University Of Florida

NSF Awards · FY 2025 · 2025-09

Humans understand language rapidly and with remarkable ease. One way that humans do this is by predicting what a conversation partner might say next. But, there remain many unanswered questions about how different language backgrounds might help, or hinder, effective predictions. There is a critical need to understand proficient second-language comprehension. This project studies brain signals while bilinguals listen to an audiobook story in their first and second language. Computational modeling using artificial intelligence (AI) language systems are used to test the kinds of predictions people make, the information that guides those predictions, and how predictions are affected by differences in language background. This project offers insight into how AI can incorporate multiple languages in a realistic way and increases awareness of bilingual language with programs targeting future teachers and the public. Other benefits to society include increased transparency and reproducibility in language research by providing a large corpus of brain and behavioral data for other scientists and engineers. To meet these aims, the project collects electroencephalography (EEG) signals from three groups of bilingual participants with different levels of experience while they listen to an audiobook story. These signals reflect fast-changing brain responses and are highly sensitive to expectations in language. AI is used to capture the linguistic features of the story, such as the relationships between nouns and verbs in a sentence and the predictability of upcoming words. Statistical analyses are used to test the alignment between these features and brain activity, showing which features best capture brain activity and how this may be different for different language backgrounds. By training computational models with different amounts of exposure to one or more languages, the project further tests how the statistical properties of different languages modulate brain responses of multilingual language users. 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

Many coastal communities face challenges with freshwater quality. This project will develop strategies to counter issues resulting from rising sea levels, climate change, urban sprawl and farming. Researchers will develop decision-support tools and recommend nature-based solutions to reduce groundwater hazards. Collaborative efforts with local governments and agricultural managers will help ensure practical, scalable solutions. This work will increase our understanding of saltwater intrusion and flooding in low-lying, densely populated regions. The project will evaluate nature-based solutions, such as mangrove restoration, wetland conservation and green stormwater infrastructure, using advanced groundwater and economic modeling. Field data collection will inform analyses to project future hazards and support adaptive management strategies. A user-friendly decision-support tool with a graphical interface will empower decision-makers to implement tailored solutions. Results will be applicable in similar coastal regions. The project will train graduate students and postdoctoral researchers in advanced techniques and interdisciplinary problem-solving. Long-term, sustainable strategies to manage groundwater resources in the face of natural hazards will benefit local populations and the broader global community. Actionable solutions will help protect crops, infrastructure, natural environments and freshwater quality, promoting community resilience. The project will explore feedback controls among natural (groundwater systems, sea level rise, and climate change) and societal (nature-based solutions, grey infrastructure, best management practices in urban planning and agricultural production) systems and processes that determine the capacity to reduce hazard risk and increase the resiliency of a coastal community. Groundwater modeling approaches and tools created from the project will enable the investigation of the interaction between fresh groundwater and seawater in coastal areas and the projection of future groundwater elevation and seawater intrusion processes. In addition, economic modeling and analysis will allow the formulation of an adaptation portfolio required for informed decision-making processes of local communities and the evaluation of the efficacy of management scenarios and strategies that the communities can implement given societal and economic situations. This study will investigate the feedback control between nature-society systems by integrating groundwater, economy modeling, and iterative evaluation, which will provide a novel approach to characterizing driving forces for Earth-system processes through the linked understanding of natural and societal processes as an integrated system. 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

