University Of Florida

NSF Awards · FY 2025 · 2025-09

Supermassive black holes live in the centers of most galaxies, including our own Milky Way galaxy. Millions to billions of times more massive than the Sun, supermassive black holes provide crucial insight for understanding how galaxies formed and evolved. When two galaxies collide, their supermassive black holes can form a binary system that spirals together and merges. This cosmic dance creates extremely powerful “gravitational waves” (GWs), which are ripples in the fabric of spacetime. This project will use computer models to study how binary supermassive black holes evolve and to improve GW predictions. The project will also expand the Gator Artificial Intelligence (AI) Camp for high school students, which was created by the lead investigator and launched in Summer 2024. This program will create research and educational opportunities to undergraduate students. The research team will design an improved framework for binary SMBH population modeling for PTAs and the upcoming Laser Interferometer Space Antenna (LISA) mission. This work will address the following fundamental research questions: (1) Which observables and theoretical assumptions dominate the uncertainty in the SMBH population characteristics inferred from the gravitational wave background and future LISA events? (2) How efficiently do SMBH binaries inspiral and merge in different environments, and what is the best way to model this process for PTA and LISA data analysis? To address these questions, the team will (i) compare GW background predictions from simulations and semi-analytic models to design a new modeling approach that leverages the strengths of each method, (ii) develop an empirically based scheme for modeling SMBHB inspiral that connects binary evolution in galactic nuclei to key host galaxy properties, (iii) produce more robust constraints on SMBH binary population characteristics using PTA detections, and (iv) optimize this approach for the low-mass, high-redshift regime and make predictions for LISA event rates and source population characteristics. This project will integrate computational research with high school and undergrad education. The broader societal impacts of this projects include: (1) expanding the Gator AI Camp into a two-week program with a larger team of residential counselors and a program coordinator, (2) developing a virtual Gator AI Camp community to provide attendees with lasting support, (3) providing research opportunities for high school and undergraduate students, and (4) integrating computational skills into undergraduate physics courses. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The aim of this project is to study and improve how Artificial Intelligence (AI) chatbots evaluate job candidates. AI chatbots increasingly are used in workplace settings to interview job candidates, offering efficiency and standardization in hiring. AI-based interview systems may unintentionally rely on irrelevant information, however, leading to inappropriate outcomes. This research investigates how AI systems might produce different outcomes based on individual characteristics, even when qualifications are equal. It also explores how people perceive the balance and transparency of such AI interview experiences. The findings inform the development of more robust AI systems and support the deployment of ethical AI in hiring practices, ultimately contributing to a stronger workforce. The project trains students in responsible AI, offers outreach through public forums, and develops interactive dashboards to help human resource professionals make better use of AI tools in hiring. The research in this project analyzes AI-based interview systems through the lens of predictors (e.g., language model embeddings), outcomes (e.g., scores or hiring decisions), and user perceptions (e.g., trust). Drawing on an existing conceptual framework and psychometric natural language processing methods, the research team examines differential functioning of AI predictors across groups, detecting group differences in outcomes, and evaluating candidate reactions to chatbot interviews. Data from both university seniors and working professionals are collected to ensure generalizability. By integrating expertise from psychology, machine learning, and business analytics, the project produces validated metrics, statistical models, and explainable AI tools that enhance transparency and balance in AI-chatbot-based interview systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT Sepsis is a life-threatening condition characterized by a dysregulated immune response to infection leading to organ dysfunction. It frequently occurs in hospitalized patients undergoing surgical procedures and is associated with prolonged hospital stays and increased mortality. Despite improved in-hospital survival, 30-50% of surgical sepsis patients will never fully recover, but develop a syndrome recently described as “chronic critical illness (CCI)” which is characterized by persistent inflammation, immune suppression, and protein catabolism (PICS) and poor long-term outcomes. Currently, two important critical questions continue to vex clinical intensivists: i) why do otherwise similar surgical sepsis patients have very different clinical trajectories where some rapidly recover while others develop CCI despite our best supportive efforts? and ii) can we “endotype” patients with sepsis and identify subsets with different immunological responses who would benefit from individualized interventions. We believe that current efforts to endotype sepsis have not been fully successful because they fail to directly assess key host-pathogen responses (e.g., cells, molecules, pathways) driving immune suppression and inflammation, instead selecting either genomic or proteomic markers of immune status which have been shown to exhibit limited predictive power due to sepsis heterogeneity and usually small sample sizes. Our recent efforts have highlighted the role of unresolving “pathologic myelopoiesis” and ensuing expansion of myeloid- derived suppressor cells (MDSCs) in driving both the persistent inflammation and immune suppression in CCI patients. The overarching goal of the PI laboratory’s research program over the next five years is to: 1) assess whether multi-omic data of MDSCs can improve the sepsis endotyping and predict clinical trajectories than the commonly-used static measurements based on protein levels and nucleic acid concentrations; 2) delineate the longitudinal change in the cross-talk between MDSCs and T cells in patients with different clinical trajectories; 3) identify potential drugs for intervention of persistent inflammation and/or immune suppression in sepsis from FDA-approved drugs. These goals will be achieved by integratively leveraging the existing and ongoing efforts (RM1 GM-139690 and R01 GM-139046) examining the MDSC-specific multi-omic landscape (transcriptomics, chromatin accessibility, DNA methylation, metabolomics, clinical variables) and large-scale of bulk blood leukocyte transcriptomes, an existent comprehensive assessment of clinical immunosuppression and functions of MDSCs and T cells, with powerful statistical modeling and artificial intelligence (AI). We believe that only through a complete understanding of the immunological endotypes surrounding sepsis can effective therapeutic interventions evolve. This MIRA would support and enable the PI and his laboratory to identify and understand immunological endotypes of sepsis, explore how MDSC driving persistent immune suppression, and how to work towards resolving this to improve patient outcomes. The outcomes of this project will provide key data, knowledge and drug candidates to apply precision medicine to sepsis therapy.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY/ABSTRACT Despite recognition of the pressing issues of high rates of childhood obesity and insufficient moderate- to vigorous-intensity physical activity (MVPA) across the lifespan, few effective programs exist to support family-based behavior change for physical activity (PA) promotion and weight gain prevention. Family-based PA promotion interventions may be well-suited to support PA improvements in parents and children in the pre- adolescent period (i.e., 9-12 years old), as parents are still seen as an important role model to children during this developmental stage, and intervention at this time could help prevent further declines in child PA seen during adolescence. Novel methods to facilitate family engagement are needed to optimize MVPA outcomes. One highly promising yet underexplored approach to facilitating family engagement in MVPA is to leverage mobile technology in concert with collaboration for support and motivation. Thus, this proposal addresses a novel goal: providing comprehensive support for collaborative MVPA—before, during, and after PA. Literature suggests that collaboration can lead to improved PA outcomes by enhancing motivation and accountability, though most studies to date have been conducted with adults, and none with families or children. Our team recently built a prototype app that was piloted in two small studies demonstrating its early usability; the app displays key data including HR zones for facilitating MVPA collaboration between parents and children. The present application proposes to refine and investigate the benefits of the FamilyCollab system, to demonstrate the ways in which it supports parent-child dyads in pre-MVPA goal setting, collaborative real-time MVPA, and post-MVPA reflection. Using smartwatch data, personalized family goal setting, and interactive AI chat, the FamilyCollab system will support collaborative MVPA and achievement of the MVPA recommendations for parents and children. Then, the FamilyCollab app will be tested in a randomized feasibility pilot, compared to the same smartwatch with its standard commercial app. The objective is to refine the FamilyCollab app and gather data on the feasibility and acceptability of this approach in preparation for a larger efficacy trial. SPECIFIC AIMS: Aim 1: Refine the FamilyCollab App. The team will refine the FamilyCollab app through two rounds of usability studies with (n=12 dyads; parents with children 9-12 years old). These studies will finalize layout and features, along with ensuring usability and acceptability, ahead of Aim 2. Aim 2: Feasibility Pilot. A total of 60 parent-child dyads will be randomly assigned to the FamilyCollab Condition (intervention) or the Comparison Condition (control), where dyads will use the same smartwatch and its standard commercial app. The primary outcomes of the FamilyCollab intervention will be feasibility, measured as rates of dyad recruitment and retention; acceptability of the approach; and engagement with the FamilyCollab system after 12 weeks. Exploratory outcomes will include changes in MVPA, BMI, and family functioning (i.e., communication, support, cohesion) at 12 weeks.

