University Of Florida

NSF Awards · FY 2026 · 2026-09

Viral infections grow and spread through processes that occur at many connected levels, from events inside infected cells, to the body’s defense against infection, and to the spread of disease across communities. However, these processes are often studied separately, making it difficult to understand how changes at one level affect outcomes at another. This project will develop new mathematical and computational tools to connect these levels in one framework. The work will help researchers better understand how viruses grow, how the body responds to infection, how treatments and prevention measures work, and how infections spread in populations. By linking these processes together, the project may provide useful guidance for evaluating strategies to reduce the impact of viral diseases. Educational and outreach activities will provide interdisciplinary training opportunities and introduce students to the application of mathematics in biomedical research and public health. This project will develop and analyze a multiscale mathematical modeling framework for viral infections by integrating intracellular and extracellular dynamics within the human body with disease transmission between host populations. At the intracellular level, the models will describe viral entry, replication, assembly, and release. At the extracellular level, the models will describe viral kinetics and immune responses, including antibody and cellular immune responses. At the population level, the models will incorporate multiple transmission routes, symptomatic and asymptomatic infection, vaccination, waning immunity, and variant emergence. These components will be coupled into unified multiscale systems to study how molecular mechanisms, host immune responses, and transmission processes interact across scales. The project will combine dynamical systems analysis, bifurcation theory, stochastic modeling, data fitting, sensitivity analysis, and numerical simulation. The resulting models will provide a mathematical basis for studying treatment effects, vaccine impacts, variant dynamics, and epidemic outcomes across multiple biological scales. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-09

Sea-breeze thunderstorms occur in warm, coastal regions due to the difference in heating of land and water surfaces. While the basic sea-breeze process is well-known, the details of why some days with seemingly similar conditions produce strong, weak, or no storms is less understood. This award provides funding for a combined observational and Interpretable Artificial Intelligence (AI) study of the specific, small-scale atmospheric conditions that promote sea-breeze events in Florida. More reliable forecasting of sea-breeze thunderstorms and their associated hazards of flash flooding and lightning will directly benefit public safety for millions of Florida residents and tourists, while also supporting critical economic sectors such as agriculture, aviation, and outdoor recreation. The project also involves training the next generation of AI-fluent students and conducting public outreach. This award aims to address the hypothesis that the temperature profile - specifically, inversion layer strength and boundary layer temperature - controls the convective depth, precipitation intensity, and lightning flash rate of Florida sea-breeze storms. Further, the researcher will investigate whether an interpretable AI framework is necessary to move beyond statistics and discover the fundamental, non-linear relationships that govern the sea-breeze thunderstorms. The project will make use of operational radar data from the National Weather Service, focused radar data from a 2022 field campaign, satellite-measured lightning data, and environmental data to construct a multi-platform convective database. That database will then be analyzed using traditional and AI tools to elucidate environmental controls and governing dynamics of sea-breeze convection. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-09

Insects provide a vital role in pollinating crops, recycling nutrients, supporting food webs, and through their influence on human and animal health. Many beneficial insect groups are declining, while some harmful ones that damage crops, spread disease, or affect property are expanding into new areas. Extreme climate and weather events, such as heat waves, droughts, cold snaps, and heavy rainfall are becoming more frequent and severe. These extreme events can rapidly impact insect populations, but we still do not know which insects are most vulnerable, which may benefit, how those effects play out across regions and over time, and what the impact with be on the U.S. human population. This project will develop a new framework to understand how extreme climate and weather events affect adult insect abundances across the contiguous United States, with a focus on three groups: mosquitoes, butterflies and moths, and ground beetles. These groups are especially important from ecological and societal perspectives, and as targets for biotechnology development. By integrating multiple dimensions of extreme events with species traits, this framework moves beyond single-event studies, with the use of artificial intelligence (AI) tools, to obtain new insight and a more realistic and generalizable understanding of how environmental extremes shape biological systems. Improving the ability to anticipate these responses is critical to developing resilience strategies for conservation, food security, and One Health. The effort will combine more than 25 million insect records from monitoring networks, community science programs, and surveillance programs with 45 years of high-resolution climate data. It will also create a publicly accessible Extreme Climate and Weather Event Atlas for the contiguous U.S. that characterizes events by their frequency, intensity, duration, timing, and sequence. Using ecological theory, life history traits, advanced statistical modeling, and machine learning and AI approaches, the project will characterize and test how both single and compound extreme events influence insect abundances across latitude and climate context. The research will also examine whether prior conditions can amplify or reduce impacts, including cases such as false springs, ecological traps, and ecological bonanzas. By identifying likely insect winners and losers under increasing climate extremes, this project will improve ecological forecasting and provide tools that can be applied to other taxa and regions. The project also includes training for students and early-career scientists in data science, ecological modeling and AI, engagement with the public through webinars and events with monitoring networks and mosquito control programs, and development of freely available data and software tools for the broader research community and resource managers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