Chronic diseases—such as cardiovascular disease, diabetes, and Alzheimer’s Disease and Related Dementias —are leading causes of morbidity and mortality in U.S. adult, place a significant burden on the healthcare system and economy. Given that most chronic diseases are preventable, it is crucial to identify a reliable measure that can effectively detect early signs of chronic disease. Allostatic load (AL), an indicator of cumulative physiological “wear and tear,” has shown promise as a pre-clinical marker. However, consensus on its measurement in population datasets is lacking, and there is a lack of longitudinal evidence capturing changes in AL over time. This project aims to address three key objectives: 1) Identify the optimal AL measure and develop a DNA methylation (DNAm) surrogate of AL that best predicts health outcomes in two population datasets; 2) Examine AL change patterns across different life stages; and 3) Identify key risk and protective factors influencing changes in AL across life stages using advanced supervised machine learning techniques. Findings will establish essential guidelines for AL scoring in two nationally representative datasets: (1) the Health and Retirement Study (HRS) and (2) the National Longitudinal Study of Adolescent to Adult Health (Add Health). Findings will also provide optimization protocols for AL measurement in other biomarker-rich population datasets. Additionally, the project will highlight key risk and protective factors and critical intervention periods, informing the design of tailored intervention programs and policies to support healthy aging. The proposed project integrates an interdisciplinary research program, mentorship, education, and apprenticeships to achieve the following core training objectives: 1) Gain expertise in life-span development with specific focuses on mid and later life; 2) Develop expertise in human population biology, biospecimen collection techniques, and the epigenetics of health and aging; 3) Gain expertise in working with field-based longitudinal nationally representative datasets rich in biosocial data; 4) Develop skills in measurement theory and data science; 5) Engage in professional development. Training in these domains will support the candidate’s long-term career goal of becoming an independent investigator who integrates social science and human biology with the emerging field of data science to examine how life experiences become biologically embedded, influencing disease etiology and contributing to the development of chronic diseases. The candidate's existing foundation and research experience position them well to pursue this critical line of inquiry, and the K01 award is crucial for providing the career development needed to pursue this critical inquiry. Receiving the K01 award would significantly contribute to their intellectual development, research skills, and readiness to become an independent scholar.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Twin pregnancies account for approximately 3% of all births in the U.S. Twin pregnancies show distinct physiological differences compared to singleton pregnancies that can impact the efficacy and safety of drugs used during pregnancy. Betamethasone is a synthetic corticosteroid that is used in pregnant women who are at risk for preterm delivery to promote fetal lung maturation and to reduce the incidence of respiratory distress syndrome (RDS) in neonates. However, our understanding of the impact of these physiological changes in twin pregnancies on pharmacokinetics and pharmacodynamics and, thus, the efficacy and safety of drugs used in women with pregnancies is currently limited. The primary objective of the proposed project is to narrow this knowledge gap using betamethasone as a prototypical paradigm compound. First, we will develop physiologically-based pharmacokinetic (PBPK) models for twin monochorionic and dichorionic twin pregnancies (Specific Aim #1). We will incorporate pregnancy- specific changes, including the induction of CYP3A4 by elevated 17β-estradiol levels in twin pregnancies. Using clinical data from both singleton and twin pregnancies, we will establish dose-exposure-response relationships for betamethasone and RDS prevention in the fetus through the integrated use of model-based meta-analysis (MBMA) and PBPK modeling and simulation approaches. This will allow us to propose optimal dosing and dose- to-delivery intervals for betamethasone to prevent RDS in twin pregnancies (Specific Aim #2). Our research will support drug label improvement, as the current use of betamethasone in RDS is still off-label. Ultimately, our research will generate quantitative insights into this underrepresented population, refining the dosing strategies for betamethasone and paving the way for safer, more effective treatments for high-risk twin pregnancies.

NSF Awards · FY 2025 · 2025-09

The three-year REU Site: Assured Autonomy and Networking is hosted by the University of Florida. Artificial intelligence (AI) will quickly transition from intelligent chatbots to controlling a wide variety of robots, autonomous vehicles, and other physical systems. The advantages include providing reliable and predictable operation of autonomous assets, such as unmanned aerial vehicles and robots, operating in unpredictable environments, and with control being carried out via networked communication. However, there are challenges affecting these communication channels such as failures in the networks that these systems rely on for communication and coordination, or even deliberate attacks. These problems require the education of a new workforce that has the technical knowledge and hands-on capability to understand these problems and develop solutions to ensure that autonomous systems work safely and as intended. This REU site will engage eight undergraduate students in scientific research on these topics. Participants will gain hands-on experience with the wide selection of autonomous vehicles and robots, networks, and sensors. They will be mentored by an interdisciplinary team of faculty and graduate student mentors across electrical & computer engineering, mechanical & aerospace engineering, civil engineering and computer & information sciences. Students will increase their knowledge and gain research experience to further pursue the challenging research problems ensuring the safe and reliable operation of AI-controlled robots, vehicles, and other physical systems. REU students will work on cutting-edge research projects and test their ideas using the wide selection of autonomous vehicles and robots, networks, and sensors of the University of Florida Autonomy Park. Cross-disciplinary projects will allow students to utilize techniques from autonomy/control, AI/machine learning, formal methods, wireless communications and networking, and cybersecurity. Participants will disseminate their research through presentations and written publications and engage with industry representatives through networking events. Undergraduate researchers will develop the ability to conduct independent research, which will transition them to learn and conduct research independently and in teams, and prepare them for future graduate studies and STEM career pathways. 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 We will develop a distributed, cloud-based, artificial intelligence (AI) tool for the prediction of chronic kidney disease (CKD) progression and modifiable risk factors for clinical use. Our tool will provide a 5-year risk profile individualized to each patient and will integrate electronic health record (EHR) data, text data, radiology images with corresponding reports, and pathology data, including brightfield histology images and electron microscopy reports. The EHR, text, laboratory, and radiology data from >200K individuals with kidney disease will drive the AI tool development supplemented with histopathologic image data from >1000 individuals. Although histopathologic information has long held prognostic significance for end-stage renal disease, such data is rarely included in clinical kidney failure risk equations due to its complexity. Because the histopathologic manifestations of CKD are broadly distributed across the kidney’s functional units, our tool will facilitate the decomposition of each kidney whole slide image into hundreds of sub-images, comprising >~3M functional units, quantitate pixel-level features, and provide balance among the different modalities for effective learning and generalization. The resulting tool will support: primary care physicians and nephrologists who will benefit from enhanced risk prediction using complex data types, patients who will benefit by identification and prioritization of modifiable risk factors, and nephropathologists who will benefit from the quantitative analytics. An iterative process will incorporate their feedback to enhance the usability and usefulness of our platform. Our platform will be self-adapting, and will ingest and learn from new data as it evolves, improving kidney disease trajectory prediction and therapeutic care.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSRACT The hippocampus has a well-established role in the encoding and recall of spatial memory. At the center of the hippocampal circuit are dentate gyrus (DG) granule cell mossy fiber (MF) to CA3 pyramidal neuron synapses. MF-CA3 synapses have been found to play a key role in learning and memory. However, little is known about the regulation of development and function of these highly plastic synapses. Neurotrophins, such as BDNF and NT3, play a critical role in neuronal development and function. BDNF is well characterized in its role in the CNS however, NT3 has long been forgotten. Both BDNF and NT3 and their receptors, TrkB and TrkC, respectively are located at MF-CA3 synapses. Results from our lab suggest the ablation of NT3, encoded by Ntf3, in the DG alone is sufficient to reduce synaptic transmission and induce contextual fear memory deficits. Despite their colocalization in the DG, initial results suggest independent or synergistic roles of BDNF and NT3 and MF-CA3 synapses. It is left to be determined what are the independent or coordinated roles of BDNF and NT3 in development and function at MF-CA3 synapses. My data indicates the ablation of Bdnf in the DG results in basal synaptic transmission deficits. Currently, knowledge about neurotrophins at MF-CA3 synapses is limited to BDNF's role in long-term potentiation, nearly nothing is known about how NT3 acts on MF-CA3 synapses. Thus, my preliminary observation offers insight into the possible mechanism underlying synaptic plasticity induced learning and memory. The overarching hypothesis is that BDNF and NT3 work independently or synergistically at MF CA3 synapses to promote development and function. To investigate this hypothesis, I will employ three integrated aims to dissect in vivo alterations at MF-CA3 synapses caused by the ablation of Bdnf or Bdnf and Ntf3 from DG granule cells. From Aim 1, I will determine how BDNF and NT3 regulate structure at MF-CA3 synapses. I have generated DG specific knock out of Bdnf (Bdnf-cKO) and Bdnf and Ntf3 (Bdnf/Ntf3-dcKO) using the Pomc-Cre mouse line. To analyze MF axon terminals and CA3 dendric spines called thorny excrescence (TE), I utilize AAV stereotaxic injection to label neuronal structures. Additionally, I utilize expansion sample preparation to image synapse immunostaining at super resolution and perform 3D reconstruction to analyze individual synapses. Finally, I will perform electron microscopy to reveal detailed structure at MF-CA3 synapses. From Aim 2, I will characterize how the ablation of Bdnf affects the function of MF-CA3 synapses by employing behavioral assay to understand to what extent of spatial and contextual memory deficits in Bdnf-cKO mice. From Aim 3, I will perform single nuclei RNA sequencing (snRNA-seq) on DGGCs to elucidate the mechanism by which BDNF regulates the active zone of MF axon terminals. The outcomes of the proposed research will elucidate the roles of two neurotrophins in the function of synaptic transmission and memory.