NIH Research Projects · FY 2025 · 2025-08

The overarching goal of the CTSA K12 Program at the University of Florida (UF) and Florida State University (FSU) is to educate and train early-stage investigators (ESIs) for leadership roles in translational science. Our highly successful KL2 program (founded in 2009), supported a group of emerging scholars who have gone on to scientific success. Specifically, our KL2 scholars had a high rate of achieving research independence, with 71% receiving their own NIH K or R awards within the two-year award period. We have also traditionally funded an additional 1-2 scholars per year from internal funds, comprising nearly one-third of all KL2/CTSA-supported scholars, demonstrating our immense institutional commitment to this program. In 2015, our KL2 was integrated with FSU, and we were able to fund four scholars from FSU. However, further steps were needed to fully integrate with FSU. This new K12 program further establishes the UF-FSU partnership and is further integrated through: (1) addition of an FSU MPI to co-lead the program across institutions and campuses, (2) integration of the K12 Advisory Committee, which is now co-chaired by UF and FSU leaders, (3) addition of representatives from satellite campuses to the K Advisory Committee (KAC), (4) provision of dedicated K12 positions for FSU ESIs, (5) FSU faculty leading approximately 1/3 of Translational Science Academy educational sessions, and (6) an increased number of K scholars with integrated mentoring teams with faculty from both UF and FSU. The new K12 will leverage the experience of MPIs with complementary expertise and oversight from an integrated advisory committee, with a tailored 2-3-year curriculum designed to support the needs of our ESIs and advance them towards independent research careers that will advance Translational Science. This K12 Program encompasses UF Health Sciences campuses and FSU’s main (Tallahassee) and satellite campuses (Panama City and Sarasota). We have also updated and enhanced our overall curriculum to include: (1) a transition from largely educating in translational research to a focus on Translational Science via several new offerings, including the Translational Science Academy, a monthly series that will educate on the 8 principles of Translational Science including an asynchronous option, (2) change in duration of K12 scholar support from two years, to a 2-3 year program, (3) more hands-on, direct education, interaction, and mentorship between K12 MPIs and scholars via the K12 core content curriculum, and (4) required mentorship training for all listed K12 mentors, with a new option for asynchronous mentor training. In addition to scholars directly supported by the NCATS K12, the K12 infrastructure will educate and train many more ESIs via additional institutional funds. Consistent with our previous CTSA efforts, our K12 program will serve as the organizational model for research career development of ESIs across all UF-FSU campuses using best practices of translational science.

NIH Research Projects · FY 2025 · 2025-08

We have developed and implemented a unique clinical and translational research “CTS Team” training program for pairs of PhD and/or combined degree (e.g., MD-PhD) trainees who are pursuing their PhD studies in different disciplines, in different colleges, and with different mentors. Prospective CTS Teams propose cross-disciplinary collaborative translational research projects that become embedded into their individual dissertation research as “team-specific aims.” CTS Team training provides authentic cross-disciplinary collaborative research experiences combining team science and clinical and translational research training. We will build on the unique strengths of the CTS Team training model by adding emphasis on translational science, by pursuing the following five objectives. Scientific and operational principles underlying each step of the translational process will be incorporated into the didactic curriculum and mentored research experiences of cross-disciplinary teams of T32 predoctoral trainees. We will use a novel combination of quantitative and qualitative methods, including Translational Science Competency-Based Assessment (TS CBA), based on a conceptual model of training progression and career success. We will fully develop, pilot-test, and implement TS CBA, which is based on seven “fundamental characteristics of a translational scientist”. We will recruit and retain a critical mass of highly qualified T32 trainees who are diverse with respect to scientific disciplines and translational research interests, and value the highest ethical standards in conducting translational team research. We will continue to emphasize training across all phases of translation, i.e., preclinical, clinical, implementation, and population-level research. We will explore the dynamics of team mentoring to optimize CTS Team training. Just as the process of cross-disciplinary collaboration poses challenges for researchers, co-mentoring across disciplines presents challenges. Mentor and mentee training materials will be developed, pilot-tested, and optimized to support development of key mentoring competencies. Finally, we will develop facilitation resources for all elements of the CTS Team training model and disseminate via national communities of practice for CTS, mentoring, and the science of team science. With a combination of NIH (six positions) and institutional funds, this predoctoral T32 program will support up to three new CTS Teams per year, for two years each (up to 12 trainees per year). Predoctoral PhD and combined degree students pursuing health-related research in different colleges will be eligible to apply and compete for funding as CTS Teams. The additional focus on translational science principles and implementation of translational science competency-based assessment will help develop the next generation of translational science leaders prepared to translate biomedical research discoveries into innovative health care.

