Florida International University

NIH Research Projects · FY 2026 · 2026-06

Project Summary Preterm birth (PTB) is a significant global health concern. The incidence of PTB in both developed and developing countries ranges from 11% to 15%. Despite ongoing research into its causes and treatments, the incidence of PTB has not shown any substantial decline in the past decade. One of the challenges in treating PTB is the lack of accurate and quantifiable methods to predict the onset of labor. It is known that the uterine cervix of a pregnant woman undergoes physiological changes throughout pregnancy that precede delivery. Recent studies in humans and animals suggest that inappropriate alterations in the extracellular matrix of the cervix, along with changes in its molecular content, occur prior to PTB. However, there is currently no method to visualize and assess these changes during pregnancy. In this proposal, we aim to develop a non-invasive, polarization-sensitive optical imaging probe to specifically evaluate cervical collagen changes during pregnancy, alongside shifts in molecular content, both of which are characteristics associated with PTB. We will achieve this objective by addressing three specific aims. In Aim 1, we will develop a miniaturized optical probe capable of delivering images of polarization-sensitive second harmonic generation and two-photon fluorescence microscopy of the uterine cervix in vivo at unmatched resolution. In Aim 2, we will conduct a longitudinal study using an animal model to quantify the changes resulting from the remodeling of the cervix from day 0 of pregnancy to day 19, as well as postpartum for 4 days. Finally, in Aim 3, we will utilize two models of preterm birth: the infection-mediated lipopolysaccharide (LPS) and the progesterone receptor antagonist, mifepristone (RU486), to characterize cervical remodeling associated with preterm labor. This proposal will provide, for the first time, a complete understanding of the remodeling of the uterine cervix in the murine model, paving the way for the development of new methodologies for assessing preterm birth risk.

NSF Awards · FY 2026 · 2026-06

The rapid advancement of artificial intelligence (AI) technologies is leading to changes in the skills employers expect from college graduates. This raises questions about how well-positioned college administrators and instructors are to updated their curricula to help students meet these evolving needs. This project will study these questions in the context of software engineering jobs and computer science departments, which are large sectors of the economy and the university that are particularly affected because of the increasing ability of AI-based tools to perform common programming tasks. Through working with hiring managers, employees, faculty, and students, the research team will develop new curricula that align the needs of both industry and universities to develop high-quality educational experiences that enhance students’ competitiveness in the workforce. The goal of the award is to build industry and academic partnerships to establish an evidence-based set of recommendations, resources, and upskilling for undergraduate computing faculty and students around cultivating AI-related competencies. As a first step, the research team will conduct semi-structured interviews with current employees and hiring decision makers on the knowledge, skills, and dispositions required for new software engineers and developers. Then, the team will apply these findings to launch a career readiness training program for computing students from two institutions, as well as opportunities for these students to apply that training through internships. Based on the feedback of the interns and their industry mentors, the training approach and resources will be refined and then launched more widely with computing faculty and undergraduate students at institutions across the U.S. The team will work to disseminate the insights and materials developed through outreach activities including a virtual expert seminar series and a website centered on AI-related competency development and career preparation. The project advances technology workforce initiatives by strategically aligning industry employees, hiring decision-makers, computing faculty, and computing majors around shared expectations and the competencies essential for practical and professional success in an increasingly AI-driven society. 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

Spinal cord injury (SCI) is a devastating condition that causes profound physical and emotional suffering, yet effective treatments remain unavailable. Developing new therapies to mitigate SCI outcomes represents a critical unmet clinical need. Disruption of the blood-spinal cord barrier (BSCB) leads to peripheral immune cell infiltration, exacerbates neuroinflammation, and worsens secondary injury, ultimately causing tissue damage and functional deficits. Protecting the damaged BSCB offers a promising strategy to limit secondary injury and improve outcomes. However, no therapies targeting BSCB dysfunction are currently available. The overall goal of this application is to investigate the previously unexplored role of perivascular fibroblasts in BSCB dysfunction and secondary injury, focusing on identifying perivascular fibroblast-derived peptidase inhibitor 16 (PI16) as a novel therapeutic target for neuroprotection after SCI. Our preliminary studies suggest that activation of the IFNγ pathway plays a key role in activating perivascular fibroblasts and promoting fibrotic scar formation. These activated fibroblasts significantly upregulate PI16 expression, which is closely associated with BSCB dysfunction and exacerbated neuroinflammation. Notably, BSCB disruption, injury size, and functional deficits were significantly reduced in PI16 knockout mice following SCI. Furthermore, a PI16-neutralizing antibody demonstrated robust neuroprotective effects when injected into the injured spinal cord. Based on these strong preliminary data, we hypothesize that upregulation of PI16 in perivascular fibroblasts via IFNγ signaling increases BSCB leakage and immune cell infiltration, exacerbating neuroinflammation and secondary injury, ultimately leading to progressive tissue damage and functional loss following SCI. Therefore, blocking PI16 activity with a neutralizing antibody could protect against tissue loss and promote functional recovery. We will test this hypothesis through three specific aims. Aim 1 will investigate the mechanisms regulating the activation of perivascular fibroblasts and PI16 upregulation after SCI. Aim 2 will validate the key role of PI16 in BSCB permeability, tissue damage, and functional deficits following SCI. Aim 3 will assess the therapeutic potential of PI16-neutralizing antibody treatment after SCI. These studies will provide valuable insights into the new role of perivascular fibroblasts in secondary injury and identify fibroblast-derived PI16 as a novel therapeutic target for SCI. Importantly, we will evaluate the therapeutic potential of a clinically translatable PI16-neutralizing antibody. These studies could pave the way for developing new treatments to improve outcomes for patients with SCI.