NIH Research Projects · FY 2025 · 2025-09

Project summary/abstract Immediate Early Genes (IEGs) are commonly used as proxy markers for neuronal activity in the context of substance abuse research. Despite a wide-ranging catalog of activity-dependent IEGs, there is a notable absence of genetically encoded markers of neuronal inactivity. This paucity is particularly noticeable in the context of substances which principally exert inhibitory effects on target neuronal function, including opioids. There is, therefore, a critical need to determine the transcripts that are upregulated following strong neuronal inhibition. The overall objective in this application is to identify and characterize markers of inhibition within neurons in the nucleus accumbens. Our central hypothesis is that distinct inhibited neurons will undergo transcriptional changes that can be used to identify and one day manipulate these inhibited neurons. The rationale for the proposed research is that understanding how neurons respond to inhibition will provide new opportunities for developing experimental therapeutics to treating opioid use disorder. To attain the overall objectives, the following specific aims will be pursued: 1) Identification of Immediate Early Genes expressed following pharmacological inhibition of the nucleus accumbens (NAc); and 2) Identification of Immediate Early Genes expressed following pharmacological inhibition with fentanyl. The research proposed in this application is innovative because it applies cutting edge methods to identify immediate early genes that are expressed in inhibited neurons for the first time. These contributions will have significant impact because they are expected to build a foundation for unprecedented analysis of patterns of excitatory and inhibitory activity throughout the brain.

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.