NIH Research Projects · FY 2025 · 2025-08

Florida is the third largest state with unique health challenges. The University of Florida (UF) and Florida State University (FSU) CTSA hub, with over 14 years of prior collaborative experience, will work within this dynamic environment to improve human health by accelerating the translation of scientific discoveries and implementing evidence-based best practices. Through pivotal leadership in catalyzing research within and across the hub’s universities, affiliated health care settings, and communities, the UF-FSU CTSA hub will target significant barriers in translation. The UF-FSU CTSA hub aligns FSU’s distributed medical education model with UF’s county extension reach and extensive OneFlorida+ Clinical Research Network and Data Trust to bring health research opportunities to Florida’s rural and urban communities. Applying clinical and translational science (CTS) practices and principles in this context lends substantial opportunities for enhancing research efficiency and improving health outcomes. The UF- FSU CTSA hub is optimally positioned to drive the integration of CTS principles and practices into the routine conduct of biomedical research and health care delivery. Over the next 7 years, such integration will drive the continued maturation of our learning health system initiative, which will evolve into a learning health community engaging state, regional, and national collaborators. The goal of this application is to catalyze cross-disciplinary translational science activities that result in meaningful gains in health for the hub’s communities. Furthermore, the UF-FSU CTSA hub aims to serve as an effective implementation and dissemination hub of best translational science practices for turning discoveries into improved outcomes. Toward that end, the UF-FSU CTSA hub will pursue four long-term strategic goals: 1) cultivate a dynamic training ecosystem to speed translation through continuous learning and skill development, 2) advance the science of translation to overcome persistent challenges in the conduct of clinical and translational research and translation of best evidence to patients and communities through innovations in clinical trial design and artificial intelligence and digital health solutions, 3) deepen stakeholder collaborations to more effectively address stakeholder health priorities and reduce health risks through strategic partnerships, and 4) create a scalable learning health community to expand the reach, sustainability, and impact of translational advances through maturation of a learning health system. As a central facilitator for implementing change, broadening access to research, and translating discoveries into practice, the UF-FSU CTSA hub is poised to impact the significant health challenges in northern and central Florida, statewide, and beyond.

NIH Research Projects · FY 2025 · 2025-08

Project Summary/Abstract In critically ill adults, evidence based guidelines for oral care are well established and known to reduce complications.1 However, in preterm infants admitted to the neonatal intensive care unit (NICU), no such guidelines exist and there is a dearth of information regarding oral care for preterm VLBW (< 1500 grams at birth) infants who because of their suppressed immunity and physiologic immaturity, are at increased risk of morbidity due to infrequent and non-standardized oral care.2 The optimal frequency and element to use for oral care to decrease oral/intestinal inflammation and microbial dysbiosis and to decrease cost of care is unknown. The overall objective of this 3-year study is to determine whether frequent standardized oral care using human milk reduces oral/intestinal inflammation and microbial dysbiosis (decreased beneficial bacteria; increased potentially pathogenic bacteria; decreased alpha diversity) leading to improved neonatal outcomes thus reducing cost of care. Previously collected oral and stool samples will be analyzed for inflammatory markers, metagenomic composition, and the cost and cost effectiveness of the intervention will be determined. The proposed study is a secondary analysis of data from an ongoing prospective randomized controlled trial of 168 racially diverse male and female preterm VLBW infants. Infants are randomized into 1 of 3 groups. Oral care is provided every 3-4 hours using human milk (Group 1); every 3-4 hours using sterile water (Group 2); and every 12 hours using sterile water (Group 3: standard care) for 4 weeks after birth. Aim 1 will evaluate the effect of frequent standardized oral care using human milk on oral inflammation in preterm VLBW infants. For Aim 2, we will characterize the metagenomic composition of oral and stool samples and their impact on inflammatory profiles. Aim 3 will determine the cost and cost effectiveness of frequent oral care using human milk. Results are expected to fill an important gap in research regarding frequent standardized oral care using human milk and whether it decreases oral/intestinal inflammation and dysbiosis and reduces cost of care.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY Recent evidence suggests that tumor-associated myeloid cells (TAMCs), which include tumor-associated macrophages (TAM) and myeloid-derived suppressor cells (MDSC), play a significant role in cancer immunosuppression and progression. These cells constitute up to 50% of the tumor's total mass in glioblastoma (GBM) and play pivotal roles in dampening the immune response, thereby negatively affecting patient survival. Therefore, targeting TAMCs could overcome the limitations of current cancer treatments. Yet, drug development in this realm remains limited. Our research focuses on leukocyte-associated immunoglobulin-like receptor 1 (LAIR1), prominently expressed on the surface of TAMs, which is essential in mediating the immunosuppressive role of TAMs. We have uncovered a previously unrecognized immunosuppressive LAIR1→ Factor XIII A (FXIII- A) → Collagen IV circuit across cancer types. The LAIR1 triggers TAMs to release the FXIII-A around tumor cells, which increases tumor collagen IV deposition and structure, shielding the tumors from immune attacks. Inhibiting LAIR1, either through genetic knock-out (LAIR1-/-) or antibody blockade (aLAIR1), effectively disrupts this immunosuppressive pathway and results in enhanced peripheral and tumor-infiltrating memory CD8 T cell populations, a phenotypic shift of TAMs towards an M1 profile, and a decrease in collagen deposition. These collective effects contribute to the normalization of the TME and facilitate improved interactions between T cells and tumor cells, leading to a more effective antitumor response. Notably, using aLAIR1 as a standalone intervention or combined with Chimeric antigen receptor (CAR) T cell therapy demonstrates enhanced antitumor efficacy in preclinical models resistant to anti-PD-1 treatment. These findings position aLAIR1 as a promising strategy for cancer immunotherapy. We hypothesize that LAIR1 is a new inhibitory immune checkpoint molecule that controls the pro-tumor function of the TAMCs. This study will elucidate mechanisms of the signaling axis of LAIR1 and its binding partners in tumor immunosuppression and progression and determine if inhibiting this molecule using 3-in-1 triple-functional CAR (L2-8R-70CAR (targeting tumor cells, enhancing CAR T cell tumor trafficking, and delivering LAIR1 blocking to TAMC inhibition) can reshape the tumor microenvironment and induce a profound antitumor response. Two aims are proposed: Aim 1: Determine the role of LAIR1 in GBM immunosuppression. Aim 2: Overcome the blood-brain barrier (BBB) using our 8R-70CAR T cells, currently being tested in a phase I clinical trial (FDA- IND#23881, NCT05353530), to deliver LAIR1 inhibitory signaling in GBM. Successfully completing this study will advance our understanding of the LAIR1 signaling axis and its role in tumor immunosuppression and progression. It will validate the use of 3-in-1, L2-8R-70CAR T cells in overcoming the blood-brain barrier to deliver targeted LAIR1 inhibition and demonstrate the potential of triple-functional CAR T cells to reshape the tumor microenvironment and induce a robust antitumor response.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY/ABSTRACT Insulin is a replacement, not a curative, therapy in the management of type 1 diabetes (T1D) which is caused by loss of immune tolerance and destruction of pancreatic beta cells. More recently, beta cell stress has also been found to contribute to T1D pathogenesis. Despite this knowledge, no disease-modifying therapies (DMTs) have been approved for the treatment of symptomatic T1D. A T cell specific immune therapy, anti- thymocyte globulin (ATG), in low doses, has been shown to lower HbA1c and preserve endogenous insulin production (measured by C-peptide) in individuals with recently diagnosed T1D. However, not all individuals who received ATG responded to the therapy. A portion who received drug had a similar decline in C-peptide as the placebo-treated individuals. Another monotherapy in T1D shown to preserve C-peptide, Verapamil, is a calcium channel blocker and is proposed to reduce beta cell stress. To advance our long-term goal, to directly inform the development and translation of DMTs in those with T1D in a precision-directed manner, this proposal seeks to address three gaps in the field: (1) identification of who is most likely to “respond” to ATG prior to drug administration, (2) use of sequential therapies to increase efficacy using both immunomodulatory and beta cell protective therapies, and (3) clinical trial design to assess efficacy earlier than the standard endpoint at 1 year allowing for shorter trials. This proposal includes a randomized, placebo-controlled, and blinded trial. Using a predictive biomarker of response to ATG we developed, participants with T1D will be stratified as “responders” and “non- responders.” This study will be powered to detect a difference at the standard 12-month time point and a novel 6-month endpoint to allow for validation of both an adaptive trial design and responder signature. Such a signature was developed by our group using data from a large ATG in T1D clinical trial. To address the beta cell-specific disease mechanism, at 1-year participants will be re-randomized to take verapamil or not for an additional year. To understand the driving mechanisms of each therapy and their potential synergy, we will perform in depth immunophenotyping and quantify biomarkers of beta cell stress and abnormal prohormone processing, as verapamil has been shown by us and others to not only affect beta cell stress but also T cell subsets. Our central hypothesis is that population enrichment via our novel ATG responder prediction tool will dramatically enhance C-peptide preservation following ATG which will be prolonged with verapamil via immune-specific mechanisms. Completion of these aims will prospectively validate the utility of responder immune profiling, alternative trial designs, and synergies emanating from the use of unique and potentially complimentary therapies in T1D. Ultimately, these efforts seek to accelerate approval of repurposed disease-modifying precision-directed therapies for T1D.