NIH Research Projects · FY 2026 · 2026-04

Project Summary Environmental and endogenous genotoxic stressors can induce DNA and RNA damage and misdeposition of the epitranscriptomic mark, N6-methyladenosine (m6A), leading to genome instability, gene dysregulation, and cancer and neurodegeneration. However, it remains unknown how DNA and RNA damage can synergize to modulate genome and epitranscriptome stability. In this project, we aim to understand the mechanisms underlying the crosstalk of RNA damage and m6A with DNA damage and repair. Our hypothesis is that environmentally-induced RNA damage, m6A, and DNA base damage and repair can interplay on R-loops to modulate RNA and DNA integrity. To test the hypothesis, we will pursue three Specific Aims. Aim 1 is to determine if environmentally-induced RNA damage can modulate RNA-guided DNA base damage repair and its fidelity on R-loops. First, we will determine if the environmental genotoxicant KBrO3 and occupational exposure level of Mn2+ can induce RNA and DNA base damage on R-loops to disrupt the expression of R-loop hotspot genes, MALAT1 and unfolded protein response regulator X-box binding protein 1 (XBP1) genes in human kidney cancer and iPSC-differentiated neural cells. Second, we will determine if RNA base damage can promote KBrO3- and Mn2+-induced DNA base damage by reducing the efficiency and fidelity of RNA-guided BER using circular RNA-mediated RNA damage systems, the artificial intelligence tool AlphaFold3, molecular dynamics simulation (MD), and steady-state enzyme kinetics. Aim 2 is to determine if m6A can modulate environmentally-induced DNA base damage repair by regulating RNA-guided DNA synthesis on R-loops. First, we will determine if KBrO3 and Mn2+ can cause the unique m6A deposition on R-loops, leading to dysregulation of the MALAT1 and XBP1 genes in kidney cancer and neural cells. Second, we will determine if m6A can disrupt DNA base damage repair induced by KBrO3 and Mn2+ on the R-loops of the MALAT1 and XBP1 genes by altering BER efficiency and fidelity using circRNA-mediated gene-targeted m6A deposition systems, AlphaFold3, MD, and enzyme kinetics. Aim 3 is to determine if environmentally-induced DNA base damage and repair can modulate m6A deposition on R-loops to disrupt RNA transcriptomic integrity. First, we will determine if KBrO3- and Mn2+-induced DNA base damage can alter m6A deposition on R-loops of the MALAT1 and XBP1 genes by disrupting the recruitment of m6A writers METTL3/METTL14 and eraser FTO and their substrate interaction on R-loops, suppressing R-loop resolution and expression of the genes in cancer and neural cells. Second, we will determine if DNA base damage can synergize with Mn2+ to cause m6A misdeposition on R-loops of the MALAT1 and XBP1 genes. Our study will create a new paradigm to reveal the crosstalk between environmentally-induced oxidative RNA damage and m6A deposition and DNA base damage and repair on R-loops in modulating genomic and transcriptomic integrity. Our results will facilitate the discovery of novel targets for RNA-based gene-targeted therapy and biomarkers for the prevention of environmentally-induced cancer and neurodegenerative diseases.

NSF Awards · FY 2026 · 2026-04

The fourth Joint Alabama-Florida Conference on Differential Equations, Dynamical Systems and Applications (JAF DEDS) will be held in Miami, Florida, on May 18--19, 2026. Organized by the mathematics departments at the University of Alabama at Birmingham, Auburn University, Florida International University, and Florida State University, this conference continues a rotating annual series that strengthens regional collaboration in applied mathematics. Growth in the applied mathematics community across Alabama and Florida has increased research activity in mathematical physics, fluid dynamics, applied harmonic analysis, and mathematical biology. JAF DEDS provides a venue for disseminating recent results and fostering collaborations among established researchers, early-career scientists, and graduate students, with special attention given to participants lacking federal funding, including graduate students, postdoctoral fellows, and early career researchers. The event will be open to anyone interested in the conference themes. The scientific program focuses on dynamical systems and partial differential equations arising in mathematical physics, including Korteweg-de Vries, nonlinear Schrödinger, and Euler-type models. These equations are central to understanding nonlinear wave phenomena -- such as traveling waves, vortices, and coherent structures -- with applications in optics, fluid dynamics, plasma physics, and new materials. The conference aims to connect researchers across diverse areas of PDEs and dynamical systems, foster exploration of their interconnections and applications, and stimulate interactions among regional experts in these areas, graduate students and junior researchers. Additional information is available at: https://case.fiu.edu/mathstat/news-events/may-26-conference/index.html. 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 · 2026-04

Project Summary Cytochromes P450 (CYPs) metabolize a wide range of drugs, carcinogens, hormones, and other xenobiotics. Their role in detoxification makes them crucial for optimizing drug safety. Steroid biosynthetic CYPs are targets for cancer, endocrine, and infectious diseases. Their ability to activate stable C−H bonds has led to ongoing interest in engineering them as novel biocatalysts. Our long-term objectives are to unravel the fundamental structure-function- dynamics relationships that govern CYP function. We have chosen the aromatase (CYP19) family as a model system. Aromatase holds significant importance as the most primitive human CYP, catalyzing a mechanistically intricate multistep synthesis of estrogens from androgens. This process is crucial for human health and plays a vital role in disease treatment. In this proposal, we aim to investigate several key questions that hold general relevance to the entire CYP superfamily. These questions include: 1. What are the critical functional dynamics of CYP19? 2. How are CYP19 and cytochrome P450 reductase (CPR) oriented in membranes? 3. How are CYP-CPR complexes organized in and scaffolded by membranes? The proposed studies will integrate classical biophysical techniques with cutting-edge tools, such as synchrotron X-ray foot printing mass spectrometry (XFMS), small X-ray scattering (SAXS), small-angle neutron scattering (SANS), and novel structural refinement methods. These advanced techniques will provide valuable insights into the critical, conserved functional dynamics underlying CYP19 function, the details of CYP19- and CPR membrane interactions, and the architecture of the CYP19-CPR complex.