NSF Awards · FY 2025 · 2025-09

Our body's surfaces that come into contact with the outside world (the lining of our nose, mouth, and gut) are protected by a layer of cells called epithelial cells. These cells form tight connections with each other to keep harmful pathogens like bacteria, viruses, and parasites from getting inside our body. These cells also produce special proteins called interferons, which help fight off infections. Recently, interferons were shown not only to fight pathogens but also to function when there's no infection. The interferons appear to be essential for creation of the tight connections in the cellular protective layer. This study will investigate how cells control the production of interferons and how these proteins help cells build their wall against the outside environment. Understanding this process could lead to new treatments for diseases that arise when this protective barrier breaks down. The project will provide education and training opportunities for high school students and teachers and for graduate students. Epithelial cells at mucosal surfaces form the primary barrier against environmental threats through tight junctions that regulate molecular diffusion and prevent pathogen access to subepithelial tissues. Type III interferons (IFNλs) primarily function at these surfaces as key protective cytokines against pathogens. Recently, the investigators discovered that basal IFNλ expression increases as epithelial cells polarize, and this increase is fundamental for regulating tight junctions and barrier function. This work identifies a previously unknown IFNλ function: controlling epithelial cells' primary role of forming tight barriers. This project will focus on understanding these mechanisms as a step towards comprehending IFNλs' complete role in mucosal surface protection beyond their established antipathogen functions. The outcomes could potentially reveal new therapeutic targets for barrier dysfunction diseases. 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 to enable infrastructure improvements at the Whitney Laboratory for Marine Bioscience, located on a barrier island in Northeast Florida. This award supports the construction of a state-of-the-art recirculating seawater system (RAS) that complements the existing open seawater facility, allowing precise control of environmental conditions such as temperature, salinity and nutrient levels. This infrastructure will significantly expand the Lab’s capacity to study and optimize the reproduction, development and behavior of local marine species, including commercially important finfish (e.g., red drum, snook, American red snapper) and shellfish (e.g., clams, oysters). The new facility will also support educational and outreach programs, including K-12 engagement, undergraduate and graduate research training and participation by the local community. These activities contribute to building a contemporary scientific workforce, enhancing aquaculture practices, rehabilitating endangered marine wildlife (e.g, sea turtles), and increasing public understanding of ocean science and conservation efforts that collectively support sustainable food systems and coastal ecosystem resilience in the face of climate change. The intellectual merit of the project lies in its ability to transform the scope and precision of marine biological research in a natural coastal setting. By enabling researchers to stimulate seasonal cues, generate multiple spawning events per year and conduct high-resolution studies in semi-natural mesocosm conditions, this facility supports cutting-edge investigations into animal behavior, developmental physiology and the effects of environmental stressors and pollutants. Integration with advanced imaging and AI-based analytics will allow researchers to explore fundamental biological processes with unprecedented clarity. The recirculating systems will serve 11 full-time tenure-track faculty, students and visiting researchers, positioning the Whitney Lab as a national model for integrative marine science in a changing world. 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

This REU Site will provide experiential learning to a cohort of participants with a focus on critical engineering challenges in healthcare. REU Participants will (i) engage in research with Faculty and Graduate Student Mentors, (ii) gain or improve foundational research and other skills via training and workshops, (iii) learn about high-priority engineering challenges in healthcare via seminars and field trips to industry and medical/clinical facilities, (iv) disseminate their research results through both oral and written communication, and (v) establish a long-lasting network of research peers. In addition to the progression of science and advancement of national health, this REU Site positively impacts the education and personal growth of the REU Participants. As a result of this REU experience, which will be customizable to individuals in the program, REU Participants will be better prepared to explore research as a future career and make decisions regarding industry, government laboratory/agency, graduate school or medical school. This REU Site will also contribute to the professional development of participating Faculty and Graduate Students Mentors through required mentorship training. REU Site activities and lessons learned will be disseminated via participation in undergraduate focused symposia at relevant professional society meetings. This REU Site focuses intellectually on cross-disciplinary engineering research within the healthcare field. Several different projects are proposed for the REU Participants to select from, which span experimental and computational methods and are aligned with research laboratories of the Faculty Mentors. For example, research on the polishing of magnesium alloys will help realize a sustainable manufacturing process for magnesium-alloy-based biodegradable stents and advance stent design. Research on the development of microfluidic devices will address a critical need to develop better pathogen isolation and detection methods. Research on how cell nuclei can sense biophysical signals and regulate cellular behavior will elucidate new pathways to utilize nuclear mechano-sensing to enhance the efficacy of a medical treatment. Research on coagulation will address the need for a personalized approach to treat inflammation-mediated coagulopathy, both for urgent care and over longer recovery trajectories. Enhanced understanding of dura structure in the skull will inform synthetic graft development and further improve the integrity of cranial dura replacements in humans. Simulation of neuromuscular human balance control has the potential to increase the well-being of individuals in society by identifying new targets for fall-prevention interventions. In addition to the societal impact of engineering research that advances healthcare, the REU addresses the need for a modern workforce to meet these engineering challenges. 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