NIH Research Projects · FY 2025 · 2025-08

ABSTRACT Red tide is an ongoing environmental health crisis that is fueled by hurricanes and tropical storms. The dinoflagellate Karenia (K.) brevis is indigenous to the waters of the Gulf of Mexico and the Caribbean and is primarily responsible for harmful algae blooms known as “red tide”. Its blooms recur in native regions and multiply to neighboring coastlines. Satellite data from Florida’s Gulf Coast shows escalating concern about red tide rooms, which can impact not only humans but also lead to extensive mortality events among marine animals and land animals that come in contact. Hurricanes and tropical storms, which are frequent in Florida during the hurricane season, stir up nutrient rich waters, fueling red tide algal blooms. Algae toxins are aerosolized with sea spray aerosol and carried inland by winds. One of the major organ systems targeted by the red tide toxin is the respiratory system. Studies suggest that coastal residents may experience 54% higher rates of respiratory diagnoses during red tide periods compared to non-red tide periods. Such diagnoses include bronchitis, pneumonia, asthma, and upper airway disease. Limited data in experimental animal models suggests that red tide can induce asthma-like features, though the mechanisms responsible remain unclear. Additionally, some data suggests that individuals with asthma or compromised airway function are more sensitive to the harmful respiratory effects of red tide algal aerosols. Building upon prior work from MPI Dr. Jang who has showed that red tide brevetoxin in sea spray aerosols undergoes degradation and produces free radicals with sunlight exposure, this proposal seeks to investigate the natural history of red tide blooms and effects of atmospheric aging on airway barrier function and inflammation using primary human airway cells. Our overarching hypothesis is that algal aerosol and toxins disrupts airway epithelial barrier function and induces airway inflammation to predispose individuals to develop asthma-like symptoms. We predict that the degree of airway inflammation and barrier disruption will coincide with the magnitude of the red tide algal bloom, with peak effects observed during peak algal bloom. We will directly collect red-tide sea spray aerosol in 3-5 locations impacted by red tide blooms until the red-tide has recessed. The collected algal aerosol will be resuspended in the Atmospheric Photochemical Outdoor Reactor (UF-APHOR) located in the University of Florida for characterizations and exposure studies. UF-APHOR is one of three large photochemical reactors worldwide that mimics the atmospheric process of algal aerosol under the ambient sunlight. The collected algal aerosol will be applied to normal and asthmatic human primary airway epithelia cultured at airway liquid interface. We will measure inflammation, ion transport mechanisms and barrier function through fluid secretion assays to investigate the adverse impacts of red tide on airway health.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY Congenital heart defects (CHDs) are abnormalities of the heart and intrathoracic great vessels present from birth, with varying degrees of health severity; some may necessitate no intervention, while others may require immediate surgical intervention. Given that CHDs are the most frequent type of birth defect, many children worldwide will undergo numerous imaging examinations utilizing ionizing radiation, spanning from initial diagnosis to treatment and long-term follow-up. Considering the latency period for the development of leukemia from 5 to 7 years, and a minimum of 10 years for solid cancers, there is a tangible risk of developing radiation- induced malignancies for patients with extended lifespans. Both the National Academies and the United Nations are currently emphasizing the need to establish low-dose epidemiology cohorts for pediatric patients undergoing cardiac catheterization procedures, computed tomography scans, and general radiography examinations. This emphasis arises from the existing paucity of data on the correlation between multi-modality, cumulative, low radiation doses, and the corresponding cancer risk in children. Currently, there is no accurate platform for calculating patient-specific organ doses from medical exposures, which hinders our understanding of the link between radiation exposure and cancer incidence in this vulnerable population. Innovations in artificial intelligence-based automatic segmentation tools have allowed efficient extraction of patient anatomy from computed tomography scans to be used in the creation of whole-body phantoms suitable for Monte Carlo radiation transport simulations. Therefore, I hypothesize that the development of a software system integrated with the clinical workflow can use available patient imaging examinations to create accurate whole-body mesh models, which can then be used to calculate organ radiation doses accrued from the diagnosis and treatment of CHDs using Monte Carlo radiation transport simulations. The proposed project will be achieved by the completion of four Specific Aims: Aim 1: develop software tools that leverage available patient imaging exams to create morphometrically matched computational phantoms of the patient suitable for radiation transport simulations. Aim 2: develop a Monte Carlo software system that integrates with the clinical workflow to compute organ doses for pediatric patients undergoing cardiac catheterization procedures. Aim 3: use existing Monte Carlo dosimetry systems that integrate with the clinical workflow to compute organ doses for pediatric patients undergoing CT and radiography examinations. Aim 4: perform a retrospective study on patients recently treated at UF Health to compute organ doses for cardiac catheterization procedures, CT, and general radiography examinations. Completion of these aims will provide an accurate platform that answers the call for more low-dose and radiation epidemiological research as well as the potential for assisting physicians in patient cancer surveillance.