Lignin is a natural substance found in plants and wood. Recently, researchers have explored ways to change lignin into very small particles known as lignin nanoparticles. These nanoparticles have promising applications. They can be used to create food packaging that fights bacteria. They can also be used as wound dressings that help prevent infections. These particles can also be placed in materials that clean water by trapping microbes. However, how these nanoparticles enter and affect bacterial cells is not well understood. This project will investigate how changing the particle properties (size, charge and others) affect their ability to kill bacteria in water. The research team will explore the effectiveness of these lignin nanoparticles in fighting different types of bacteria. The team will identify how the design of the lignin nanoparticles and the specific types of bacteria they target can influence their antibacterial strength. By understanding what helps lignin nanoparticles bind to and impact bacteria, the team will improve the performance of the particles. The team will use microscopes to examine the tiny pores of bacteria cells to understand how size affects the ability of the lignin nanoparticles to move through pores and enter the bacteria. The team will also explore how the stickiness of both bacteria and the lignin nanoparticles impact particle-bacteria connections. Lastly, the team will investigate how the electric charges of bacterial cells and lignin nanoparticles change the ability of these nanoparticles to kill bacteria. The project also includes educational programs aimed at helping students understand the importance of using natural plant-based materials in daily use. The team will create a minicourse and offer hands-on laboratory experience for both college and high school students. Students will learn both the basic concepts of lignin nanoparticles and participate in practical experiments stemming from this research. The educational activities will encourage students to think critically about how natural materials, microbiology, and engineering all play a role in the overall quality of human life. The team is committed to inspiring students to see how using more natural materials can improve well-being and support the national economy. Advancing the development of lignin nanoparticles with tailored antimicrobial properties will require a comprehensive understanding of the interplay between cell surface characteristics and the chemical and morphological features of lignin nanoparticles. The overarching objective of this project is to elucidate the critical factors determining the toxicity of lignin nanoparticles to microorganisms. The research will assess how cell characteristics, including hydrophobicity, surface charge, and cell envelope topography, influence particle-cell interactions. Concurrently, the research team will evaluate the extent to which the size, hydrophobicity, and surface charge of the lignin nanoparticles modulate their antimicrobial properties. This dual approach will provide mechanistic insights into the toxicity of lignin nanoparticles and enable the rational design of nanoparticles with targeted antimicrobial activity, addressing a significant knowledge gap in lignin-based nanomaterials research. The project team will: (1) utilize advanced imaging techniques to characterize the presence and dimensions of pores in the cell walls of Gram-positive and Gram-negative bacteria, and determine how lignin nanoparticles of varying sizes traverse these structures to access the intracellular environment, thereby clarifying the role of cell topology and the size of the lignin nanoparticles in toxicity mechanisms; (2) investigate the influence of hydrophobicity in both bacterial cells and lignin nanoparticles on the toxicity, studying the intensity of cell-particle adhesion through hydrophobic interactions; (3) examine the impact of cell surface and particle charges on the antimicrobial activity of lignin nanoparticles, with emphasis on the contribution of cell wall teichoic acid esterification with D-alanine to the density of positive surface charges and the resulting interactions with the negatively charged particles. The antimicrobial effects will be quantified using both traditional plate counting and advanced techniques, including confocal microscopy and flow cytometry. Successful completion of this project is expected to generate transformative advances in the field of biopolymers, particularly in the valorization of lignin through its conversion into high-performance nanomaterials, thus deriving value from a polymer historically regarded as waste in the United States. Furthermore, this research will contribute to the development of a multidisciplinary workforce skilled in chemistry, materials science, and microbiology, and foster expertise at the intersection of these disciplines. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

NONTECHNICAL Summary This award supports computational research and education to advance understanding of physical polymer aging, the process where the configuration of atoms relaxes over time leading to changes the properties of polymers. Polymers are long chain molecules made of several repeating units. When these chains are tangled and randomly arranged without any order, the polymer is said to be ‘amorphous’. Below the glass transition temperature (the point at which the polymer changes from soft and flexible to hard and glass-like), amorphous polymers are widely used in applications ranging from storage and packaging to separation technologies. Owing to their low cost and ease of manufacturing, polymer membranes have the potential to dramatically reduce the energy required for industrial separations, which currently account for a substantial fraction of global energy consumption. Polymers of intrinsic microporosity are especially promising membrane materials because their loosely packed molecular structure creates extra internal space (also known as free volume), which increases how quickly and efficiently gas molecules can move through the material. However, these polymers are not widely used in industrial applications, because they undergo physical aging, a slow and irreversible process in which the polymer relaxes and densifies over time. This relaxation leads to reduced separation efficiency, loss of mechanical integrity, and ultimately diminished membrane performance. At present, physical aging in high free volume polymers like polymers of intrinsic microporosity is captured indirectly through changes in membrane performance, and the underlying structural changes at the molecular level remain poorly understood. This CAREER award addresses this critical knowledge gap by uncovering how polymer chains rearrange during aging and how these rearrangements affect membrane performance. Molecular simulations will allow the PI to directly observe these small-scale rearrangements over time, revealing how the material’s internal structure and movement change in ways that cannot be observed experimentally. By developing a predictive framework for aging behavior, this research will enable the rational design of polymer membranes that maintain their performance over long times, facilitating the transition of energy-efficient membrane technologies from the laboratory to industrial use. In addition to advancing membrane science, the project integrates education and workforce development. While molecular simulations are used across many science and engineering disciplines, access to simulation-based training at the K-12 and undergraduate levels remains limited. To address this gap, the project includes mentoring and training activities that are open to all students at the K–12 and undergraduate levels. In addition, the project will increase the accessibility of molecular simulations for blind and visually impaired students, enabling their early engagement in STEM research. Successful completion of these educational goals will broaden access to molecular simulation tools across multiple educational stages while also encouraging blind and visually impaired students to pursue STEM degrees and enter the STEM workforce. TECHNICAL Summary This award supports computational research and education to advance understanding of physical polymer aging with potential to help guide polymer design and discovery. Physical aging in amorphous polymers arises from the gradual relaxation of a non-equilibrium glassy structure toward a thermodynamically favorable state. In polymer membranes, these microscopic relaxation processes manifest as macroscopic declines in permeability and mechanical properties, limiting the long-term viability of state-of-the-art high free volume materials such as polymers of intrinsic microporosity. Despite their technological importance, the molecular mechanisms governing aging in these materials, including the roles of chain rigidity, cooperative dynamics, and free volume redistribution, remain poorly understood. This project uses atomistic molecular simulations in conjunction with glassy aging theories to elucidate the molecular origins of physical aging in high free volume polymer membranes. The research focuses on quantifying the interplay between monomer rigidity, segmental mobility, and free volume distribution, and on identifying the dominant relaxation modes that control aging behavior. Temperature jump protocols are employed in simulations to accelerate and probe aging dynamics, enabling systematic investigation of relaxation pathways that are inaccessible to conventional experimental techniques. The project further develops a computational protocol to predict aging behavior in high free volume polymers based on molecular-level descriptors. The outcomes of this research will bridge polymer physics and membrane science by providing a mechanistic framework for understanding aging in glassy polymers. The resulting insights will guide the design of rigid, solvent-cast polymer membranes with improved resistance to aging, advancing the fundamental understanding of non-equilibrium polymer dynamics while enabling the development of durable, energy-efficient separation technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

