’s extensive RWD resources. Ultimately, this training will equip me with the skills to lead multidisciplinary teams, develop strategic leadership abilities, and establish a nationally recognized research program in sleep and Alzheimer’s disease using RWD.

NIH Research Projects · FY 2025 · 2025-09

Sepsis is a life-threatening polymicrobial infection that affects approximately 49 million individuals worldwide each year and accounts for nearly 20% of global mortality. Among survivors, up to 75% develop a persistent myopathy characterized by muscle wasting, weakness, and impaired regenerative capacity, for which no effective therapies currently exist. Prolonged immobilization during critical illness further exacerbates muscle loss and contributes to long-term frailty. Despite its clinical relevance, the mechanistic interplay between sepsis, disuse, and redox-dependent muscle dysfunction remains poorly defined, limiting the development of targeted interventions. Excessive production of reactive oxygen species (ROS) is a hallmark of sepsis-induced muscle pathology. NADPH oxidases (NOX) are major enzymatic sources of ROS in skeletal muscle, with NOX2 and NOX4 being the predominant isoforms upregulated during sepsis. Our preliminary data demonstrate that NOX activation drives oxidative stress, myofiber atrophy, and contractile dysfunction, and that combined inhibition of NOX2 and NOX4 preserves muscle strength and protein synthesis in septic mice. These findings implicate NOX-derived ROS as a causal mediator of sepsis-induced myopathy. The overall goal of this project is to define the specific contribution of myofiber-derived NOX2 and NOX4 signaling to muscle weakness, wasting, and impaired regeneration during sepsis complicated by disuse. To achieve this, we will use a novel myoAAV-mediated, myofiber-specific deletion strategy that allows efficient and temporally controlled targeting of Nox2 and Nox4 without the need for inducible transgenic breeding. In Aim 1, we will determine whether myofiber-specific deletion of Nox2, Nox4, or both isoforms mitigates sepsis-induced muscle weakness and atrophy during hindlimb disuse by preserving contractile function, promoting protein synthesis, reducing proteolysis and inflammatory infiltration, and normalizing redox-sensitive proteomic signatures. In Aim 2, we will test whether myofiber NOX-derived ROS contribute to satellite cell dysfunction in sepsis by assessing satellite cell abundance, proliferative and myogenic capacity, and transcriptional and epigenetic programs governing regenerative potential. The proposed studies will establish a direct mechanistic link between myofiber-specific NOX signaling, redox imbalance, and impaired muscle regeneration during sepsis and disuse. By integrating a clinically relevant model of sepsis-associated immobilization with cell-type–specific genetic manipulation, this work will identify tractable redox targets for preserving skeletal muscle mass and function in septic patients and advancing therapeutic strategies to improve long-term recovery after critical illness.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT A high percentage of children do not eat or drink at sufficient levels, causing substantial health consequences, as well as psychological and financial stress for caregivers. Pediatric Feeding Disorders or Avoidant/Restrictive Food Intake Disorder (ARFID) are especially common in children diagnosed with intellectual and developmental disabilities and results from complications anywhere in the complex chain of reflexive and learned behaviors. Children with a severe feeding difficulty (SFD) can learn to associate feeding with pain and discomfort, leading to intense refusal behavior that leaves caregivers with the inability to feed their children. When children learn that engaging in refusal behavior successfully avoids meals, refusal behavior can persist even after amelioration of underlying medical conditions. If refusal persists, a two-phase behavior-based method (1) assesses conditions maintaining refusal behavior for individual children and (2) bases treatment of refusal behavior on the assessment through teaching developmentally appropriate feeding skills both to children and caregivers. Though highly effective, most evidence supporting behavioral treatments for SFD comes from a large number of published studies using single-case experimental designs. This approach is useful for conducting individualized treatments, but their format impedes evaluation of the most effective methods and generality of outcomes, and limits dissemination across scientific communities, caregivers, insurance companies, and healthcare providers. Limited dissemination among primary health-care providers is particularly problematic because pediatricians are typically the first contact for child-feeding difficulties, which can result in alternate treatment recommendations that have little empirical support. Moreover, most insurance companies require prior authorization on a case-by- case basis for behavioral treatment of SFD, often resulting in delayed or denied coverage for a myriad of reasons (payor policies not matching evidence-based practice), leading to invasive interventions (g-tube) and deleterious outcomes for developing children. Therefore, the present research proposes to synthesize and disseminate the ample evidence for behavioral treatments for SFD with aims to (1) systematically identify and meta-analyze the range of independent and dependent variables examined from the diversity of methods used, (2) explore the degree to which demographic variables, number/types of medical diagnoses, and dependence on supplemental feeding moderate treatment effectiveness, and (3) explore approaches to disseminating treatment effectiveness by surveying and receiving feedback from key stakeholder groups (caregivers, health-care providers, insurance companies). Conducting a meta-analysis of these treatments for SFD is a critical step (1) preceding evaluation through a randomized-controlled trial, (2) toward recognizing it as a medically necessary, highly effective evidence-based medical approach, (3) that needs to be disseminated across scientific communities, caregivers, insurance companies, and healthcare providers. Such dissemination would support greater insurance coverage, increased access to care, and improved health and quality of life of children and their families.