NIH Research Projects · FY 2025 · 2025-08

Project Summary/Abstract: α-synuclein (αS) is an abundantly expressed neuronal protein which forms pathological inclusions in a spectrum of neurodegenerative diseases. Although αS inclusions are a commonality in Parkinson’s disease (PD), Lewy body Dementias (DLB), and Multiple System Atrophy (MSA), the cellular and regional distribution of pathological inclusions varies widely between diseases. Importantly, the progression of these diseases corresponds with the spread of αS pathology through the CNS. The progression of αS pathology requires the aggregation and propagation of αS inclusions, although the origins and mechanisms of this process are not well understood. Biochemical and ultrastructural studies of aggregates from human disease brains and prion-type αS propagation models have revealed distinct properties of αS in various disease contexts, lending to the hypothesis of disease strains. In this strain hypothesis, it is thought that the cell types and regions in which αS inclusions form imbue conformational distinctions to aggregates, leading to distinct pathological courses. Post-translational modifications (PTMs) can imbue distinct biochemical characteristics and represent a mechanism for generating disease strains. Of these PTMs, extensive cleavage of the acidic, carboxy (C)-terminal residues is a common modification to αS found in pathological inclusions and one that leads to increased aggregation of αS in vitro. In mouse models seeded with human disease brain extracts, the abundance of αS pathology and specific C-terminally cleaved αS rich pathology reveals differences in disease strains. Our lab has generated a large panel of αS antibodies, including those to C-terminally cleaved αS, which show the heterogeneous nature of αS pathology in both LBD and MSA. Most notably, we have found populations of neuronal pathology in the pons of MSA brains which is distinct from the predominant oligodendrocytic pathology that defines MSA. αS in the CNS is primarily expressed in neurons, and therefore newfound neuronal pathology represents a point of origin for the broader oligodendrocytic pathology in MSA. The connection between affected cell types, patterns of C-terminal cleavage, and the potency of tissue-derived seeds is not understood. For this reason, the present study will leverage the seeding properties of human brain-derived and recombinant αS aggregates to study seeding dynamics in mouse lines that express human αS and which have a known disease phenotype. Aim 1 will compare seeding using brain extracts from the pons or cerebellum from MSA cases, distinguishing their seeding activity immunohistochemically with a panel of novel, truncation-specific antibodies. Aim 2 will investigate the differential seeding properties of synthetic αS fibrils comprised of specific disease- relevant C-terminally truncated forms of αS compared to that of full-length αS. These studies will provide insight into the pathogenesis of αS related diseases, differentiating regional pathology, and exploring C-terminal truncation of αS as a disease mechanism.

NIH Research Projects · FY 2025 · 2025-08

Project Summary/Abstract Malignant brain tumors are the leading cause of cancer-related death in children and a significant cause morbidity and mortality in adults. Despite aggressive treatment, outcomes remain dismal and limited by a highly immunosuppressive tumor microenvironment which includes myeloid-derived suppressor cells (MDSCs). A promising therapeutic alternative developed by our group is called adoptive cellular therapy (ACT) which includes myeloablative host conditioning accomplished using total body irradiation (TBI), hematopoietic stem cell rescue, tumor-specific T cell transfer, and dendritic cell vaccine. ACT demonstrated significant improvements in overall survival across multiple brain cancer models, leading to pivotal approvals by the FDA for several investigational new drug applications. Our group has observed that ACT dramatically alters the glioma microenvironment. Hematopoietic stem cells migrate to the glioma microenvironment and differentiate to dendritic cells, which displace host-derived myeloid-derived suppressor cells. Additionally, hematopoietic stem cells secrete CCL3, promoting T cell infiltration into the glioma microenvironment. These observations suggest that the glioma microenvironment is radically altered after ACT and that other mechanisms may influence the cellular milieu underlying ACT’s efficacy. It is the goal of this proposal to elucidate the impact of ACT as it related to overcoming glioma immunosuppression and specifically the MDSC niche. Our preliminary data support our notions that ACT dramatically impacts the MDSC niche in glioma: (1) ACT mediated intratumoral depletion of MDSCs and reduced their proliferative capacity, (2) ACT promoted accumulation of MDSCs in secondary lymphoid organs, (3) repopulation of the glioma microenvironment with MSDCs was abrogated by ACT, (4) myeloproliferative chemokines were significantly reduced in the glioma microenvironment following ACT. Given these premises, two aims have been proposed to study this novel relationship in glioma. Aim 1 will identify the mechanisms governing MDSC depletion in ACT. To achieve this, flow cytometry-based assays will quantify cell apoptosis and cell death in addition to innovative use of gold nanorods and computed tomography to translationally measure cell migration in vivo. This aim is supplemented by multiplex analysis of cytokines and chemokines found in the glioma microenvironment to sustain future efforts in identifying the specific soluble molecules governing MDSC migration. Aim 2 will assess the impact of brain irradiation on MDSC accumulation and ACT efficacy. Following a survival study, we will leverage geo-spatial genomics approaches in conjunction with our gold nanorod imaging modality to track cells. This work is significant because depletion of immunosuppressive MDSCs represents a significant barrier to efficacious immunotherapy development. This project is innovative because it will utilize gold nanorods (GNRs) to elucidate in vivo migration of MDSCs in ACT-treated mice. These translational findings will optimize host conditioning protocols to maximize antitumor immune responses, limit iatrogenic toxicity, and reveal cellular mechanisms of overcoming cancer-driven immunosuppression.

NIH Research Projects · FY 2025 · 2025-08

Project Summary The proposed fellowship plan is a transdisciplinary research project integrating training in implementation science, pragmatic health services research, quality improvement, and medicine. This project is supervised by Drs. Ramzi Salloum (Dept. of Health Outcomes and Biomedical Informatics), Carma Bylund (Dept. of Health Outcomes and Biomedical Informatics), Lisa Carter-Bawa (The Cancer Prevention Precision Control Institute), and Ji-Hyun Lee (Dept. of Statistics) with the resources and support of the University of Florida (UF) College of Medicine, the UF Health Cancer Center, and the UF Clinical and Translational Science Institute. The proposal is designed to equip the trainee with the skills necessary to become a physician-scientist who bridges the divide between health systems science and clinical medicine. In addition to MD/PhD-specific professional development and expanded clinical/translational training, much of this preparation will come from technical education gained from the execution of this proposal’s research aims. Lung cancer is the leading cause of cancer-related deaths in the United States. Despite this, rates of screening have remained persistently low nationwide hovering around 6%. Rates in Florida are even lower with only 3% of eligible individuals screened in 2022, and rural areas consistently face lower rates of screening and higher rates of lung cancer mortality. The vast majority of those eligible are willing to get screened if advised to do so by their provider. However, counseling on lung cancer screening would ideally include patients actively through shared decision-making (SDM) and use of a decision aid, as required for Medicare reimbursement of lung cancer screening. Currently, providers are not consistently able to integrate either decision aids or SDM for lung cancer screening into their visits due to barriers including competing priorities during the visit, lack of provider awareness of eligibility for screening, and limited patient knowledge on lung cancer screening. This proposal will focus on evaluation of a UF Cancer Center initiative to integrate a pre-visit decision aid and outreach contact into both an urban and a rural primary care clinic workflow. Additionally, given the dedicated personnel time necessary for this strategy, this proposal also includes an economic evaluation component to facilitate future adoption. The overarching hypothesis is that a pre-visit decision aid and outreach contact will increase uptake of SDM for lung cancer screening to the extent that downstream revenue from screening, diagnostic testing, and treatment of lung cancer will offset the increased personnel costs of implementation and outreach. This will be tested via three aims: 1) assessing the feasibility of this strategy 2) evaluating its budget impact 3) identifying the determinants for successful implementation. Successful completion of this project will enhance the understanding of the barriers and facilitators to SDM in primary care more generally as well as the costs and benefits of a pre-visit decision aid and outreach contact.