Despite centuries of study, scientists still lack the tools to accurately describe what animals eat in a way that captures the true complexity of their diets. Most existing approaches force species into broad, oversimplified categories such as "carnivore" or "omnivore" that obscure meaningful ecological differences and limit our understanding of how diet has powered the evolution of life on Earth. This project addresses that fundamental gap by developing a new framework for measuring the diets of mammals with unprecedented precision and scale. By combining cutting-edge Artificial Intelligence tools with detailed three-dimensional measurements of mammal teeth, this research will reveal how dietary diversity has evolved across the placental mammal radiation, one of the most spectacular events in vertebrate history. Beyond its scientific contributions, this project advances AI literacy in the biological sciences, produces a large publicly accessible database of mammalian dietary and morphological data, and supports STEM education through partnerships with Florida K-12 teachers and undergraduate internship programs at the University of Florida's Florida Museum of Natural History and George A. Smathers Libraries. This project leverages Large Language Models and advanced Natural Language Processing (NLP) techniques to extract and quantify dietary information from thousands of scientific studies spanning more than two centuries of biological literature. Rather than assigning species to discrete diet categories, the contextual NLP extraction identifies diet as the ranked importance of biologically defined food classes, generating a high-dimensional multivariate dietary dataset for more than 1,000 placental mammal species. A human-in-the-loop validation workflow ensures accuracy and repeatability of AI-assisted dietary rankings. Using a novel multivariate liability threshold model, the project will characterize the tempo and mode of dietary macroevolution and its ecological consequences across the placental mammal radiation. In parallel, 3D dental topographic metrics will be generated for more than 1,000 species, and the form-function relationship between tooth morphology and diet will be interrogated at a scale never before attempted. This project will produce open-access datasets, novel macroevolutionary analytical tools, and a replicable AI-driven pipeline applicable to other axes of ecological variation across the tree of life. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

Angiosarcomas are malignant vascular cancers that are rare in humans, accounting for 1-2% of all sarcomas. These aggressive malignancies have a high metastatic propensity, leading to a poor prognosis. No effective targeted therapy for angiosarcomas has been established yet, with previous studies showing unfavorable clinical benefits. The rarity of angiosarcoma and its underrepresentation in cancer research underscore the urgent need for foundational studies and the development of reliable translational animal models. A major challenge in this field is the limited availability of research tools and faithful animal models for studying rare human cancers. This limitation often leads to ineffective studies and a high risk of translational failure, as existing models fail to capture the complex clinical and biological features of human disease. One promising solution is the use of naturally occurring tumors in dogs. Hemangiosarcoma (hereafter referred to as angiosarcoma), a histologically similar counterpart to human angiosarcoma, occurs more frequently in domestic dogs. Canine angiosarcoma closely mirrors the clinical presentation and pathological characteristics of the human disease, offering a rich and translationally relevant research resource. Our previous work has demonstrated the experimental feasibility and technical advancements made possible by leveraging canine models for a variety of preclinical research applications. The use of animal models is justified as mouse xenograft systems derived from human and canine angiosarcoma tissues provide a controlled in vivo platform to evaluate tumor growth, RAS signaling, and therapeutic response in a pathologically relevant context that cannot be adequately modeled in vitro. The primary objective of this project is to establish a reliable translational model using canine angiosarcomas, with a particularly focus on cross-species molecular convergence into RAS signaling pathways. This pathway represents a promising therapeutic target for intervention using a tri-complex RAS(ON) multi-selective inhibitor. Specifically, we aim to: 1) establish multi-platform research resources from naturally occurring angiosarcoma in dogs; 2) assess the potential of canine angiosarcomas as a model for targeting active RAS signaling as a point of molecular convergence; and 3) develop AI models to assess the molecular relevance and translational potential of canine angiosarcoma. At the completion of the proposed project, we expect to generate comprehensive research resources to establish a reliable translational model for human angiosarcoma using naturally occurring cancers in dogs. This resource will enable us to demonstrate the therapeutic potential of the RAS(ON) inhibitor by effectively targeting convergent signaling pathways in angiosarcomas. AI models developed from canine angiosarcomas will support the clinical assessment of RAS-activated angiosarcomas by integrating complex genomic, molecular, and pathogenic characteristics. Through this approach, this project will offer strong translational applications for angiosarcoma, a rare and aggressive human vascular cancer. Furthermore, this project has the potential to establish a foundational modeling pipeline that can be adapted to support the use of naturally occurring cancers in dogs for translational research across a broad range of cancer types.

NIH Research Projects · FY 2026 · 2026-06

Project Summary. Many drugs of abuse are inhaled by users to achieve a more immediate or intense high including cocaine, amphetamine, prescription stimulants (e.g., Adderall), heroin, prescription opioids, benzodiazepines, and ecstasy. Nasal inhalation of these drugs (viz., “snorting”, “taking a bump”, “sniffing”, or “blowing”) holds substantial public health and treatment implications in addition to destruction and necrosis of oral, nasal, and facial structures. Despite the widespread use of the nasal route of administration by human drug users, there lacks an established model to capture this route of administration preclinically. Thus, there is a critical gap in our understanding of the mechanisms of these drugs on the brain and behavior. We reason that the lack of safe and effective therapies to combat drug addiction is due in part to this fundamental knowledge gap. Indeed, inhalation of drugs into the nose exposes them to unique entry points whereby they can eventually reach the CNS, including a unique enzymatic milieu which will impact metabolism and unique plasma permeability which altogether will influence the total amount of drug to reach the brain in addition to the kinetics of how it does so. While we are learning more about how routes of drug administration can influence brain circuits recruited by those drugs as well as dopamine increases, we have no benchmark for outcomes from intranasal delivery. To overcome this knowledge gap, a paradigm is needed which achieves reliable and precise delivery of drugs into the nose in preclinical models. We have developed a surgical device, that when implanted upon the nasal bone of mice, accesses the nasal cavity to allow reliable and precise intranasal (IN) drug delivery during freely-moving behavior. We validated that this device (called the Nasal Access Port or NAP) achieves precise and reliable administration of drugs. In this R21 we seek to compare the outcomes of cocaine when administered intranasally (IN) versus intravenously (IV) or intraperitoneally (IP) on common behavioral and neurobiological measures. Our hypothesis is that IN cocaine will induce distinct behavioral and neural alterations compared to other routes of administration. Data to support this hypothesis will highlight a unique liability profile and specific neurobiological mechanisms underlying cocaine’s effects when inhaled. In Aim 1 we will determine differences in how IN cocaine administration impacts behavior and then in Aim 2 determine differences in activation of the mesolimbic dopaminergic system by IN cocaine. From the results in Aim 1 we will also generate an integrated PK/PD model to bolster rigor and outcomes. The results from this pilot R21 study will establish a valuable platform for investigating the long-term effects of IN drug use, including addiction liability, neurotoxicity, and the development of targeted interventions.