NSF Awards · FY 2026 · 2026-02

Urban water and wastewater networks are essential to public health, environmental protection, and economic stability, yet the information used to manage these systems is often fragmented, incomplete, or difficult to retrieve and interpret. Aging infrastructure, fragmented geographic information system records, and large volumes of unstructured inspection data make it challenging for operators to detect leaks, blockages, or structural weaknesses before they cause service disruptions or environmental harm. This project addresses these challenges by developing new artificial intelligence (AI) methods to organize and interpret complex water and wastewater network data. By transforming scattered information into a coherent integrated network representation, the project aims to make water infrastructure management more efficient and reliable. The tools developed will help reduce the need for manual data review and support informed decision-making. This in turn will result in protecting community health and local environments through early detection of anomalies in water networks before they turn into costly emergencies. By making water management smarter and faster, these tools ensure more reliable service and help stabilize utility costs. Improvements to water and wastewater network data will be facilitated by a new computational framework that combines machine learning, graph-based modeling, and multimodal data integration to analyze urban water networks. The research advances methods for completing and repairing directed network representations using physics-informed flow models and attention-based neural networks, enabling the identification of missing or inconsistent connections in network data. The approach incorporates interpretable, multiresolution uncertainty quantification to assess confidence in inferred network structures and detected anomalies. In addition, the project develops multimodal learning techniques that integrate network data with video, imagery, audio, and text from inspection reports and maintenance records. The developed computational infrastructure will allow automated extraction of actionable information from traditionally unstructured sources. This enables cities to allocate resources more efficiently to ensure long-term water security and infrastructure sustainability. The methods will be trained, validated, and tested using independent wastewater network data sets from France and the United States, enabling robust evaluation and generalization across different data modalities, networks, geographic information, cities, and countries. The project also supports workforce development and transferability of skills through the training of graduate students and postdoctoral researchers in data science and AI, network modeling, and infrastructure analytics. 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 · 2026-01

Friedreich’s ataxia (FRDA) is the most common autosomal recessive ataxia. It is caused by expanded GAA repeats at the first intron of the frataxin (FXN) gene. No effective treatments are available for the disease due to the inherited expanded GAA repeats in the patient’s genome. Thus, there is an urgent need to develop FXN gene-targeted GAA repeat contraction for FRDA treatment. We have recently found that inhibition of histone H3 lysine 9 (H3K9) methylation can induce large GAA repeat contraction in FRDA neural cells and transgenic mice by opening the chromatin and promoting DNA base excision repair (BER) at the FXN gene. We hypothesize that FXN gene-targeted histone demethylation interplays with BER to contract GAA repeats and activate the FXN gene in FRDA. We will test the hypothesis using CRISPR/deadCas9 (dCas9)-mediated FXN gene-targeted histone demethylation to disrupt heterochromatin, induce BER-mediated GAA repeat contraction, activate FXN gene and relieve FRDA phenotypes. This will be achieved by the integrated indispensable expertise of all the PIs through their synergistic team efforts. First, we will determine if CRISPR/dCas9-mediated FXN gene- targeted histone demethylation can alleviate heterochromatinization and induce BER, leading to GAA repeat contraction and relief of FRDA phenotypes. We will determine if FXN gene-targeted histone demethylases KDM4D, KDM6A, and KDM6B can induce BER to contract GAA repeats, relieving FRDA phenotypes in FRDA neural and cardiac cells. Second, we will determine if the human vault nanoparticle-mediated FXN gene-targeted histone demethylation can lead to GAA repeat contraction and relief of FRDA phenotypes. We will encapsulate sgRNA-dCas9-histone demethylases into recombinant vault nanoparticles. We will then test if the vault-mediated histone demethylation can induce GAA repeat contraction via BER, activate the FXN gene, alleviate FRDA phenotypes, and interplay with the function of the blood-brain barrier. Third, we will determine if FXN gene- targeted histone demethylation can lead to cellular differential effects on GAA repeat contraction, FXN gene activation, and relief of FRDA phenotypes via cell-cell interaction. Using a single-cell analysis, we will determine if FXN gene-targeted histone demethylation can cause cellular differential effects on GAA repeat contraction, FXN gene activation, and energy production via cell-cell communication. Fourth, we will determine if FXN gene- targeted dCas9-histone demethylases can coordinate with a nucleosome to demethylate histones on expanded GAA repeats at the FXN gene. We will use molecular dynamics simulation, machine learning, and the AI tool AlphaFold3 to determine the FXN gene-targeted substrate recognition of dCas9-histone demethylases through their interaction with a nucleosome. Our study will reveal the novel mechanisms of FXN gene-targeted GAA repeat contraction, create a bionanoparticle-mediated FXN gene-targeted platform for GAA repeat contraction, and open a new horizon for developing AI-assisted mechanism-based gene therapy for FRDA. Thus, it will fundamentally transform the research and treatment for FRDA and other neurological diseases.

NSF Awards · FY 2026 · 2026-01

This BRITE Pivot award supports research to enable high-fidelity computer simulations of granular materials with unprecedented scalability and efficiency by fundamentally reimagining the discrete element method (DEM). Granular materials – spanning soil and powders to lithium-ion battery particles – are central to many natural and engineered systems. DEM has become an essential tool across disciplines for modeling granular materials, offering critical insight into how microscopic interactions among individual particles translate into the macroscopic behavior of granular systems. It effectively bridges particle-scale mechanisms and bulk material response. However, for nearly five decades since its inception, prohibitively high computational costs have constrained the scale and duration of DEM simulations. This research addresses the long-standing challenges by eliminating key computational bottlenecks, aiming to enable real-time, large-scale simulations on standard computing platforms. These advances will broaden access to particle-scale modeling and accelerate discovery across civil and natural hazards engineering, mechanical and materials science, energy systems, additive manufacturing, and other fields where granular materials are central. The outcomes will advance fundamental scientific understanding, improve predictive capabilities in engineering, and enhance the performance and resilience of complex particulate systems. To achieve this, the project introduces a novel computational framework that integrates artificial neural computations into DEM, enabling simulation speeds five orders of magnitude faster than conventional techniques. This approach will support real-time simulation of systems with up to a hundred million sand-sized particles. The framework, termed DEMIAN (Discrete Element Method Infused with Artificial Neural computations), is built upon four key innovations: a perturbation network for estimating long-range particle interactions with reduced computational overhead; a particle geometry space representation that efficiently encodes various particle shapes and sizes; an on-demand contact force strategy that avoids costly per-timestep updates; and optimized floating-point operations to accelerate computation and reduce memory usage while maintaining simulation fidelity. Training data will be generated using an impulse-based DEM approach that has already shown significant performance gains over conventional approaches. Together, this research will define a new standard for high-speed, high-fidelity, and large-scale simulations in granular materials research. 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 · 2026-01