NIH Research Projects · FY 2025 · 2025-08

Dopamine signaling in the brain is essential for movement, cognition, motivation and reward. Conversely, dopamine dysregulation has long been implicated in parkinsonism, dystonia and neuropsychiatric disorders. Recently, our laboratory discovered loss-of-function mutations in a protein chaperone, DNAJC12, as a cause of young-onset parkinsonism. Our findings coincided with another report of similar mutations in infants with dystonia, hyperphenylalanemia and intellectual disability. Now frequently identified through newborn screening, biallelic DNAJC12 mutations in infants and adults lead to a reduction in brain dopamine that is responsible for their movement disorders. However, the fundamental roles of DNAJC12 protein in catecholamine production are poorly described. To fill this knowledge gap, we have characterized a genetic mouse model in which Dnajc12 has been constitutively ablated and showed that at three months of age, these mice have reduced locomotion/exploratory behavior and reduced striatal dopamine, congruent with the function of Dnajc12. We now aim to specifically ablate Dnajc12 in dopaminergic neurons, which are prominently affected in Parkinson’s disease, to ascertain the effects of this gene dysfunction on dopamine synthesis, physiology and related behavior. Hence, our studies are to provide regulatory insights into dopamine biosynthesis from a unique biological perspective. The findings may reveal novel approaches to maintain or restore brain dopamine, which would have wide applicability.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY Dendritic cells (DCs) are pivotal in mediating anti-tumor immunity, and an effective DC vaccine devoid of intolerable immune-related toxicity would be groundbreaking. Clinical trials underscore the therapeutic promise of next-generation DC vaccines and non-invasive imaging in patients suggests an important link between DC to lymph node (LN) migration and treatment success. However, established clinical and pre-clinical imaging technologies suffer limitations that preclude systematic studies that link DC vaccine therapy and migration to LN. This project will enhance understanding of DC migration and its impact on DC vaccine efficacy through an innovative nanotechnology-enabled imaging technology called magnetic particle imaging (MPI), where signal arises from the unique response of superparamagnetic iron oxide nanoparticles (SPIONs) to an alternating magnetic field. MPI overcomes the limitations of other imaging approaches by enabling non-invasive, tomographic, quantitative, unambiguous imaging with high sensitivity and negligible tissue background signal or attenuation, while using biocompatible tracers with long shelf life. Preliminary studies demonstrate that MPI enables longitudinal tracking of DC vaccines that capture the biological diversity of DC to LN migration and is predictive of treatment response, while suggesting that SPION-labeling enhances therapeutic outcome of DC vaccine by activating immune-related gene pathways. The proposed research combines SPIONs with enhanced MPI performance, optimized protocols for DC labeling coupled with comprehensive characterization of the effects of tracer uptake on DCs, and state-of-the-art MPI acquisition, analysis, and validation approaches to study DC migration and its correlation to treatment response in DC vaccine immunotherapy through three specific aims: Specific Aim 1: Obtain a robust quantitative understanding of DC to LN migration dynamics. Specific Aim 2: Investigate theranostic SPION-DC vaccine in models of early- and late-stage melanoma. Specific Aim 3: Investigate theranostic SPION-DC vaccine in a model of glioblastoma (GBM). These studies will leverage MPI to evaluate the influence of sex, administration site, DC dose, and use of pre- sensitizing and co-stimulating factors on DC to LN migration and will systematically investigate the use of MPI in predicting response to therapy in murine models of immunotherapy. Complementary studies will compare MPI measures of DC migration to flow cytometry and study the DC microenvironmental niche in the LN to gain mechanistic insights into the enhanced therapeutic response to SPION labeled DC vaccines. As a result, the proposed work will contribute new fundamental knowledge on the use of nanotechnology in monitoring cancer treatment response and will establish specific and sensitive tracking of DC migration using MPI as a powerful tool linking therapy and mechanism of action. The proposed research benefits from a synergistic collaboration between a nano-bioengineer specializing in MPI development, a clinician-scientist with translational expertise in cancer immunotherapy, and a leading developer of MPI instrumentation.

NIH Research Projects · FY 2025 · 2025-08

Project Summary & Abstract Advanced age is associated with compromised outcomes in differentiated thyroid cancer. This observation suggests that there is a biologic change due to aging that causes the poorer prognosis for the older adult. Studies of the aging thyroid have reported that detrimental immune modulation and cellular senescence contribute to thyroid diseases in the older adult, both of which have been implicated in the progression of malignancies. Aging is associated with a state of chronic inflammation due to the secretion of inflammatory proteins collectively known as the senescence-associated secretory phenotype (SASP) from senescent cells. There is a critical need to understand the connection between immune modulation and cellular senescence via the SASP and tumor progression in older adult patients. Our long-term goal is to better characterize the role of age-associated immune modulation in the development and progression of papillary thyroid carcinoma (PTC) in the older adult population. The overall objective of this proposed study is to elucidate the interplay of age and SASP production in thyroid tissue to guide personalized management of older adult patients with PTC. Our central hypothesis is that advanced age induces an SASP transcriptome in senescent thyroid fibroblasts that leads to enhanced progression of PTC. This hypothesis will be tested using publicly available large RNA sequencing datasets and single-cell RNA sequencing technology to pursue two specific aims: (1) to identify the contribution of advanced age to the SASP in thyroid tissue and (2) to identify alterations in the SASP transcriptome of aged thyroid fibroblasts due to local and locoregionally advanced PTC. This project is innovative because it is the first study to characterize the contribution of age to SASP production in normal thyroid tissue and leverage deconvolution techniques to isolate senescent thyroid fibroblasts of older adults to examine how the SASP contributes to PTC progression. The proposed research will establish validity for the unique SASP transcriptome in thyroid tissue and thyroid disease and lay the groundwork for further mechanistic studies into the role of older adult immune modulation in thyroid diseases.