NSF Awards · FY 2026 · 2026-06

Generative artificial intelligence (AI) is rapidly transforming how people find, analyze, and use information. In biodiversity science, where research depends on integrating data from thousands of specimens, publications, and observations, these tools hold enormous promise but also pose new challenges. Scientific progress, environmental resilience, and biosecurity all depend on trustworthy, accessible, and reusable data. Despite significant investments in biodiversity data infrastructure, many researchers still struggle to navigate complex data repositories, apply FAIR (Findable, Accessible, Interoperable, and Reusable) practices consistently, or take full advantage of available digital resources. This project advances the national AI research priority by ensuring that AI-powered research systems produce results that are FAIR, while following practices of Open Science, hence FAIROS. The project enhances established national and NSF cyberinfrastructure with the agent-based research platform iChatBio so that AI-assisted scientific workflows seamlessly comply with FAIROS principles. By lowering technical barriers to high-quality data use, the project expands participation in biodiversity research, supports education and workforce development, strengthens open science practices, and provides a model for responsible AI integration across scientific disciplines. The project designs, implements, and evaluates a suite of interoperable software agents that enable FAIROS-compliant biodiversity research within iChatBio. Four integrated tasks guide the work: (1) developing agents that leverage existing FAIR mechanisms in major biodiversity repositories and standards; (2) creating agents that mitigate gaps where FAIROS mechanisms are incomplete or absent, including automated data harmonization and identifier strategies; (3) implementing management and validation agents that generate machine-actionable workflow archives capturing inputs, outputs, provenance, metadata, and persistent identifiers; and (4) evaluating effectiveness, usability, and performance through research workflow templates, user surveys, and usage analytics. The project employs a co-development model in which computer scientists, biodiversity informaticians, and domain researchers collaboratively design reusable agent templates, validation strategies, and workflow templates. Outcomes include deployable FAIROS agents, standardized workflow-archive structures, documented design patterns, and evaluated best practices for AI-enabled research. These contributions establish a scalable framework for making agent-based scientific workflows reproducible, transparent, and reusable in biodiversity science and beyond. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Directorate for Biological Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

Project Summary/Abstract Ribonuclease L (RNase L) is a key antiviral enzyme that degrades host and viral mRNA in response to viral infection. Dysregulation of RNase L is implicated in viral pathogenesis, cancer, and immune disorders, yet if cells resolve this activation and how RNase L shapes adaptive immunity remains poorly understood. My preliminary data indicate that cells can recover from RNase L activation and that it promotes cellular immunity. Thus, RNase L activation is non-terminal and enhances immune cell-mediated clearance of infected cells. My overarching hypothesis is that RNase L-promotes apoptosis by enhancing immune cell-mediated targeting. I will test this hypothesis through two central aims: (1) Identify the mechanisms and consequences of RNase L deactivation. (2) Identify how RNase L promotes immune cell-mediate apoptosis.. These studies will define a novel mechanism by which RNase L bridges innate and adaptive immunity. Furthermore, my findings may inform treatments for long-term inflammation and uncover new immune-modulating therapies. In parallel, I will receive training in immunology, RNA biology, and quantitative proteomics under the guidance of an interdisciplinary mentorship team at Scripps Research, preparing me for a career at the intersection of RNA biology and immune regulation.

NIH Research Projects · FY 2026 · 2026-06

ABSTRACT Millions of older adults are at increased dementia risk because of prior exposure to repetitive head impacts (RHI), such as those from contact sports, military service, intimate partner violence, and other sources. Investigating common, globally relevant, environmental dementia risk factors like RHI is a public health imperative. We lack a fundamental understanding of how RHI leads to cognitive decline, including the distinct biological pathways and brain regions that are disrupted. Further, the intersection of prior RHI with common diseases of aging, such as Alzheimer’s disease (AD), cannot be ignored given the potential for a synergistic negative impact on brain health. Progress towards accurately identifying (diagnosis) and effectively treating RHI-related brain changes depends on defining key molecular and brain structure correlates of RHI. Our multi-site, multidisciplinary team is uniquely positioned to address these critical gaps and make substantial strides towards precision care for aging adults with prior RHI. This proposal builds directly on our strong portfolio of published research and compelling preliminary data pointing us towards the importance of 1) altered inflammation and immune-signaling pathways and 2) vulnerability of limbic system structures seen several years downstream of RHI, distinct from the effects of AD, and potentially explaining later-onset of RHI-related cognitive decline. We aim to accelerate our recent progress through a longitudinal study of fluid biomarkers, neuroimaging, and cognition in aging adults with and without RHI to parse RHI effects on brain aging (i.e., RHI “signatures”). In this R01 from an Early-Stage Investigator (ESI), we will investigate intersections of aging, RHI, and AD using the collective infrastructure of 3 ADRCs (1Florida, Boston University, University of California-San Francisco). We will identify data-driven molecular (plasma proteomics; Aim 1) and brain structure (multimodal MRI; Aim 2) signatures of RHI and map onto cognitive trajectories (Aim 3). The primary cohort will be strategically sampled to study 400 older adults with representation across the RHI, AD pathology, and cognitive continuums. Robust validation of our findings will be performed two ways: 1) internal validation – longitudinal subset within the primary study cohort, 2) external validation – independent sample enriched for RHI across the cognitive and AD pathology continuum (DIAGNOSE-CTE II). Our methods are conceptually and technically innovative: multi-site gold standard head trauma measurement integrated with deep clinical and biological phenotyping, Subtype and Stage Inference modeling (SuStaIn), large-scale proteomics and bioinformatics from ~11,000 proteins (SomaScan®), leading- edge brain MRI (hippocampal subfield analysis, free water diffusion), and modeling the full continuum of RHI exposure. Executing this ESI-led project will provide foundational knowledge of the relationships between RHI, AD, and aging. Identifying RHI-related molecular targets, brain atrophy and white matter injury patterns, and cognitive correlates are essential steps for understanding mechanisms, accurately modeling disease progression, and developing interventions for millions of at-risk older adults with prior RHI.