The LLMDaL project leverages generative Artificial Intelligence (AI) along with data from the AmLight international research and education (R&E) network to provide an essential and previously unavailable building block necessary for automated network defense. The growing complexity and sophistication of modern networks are driving the need for automated cybersecurity and management. Operators of critical infrastructure must increasingly rely on AI to cope with the sheer scale of information and the growing use of AI by adversaries. However, effective AI defenses depend on both the quantity and quality of data for training. The lack of high-quality, labeled datasets from production environments presents a significant barrier. Without access to such datasets, advanced models often remain untested in real-world scenarios, limiting their effectiveness, as they fail to learn the complexity and uncertainty of production environments. Without labeled packet-level data from production networks, the AI models essential for critical infrastructure defense will fail. The LLMDaL project utilizes Large Language Models (LLMs) to automatically label packet-level data collected from AmLight maintained at Florida International University. Technical, financial and privacy challenges of providing such data remain substantial. To accurately and quickly label this real-world data, open-source LLMs are fine-tuned using data gathered from AmLight, along with known threat signatures, and expert-annotated cybersecurity events. Validation is performed through a Retrieval-Augmented Self-Refinement process, cross-checking with an ensemble of LLMs, and verification through a human-in-the-loop approach. LLMDaL fills a critical gap in automating dataset labeling, enabling effective testing of AI models for real-world environments. LLMDaL will release datasets from AmLight in batches to reflect the evolving threat landscape. 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 · 2025-12

The Association for the Advancement of Artificial Intelligence (AAAI) is one of the world’s premier venues for foundational and applied research in artificial intelligence (AI). The AAAI Conference on Artificial Intelligence (AAAI-26), to be held in Singapore in 2026, will provide a unique opportunity for early-career researchers to engage with cutting-edge advancements and participate in an internationally recognized scientific forum. This proposal supports student travel to attend AAAI-26. Providing opportunities for students to engage with the global AI research community promotes the progress of science by building the next generation of researchers equipped to address critical challenges in AI. Supporting their attendance also strengthens the broader AI research ecosystem by enabling interactions across academia, industry, and government, advancing national competitiveness and long-term innovation in trustworthy and safe AI. This project will provide travel assistance to competitively selected student participants whose research has been accepted at AAAI-26 or who have demonstrated a strong commitment to scholarly contributions in AI. The selection process will be based on academic merit, relevance to the conference themes, and recommendation letters. Recipients will be expected to attend the full conference, participate in mentoring activities, and share their experiences with peers. By facilitating participation in a global research venue, this initiative will help students gain exposure to leading-edge AI research, build lasting collaborations, and accelerate their research careers. The proposal aligns with national priorities in AI and helps build capacity in critical areas including reasoning, decision-making, and machine learning at scale. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

The AmLight: The Next Frontier Towards Discovery in the Americas and Africa project continues its mission to maintain a purpose-built international network infrastructure to support the Vera Rubin Observatory and U.S. astronomy projects, major facilities, scientific workflows, network testbeds, and the R&E communities in the U.S., Latin America, and Africa by maintaining critical long-haul connectivity, colocation, and software services that directly support U.S.-led global science initiatives. The project enhances the network operation and automation to guarantee new levels of observability, programmability, integration with science workflows and testbeds, and data transfer performance. Research contributions include new models for predictive fault detection, telemetry-driven automation, Smart Network Interface-based monitoring, and multi-domain orchestration. These innovations serve as live, reproducible platforms for computer networking, cybersecurity, machine learning, and distributed systems research. The project enables transformational science and education by supporting long-term data-intensive workflows across Latin America and Africa, directly benefiting U.S. access to globally distributed facilities, such as the Vera Rubin Observatory, U.S. astronomy projects, the Large Hadron Collider, and environmental sensing. The project contributes open-source tools; Findable, Accessible, Interoperable, and Reusable datasets for machine learning and cybersecurity research; and operational telemetry used by national laboratories and academic researchers. By sharing large-scale telemetry data, automation frameworks, and experimental platforms, the project enables broader engagement in reproducible research and infrastructure science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