NIH Research Projects · FY 2026 · 2025-08

Over 5.5 million Americans had opioid use disorder (OUD), and >81,000 opioid overdose deaths occurred in 2023. Buprenorphine, one of the most prescribed medications for OUD reduces opioid use, overdose risk, and saves lives. Yet, 20%-60% of patients relapse to OUD while on buprenorphine. Relapse undermines the benefits of the treatment and increases the risk of overdose-related death and healthcare utilization. To combat the opioid crisis, relapse prevention is crucial; however, practitioners are not equipped with accurate tools for identifying patients at an elevated likelihood of relapse. Currently, they rely on individual risk factors such as low adherence, younger age, presence of psychiatric comorbidity, history of other substance use, and limited social support. However, how these factors are intertwined and influence the overall relapse likelihood is unclear, making it difficult for practitioners to predict the likelihood accurately. Hence, there is a critical need to develop a clinical decision support (CDS) tool to help primary care practitioners (PCPs) identify patients with an elevated likelihood of relapse and implement timely and targeted relapse prevention interventions such as behavioral therapies, mindfulness-based approaches, contingency management, residential rehabilitation, and connection to peer recovery specialists. Machine learning (ML) can uncover hidden patterns in complex data to create precise relapse prediction algorithms and risk stratification subgroups, thereby enhancing clinical care and intervention development. Leveraging our prior work in building ML prediction models in OUD, our goal is to develop an innovative ML algorithm to predict relapse on OUD treatment and build an evidence-based clinical decision support (CDS) e-tool (PROTECT) designed for front line practitioners treating patients with OUD with buprenorphine. We will achieve this objective through the following three specific aims. In Aim 1, we will develop and validate ML algorithms to identify buprenorphine patients with an elevated likelihood of relapse using electronic health records (EHR) data from OneFlorida+ and then externally validate the algorithms using EHR data from the PaTH network. In addition to demographic and clinical conditions, we will include information of patients’ health- related social needs (e.g., homelessness, financial constraint/unemployment), extracted from unstructured clinical notes via natural language processing. In Aim 2, we will prototype the PROTECT CDS tool and identify targeted interventions for relapse prevention. Building on Aim 1’s best-performing model, we will employ a user-centered design process involving iterative user interface development and user-feedback session, collecting feedback from PCPs, addiction psychiatrists, and community partners to assess PROTECT’s usability and optimize linkage to existing relapse prevention interventions. In Aim 3, we will conduct an economic evaluation of the PROTECT tool using a simulation model to demonstrate the potential value of the tool to end-users (health systems and practitioners). Our proposed research is highly innovative and clinically relevant in its use of an ML-based CDS tool to guide clinical practice and tailor evidence-based relapse prevention interventions, thereby optimizing resource allocation and improving health outcomes in patients with OUD.

NSF Awards · FY 2025 · 2025-08

This award provides support to U.S. researchers participating in a project competitively selected by a 55-country initiative on global change research through the Belmont Forum. The Belmont Forum is a consortium of research funding organizations focused on support for transdisciplinary approaches to global environmental change challenges and opportunities. It aims to accelerate delivery of the international research most urgently needed to remove critical barriers to sustainability by aligning and mobilizing international resources. Each partner country provides funding for their researchers within a consortium to alleviate the need for funds to cross international borders. This approach facilitates effective leveraging of national resources to support excellent research on topics of global relevance best tackled through a multinational approach, recognizing that global challenges need global solutions. Working together in this Collaborative Research Action, the partner agencies have provided support to foster global transdisciplinary research teams of natural, health and social scientists and stakeholders from across the globe to improve understanding of climate, environment and health pathways to protect and promote health. The projects will provide crucial new understanding into the health implications arising from the impacts of climate change and variability on; 1) decision-science approaches to adaptation and implementation, 2) food, environment, and biological security and 3) risks to ecosystems and populations. This award provides support for the U.S. researchers to cooperate in consortia that consist of partners from at least three of the participating countries to increase our knowledge of the complex linkages and pathways between the climate, environment and health to help solve complex challenges that face societies. The ComDisp project aims to develop an internationally proven, collaborative, iterative process for grassroots modeling of health with predictive capability across weather-variability scenarios. The project responds to health-related challenges related to housing conditions in the USA, Vietnam, Turkey and Ecuador - with broad relevance in every global society - where living environments are shaped by historical and contemporary patterns of change. The project focuses on populations who live in areas with high exposure to weather-variability and local hazards. In these areas, elderly, children, and individuals that have pre-existing health conditions are at elevated risk. The project team will leverage existing partnerships with non-academic stakeholders to include local governments (land use, housing, public health), and community health & housing organizations. The team will build on the approach developed by a team member’s study in Florida which developed a unique agent-based model and a network of indoor and outdoor air quality sensors with local community members in a participatory action research process. The project will combine spatial analyses of multiple environmental hazards that are impacted by weather-variability - in particular heat island effects, heatwaves, hurricanes and flooding - pertinent to cardiovascular and respiratory health outcomes. The team will develop local models of housing and health in USA, Vietnam, Turkey and Ecuador to connect and augment national datasets and models, to not only project future localized scenarios but to develop a strategy with affected communities to alter their present and future through science and action. 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 · 2025-08

TITLE: Elucidating social competence: neural mechanisms for competition and cooperation ABSTRACT The ability to shift social behavior based on available information, known as social competence, is vital for social animals’ group cohesion and survival. Deficits in social competence are a hallmark of neuropsychiatric disorders such as autism spectrum disorders, major depression, and schizophrenia, significantly impairing patients' quality of life. Past neuroscience research has largely failed to capture the dynamics of behavioral shifts because it has primarily isolated and studied one type of behavior. Furthermore, most past research has ignored the social factors that may influence behavioral changes. Competition and cooperation are two evolutionarily intertwined social behaviors that are crucial for social competence and group survival across species. Past studies show that the prefrontal cortex is important for competition and cooperation, but these behaviors have been mostly studied in isolation limiting our understanding of how neural computations shift behavior. This project aims to fill this gap by combining optical methods, chemogenetics and machine learning to investigate the role of the prelimbic cortex (PL) and its projections to the lateral hypothalamus (LH) and basolateral amygdala (BLA) in modulating both competitive and cooperative behaviors. We hypothesize that these two PL subpopulations are part of a network that shifts behavior towards competition or cooperation depending on the context. Using our novel behavioral tasks for mice, we can study a wide array of behavior states, including high competition, low competition, selfish choices, cooperative behavior, and shifts between cooperative and competitive states. In Aim 1 we will record the same PL-BLA or PL-LH neurons across cooperative and competitive states and identify how they encode behavioral shifts in these states. In Aim 2 we will turn to multi-site electrophysiology to record local field potentials from the PL-LH-BLA network simultaneously to study how network level activity encodes behavioral shifts between competition and cooperation. In Aim 3, we will investigate the causal roles of PL-LH neurons, PL-BLA neurons and reciprocal projections from BLA and LH to PL in competition and cooperation. The findings will advance our understanding of the prefrontal mechanisms that govern adaptive social behaviors and may inform therapeutic strategies to enhance social functioning in neuropsychiatric disorders.

