Florida International University

NSF Awards · FY 2024 · 2024-09

The second quantum revolution is paving the way for a future with unprecedented technological capabilities. The National Quantum Initiative Act catalyzed the demand for a skilled and diverse workforce by identifying quantum technologies as a national priority. The practical realization of each of these technologies cannot be accomplished without a toolbox of materials building blocks that display controllable quantum properties demanded by each application. This National Science Foundation Research Traineeship (NRT) award to the Florida International University will provide a coherent research and education framework to address these imperative needs in quantum information science and engineering (QISE) by training graduate students for the quantum workforce. The project anticipates training one hundred and ninety (190) M.S. and Ph.D. students, including thirty (30) funded trainees, from Materials Science and Engineering, Chemistry, and Physics programs. The research focus of the NRT Q-STAR project is on novel quantum materials discovery, synthesis, modeling, and validation of their quantum behavior. The educational component involves a 360-degree student-centered graduate education model encompassing elements that deliver: A. Technical expertise training: these elements are team-research projects, internships at partner institutions, and a designed coursework; and B. Career-success training including the ability to lead projects, work in teams, communicate science to public and policymakers, and make ethical decisions. Specific elements include training mechanisms to promote learning of science communication, ethics, and teamwork, and a workshop on transferable skills including leadership and entrepreneurship is included. The program will provide all trainees a pathway toward technical and leadership roles in QISE across the nation. The convergent research will bridge the chemistry and materials science that will enable novel quantum materials, with the physics of the quantum phenomena that will render these materials useful in future quantum technologies. The discovery process will be data-driven, using machine learning and density functional theory (DFT) to guide experiments. The scientific advancements anticipated by exploring novel non-centrosymmetric chalcogenide materials for quantum frequency converters, 2D and 3D semiconductor qubits, and materials for quantum battery technologies will be complemented by new knowledge gained through the implementation of a 360-student training model. At the core of the program are proven strategies to enhance recruitment, retention, and persistence in STEM of all students, including the cohort model, team-training, and a comprehensive, Individual Development Plan-based mentoring program. This project will strive to extend the professional development and career-choice program elements to all participants, thereby benefiting a large population of FIU graduate students, especially through the QISE concentration to be developed. The project will foster collaborative efforts with industry, government laboratory partners, and other academic institutions, offering trainees a wide range of opportunities for internships. The training model outcomes will be evaluated and disseminated to enable its facile implementation to other departments at FIU and other institutions, especially minority serving institutions (MSIs). The program will advance transformative research in QISE while training a diverse population of graduate students in this area of high priority to the nation. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs. 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 2024 · 2024-09

In the nearby Universe astronomers have discovered that most galaxies are either actively forming new stars (appearing blue in color) or have ceased to form them (appearing red in color). In many ways, producing new stars is the most pronounced feature of galaxies in the local Universe, but there is no scientific consensus explaining why some galaxies cease forming stars. Part of the problem is that the end of star formation occurred in most systems billions of years ago, where we currently lack the necessary observations to fully understand their physical properties. This team of researchers will address this issue by utilizing data from an upcoming survey of galaxies targeting early cosmic times. In so doing, they will constrain this crucial evolutionary stage in galaxies, helping to resolve one of the long-standing mysteries in all of astrophysics – why are there two types of galaxies? The PI will establish a new program providing hands-on training in observational astronomy to undergraduate students of all majors. For this purpose, the PI will use the 24-in telescope on the campus of Florida International University, a Hispanic-Serving Institution. This research team will utilize ‘VLT-MOONRISE’, an unprecedented study of about 500,000 galaxies observed with rest-frame optical spectroscopy at the peak epoch of galaxy formation (‘cosmic noon’: z = 1 – 2.5). This is a large (190-night) spectroscopic survey targeting early galaxies to be conducted on the Very Large Telescope in Chile over the next five years. This research project will combine these transformative spectroscopic observations with state-of-the-art cosmological simulations and sophisticated machine learning tools to rigorously test theoretical models of galactic star formation quenching at cosmic noon. In particular, this project will: (1) Reveal the fundamental physics of galaxy quenching at cosmic noon: ‘fuel’ vs. ‘efficiency’; (2) Test the AGN feedback paradigm of massive galaxy quenching at cosmic noon; (3) Identify the triggers of massive galaxy quenching – e.g., mergers, starbursts vs. AGN; (4) Determine the role of environment in quenching satellite galaxies at cosmic noon; and (5) Enable a census of star forming and quiescent galaxies across 10 billion years of cosmic history. Ultimately, this research will aim to reveal where and when in the Universe star formation is possible, explaining the fundamental bimodality in galaxy properties, which has remained elusive for decades. 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 2024 · 2024-09