The exploration of Terahertz (THz) communications (above 100 GHz) is promising for achieving ultra-high data rates and bandwidth in next-generation wireless networks. The combination of large transmitting arrays and smaller wavelength in THz communications introduces new challenges where the near-field range of a transmitter can extend to several meters. A wireless base station or access point may serve multiple near-field and far-field users. This project investigates the impacts of near-field complexities and the interplay between near- and far-field communications. It leverages near-field properties, such as the generation of curved beams for going around the obstruction or reconstructing the beams after interacting with an obstacle, to improve blockage resilience and system performance of THz wireless networks. This project also includes significant efforts to motivate participation in STEM, at all educational levels. This project aims to explore near-field propagation properties of wireless signals to enable blockage-resilient high-speed multi-user wireless communications above 100 GHz. The proposed research comprises four thrusts, (1) understanding and modeling the fundamental limits of near-field wavefront shaping for realizing agile and high-speed communications in the scenarios with link blockage, (2) investigating the design and fabrication of a new programmable array architecture that can generate near-field wavefronts and dynamically adapt them according to the mobility and signal transmission blockage conditions, (3) designing and implementing digital signal processing algorithms to support 10s of GHz of bandwidth in a power-efficient manner, and (4) developing foundations for interference modeling and multi-user communications with the consideration of both near- and far-field regions. Through the physics of wave propagation, mathematical modeling, hardware design, and experimental demonstration, this project will lay out the foundation to bridge the theory of near-field electromagnetics with practical wireless networks. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This Level 3 IUSE project, a collaborative effort between Florida International University (FIU), California State University San Marcos (CSUSM), Clark College (CC), and the University of Virginia (UVA), aims to improve teaching and learning in introductory calculus courses. Specifically, the project adapts and implements the Modeling Practices in Calculus (MPC) approach, which has been shown to significantly improve learning and success for a broad collection of Calculus I students at FIU. At the core of MPC is the opportunity for students to work cooperatively and discuss key mathematical ideas in a face-to-face, small group setting. The curriculum engages students in the practices of mathematicians, supports students argumentation, and builds important mathematical reasoning and communication skills. This project implements MPC at multiple sites across two overlapping phases, first facilitating adoption and implementation at CSUSM, CC, and UVA, and then identifying three additional partner sites that will join the project in its second year. Calculus remains a key course for many STEM degree programs and can represent a bottleneck for many students. This project uses evidence-based teaching and engagement practices to provide an enriching, collaborative learning environment that has potential to improve student learning and outcomes, thereby increasing the number of students successfully completing STEM degrees in multiple areas of national importance. Project research focuses on three research questions: 1) Faculty focus: How do faculty develop their MPC instructional expertise and what professional development elements are necessary for future propagation efforts? 2) Student focus: How do students develop cognitive and affective skills arising from the adapted MPC instructional designs? 3) Propagation focus: How are Phase 1 partners prepared for establishing a national calculus propagation network? How are new sites successfully recruited and onboarded into project? Data sources include interviews, surveys, weekly faculty reflections, and shared exam questions across project and non-project courses. Project evaluation, carried out by an experienced evaluator and supported by an expert advisory board, will assess project implementation, project and site operations, and overall progress towards goals. Outcomes and findings will be disseminated broadly to encourage further adoption of the MPC approach across institutional contexts. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Graphs are powerful tools for representing relationships in complex systems, from social networks to weather monitoring stations. Graph Neural Networks (GNNs) have emerged as effective methods for analyzing these interconnected systems, but their "black box" nature poses significant challenges in critical applications such as environmental monitoring, healthcare, and finance. This project develops a comprehensive framework for making GNN predictions explainable and trustworthy. The research addresses the urgent need for artificial intelligence systems that can not only make accurate predictions but also explain their reasoning in ways that domain experts can understand and verify. For instance, in South Florida's water management network where monitoring stations form a graph connected by hydrological pathways, emergency managers need to understand which stations and their interconnections are most influential in flood predictions. This capability is essential for building trust in these systems and ensuring their responsible deployment in applications that affect public safety and welfare. The project will train students in interdisciplinary research combining machine learning, information theory, and practical applications, while developing educational materials that bridge theoretical foundations with real-world implementations. This project establishes a unified framework using information theory concepts for explainable GNNs through two complementary research thrusts. The first thrust develops rigorous mathematical foundations for quantifying explainability in graph learning, including necessary and sufficient conditions for classifier explainability, methods to address out-of-distribution challenges, and ways to demonstrate how accurate the finding are. The second thrust translates these theoretical insights into practical architectures and algorithms, including computationally efficient explainers, generative models for robust explainers, and co-design frameworks that balance prediction accuracy with explainability. The research introduces novel concepts such as nonverbal signatures for characterizing explanation patterns and explanation-assisted learning mechanisms that leverage extracted explanations to improve model performance. Extensive evaluations will be conducted on benchmark datasets and specially curated weather forecasting datasets from South Florida's water management systems. The project advances the state-of-the-art by providing both theoretical rigor in quantifying explainability and practical solutions for deploying trustworthy GNN systems in critical applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

This is a proposal for the continuation of the well-characterized Miami Adult Studies on HIV (MASH) cohort of middle-to-old, aged people living with and without HIV and substance use followed biannually, with a history of multidisciplinary collaboration. After 3% yearly dropout, MASH (n=1,439) has people living with HIV & cocaine use (n=231), HIV & no cocaine use (n=445), no HIV with cocaine use (n=324), and no HIV with no cocaine use (n=439), followed biannually. The MASH infrastructure has accrued >8,000 visits, >500,000 biospecimens, >900 MRI scans, microbiome and epigenetic data, and a database of >6,000 variables on HIV, substance use, structural factors, and comorbidities. As the only NIDA HIV Cohort in the South, our work addresses NIH-HIV research priorities on the intersection of HIV and substance use, specifically cocaine, which is widely used by people living with HIV (PLWH), and comorbidities including neurocognitive impairment (NCI) and physical frailty, a phenotype of physiological deterioration linked to disability and mortality. We propose to expand the MASH cohort to 1,879 people to increase representation of women while following the participants for HIV, substance use patterns, emergent comorbidities, and poly-social risk. With the new recruitment, MASH will become a cohort of predominantly women, representing a population lacking among NIDA cohorts, yet remains impacted by HIV and substance abuse. The scientific aims of this proposal involve epigenetic, neuroimaging, and neurocognitive patterns of cognitive frailty, and are to (1) longitudinally investigate differential epigenetic patterns in people living with and without HIV and cocaine use that predict cognitive frailty. Epigenetic modifications, an interface between the genome and environment, are associated with HIV, drug abuse, physical frailty, NCI, and social factors, and can predict health outcomes, yet remain understudied in the context of HIV and substance use. Scientific aim (2) is to investigate whether the epigenetic signatures predict structural and functional neuroimaging patterns, which then can predict neurocognitive and physical components of cognitive frailty over time, considering sex and social risk. This work integrates the Health Promotion Research Center Framework to leverage existing relationships with end-users to bridge research and practice. Our goal is to implement research into actionable strategies such as colocation of HIV care and substance abuse treatment, identification of early cognitive frailty diagnostic markers, and prediction of clinical trajectories. By employing multimodal neuroimaging combined with epigenetics, neurocognitive, and physical frailty assessments, this project aims to enable precision medicine approaches in the context of cognitive frailty by identifying specific pathologies in PLWH with cocaine use. By leveraging the unique characteristics of MASH, this study has the potential to generate valuable insights into the interplay of HIV, cocaine use, and cognitive frailty, while the integration of implementation science will inform effective solutions.