NIH Research Projects · FY 2026 · 2026-06

Summary Pathogenic Vibrio spp., including V. vulnificus, V. parahaemolyticus, and V. alginolyticus, are naturally occurring seawater bacteria that pose a growing threat to public health, particularly in coastal regions of the United States. These organisms are highly sensitive to geophysical conditions such as sea surface temperature, salinity, and nutrient availability—factors increasingly influenced by ecological changes and extreme weather events. Florida, with its extensive coastline and frequent exposure to hurricanes and harmful algal blooms (HABs), reports the highest incidence of vibriosis in the nation. This project aims to develop a predictive, satellite-based public health forecasting system—VIGOR (Vibrio Infection Genomics for Outbreak Risk Forecasting)—to identify and monitor high-risk zones for Vibrio spp. proliferation. The long-term objective is to reduce the burden of waterborne infections by enabling early detection and targeted interventions. The specific aims are: (1) to detect and characterize pathogenic Vibrio spp. in coastal water microbiomes using molecular and genomic tools; (2) to determine the impact of severe weather events and geophysical parameters on prevalence and genomic diversity of pathogenic Vibrio spp; and (3) to develop and validate ecological niche models (ENMs), particularly using MaxEnt architechture, to forecast seasonal and spatial hotspots of Vibrio spp. risk for human populations. The research design integrates biweekly field sampling, high-throughput sequencing, satellite remote sensing, and machine learning-based pathogen modeling, and develop a platform for real-time public health information dissemination. Environmental and microbial data will be used to train and validate predictive models, which will be overlaid with demographic and infrastructural vulnerability indicators to inform public health decision-making. This work aligns with NIAID’s mission by advancing predictive capabilities for environment-sensitive infectious diseases and supporting proactive, data-driven public health responses.

NSF Awards · FY 2026 · 2026-06

This CAREER project will develop new methods that allow cyber-physical systems, such as robotaxis, service robots, and autonomous drones, to adapt to individual human preferences using simple feedback. Today, many such systems rely on fixed rules designed in advance, which makes it difficult for them to respond to differences in how people prefer to work or interact with them. This project will enable these systems to learn from intuitive input that non-expert users can provide, such as choosing between options or ranking outcomes, while maintaining safety, privacy, and security during operation. The project will develop new theory and algorithms, release open-source tools, and validate the resulting methods on real-world robotic platforms. It will also advance education and workforce development through new course materials, immersive learning platforms, student mentoring, and outreach activities for undergraduate and K-12 students, in collaboration with the University of Florida Transportation Institute, the Florida Institute for National Security, and the Florida Institute for Cybersecurity Research. This project will establish a comprehensive framework for assured reinforcement learning from human feedback for cyber-physical systems, enabling systems to learn personalized control policies directly from simple human feedback while providing guarantees for safe, secure, and privacy-preserving operation. The research is organized into three integrated thrusts. Thrust 1 will develop logic-aware methods that translate simple human feedback into logic-based reward models to update system control policies, addressing noisy or limited human feedback through uncertainty quantification, robust estimation, and active learning. Thrust 2 will develop methods for deploying these systems in human environments with temporal constraints, including both predefined constraints and those inferred from human interaction, while supporting bounded violation during exploration, risk awareness for worst-case outcomes, and robustness to distribution shifts. Thrust 3 will address privacy and security concerns associated with integrating human feedback into cyber-physical systems by protecting human data, defending against data poisoning attacks, and investigating federated learning architectures for large-scale systems. All developed methods will be evaluated through real-world case studies involving autonomy with human interaction. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY There is a fundamental gap in understanding how, in neurodegenerative disorders called tauopathies, such as Alzheimer’s Disease and Progressive Supranuclear Palsy (PSP), the protein tau affects memory and neuronal function. One pathological mechanism involves the association of aberrant tau with components of the translation machinery: ribosomes, mRNA, or both. However, the consequences of these interactions remain unknown. The long-term goal of this work is to better understand the link connecting tau abnormalities and memory impairment. The overall objective of this proposal is to determine the impact of pathological tau on translation and, potentially, the impact of translational changes on tau pathogenesis. We will use human brain tissues as well as in vitro and in vivo models to study translation, tau-RNA interactions, and the regulation of protein synthesis in cells and mice neural tissue. Our preliminary results demonstrate an association between tau and ribosome complexes, as well as an impaired protein synthesis network in disease. Therefore, the central hypothesis is that pathological tau impairs translation of proteins critical for memory. The rationale for the proposed research is that understanding the tau-mediated mechanism of translation dysfunction will aid in the design of therapeutic targets for tauopathies, which currently afflict nearly 50 million people worldwide. Our strong preliminary data serve as support for identifying the RNA transcripts that bind to tau in primary tauopathies, such as PSP (Aim 1). Our results also substantiate identifying the compendium of newly translated proteins in the presence of pathological tau (Aim 2). The proposed experiments are highly rigorous by following good practices, statistical design, and controlled validation experiments in each aim. This proposal is significant because it tests a new mechanism in which translation dysfunction promotes symptoms classical of tauopathies, such as memory loss and cognitive impairment. The proposed strategies use novel approaches in human brains and mouse models of tauopathy, which adds substantial innovation. This work is expected to advance the field by filling the gap in understanding of tau-mediated brain dysfunction. This knowledge will serve to better characterize the link between tau and memory impairment in order to develop novel therapeutic strategies.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY Lung transplantation is the treatment of last resort for a large number of patients with end-stage lung diseases, but is complicated by a modest post-transplant survival. Mortality after transplantation is mostly driven by chronic lung allograft dysfunction (CLAD). An early complication of lung transplantation, primary graft dysfunction (PGD), is a major risk factor for subsequent development of CLAD. Tools to more accurately predict these complications are urgently needed. This proposal hypothesizes that integrating clinical and biological markers from donors and recipients will predict the incidence and severity of PGD and CLAD. This award will support the career development of a mathematician–data scientist toward a critical and underexplored area. The candidate has a strong foundation in mathematical modeling, machine learning, and biomedical data science, and aims to apply these skills to improve outcomes after lung transplantation. Through this K25 award, he will transition to an independent research career centered on translational, data-driven solutions to clinical challenges in transplant medicine. This will be achieved under the following Aims: Aim 1: Identify and validate predictors of PGD incidence and severity. Aim 2: Identify and validate predictors of CLAD onset and subtype. In pursuit of both Aims, we will obtain biospecimens from 3 anatomical sites (donor lung pre-transplant, donor lung 2 hrs post-transplant, and serially blood samples from the recipient) and donor and recipient clinical data from ≥200 patients at two large lung transplant centers. We will perform a multiplex assay measuring 71 cytokine/growth factors to screen samples for markers of cell activation, chemotaxis, injury, angiogenesis and growth factors. We will use computational phenotyping and machine learning to predict outcomes from these comprehensive biological and clinical data. These models will also allow us to identify novel markers for both outcomes that could improve mechanistic understanding and support point-of-care test design. Trained models and candidate markers will be validated using multiplex and ELISA assays of prospectively collected samples from recipients at two major transplant centers over the course of the study. The ability to predict PGD and to forecast CLAD onset are essential for the development of life-, health-, and graft-extending interventions. Our proposed project builds upon important ongoing work while introducing five advances: multi-site specimens, a broad-scope injury/immune response cytokine panel, analysis of conventional storage solution, specialized computational modeling, and multi-center validation. Successful completion of this project will yield externally validated biomarker panels for PGD incidence and severity and CLAD onset and subtype suitable for developing point-of-care tests.