Technical interviews are a component of hiring for computing positions pervasive in the industry that necessitate solving coding problems in real-time. Apart from obtaining a solution to the task given, job candidates are also encouraged to consider the algorithmic efficiency, to test their approach, and to verbally communicate decisions made throughout. While students are taught programming, algorithms, and data structures during their education, applying these principles under duress can be challenging and often requires immense practice. This project at Florida International University will aid in students’ preparation for these interviews while cultivating professional and technical competencies for undergraduate students at Hispanic-Serving Institutions (HSIs) in the southeast. Through a combination of research and education, the project will create and disseminate resources using meaningful examples to further understanding of computing concepts. Moreover, this content will provide faculty with lessons and learning activities that they can incorporate throughout curricula and programs to aid in better preparation for a computing career. Ultimately, this initiative will establish opportunities across the participating institutions to empower the next generation of students in computing. This project seeks to: 1) construct culturally relevant materials for undergraduate students, which can be integrated into courses and programs at HSIs in the southeastern region of the U.S.; 2) increase awareness of and familiarity with the technical interview process in academia.; and 3) establish opportunities for students to gain exposure, transfer knowledge, and/or practically apply computing principles needed for career attainment. The project includes a qualitative investigation exploring the conceptions of educators at HSIs in the southeast on the hiring process in computing and how preparation could align with existing institutional structures. Through semi-structured interviews and thematic analysis, approaches that could be effective in enhancing students’ graduate employability will be examined. Alongside an advisory board with combined expertise on our population of focus, academia, and industry, the findings will then be used to develop content that can be integrated throughout curricula and offered as training by student-led groups. In the following year, the findings will be disseminated through a workshop hosted for faculty and student leaders at HSIs in the southeastern region of the U.S. to share the established resources and collaboratively discuss how these lessons and learning activities could be integrated into existing courses and extracurricular programs. 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 · 2024-09