NSF Awards · FY 2025 · 2025-09

Transformations in technology and global communication now require a multiliteracies pedagogy, one that uses analogy to develop cross-disciplinary, multi-modal learning. This Level 1 Engaged Student Learning project plans to introduce a STEM multiliteracies framework into the undergraduate curriculum at Florida International University. This cognitive approach to analogy suggests one reasons about a novel concept in terms of one that is already familiar, and identifying commonalities between two concepts involves recognizing they share a pattern. The project's curricular model thus leverages pattern mapping as a cognitive tool and learning strategy. Given complex worldwide issues that must be addressed by a STEM-literate public, it is crucial that students strengthen interdisciplinary competencies. Such enhancement will position them to design better integrated solutions across STEM and non-STEM fields and become more proficient in science communication. This project exposes STEM students to literacies that help them better communicate STEM concepts with multiple audiences in the future and integrates STEM with the regular curriculum of non-STEM majors. The project team will house faculty-designed modules in an open-access learning platform, where they will be freely available. The project seeks to develop, test, and disseminate a STEM multiliteracies curricular model that can be implemented across a wide range of undergraduate courses. The curricular intervention takes the form of faculty-designed teaching modules that engage students in analogical reasoning by asking them to make interdisciplinary connections. Studies show that pattern mapping supports student engagement and knowledge retention, enhances critical thinking and problem-solving, and facilitates relationship identification and pattern recognition. The project will first create an instrument to measure students' proficiency in STEM multiliteracy and test this instrument's validity and reliability. A professional development institute that guides faculty in designing the pattern mapping modules will inform the curricular model. Faculty will then integrate their modules into their existing courses, resulting in students producing two writing assignments: a critical reflection and an original pattern map of a course-related STEM subject of their choosing. The project team will assess these artifacts with the developed instrument and refine both the modules and the instrument accordingly. Finally, the same faculty cohort will implement the refined modules in new courses, and the project team will iterate the process of assessment and refinement. Project evaluation utilizes a Participatory Evaluation framework, which actively involves all stakeholders in the evaluation process, ensuring their perspectives and experiences shape the assessment and interpretation of project outcomes. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) manifest with acute lung inflammation with increased vascular permeability. Treatment of ALI/ARDS patients with low-volume mechanical ventilation is the only proven therapy for ALI/ARDS, and mortality rates remain unacceptable. Because the syndrome of acute respiratory failure is so common in the United States and worldwide, especially in the face of relatively new respiratory viruses such as SARS-CoV-2, ALI/ARDS is an unmet medical need. Novel pharmacological therapies need to be developed to further improve clinical outcomes in ALI/ARDS patients. RhoA and Rac1 exert distinct effects on epithelial and endothelial barrier function via selective structural and biochemical modulation of junctional proteins. Rac1 and RhoA have antagonistic effects on endothelial barrier function in the lung. Rac1 is required for the assembly and maturation of endothelial junctions, whereas RhoA destabilizes endothelial junctions by increasing the isometric tension at the cell membrane, increasing myosin contractility. Our prior studies have linked RhoA nitration at (Tyr (Y)34) with its activation, endothelial barrier disruption, the disruption of mitochondrial network dynamics, mitochondrial function, and the activation of NF-kb dependent inflammation. A strategy has been designed to shield RhoA from nitration at Y34, protect endothelial barrier function, preserve mitochondrial function, and reduce inflammation during conditions that can cause ALI/ARDS. This strategy identified a small peptide, NipR1, which protects RhoA from nitration at Y34 and prevents LPS-induced peroxynitrite attack. NipR1 contains nine amino acids from 31–39 of RhoA fused with the cell-permeable TAT sequence. Natural peptides have poor absorption, distribution, metabolism, and excretion (ADME) properties with rapid clearance, short half-life, low permeability, and sometimes low solubility. Thus, in response to this special NHLBI RFA, a chemical approach will be utilized to identify more potent peptidomimetics of NipR1. In the R61 phase, we will generate ~100 compounds based on our newly identified pharmacophores and screen them using both in vitro and in vivo assays in multiple mouse models of ALI. In the R33 phase, we will generate two optimized NipR1 derivatives, conduct pharmacokinetics/pharmacodynamics (PK/PD) and safety studies, and confirm therapeutic effects using a pig model of sepsis. We anticipate these studies will allow us to identify a lead compound with desired in vitro and in vivo characteristics as a novel therapy for ALI/ARDS.