The objective of this University of Florida/Advent Health proposal within The Chronic Pancreatitis Research Consortium (CPCRC) is to complete recruitment and follow-up of the longitudinal prospective cohort of patients with acute, relapsing acute, and chronic pancreatitis that comprise the PROCEED cohort. The CPCRC is a continuation of the Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer (CPDPC). The major collaborative effort within the CPDPC, and for the CPCRC, is the establishment of a deeply phenotyped cohort with an associated annotated repository of biospecimens (blood, pancreatic and duodenal juice, urine, stool, saliva and when feasible pancreatic tissue), clinical data, and imaging to allow for the identification and validation of biomarkers for risk stratification, early and accurate diagnosis, prognostic modeling, and personalized therapy. Through the completed acquisition of cohorts of these patients and associated biospecimens, the CPCRC will continue to provide the resources and collaborative opportunities necessary for achieving many of the research objectives identified in the strategic plans of NIDDK- to gain mechanistic insights into the pathophysiology of acute and chronic pancreatitis and develop treatments to prevent or more effectively treat their sequelae- chronic pain, relapse of acute pancreatitis, diabetes, exocrine insufficiency, and the variety of other negative outcomes (metabolic bone disease, sarcopenia, reduced quality of life). Our proposal addresses several key needs of the CPCRC. 1) A successful platform for recruiting subjects with relapsing acute and chronic pancreatitis, particularly those from subgroups which are inadequately represented in the current PROCEED cohort (Blacks, Hispanics). 2) Completion of two approved and ongoing ancillary studies- one in sarcopenia in relapsing acute and chronic pancreatitis and one assessing a novel DAMP (eNAMPT) that serves as a master regulator of innate immunity. Sarcopenia is a highly significant clinical issue in patients with pancreatic diseases, and the studies of eNAMPT may lead to novel strategies for clinical trial stratification, and potentially even to therapy as an agent against this molecule is in clinical trials. 3) Mechanistic and pathobiological studies of diabetes across the spectrum of pancreatitis, using archived specimens from cohorts within CPDPC, another UO1 T1DAPC, and the D2d trial (biorepository at AdventHealth). In addition, we propose a novel proof of concept trial for pancreatogenic diabetes, and a proposal to utilize human pancreatic tissue available at UF and AdventHealth to study the molecular pathology of insulin secretory dysfunction in pancreatitis. 3) To determine the timing, efficacy, and durability of surgical therapy for treating painful chronic pancreatitis in the PROCEED cohort, and to use these findings to establish a clinical trial of surgery across the CPCRC. We believe the types of analyses and studies proposed will be necessary for the work of the CPCRC, and we are able to fully support these aims or related aims selected by the CPCRC.

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 · 2025-09

Abstract Project Summary and Abstract. Over 17,000 new traumatic spinal cord injuries occur in the United States each year. Following the initial traumatic insult (i.e. primary injury), blood vessel disruption leads to pervasive hypoxic and inflammatory cell damage (i.e. secondary injury). Our inability to detect ongoing cell damage impairs our capacity to develop effective strategies to limit debilitating secondary injuries. The fundamental goal of this proposal is to test the hypothesis that cerebrospinal fluid (CSF) 5-hydroxyindoleacetic acid (5-HIAA), serotonin’s primary metabolite, is a reliable biomarker of secondary cell damage. Descending raphe projections deliver and store serotonin near spinal sensory and motor synapses. Due to the proximity of raphe projections to sensory and motor fibers, insults that damage sensory and motor tracts inadvertently injure raphe fibers resulting in a transient release of stored serotonin into the extracellular spinal tissue. 5-HIAA is a highly stable serotonin metabolite with CSF levels mirroring extracellular serotonin levels. Building from well-published animal data, and recent human studies from our group, we further hypothesize 5-HIAA to be a reliable biomarker for structural injury to spinal sensory/motor tracts, therefore 5-HIAA is an important tool to acutely prognosticating long-term outcomes in humans with spinal cord injuries. This study represents the first step in developing strategies to limit secondary injuries that exacerbate disability and undermine recovery potential in humans with acute traumatic spinal cord injuries. Within the first phase of this proposal we have 1) refined our understanding of acute management strategies to preserve motor control in patients with acute traumatic spinal cord injuries (Swaroop et al., 2024), 2) demonstrated our capacity to reliably measure CSF metabolites in humans (Anand et al., 2024), and 3) demonstrated 5-HIAA to be a stable molecule with spinal levels NOT influenced by the circadian cycle (Anand et al., 2024). Within this second phase of the project (R00) we will perform a proof-of-concept study evaluating CSF 5-HIAA’s sensitivity in detecting secondary insults in humans with spinal cord injuries. This study will set the foundation for a future PHASE III study in which we will compare the accuracy of CSF 5-HIAA vs standard physical exams, to monitor ongoing secondary injuries and prognosticate long-term clinical outcomes for spinal cord injury patients.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Menopause is midlife event that promotes adverse outcomes in the female brain. The systemic proinflammatory state induced by large fluctuations and changes in ovarian and pituitary hormones is thought to exacerbate peripheral and centrally mediated neuroinflammation that increase the risk of AD in women compared to men. Our previous work has demonstrated that the gonadotropin LH/CG receptor-ligand complex is as a therapeutically relevant nexus for menopausal and AD-related cognitive and neuroplasticity loss. We have now identified that these benefits may be facilitated through immune regulating properties; known for this receptor complex in the periphery but completely unexplored in the CNS. We have identified that LH/CG receptor activation using hCG is particularly effective at normalizing myeloid cell peripheral infiltration exacerbated by ovariectomy (to model menopause) in the rodent AD model brain. This proinflammatory mechanism is regarded as a key driver of AD pathogenesis, underscoring the significance of evaluating this hormone-receptor complex in this context. A potential immune-regulating role for the LHCGR in the CNS are bolstered by its expression in microglia and excitatory neurons, both key mediators of neuroinflammation in disease states. Therefore, here we seek to address if this hormone-receptor complex yields CNS benefits through regulation of neuroinflammatory mechanisms that are relevant in AD development. Specifically, AIM 1 will evaluate the cell specific role of the LHCGR signaling on CNS function, neuroplasticity, and inflammatory processes through conditional deletion. AIM 2 will address whether the functional and neuroplasticity benefits associated with stimulating this receptor complex involve the inhibition of peripheral infiltration mechanisms; also, how this proinflammatory mechanism and therapeutic benefits are impacted by age, reproductive status, and amyloidosis. AIM 3 will determine how age and reproductive status specific baseline levels of centrally produced ligand (LH), receptor expression levels, and/or its cellular localization influence the therapeutic outcomes of this receptor’s target engagement. This work will advance our understanding of this understudied receptor complex in the brain and its relation to incrased risk of AD development in women. More broadly, it will expand our knowledge base on neuroendocrine regulation of important disease driving neuroinflammatory processes. Notably, given the involvement of peripheral immune cell infiltration-based inflammatory processes, in several neurological conditions (TBI, neurogenerative disorders, stroke, epilepsy, severe infection) the mechanistic evaluation of LHCGR signaling in this context can inform therapy beyond AD risk factors.