NIH Research Projects · FY 2026 · 2026-06

ABSTRACT Renal masses, both benign and malignant, pose significant diagnostic and management challenges, with kidney cancer ranking among the top 10 most common cancers in both men and women in the U.S. Treatment options vary based on patient and tumor characteristics and include active surveillance, biopsy, surgical resection, and thermal ablation. While surgery can be effective, many small renal masses—particularly those under 4 cm—are benign, and unnecessary surgical removal can expose patients to avoidable risks. Imaging techniques like CT are critical for diagnosis, but manual interpretation is time-consuming and subject to inter-reader variability, contributing to inconsistencies in diagnosis and treatment planning. Integrating imaging with electronic health records (EHRs), which capture key risk factors such as obesity and smoking, can support more accurate risk stratification and clinical decision-making. Despite the promise of artificial intelligence (AI) and real-world data (RWD) to improve renal mass diagnosis and management, adoption in clinical settings remains limited. Barriers include challenges in developing reliable segmentation algorithms, integrating multimodal data, ensuring usability within clinical workflows, and the lack of clear, evidence-based guidelines for managing renal masses— particularly small, incidentally detected lesions—which contributes to clinical uncertainty and variability. To address these gaps, our multidisciplinary team—drawing on expertise in data science and renal research and leveraging large-scale datasets from the UF Health Integrated Data Repository (IDR) and public imaging datasets—proposes the following aims: 1) Develop transformer-based vision-language models (VLMs) for medical image segmentation, clinical concept extraction, and automated radiology report generation, trained on UF and public datasets and validated against expert annotations. 2) Create a robust multimodal framework that integrates EHRs, imaging, and clinical notes—while addressing missing modalities—to support renal mass risk stratification and identification of key clinical factors. 3) Design, develop, and evaluate the RENAL MASS DIAGNOSIS AND MANAGEMENT DECISION SUPPORT SYSTEM (RMM-DSS) using a user-centered design (UCD) approach. This tool will deliver personalized diagnostic and treatment insights and integrate seamlessly into clinical workflows. Iterative co-design and usability testing—including deployment in the Epic sandbox—with radiologists, urologists, and other stakeholders across multiple health systems will ensure clinical relevance and usability. Expected outcomes include: (1) novel AI-driven tools for medical imaging and text processing that enable automated segmentation and report generation; (2) a robust multimodal framework to enhance decision- making; and (3) a high-fidelity, usable prototype of the RMM-DSS. This work has broader potential to improve small renal mass management and inform similar efforts in other clinical domains.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY/ABSTRACT The application of mathematical modeling to biological processes has grown exponentially in recent years, fuelled by an explosion in the quantity of generated experimental data and a parallel explosion in computational capacity. This is a relatively young field that holds great potential for accelerating biomedical research, but is hampered by a dearth of investigators who are facile both in mathematical concepts and experimental pathobiology. The University of Florida Mathematical Biology of Lung Disease training program seeks to fill this gap by providing postdoctoral trainees with a Ph.D. in mathematics or related fields the necessary training in both pulmonary biology and mathematical modeling to launch successful independent investigative careers in this field. To our knowledge, this is the only training grant of this type, seeking to train postdoctoral fellows to apply mathematical methodologies to the study of lung biology. The proposal is born out of an ongoing collaboration between a group of experimental lung researchers and mathematical biologists at the University of Florida. The program is co-directed by a physician-scientist and a mathematician, and the program faculty mentors are a diverse and multi-disciplinary training force of 24 outstanding mentors with complementary expertise in mathematical sciences and experimental research. This uniquely focused training program resides in the Division of Pulmonary, Critical Care, and Sleep Medicine with affiliations with multiple other University Departments. The trainees will be recruited from a large national pool by a Steering Committee, and will receive mentorship from a team of mentors with complementary skill, expertise, and background; rigorous laboratory-based research training; an extensive didactic curriculum that includes mechanisms of disease and introduction to lung diseases; as well as professional development. This application is the recipient of substantial institutional backing and requests support for 4 postdoctoral trainees who will each have a 2 year appointment on the training grant.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY/ABSTRACT The goals of this work are to understand the role of the glucocorticoid receptor in integrating stress responses from fasting and exercise in skeletal muscle and how synthetic glucocorticoids (GCs) show tissue and pathway selective activity profiles. CGs are known for their pharmacological anti- inflammatory effects, they display a suite of metabolically undesirable effects including gluconeogenesis, and insulin resistance, obesity, diabetes and muscle wasting. This is due to the role of the natural stress hormone, cortisol, as a primary effector of the adaptive response to fasting. However, GCs are also released during exercise, and episodic treatment with pharmacological GCs can be performance enhancing, showing stressor-selective outcomes. We have also developed selective GR modulators (SGRMs) that are muscle sparing, using a structure-based design strategy. These studies lead us to our hypothesis that pathway selective ligands utilize naturally occurring transcriptional modules that GR uses physiologically in response to different stressors. To study the adaptive responses to exercise without the heterogeneity of type of exercise and effects outside of skeletal muscle and model exercise signaling in vitro, we will genetically active the Creb1 transcription factor, which is normally activated by the combination of muscle contraction and b-adrenergic signaling. We showed that skeletal muscle Creb1 activation was performance enhancing, improved fuel storage and flux capacity, and reduced body weight in response to dieting or high fat diet. We will use a combination of genomics, proteomics, and a mitochondrial diagnostics and analysis platform to build causal models for how SGRMs mediate different effects on glucose disposal, protein balance, and mitochondrial/energetic rewiring and the roles of fasting and Exercise in modulating these responses. We will use machine learning to identify GR pathways \that are differently utilized by two different structural classes of SGRMs, we will use deep learning with molecular dynamics simulations to identify how receptor structure transduces ligand chemistry to regulate recruitment of epigenetic regulatory proteins and selective biological outcomes of the SGRMs. Models will be validated with structure-based design and analysis of a new set of SGRMs, with selected compounds studied in vivo.