NIH Research Projects · FY 2025 · 2025-09

There is an urgent need for cardiac valve replacements capable of growth and self-repair for children with congenital valve disease, for whom the current standard of care is multiple open-heart surgeries to repair or replace structurally-degraded valve prostheses. Living allogenic valve transplantation (LAVT) has recently emerged as a means of delivering valve replacements capable of achieving these goals. However, there are key limitations: (i) restricted availability, (ii) limited ex vivo viability, (iii) the cost, time and resource constraints related to minimizing tissue ischemic time, and (iv) immunogenicity. There is an urgent clinical need to develop methods for storing and preserving living valvular allografts ex vivo, that remain viable and available off-the-shelf for implantation. Our central hypothesis is that integrating a preservation solution which supports interstitial cell metabolic activity with biomimetic mechanical stimulation will maintain valvular viability and growth-capacity for at least 6 weeks ex vivo, while reducing immunogenicity. The rationale is that extending the ex vivo viability of valvular tissue would significantly improve the availability, cost-effectiveness, logistics and widespread clinical adoption of LAVT. First, a biomimetic strategy for the extended storage, preservation and rehabilitation of living valvular allografts will be validated (Aim 1). Tissue preservation over 8 weeks of storage will be evaluated via a multi-scale approach, from the macrostructure to resident cell phenotype. Anticipated outcomes include (i) a preservation solution for valvular tissue, and (ii) a custom bioreactor for long-term storage, which continuously exposes valves to physiologic open/close cycles and transvalvular pressure gradients in a sterile, low-cost fashion. Second, we will evaluate the impact of the storage and rehabilitation strategy on the allograft's capacity for growth and self-repair in a growing piglet model (Aim 2). We anticipate that stored valves will demonstrate growth and self-repair capacity non-inferior to that of freshly transplanted valves and superior to cryopreserved ones. Third, we will characterize the effect of IL-10 and storage time on the allogeneic immune response to valvular allografts in vitro and in vivo (Aim 3). Outcomes evaluate the host immune response to valvular transplants in a large animal model with or without immunosuppression, while simultaneously evaluating whether the risk of an adverse immune response can be mitigated through immunomodulation. The project's success would enable off-the-shelf availability of living allogenic valves, significantly enhancing the spatiotemporal availability and logistic-ease of LAVT. Understanding the allogeneic immune response to valvular allografts will inform clinical immunosuppression protocols, as well as techniques for immunomodulation of valve tissue. Altogether, this innovative approach to living valve storage is paradigm-changing, offering patients the first-ever off-the-shelf available living valve replacement option capable of somatic growth and self-repair, which can act as a life-lasting valve. This work will be carried out by an multidisciplinary team which is uniquely poised for success with an established history of collaboration synergizing translational research with clinical experience.

NIH Research Projects · FY 2025 · 2025-08

SUMMARY Our prior work has shown that TLR4-mediated increases in mitochondrial (mt)-ROS play an essential role in the inflammatory phase of acute lung injury (ALI) by stimulating NF-kB and nucleotide-binding domain-like receptor protein 3 (NLRP3) inflammasome activity. However, the molecular mechanisms by which mt-ROS stimulates inflammatory response are unresolved and are the focus of our application. The mitochondrial network constantly forms elongated tubes through fusion and splits into small, less-connected mitochondria through fission. Recent work has highlighted the importance of pathological mitochondrial network remodeling in human disease. Thus, optimizing mitochondrial network dynamics is critical for mitochondrial homeostasis. We recently discovered that TLR4 activation increases mt-ROS in lung EC by stimulating mitochondrial fission. Based on these exciting findings, this proposal will test the hypothesis that TLR4-mediated pathological mitochondrial network remodeling is a previously undescribed mechanism that prolongs the inflammatory phase during sepsis- mediated ALI. Specific Aim (SA) #1 will define how TLR4-mediated mitochondrial fission requires RhoA/ROCK- dependent cytoskeletal remodeling and evaluate the role of increased activation of the fission protein, Drp1, and reductions in the levels of the fusion proteins MFN1 and MFN2. The involvement of nitration-mediated post- translational modifications (PTMs) in RhoA will also be evaluated. The effect of fission-mediated mt-ROS on NF- kB activity and NLRP3 inflammasome assembly and activation will also be evaluated. Mechanistically, we will focus on the mt-ROS-mediated inhibition of the NF-kB regulatory protein IkBa in the sustained activation of NF- kB. SA #1 will also delineate the involvement of JNK as a downstream target of ROCK signaling in Drp1 activation and the decrease in MFN1/2 levels. The inhibitory effect of pathologic mitochondrial network remodeling on the autophagy/mitophagy response in the inflammatory response will also be evaluated. Using multiple pre-clinical mouse models, SA #2 will test new therapeutic strategies to preserve/restore the mitochondrial network balance and determine their efficacy in attenuating the inflammatory injury associated with sepsis-induced ALI. Our explorations are highly innovative, as they will define a concept-advancing interplay between TLR4 activation, pathologic mitochondrial network remodeling, autophagy/mitophagy inhibition, and the inflammatory response, opening a new avenue for therapeutic development in lung injury. Finally, these findings are highly significant, as they will promote a more thorough understanding of sepsis pathobiology and highlight the application of novel therapies for the critically ill.

NSF Awards · FY 2025 · 2025-08

This planning project will integrate several existing wireless testbeds that encompass a wide range of radio frequency bands and communication techniques into a cohesive, software-defined virtual platform, offering novel capabilities. The project will create a strategic plan for testbed design, integration and workforce development. The base design of existing testbeds, once integrated, will form preliminary results for a highly scalable community infrastructure. By adapting existing testbeds as a prototype for incorporating AI-driven capabilities, the team expects new competencies to be spawned for US-based academic and industrial partners that build toward the national interests of economic development in emerging 6G and next-generation networking markets. The proposed testbed will integrate wireless testbeds at collaborating institutions into a single ultra-broadband, multifunctional, and uniquely capable platform for emerging experiments under AI control. The overarching goal is enabling cross-level autonomy and cognition in wireless systems. During this project, the team will develop a plan that helps with long-term goals of a unified wireless testbed infrastructure, including creating software and hardware tools necessary to provide a single-stop solution for researchers to deploy and run multi-site experiments, and developing the tools necessary to run AI solutions both within and across individual network layers, and multi-site experiments with real-time data streaming and visualization capabilities. The team will engage in joint development of high-quality workforce development material, mentoring, education, and scientific outreach activities in a manner that benefits everyone interested in science, technology, engineering and mathematics fields with particular focus on AI and wireless 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 · 2025-08