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.

NSF Awards · FY 2025 · 2025-09

This project establishes an artificial intelligence (AI)-driven framework to accurately detect sinkhole precursors based on multiple Earth monitoring data sources. Sinkholes are a pervasive and destructive geohazard, threatening critical infrastructure and public safety. Yet accurately detecting sinkhole precursors is challenging due to the subtle and varying nature of these signals. While recent advances in remote sensing and AI have improved our ability to monitor ground movement, existing methods often rely on general-purpose AI algorithms that overlook the specific complexities of sinkhole development. This project introduces novel AI algorithms specifically designed to meet these unique challenges. In particular, the proposed AI-driven framework is capable of integrating different data types, automatically identifying and grouping environmental conditions and adapting detection criteria accordingly. The findings from this research will improve the early detection of sinkhole precursors, supporting public safety and hazard mitigation. The findings will also help city planners make better decisions about zoning, site stability, and infrastructure resilience. Additionally, the project will develop educational materials and outreach programs by organizing a geoscience AI challenge for students and creating new AI-driven geoscience curricula for undergraduate and graduate courses. This research aims to establish advanced multimodal AI methodologies that fuse large heterogeneous geoscience data streams using a shared latent representation and a nonparametric Bayesian clustering algorithm. The primary objectives include: (i) establishing a structured data processing pipeline for collecting, organizing, and aligning multimodal geoscience datasets; (ii) creating a multimodal dual-variational autoencoder architecture for accurate detection and localization of sinkhole precursor signals; (iii) developing an adaptive nonparametric Bayesian clustering algorithm to support interpretable analysis; and (iv) validating the proposed framework using real-world datasets from known sinkhole-prone regions. The contributions of this research span both geoscience and AI. In geoscience, it will advance our understanding of the predictive mechanisms of sinkhole formation. In AI, it will introduce novel algorithms capable of handling multimodal and incomplete data, performing adaptive anomaly detection, and enhancing model interpretability in complex settings. 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

Non-technical Abstract: Current flowing in a wire can induce electron movement in the adjacent wire. The naive expectation is that the electron motion will mirror the direction of the current. However, recent findings in quantum wires have revealed that these systems can sometimes behave like a diode, allowing electrons in the nearby wire to move in only one direction, regardless of the current's direction. By studying this effect in detail, the research team aims to deepen our fundamental understanding of electron-electron interactions in one-dimensional systems. This knowledge could pave the way for the development of heat-harvesting devices and the discovery of novel quantum phases of matter. Additionally, the project offers valuable opportunities for educating and training undergraduate and graduate students in quantum devices, nanodevice fabrication, and cryogenic operations. It also promotes careers in quantum science and technology at the University of Florida. TECHNICAL SUMMARY: The project aims to investigate interactions between quasi-one-dimensional quantum wires coupled at the nanoscale using Coulomb drag measurements. While electron-electron interactions in individual Tomonaga-Luttinger liquids are well understood, the physics of Coulomb-coupled Tomonaga-Luttinger liquids remains less established. This project seeks to map the phase space of various drag-inducing mechanisms by examining the dependence of one-dimensional Coulomb drag on magnetic field, interwire separation, and disorder. Special emphasis is placed on studying Coulomb drag in the spin-polarized regime and on exploring the recently observed non-reciprocal contribution to 1D Coulomb drag, which is identified through measurements with reversed drive current directions. Furthermore, by quantifying the strength of electron interactions in 1D wires with proximity-induced superconductivity, the project addresses a key question regarding the fate of superconductivity in strongly interacting one-dimensional systems. Both laterally and vertically coupled quantum wire devices will be employed to achieve these objectives. The outcomes will expand our understanding of TLLs in closely coupled systems—particularly their magnetic field dependence—and pave the way for engineering next-generation quantum devices for heat harvesting and topological superconductivity. In addition, the project provides valuable opportunities for educating and training undergraduate and graduate students in quantum devices, nanodevice fabrication, and cryogenic operations. It also supports the promotion of careers in quantum science and technology at the University of Florida. 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 Number sense, the cognitive ability to represent and manipulate numbers, is found across various species, including mammals, birds, and insects, and is crucial for their survival. Among various species, number sense is particularly important for humans for their social and economic activities. People with numerical disorders, such as dyscalculia, which affects 3-5% of the population, face significant challenges in their lives. Despite the importance of number sense, the underlying neural circuit mechanisms remain poorly understood. Previous studies with monkeys have found neurons in the parietal and prefrontal cortical areas that selectively respond to specific numbers. These number coding neurons are the candidate neural substrates for numerical perception. However, the neural circuit mechanism that generates and maintains the number coding remains unknown. Understanding of the mechanism is crucial to understand the basis of numerical disorders and develop effective treatments. This research program investigates the neural circuit mechanisms that are responsible for the transformation of visual inputs into number coding in the brain. The vision-to-number transformation process involves multiple cortical areas spanning from sensory to association areas. To understand how sensory inputs are transformed into numbers across the cortical hierarchy, we developed novel head-fixed number discrimination behavior tasks for mice using virtual reality (VR). By combining the head-fixed VR tasks with a large field-of-view 2-photon calcium imaging and optogenetics, we will reveal how the brain transforms the extracted visual features into number coding and how the number selectivity is formed through inter-areal and local neural circuits. These animal experiments will be corroborated with network simulations using artificial intelligence (AI) trained to perform the same VR tasks. The results from this project will provide critical insights into the neural basis of number sense, an essential aspect of human intelligence, by revealing the transformation mechanism that bridges the gap between sensory and cognitive systems. Furthermore, our unique approach to combine large-scale in vivo imaging, optogenetics, AI-based network simulations, and novel virtual reality tasks that are compatible with training both mice and AI has the potential to become a powerful approach to understand neural mechanisms underlying cognitive functions that involve many brain areas in general. Therefore, the success of our project will have a broad impact on systems and cognitive neuroscience, and on the development of more effective treatment for number-related disorders.