NIH Research Projects · FY 2026 · 2026-05

Project Summary/Abstract Adeno associated viruses (AAVs) are premier viral vectors for gene therapy. AAV vectors consists of the capsid of an AAV packaging a transgene intended to treat a particular genetic disorder. A major obstacle to AAV mediated gene therapy is pre-existing neutralizing antibodies (NAbs) to the AAV serotype employed. AAVs are non-pathogenic, however they are endemic in the population and seropositivity for anti-AAV antibodies ranges from 20%-80% depending on the serotype. Prior to treatment patients must be screened for pre-existing NAbs, patients that fail to meet inclusion criteria are unable to receive treatment. Furthermore, the mechanism by which many of these NAbs inhibit viral transduction is not fully understood. Overcoming the barrier to treatment presented by NAbs is of utmost importance to develop the use of AAVs as gene therapy vectors. The McKenna lab has demonstrated that NAbs can be evaded by structure-guided rational design of the AAV capsid surface. Additionally, it has been shown that mouse derived NAbs favor interacting with different regions of the capsid surface than human derived NAbs, thus highlighting the importance of using human derived NAbs for structure-guided capsid engineering. The project proposed for this fellowship aims to employ structure-guided rational design of a recombinant AAV2 antibody escape variant by investigating the key contact residues between wild-type AAV2 and a cohort of 31 human NAbs derived from a naturally occurring AAV2 infection in pediatric hepatitis patients (Aim 1). Additionally, the mechanism by which these NAbs inhibit viral transduction will be investigated by in vitro cell binding and trafficking assays (Aim 2). To the best of our knowledge, this is the first time such a study has been conducted using NAbs derived from a naturally occurring AAV2 infection in humans. Production of a novel AAV2 antibody escape variant will increase the cohort of treatable patient s and allow for redosing in the future, thereby reducing patient burden. Elucidation of the NAb mechanism will serve to guide vector and treatment design reducing costs and increasing patient safety. Furthermore, the research entailed in this project will greatly aid my training to become and independent scientist and the environment at University of Florida will ensure my successful training.

NIH Research Projects · FY 2026 · 2026-05

PROJECT ABSTRACT Fetal alcohol spectrum disorders (FASD) can occur in a person when they are exposed to alcohol before birth and are characterized by impairments in physical, cognitive, and behavioral functions. Common signs and symptoms associated with FASD include low body weight, abnormal facial features, poor coordination, hyperactive behaviors, difficulty with attention, and difficulty in school. There is also growing research that individuals with FASD have sensory processing deficits that can impact self-regulation and adaptive behavior. Sensory enriched occupational therapy intervention may improve the neurological sensory processing deficits experienced by individuals with FASD, which may lead to lifelong improvements in self-regulation and adaptive behavior. Here, we propose a Phase 1 clinical trial to assess the following: 1) characterize changes in brain white matter microstructure of sensory integration in 6 to 8-year old children with FASD following intervention; 2) characterize changes in sensory processing, self-regulation, and adaptive behavior; and 3) develop a conceptual model of mediation, whereby a sensory enriched intervention alters brain sensory white matter networks, leading to improvements in sensory processing, self-regulation, and adaptive behavior in children with FASD. We predict that children with FASD who receive a sensory enriched intervention will demonstrate positive changes in white matter neural architecture when compared to controls, and that we will see improvements in sensory processing, self-regulation, and adaptive behaviors. This two phase R61/R33 will allow us the time to adapt and assess the study procedures and intervention to inform the development of future larger clinical trials.

NSF Awards · FY 2026 · 2026-05

This project addresses a fundamental challenge in synthetic biology: how to design cell-like systems that can sense their environment and process information in a controlled and programmable way. Living cells rely on asymmetric membranes and precisely organized proteins to convert external signals into internal responses, enabling essential functions such as communication, recognition, and adaptation. However, recreating these capabilities in synthetic systems remains a major scientific barrier. This research will develop a new class of fully protein-based vesicles that mimic these key cellular features, enabling directional sensing and programmable signal processing. By establishing fundamental principles that link molecular organization to biological function, the project promotes the progress of science and advances the frontiers of biotechnology—an area of strategic importance for national health, economic competitiveness, and innovation. The outcomes will enable new approaches in biosensing and bio-inspired materials. In addition, the project will provide interdisciplinary training for undergraduate and graduate students in protein engineering, biomaterials, and synthetic biology, while engaging K–12 students through hands-on modules and outreach programs. These activities will broaden participation in STEM, strengthen the future workforce, and enhance public understanding of emerging biotechnologies, thereby advancing the nation’s scientific enterprise and societal well-being. This research aims to engineer recombinant globular protein vesicles (GPVs) with controlled membrane asymmetry and integrated signal transduction capabilities. The project will pursue three objectives: (1) design and synthesize modular bolaamphiphilic fusion proteins that self-assemble into asymmetric vesicle membranes with defined protein orientation; (2) construct inward signal transduction pathways that convert ligand binding at the vesicle surface into lumenal biochemical outputs using mechanisms such as split enzyme reconstitution, protease cascades, and cell-free gene expression; and (3) quantitatively determine how protein orientation, spatial distribution, and lateral mobility within the membrane influence signaling efficiency. The approach integrates recombinant protein engineering, bioconjugation chemistry, advanced fluorescence imaging, cryo-electron microscopy, and small-angle scattering to characterize membrane structure and dynamics across multiple length scales. By correlating membrane architecture with functional outputs, the project will establish predictive design rules for programmable synthetic cells. These outcomes will advance synthetic biology and biomolecular engineering by providing foundational knowledge and enabling technologies for constructing adaptive, cell-like systems with tunable sensing and response behaviors. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-05

Summary There is an urgent need for objective and robust diagnostic indices related to psychiatric illnesses, including obsessive-compulsive and anxiety disorders. Brain-based indices are particularly suited for this purpose, but many of the available brain-based markers have limited scope, require specialized equipment, or have unknown reliability and validity. The proposed research aims to examine the value of data-based, quantitative, and objective markers of dysfunctional electrocortical processes associated with obsessive-compulsive and anxiety psychopathology. The proposed research aims to establish advanced computational indices based on resting electroencephalogram (EEG) recordings that are capable of (1) discriminating between diagnostic categories but also (2) predict transdiagnostic variables such as severity and comorbidity. Specifically, we use state-of-the- art data transformations to extract mechanistically informative indices of spectral shape and alpha-frequency phenomena inherent in EEG recordings during resting states. We will then establish their reliability and internal consistency—prerequisites for using them as markers of inter-individual differences. The indices will then be related to clinical data collected in a large sample of individuals presenting with symptoms on the obsessive- compulsive and anxiety spectrum. A Bayesian Hierarchical Model will be used to aid in data reduction and to measure and heighten reliability of the EEG-derived variables. Finding reliable and valid biomarkers of electrocortical processes has the potential of transforming diagnostic assessment by providing continuous indices of cortical dysfunction. If the goals of this application are met, then reliable and valid indices of electrocortical (dys)function may help to significantly shift clinical practice: In assessment, objective measures of EEG alpha reactivity could be used, for example, to objectively identify patients with perception/attention dysfunction, versus those with generally delayed oscillatory activity and thus more general cortical dysfunction. These inter-individual differences may in the future guide how patients are assigned to individualized treatment protocols as well as for predicting treatment outcome.