NIH Research Projects · FY 2026 · 2025-09

PROJECT SUMMARY / ABSTRACT Epidemiological projections indicate a substantial rise in Alzheimer's disease (AD) cases, reaching an estimated 152 million by 2050. Among the multifaceted factors influencing AD progression, chronic alcohol consumption is implicated as a modifiable risk factor for AD. My long-term goal is to uncover mechanisms that contribute to the risk of developing early progression of AD upon alcohol consumption and develop therapeutic interventions to mitigate this risk. While recent studies indicate metabolic disorders and accelerated aging in individuals with Alcohol Use Disorder (AUD), energy metabolism emerged as a significantly altered pathway triggering early AD pathology. This project employs an interdisciplinary approach to understand the impact of alcohol on the brain, with a specific focus on elucidating the complex relationship between metabolic and epigenetic function. The overall objective of this study is to comprehend how these factors collectively contribute to the progression of AD following ethanol exposure. In Specific Aim 1, I will assess the influence of chronic ethanol exposure on metabolic function and tau pathology in the hippocampus using an AD model (rTg4510) expressing the mutant human tau MAPTP301L. Specific Aim 2 involves applying cutting-edge techniques, such as single nuclei multi-omics, to identify cell-type-specific epigenomic and transcriptomic signatures associated with alcohol-induced early progression of tau pathology. The focus of Specific Aim 3 is on creating a novel mouse model (rTg4510-ALDH2*2) expressing both human genetic mutations, ALDH2E504K and MAPTP301L to assess compromised metabolism and its impact on early AD progression. Additionally, we will investigate the potential rescue from ethanol-induced tauopathy and cognitive deficits using a pharmacological ALDH2 activator (Alda-1). The innovative findings from this study are expected to reveal the molecular signatures governing the detrimental effects of ethanol metabolism and their involvement in the progression or early development of AD. This, in turn, will lay the foundation for novel therapeutic approaches aimed at mitigating the risk of developing AD due to alcohol consumption. This project provides training in cutting-edge research skills, including single-nuclei multi-omics, and tailors toward understanding cell-type specific tau pathology. The University of Iowa is home to experts on neuropathology, Alzheimer’s disease, computational psychiatry, and molecular biology and offers an ideal collaborative environment for obtaining the necessary skills to transition into a successful independent research career. During the mentored (K99) phase, I will engage in activities designed to prepare for independence, including training in scientific presentations, laboratory management skills, grant writing, scientific peer-review, and interview preparation. This award will collectively equip me with cutting-edge skills and expertise in metabolic phenotyping, molecular biology, and tauopathy, ensuring a strong technical and conceptual foundation for starting my independent laboratory investigating mechanisms for the treatment of alcohol misuse and Alzheimer’s disease.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Patients with chronic limb threatening ischemia (CLTI; aka critical limb ischemia, CLI) who undergo revascularization face high rates of readmissions and complications. Prior work has attempted to better understand the drivers of these poor outcomes, with several factors reliably replicated across studies (e.g., various demographics, comorbidities, procedure type, etc.). Despite this, and methodological strengths of these studies such as large sample sizes, a substantial proportion of variance remains unaccounted for, with these predictive models resulting in Area Under the Receiver Operating Characteristic Curves (AUCs) in the .60s. A likely contributor is the data sources used in these studies, which to date have been largely limited to use of state or nationwide registries and/or structured Electronic Health Record (EHR) data. However, one in three post- surgical complications occur after hospital discharge—a time period in which data are largely unaccounted for in the aforementioned sources. Further, physicians who treat CLTI have indicated that there are likely several other factors associated with poor