NSF Awards · FY 2025 · 2025-09

This research project focuses on aligning artificial intelligence models with real-world operational goals in predictive maintenance for manufacturing systems. Predictive maintenance plays a crucial role in manufacturing by optimizing equipment reliability, minimizing downtime, and reducing costs, yet current artificial intelligence-driven solutions often underperform in practice due to a misalignment between how models are trained and how maintenance decisions are made. Many artificial intelligence models rely on standard statistical metrics that do not directly reflect maintenance objectives, leading to costly and inefficient decision-making. This research looks to introduce a systematic approach that ensures artificial intelligence models are designed to improve actual maintenance outcomes, not just predictive accuracy. By embedding operational objectives and constraints directly into artificial intelligence model training, the project seeks to enable manufacturers to deploy predictive maintenance solutions that are both effective and practical. The findings from this research look to support the broader adoption of artificial intelligence in predictive maintenance for manufacturing systems, contributing to national economic prosperity and the progress of science and engineering. Additionally, the project seeks to develop educational materials and outreach programs to integrate artificial intelligence alignment concepts into engineering curricula and prepare future professionals for artificial intelligence-driven industries. This research aims to establish model-agnostic strategies that improve the decision-making impact of predictive maintenance models in manufacturing. The objectives include: (i) designing machine learning models that explicitly incorporate maintenance cost and operational constraints into predictive modeling; (ii) extending the unit-level prognostics to fleet-level maintenance decision-making through Bayesian optimization to enhance scalability and adaptability; and (iii) assessing the framework’s effectiveness under various industrial settings using real-world and simulated manufacturing datasets. The project seeks to answer fundamental questions such as: (1) how can artificial intelligence models be trained to optimize for real-world operational objectives rather than simple proxy metrics, such as average prediction accuracy? and (2) how can unit-level predictions be integrated into fleet-level maintenance decision-making? By addressing these questions, the project strives to advance artificial intelligence alignment for decision-making in predictive maintenance and contribute to the broader field of artificial intelligence for manufacturing applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Every individual has a unique way of learning. Recent advances in virtual reality (VR) technology—particularly the integration of eye-tracking and motion sensors—now make it possible to create personalized learning experiences by capturing where students look and how they perform tasks in real time. However, most educational VR tools still do not fully account for these individual differences. This project investigates how real-time interaction data in VR can be used to provide adaptive learning, driven by artificial intelligence (AI), to enhance students’ understanding of complex scientific concepts, specifically in the area of semiconductor fabrication. By emphasizing personalized support in semiconductor education, this project aims to enhance STEM learning and strengthen the domestic semiconductor workforce. The overarching aim is to ensure that Americans—regardless of individual ways of learning—can meaningfully engage with emerging technologies. This project investigates how adaptive, AI driven, support in VR environments can enhance students’ understanding of semiconductor fabrication, a complex STEM domain that requires both procedural knowledge (e.g., how to perform a task) and conceptual understanding (e.g., why and how it works). The research examines two key variables: (1) the type of knowledge being constructed—procedural versus conceptual—and (2) individual cognitive capacity, particularly visual-verbal working memory. The central hypothesis is that gaze-based adaptive scaffolding—such as prompts triggered when learners lose focus—can be especially beneficial for students with lower working memory capacity during tasks that require integrating verbal and visual information. Using a quasi-experimental design, the project team develops and evaluates the effectiveness of adaptive VR instructional modules that provide real-time personalized feedback versus non-adaptive VR on learning outcomes such as recall, recognition, and knowledge transfer. 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