NSF Awards · FY 2025 · 2025-08

The objective of this Faculty Early Career Development Program (CAREER) project is to support research on how to integrate artificial intelligence (AI) into spatial planning methodologies and processes. This knowledge is particularly important to informing long-term disaster recovery. By exploring Generative deep learning (GenAI) based approaches, the project promotes economic vitality, housing affordability, and other planning objectives while ensuring their responsible use. The methodologies developed are applicable to various planning and disaster contexts. The outcomes can significantly enhance disaster management capabilities and contribute to workforce development in AI and urban planning. The research addresses key challenges in formulating, training, and calibrating GenAI models and in preparing for their anticipatory governance in spatial regeneration planning processes. Specifically, it develops novel algorithms to balance multiple planning objectives, consider the effects of connected areas, respond to incremental planning across spatial scales, enable transactive planning, and avoid repeating past spatial disadvantages. It creates augmented spatial datasets and makes them available for public use. The project also examines how planners perceive and interact with these GenAI models, establishing responsible ways to deploy them. Educational efforts are designed to innovate curricula and teaching methods in urban analytics programs by combining learner persona-tailored pedagogies, new authentic learning modules, an online course, and original curricular studies. Integrated activities have the potential to advance cyberinfrastructure for planning research and education and contribute to building more resilient cities. The project is jointly funded by Humans, Disasters, and the Built Environment Program and Human-Environment and Geographical Sciences Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project addresses fundamental mathematical problems at the intersection of classical and quantum fluid dynamics, with a focus on compressible and quantum Euler systems. These models capture a range of complex physical phenomena, from high-speed gas flows to quantum fluids like super-fluids and Bose-Einstein condensates. The research aims to advance understanding of the transition from compressible to incompressible flow in the low Mach number regime and to explore how quantum effects influence solution structure and stability. The project also supports graduate training in mathematical analysis and applied partial differential equations (PDEs). The first component focuses on the incompressible limit of global weak solutions to the compressible Euler equations using convex integration, addressing key challenges such as nonlinear oscillations and weak convergence. The second component studies the quantum Euler equations derived from the Schrödinger equation via Madelung’s transformation, with a focus on whether quantum pressure intrinsically regularizes solutions. Analytical tools include polar decomposition and dispersive PDE theory. This research introduces new techniques in nonlinear PDEs and contributes to a deeper theoretical understanding of hyperbolic and dispersive systems in mathematical physics. 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 · 2025-08

This project addresses a critical challenge for coastal communities and the nation: how to sustain and strengthen natural salt marshes. These ecosystems are essential to the health and prosperity of coastal communities across the United States. They help protect shorelines from storms, support biodiversity, filter pollutants, sequester carbon, and sustain local economies through fisheries, recreation, and tourism. Rising sea levels threaten these ecosystems and the benefits they provide. Ribbed mussels, which form clusters known as mussel mounds, may help marshes keep pace with rising waters by filtering sediments, depositing material, and enhancing plant growth. However, the extent of their impact at the scale of an entire marsh remains unknown. This research will fill that gap by quantifying how mussels influence sediment accumulation and marsh resilience to sea level rise. In doing so, it directly promotes the progress of science and advances the national interest by contributing to sustainable coastal protection. The project also engages rising high school seniors in hands-on research, increasing engagement in coastal engineering and training the next generation of scientists and engineers, on which national competitiveness depends. Results will guide restoration efforts and support nature-based solutions for coastal protection. To achieve these goals, this project will combine field measurements and numerical simulations. Field work at Little Sapelo Island, Georgia, will include mapping marsh topography and mussel mound distribution using drone-based LiDAR, tracking water flow with dye release and aerial imagery, measuring current profiles and sediment concentration with acoustic and laser sensors, and collecting sediment samples with traps and surface elevation tables. Sediment will be analyzed by size and composition. An open-source numerical model, solving for hydrodynamics and morphological evolution, will be extended to simulate mussel filtration processes and will be calibrated and validated against field data. Simulations will compare scenarios with and without mussel mounds to quantify sediment budgets and the increase in marsh elevation due to presence of mounds. The project will deliver open-source software, detailed sediment budgets for different sediment sizes, and practical guidelines for using mussel transplants as a nature-based restoration strategy. 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 · 2025-08

Autonomous vehicles offer profound societal benefits, promising increased productivity and enhanced quality of life by reducing traffic congestion and improving transportation accessibility. Ensuring the safety of autonomous vehicles is paramount, given their operation on public roads and interaction with human beings. The project aims to develop MELIOREM, an automated tool designed to enhance the safety of autonomous vehicles. By utilizing our nation's high-performance computing infrastructure, MELIOREM will conduct rigorous testing to identify and address potential safety issues before they impact public roads. This initiative ensures that autonomous vehicles are dependable and safe for all road users. Using advanced search techniques, MELIOREM will simulate various driving scenarios to assess how well these vehicles perform under different conditions, leveraging extensive computational power for complex calculations and analysis. By bolstering the safety of self-driving technology, this project not only advances transportation safety but also provides a valuable resource to academia and industry, contributing to the broader professional community. It also creates educational opportunities by training students from diverse backgrounds in higher education. Autonomous vehicles (AVs) promise vast societal benefits of increasing productivity and improving quality of life, from reducing traffic congestion to improving access to transportation. Ensuring AV safety is critical to success in the marketplace, and an essential aspect of AV development to ensure safety is testing. Existing techniques incorporate computerized simulation-based iterations, where the AV under evaluation is stress tested by perturbing traffic parameters and AV internal states to generate safety cases for analysis, identify AV vulnerabilities, and mitigate safety hazards. This process largely involves using high-performance computing (HPC) infrastructure given the enormous amount of computation resources demanded by the simulations. However, current approaches often face state space explosions due to the large search spaces in both internal program executions and external environment parameters when searching for safety cases, making existing tools far from being comprehensive and efficient in HPC. Furthermore, due to the complicated structure of AV software stack, error resilience is not yet well understood, making diagnosis and protection extremely time consuming. This project will develop an efficient and comprehensive testing infrastructure, MELIOREM, for characterizing, assessing, and identifying vulnerabilities in AV software systems in evolving traffic situations. The core purpose of this work is practicality, enabling domain scientists to generate safety cases for characterizing and understanding AV safety, and AV developers to identify AV safety vulnerabilities using existing HPC infrastructure. This project will develop a series of algorithms to optimize test coverage, emulation efficiencies, and identify representative safety cases for an AV under test. This work will resolve these AV development issues with respect to their practical analysis by applying MELIOREM in intelligent cyber-systems in transportation and crash analysis research domains. This project is jointly funded by the OAC Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program and the Division of Information and Intelligent Systems (IIS). 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.