NIH Research Projects · FY 2026 · 2026-05

Project Summary/Abstract: Candidate: My goal is to become an independent investigator running a multidisciplinary, translational, systems neuroscience lab which investigates mechanisms underlying specific symptoms of movement disorders and designs translational strategies. My lab will use electrophysiology and optical neuroscience techniques paired with behavior to investigate how cellular ensembles and circuits contribute to movement disorders symptoms. I have a strong background in behavioral neuropharmacology and electrophysiology. I propose to learn optical neuroscience techniques including 1-photon in vivo calcium imaging and data analysis methods which use computational neuroscience and machine learning to analyze behavior. This will allow me to make inferences about the role of specific neurons and circuits in motor symptoms and to have the necessary skills to produce high-impact publications and successful R01 submissions. I received my PhD in June 2020. After receiving an extension due to major medical issues, this is my last eligibility cycle for the K99/R00. Training: In addition to my mentor Dr. Moehle, and co-mentor Dr. Wesson, I have assembled an advisory committee of experts in opioid pharmacology and electrophysiology, Dr. Varga, and in using machine learning strategies and analyzing large datasets, Dr. Vaillancourt. This committee will provide training and guidance to accomplish this proposal. I have also identified both local and external courses, seminars, and meetings which will provide technical training, presentation experience, responsible conduct in research, and necessary skills (offer negotiations, tenure, lab management, etc.) to facilitate my transition to independence. Research: After chronic L-DOPA treatment, up to 90 percent of Parkinson’s disease patients will develop debilitating abnormal, involuntary movements called L-DOPA-induced dyskinesia (LID). The mechanisms behind LID development are not understood. A common single nucleotide polymorphism in the mu opioid receptor (MOR) gene, OPRM1 increases susceptibility to LID. The mechanisms whereby OPRM1 variants increase LID susceptibility and how MOR+ spiny projection neurons (SPNs) contribute to LID is unknown. My underlying hypothesis is that striatal MOR+ neurons cause LID through increased dSPN activity. In AIM1 I will use fiber photometry and machine learning methods in a mouse expressing an equivalent Oprm1 variant to understand the mechanism by which this Oprm1 variant influences neural activity and specific LID behaviors. In AIM2 I will chemogenetically stimulate or inhibit MOR+ dSPN and iSPNs to determine the effects of MOR+ SPNs on LID behavior and striatal cellular ensemble activity as determined by 1-photon calcium imaging. In AIM3A, I will test the physiological changes occurring in MOR+ and MOR- dSPNs and iSPNs over the course of chronic L-DOPA treatment. In AIM3B I will test how MOR+ vs. MOR- dSPN ensembles are involved in LID using 1P calcium imaging with chemogenetic excitation and inhibition.

NIH Research Projects · FY 2026 · 2026-05

ABSTRACT / SUMMARY The Biennial Conference of the Society for Research on Biological Rhythms (SRBR) is the leading international meeting in circadian biology and the largest gathering of researchers in the field. For the past four decades, it has served as the premier forum for advancing research, fostering collaboration, and supporting career development in biological rhythms. The 20th edition, SRBR 2026, will be held May 9-13, 2026, in Amelia Island, Florida. This milestone conference will spotlight the critical role of biological timing in health, disease, and therapeutics, with a major emphasis on the emerging field of Circadian Health and Medicine. SRBR 2026 expects to welcome over 800 attendees and feature over 600 presentations spanning basic, clinical, and translational research. Historically, nearly 50% of conference attendees have been trainees, making it a critical event for supporting young investigators. We request partial support to provide travel awards for students and postdoctoral researchers and to support their attendance and participation. The program includes extensive opportunities for students, post-docs, and junior faculty, as well as those who are new to the field, including a dedicated trainee day, multiple mentoring activities, and continuing education sessions for health professionals. Biological rhythms are fundamental features of life that regulate key aspects of metabolism, physiology, and behavior. The SRBR conference has long integrated basic research across species with translational studies that link this science to improving human health. The central theme of SRBR 2026 is “Time Matters: Biological Rhythms Shaping Life and Health”. The meeting will convene investigators across career stages, disciplines, model systems, and levels of biological organization (from cells to organisms) to exchange advances and expand access to circadian biology training. SRBR 2026 will accelerate the integration of circadian principles into broader biomedical research and clinical practices to better human health.

NSF Awards · FY 2026 · 2026-05

The Security, Privacy, and Trust in Cyberspace (SaTC) program, a flagship initiative by the National Science Foundation (NSF), addresses critical cybersecurity challenges from a socio-technical perspective. By delving into deep scientific and engineering issues and considering human behaviors, SaTC aims to advance the field of cybersecurity and privacy. Given the escalating national significance of cybersecurity, effective communication between program officers, researchers, and government funding agencies becomes paramount. A robust SaTC community will drive innovation, identify novel research avenues, prevent duplication, and enhance graduate education opportunities. This project encompasses the 2026 SaTC PI meeting venue and conference logistics, including registration, audio-visual support, communications, and meeting space in College Park, Maryland. The planning date for the conference is August 6-7, 2026. The 2026 SaTC Principal Investigator (PI) meeting will help as follows. 1) Stimulating research ideas: By bringing together PIs working on different projects, the meeting encourages cross-pollination of ideas. Discussions, workshops, collaborative sessions, and networking opportunities foster creativity and may spark novel research directions. 2) Exploring new opportunities: PIs can explore interdisciplinary collaborations beyond their immediate domains. Interactions between researchers from different disciplines can yield new insights, foster synergies, and prevent redundancy in existing research efforts. 3) Transitioning Research into Practice: Sharing experiences and learning from others helps PIs refine their research approaches. Practical insights gained during the meeting contribute to more effective research outcomes. 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.