New and improved technology is allowing for the discovery of lightning processes that were previously unable to be observed. Lightning researchers are using this new data to adjust their theories on fundamental questions related to how lightning initiates and propagates. In this award, new instrumentation will be deployed at the Lightning Observatory in Gainesville (LOG) in Florida to capture the details of lightning across a broad range of the electromagnetic spectrum, including new measurements of high-energy X-rays. Improved understanding of lightning processes are important for improved predictive capabilities and development of lightning protection options. The project will also provide training and mentorship for students and a post-doctoral research scientist. This award will address a number of open scientific questions about lightning using data across the electromagnetic spectrum. For this study, the researchers will use high-speed cameras in the visual range, an infrared camera, an ultraviolet imaging system, and a new high-speed X-ray imaging system, all deployed from a rooftop on the University of Florida campus. The specific tasks are to: 1) Study the mechanisms of leader-stepping and attachment processes in negative and positive lightning, 2) Investigate the post-return-stroke processes, including continuing current and channel decay, 3) Conduct high-speed imaging of lightning in the X-ray/gamma-ray bands, and 4) Study bipolar lightning flashes with multiple ground terminations. 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

This proposal requests partial support for the 2025 Cancer Cachexia Conference (CCC). The conference will be organized by the Cancer Cachexia Society (CCS), and hosted in Turin, Italy from September 25-27, 2025. Cachexia is present in a significant number of patients at their time of cancer diagnosis and ultimately affects approximately 75% of patients with advanced disease of any tumor type. Cachexia decreases physical function, functional independence, and quality of life - causing considerable patient and family distress. Cachexia also predisposes patients to illness, increases treatment toxicity, reduces treatment efficacy, and decreases survival, with cachexia estimated to be causative in 30% of cancer deaths. Though emerging evidence suggests this number may be much higher. The overall goals of the conference are to foster awareness, understanding, and research of cancer cachexia among scientists at all career stages, clinicians, patients, and caregivers, for the purpose of advancing identification, current management, and future treatment, for this major unmet medical need. The CCC serves as the major conference to help grow this awareness and research around cancer cachexia and remains the only conference devoted solely to this topic. Indeed, other cachexia-related conferences cover a broad array of topics including aging, sepsis, covid, chronic kidney disease, obesity therapy, cardiovascular disease, burn injury, critical illness myopathy, as well as cancer. Importantly, although there has been previous iteration of the CCC, each conference is unique in focusing on new topics within the rapidly evolving cancer cachexia field. In this regard, the 2025 CCC will include several new sessions and speakers and cover topics within the scope of the Division of Cancer Biology’s scientific branches, including a double session on how the tumor microenvironment, tumor metastasis, and immunology impact cachexia, an expanded session on integrated and systems biology (inter-tissue crosstalk) and cachexia, as well as new sessions on central regulation systems of feeding and metabolism in cachexia, and emerging topics including pediatric cancer cachexia. The 2025 CCC will also include a dedicated session on current management strategies for cachectic cancer patients that will be recorded and made publicly available. These novel sessions and topics will be covered whilst simultaneously providing speaking opportunities, training and mentorship for graduate students, postdoctoral associates, physicians in training and early-stage investigators. The impact of this 2025 Cancer Cachexia Conference will be to promote new progress and growth in the clinical investigation of anti- cachexia therapies and to strengthen education and research in cancer cachexia.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Rational enzyme design holds the promise to transform biological research and therapeutic development by enabling the creation of efficient biocatalysts. However, current methodologies in rational enzyme engineering remain limited; computationally designed enzymes often fall short of natural enzymes and require extensive experimental optimization. A fundamental barrier to progress is the incomplete understanding of enzyme sequence-activity relationships. In fact, even the most advanced data-driven and physics-based models cannot reliably and systematically predict mutation effects on enzyme activity. Such a prediction is critical for designing optimal enzyme sequences for efficient catalysis. This limitation underscores the need for novel approaches. Generative AI offers a compelling solution to overcome the barrier, analogous to how language models like ChatGPT interpret semantics in human language. Generative AI can learn from sequences evolved in nature to predict protein function. In particular, the PI has pioneered the application of generative AI to predict enzyme activity using sequence data. Our research has demonstrated that natural sequence information can reliably predict the impact of mutations on enzyme catalytic activity, especially in active sites, enabling the successful optimization of multiple enzymes in biochemical assays. Additionally, we found that loop sequence within TIM- barrel scaffold enzymes can predict novel activity when integrated into de novo designs. These insights represent a new paradigm for studying enzyme catalysis, highlighting how different enzyme regions are evolved for catalytic efficiency in nature. This research program aims to further advance our understanding of enzyme sequence-activity relationships through generative AI, with the long-term goal of achieving fully rational enzyme design. In Direction 1, we will evaluate the generalizability of these relationships across diverse enzymes, laying the groundwork for future sequence-based enzyme engineering. Directions 2 and 3 will overcome the current limitations of black-box AI models by incorporating physics-based modeling to elucidate the sequence-activity relationships identified by generative AI. Specifically, Direction 2 will investigate how active sites are preorganized for efficient catalysis. Direction 3 will explore the role of loops in TIM-barrel enzymes and develop strategies to enhance catalytic activity by engineering these loops. Overall, this proposal aims to introduce innovative methodologies to link enzyme sequences with their catalytic activities, deepen our understanding of enzyme catalysis, and pave the way for fully rational enzyme design.