Coastal ecosystems provide many benefits and services to society, including protection from storms, habitat and food for important fisheries, support of tourism and local economies, filtration of freshwater, and burial and storage of carbon that offsets greenhouse gas emissions. The Florida Coastal Everglades Long Term Ecological Research (FCE LTER) program addresses why coastal ecosystems and services are changing and how this may be explained by shifts in hydrology. Like many coastal ecosystems, the Florida Everglades has been threatened by diversion of freshwater to support urban and agricultural expansion but is undergoing widespread restoration of seasonal pulses of freshwater. Rapid sea level rise is also causing saltwater intrusion of coastal ecosystems, which stresses freshwater species, causes elevation loss, and salinizes municipal water resources. Researchers are continuing long-term studies and experiments to understand how changes in freshwater supply, sea level rise, and disturbances like tropical storms interact to influence ecosystems and their services. The interdisciplinary research team includes resource managers who use discoveries and knowledge from the program to guide effective freshwater restoration and engage an active community of academic and agency scientists. The program has a robust education and outreach program that educates and engages students, educators, and the general public regarding scientific discoveries of coastal ecosystems. The FCE LTER program integrates disturbance ecology and ecosystem development theories to understand and test how climate variability and water management drive hydrologic presses and pulses as well as how disturbance legacies can result from hydrologic changes. Further, the research will investigate how governance of freshwater and changing values of ecosystem services interact with structural and functional responses in social-ecological landscapes to influence resilience and long-term ecosystem trajectories. Hypotheses will be tested through collection of continued long-term and new data, human dimensions research, landscape-level experiments, as well as process and landscape-scale modeling. Freshwater restoration is predicted to reduce the effects of sea level rise on saltwater intrusion (a hydrologic press), and fresh and marine hydrologic pulses will likely control resource distribution and long-term trajectories of social-ecological systems and services. Syntheses will use data from national and international research networks to holistically understand how changing hydrology and disturbance legacies drive ecosystem trajectories, addressing one of the most pressing challenges in contemporary ecology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project requests NSF support for travel awards that will enable students based in the United States to attend the 8th AAAI ACM Conference on AI, Ethics, and Society (AIES 2025), to be held October 20 to 22, 2025, at IE University Tower in Madrid, Spain. AIES is a leading interdisciplinary venue that brings together researchers from computer science, law, philosophy, economics, sociology, and public policy to explore the ethical and societal impacts of artificial intelligence. The conference focuses on topics such as fairness, transparency, accountability, privacy, and the broader implications of AI systems in society. These travel awards will provide students from a range of institutions, including those with fewer international research opportunities, with valuable access to the AIES community. Participants will benefit from mentoring by senior researchers, exposure to new ideas in responsible AI, and engagement in discussions at the intersection of AI technology and society. The program supports NSF priorities by strengthening the pipeline of future researchers prepared to address complex challenges at the intersection of AI and society. The awards will also enhance international collaboration and broaden the intellectual diversity of participants, contributing to the United States’ leadership in advancing responsible and innovative AI practices globally. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

Title: Optimization of a novel antimicrobial for pulmonary delivery to fight respiratory infections Antibiotic resistance (AR) has reached alarming levels in the US and other parts of the world in recent decades. Increased infections with AR bacterial pathogens result in increased healthcare costs and a decline in positive clinical outcomes. Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species together are known as ESKAPE pathogens because they are the six top-priority dangerous ‘superbugs’ easily generate resistance and require the most urgent attention for novel antibiotics. Multidrug-resistant (MDR) ESKAPE infections are emerging causes of AR infections associated with high morbidity and mortality rates. Despite tremendous efforts, the lack of efficacious new antimicrobials is an enormous concern because of the potential threats posed by emerging/re-emerging AR pathogens to public health and global healthcare. It’s a significant medical challenge to lack novel, effective antibiotics to treat AR bacterial infections, and our long-term objective is to develop novel antimicrobials to combat urgent AR bacterial infections efficiently. We have recently developed a natural airway host defense molecule SPLUNC1 inspired lead antimicrobial peptide, A4-X7, that has enhanced stability, potency, and safety, making it an ideal therapeutic candidate for treating a broad range of AR bacterial respiratory infections. In this application, we hypothesize that formulating A4-X7 for aerosolized pulmonary delivery is an effective and well- tolerated novel approach to successfully treating AR bacterial respiratory infections. Our proposed studies will prepare, characterize, and evaluate the antimicrobial and antibiofilm activities of formulated A4-X7 for aerosolized pulmonary delivery. We will then determine aerosol dispersion performance, lung deposition, pharmacokinetics, pharmacodynamics, and inhalation aerosol safety as liquid aerosols and inhaled dry powders of formulated A4-X7. By focusing on developing novel A4-X7 for targeted pulmonary delivery, our study will provide an alternative and effective treatment to fight MDR ESKAPE respiratory infections.

NSF Awards · FY 2025 · 2025-08

The use of AI for abuse against persons is a growing threat. Decades of research in computer vision and AI has led to the broad availability of tools that can be misused to distort an image of a person, such as turning a clothed image of the individual into an unclothed image without the person’s consent. Attackers profit from this kind of abuse and use these abuses in a range of cyberattacks including blackmail, extortion, and ransomware. To address this problem, the research team is conducting technical and content analysis of the ecosystem of tools used to create abuse material, leveraging principles from decades of psychological research to deter attackers from creating and viewing this material, and engaging in technical forecasting to predict and stop future attacks. Overall, this project aims to protect Americans by technically characterizing the current and future ecosystem of online abuse attacks and developing sociotechnical mitigations against such attacks. This team takes a defense-in-depth approach to abuse reduction. The project aims to make current and future abuse tools harder to access and use and to decrease attacker’s inclination to use those technologies. They combine approaches from internet measurement and content analysis to scope the severity of AI use in abuse and identify signals that can be used to proactively remove abuse tools and content. They also rely on decades of psychological and communications theory and research to develop effective deterrence systems that target attacker motivations and are delivered at the time of action as a form of secondary intervention. They use a series of computational and human-subjects experiments to upper- and lower-bound future attacks that use AI for online abuse. Together, these efforts will develop a robust set of threat models, forecasts and interventions against online abuse. -- 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.