outcomes that are not reliably available in registry or structured EHR data fields, such as caregiver quality and home environment. This project will close these gaps by supplementing EHR and clinical notes data with prospectively collected data from CLTI patients and caregivers after revascularization. Specifically, data sources will include: validated questionnaires from patients, an Artificial Intelligence (AI)-enabled mobile health (mHealth) app using an Ecological Momentary Assessment method to collect daily surveillance data and wound photos from discharge to the first surgical follow-up (14 days post- discharge), and Natural Language Processing (NLP) tools specific to this population to examine clinical notes. These will be merged with data from a large real-world data (RWD) resource, the OneFlorida+ (~19 million patients) Patient-Centered Clinical Research Network (PCORnet) clinical research network. Together, this will yield more comprehensive predictive modeling of readmissions and complications in CLTI revascularization using machine learning (ML) methods, to provide a more complete picture of the factors that contribute to these poor outcomes. Finally, we will begin an implementation mapping approach by working with patients, caregivers, physicians and clinical staff who treat CLTI patients to determine predictors that are most modifiable. Aims include: (1) Leverage a large retrospective cohort to develop NLP tools and determine initial significant predictors of poor outcomes in CLTI revascularization from RWD; (2) Prospectively collect patient-reported and post- discharge data (via an AI-enabled mHealth app) from a cohort of CLTI revascularization patients, and use ML to model these factors with RWD and NLP to predict readmissions and complications; (3) Identify the most feasibly modifiable determinants based on stakeholders’ perspectives to inform future work on intervention development, adaptation, and implementation. This work will inform interventions to improve CLTI outcomes and provide a path to better data modeling and post-discharge support for other clinical conditions.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY / ABSTRACT Selenium is a trace element co-translationally incorporated as the 21st amino acid selenocysteine into selenoproteins. Due to the unique properties of selenium, many selenoproteins have been implicated in maintaining the reduction-oxidation homeostasis. The glutathione-dependent peroxidase GPX4 reduces lipid peroxide into non-toxic lipid alcohol. It provides one of the primary cellular mechanisms to prevent ferroptosis, a regulated cell death caused by the excessive accumulation of oxidatively damaged lipids. Using synthetic lethal screens, we and others have recently discovered the low-density lipoprotein receptor-related protein LRP8 impacts cell sensitivity to ferroptosis by maintaining selenium levels. The decrease in cellular selenium leads to ribosome collision, a premature termination of selenoprotein translation, and systematic changes in transcriptome and proteome, indicating the importance of this essential micronutrition in maintaining cell functions and viability. Yet, how selenium metabolism is regulated and how cells deal with low selenium remain incompletely understood. This is mainly due to a lack of tools to consistently and efficiently deplete cellular selenium, as removing selenium from cell culture supplements is complicated and expensive. To overcome this critical gap in knowledge, we create a low selenium cell system with a 60% decrease in total selenium levels, which mimics the effect of a selenium deficiency diet that causes several diseases in humans. In the next five years, taking advantage of this cell system, we will investigate mechanisms underlying the regulation of selenium metabolism and address two fundamental questions: 1. How does the regulation of selenium impact cell viability? 2. How do cells respond to low levels of selenium? Our research will not only advance our understanding of selenium function and mechanisms governing cell response to stress but also will provide the basis for developing new therapeutic tools to treat diseases associated with the dysregulation of selenium and its metabolism. The techniques and experience acquired in this project will prepare our group for future research exploring the molecular machinery of selenium-mediated maintenance of cellular homeostasis.