Project Summary Digital technology and screen media have become increasingly ubiquitous in the lives of young children, fueling urgent calls for better understanding how screen media use impacts child development and mental health. Emerging research has suggested associations between child media use and child emotional and behavioral difficulties, including problems with attention, self-regulation, and disruptive behavior, and some evidence further suggests that early behavioral functioning may also predict later screen media use. However, the nature of the association between early screen media use and child emotional and behavioral functioning remains under debate due to inconsistent findings, limited availability of robust long-term studies, and a lack of understanding regarding the underlying mechanisms driving these effects. Additionally, much of the existing research on early screen media use and child development has been limited to considering only a child's overall exposure to screen media (i.e., duration of screen media use), disregarding crucial contextual factors, such as, content, purpose, and caregiver media use, that are essential for clarifying how early screen media use impacts (and is impacted by) young children's emotional and behavioral functioning. To address these gaps, we propose to follow an existing cohort of 200 children and their caregivers, who were previously enrolled in an NICHD-funded randomized trial for infants at-risk for externalizing behavior problems. The primary aims of this R01 application are to investigate transactional associations between characteristics of family media use and developmental trajectories of early child emotional and behavioral functioning, and to investigate caregiver-child interactions, parenting, and caregiver distress as key mechanisms of these associations. Measures of parenting, caregiver-child interactions, and child functioning, as well as screen media use, were administered in the prior trial when children were 1-2 years old. We propose to follow these caregiver-child dyads yearly from age 3 years to kindergarten entry (age 5 years). Specifically, we will examine family screen media use (including duration of parent and child media use, content, purposes of use, co-use), caregiver-child interactions, child behavior and self-regulation, and caregiver wellbeing at each time point via robust, noninvasive, and validated means. Using growth curve modeling, we will investigate 1) transactional associations between family media use and child self-regulation and behavior, 2) longitudinal associations between family media use and caregiver-child interactions, and whether a brief parenting intervention moderates these associations, and 3) caregiver-child interactions and caregiver distress as mediators. The proposed study responds to NICHD priorities to investigate impacts of early exposure to technology and digital media on caregiver-child interactions and child outcomes, and the results of the proposed research will help inform healthy family media use guidelines and interventions that are relevant to modern digital age.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY People living with HIV demonstrate increased incidence of lung inflammation and HIV is an independent risk factor development of COPD. In the era of antiretroviral therapy (ART) with markedly improved life expectancy, lung related chronic conditions like COPD and bacterial pneumonia are primarily responsible for increased morbidity and mortality in people living with HIV (PLWH). HIV patients die of non-AIDS comorbidities almost a decade earlier that their non-HIV counterparts. Identifying the pathophysiological mechanism and treatment of HIV-associated comorbidities is very complex in chronic lung diseases, even with ART. TGF-β signaling upregulated by HIV Tat, and Tat itself alters the microRNAome of the airway epithelium. MicroRNAs play important roles in lung health and diseases and their dysregulation can serve as pathological hallmarks of several lung diseases. TGF-β signaling, plays a vital role in the progression of chronic airway diseases like COPD and lung infections. This is important since we have shown that TGF-β can increase the viral burden in the airway thereby establishing a positive feedback loop mechanism. HIV Tat and TGF-β1 upregulates miR-126-3p, a microRNA known to suppress IRS-1 which leads to upregulation of ADAM17 in airway epithelial cells which leads to impaired mitophagy, senescence and lung inflammation in vitro and in vivo. The current proposal focuses on determining the pathophysiological mechanism and rescue by which HIV- associated COPD through TGF-β alter the microRNAome to mediate suppression of critical genes in mitophagy leading to defective mitophagy, senescence and inflammation. Aim 1 will identify a novel crosstalk between the defective microRNAome, IRS-1 and ADAM17 with downstream effects on mitochondrial homeostasis, senescence, and inflammation in the context of HIV and CS. We will validate this pathway using miRNA mimics and antagomiRs in vitro and in vivo on lung inflammation in air and CS-exposed SP-C Tat transgenic. Aim 2 will test therapeutic approaches given that miRNA target sites differ among different genes regulated by the same miRNA, editing the miR-126-3p target site on IRS-1 3’ UTR will preserve IRS-1 levels in the context of HIV and CS thereby restoring baseline mitophagy, senescence and mitigating inflammation in EcoHIV mouse models of HIV infection.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Globally, depression is the leading cause of disability world worldwide, yet over 75% of people in low and middle-income countries (LMIC) do not receive treatment. This gap is especially large for men. Treating men is essential to reducing the global burden of depression given its impact on families and children with men’s mental health (MH) predicting poor parenting and child MH. Although treating parent depression has been shown to improve parental MH, parenting, and child MH; men and fathers have been missing MH care and clinical trials. MH care relevant to fathers that address common barriers to MH, such as masculine norms and economic factors, are needed. The goal of the K23 is to expand the candidate’s skillset and prepare her to conduct independent research on scalable interventions for adult MH to improve child MH in low-resource settings. Through a combination of coursework and seminars, scientific conferences, collaborative publications, and mentorship from leading experts, the candidate will develop competency in 1) execution of randomized control trials using implementation science designs; 2) exploration of mechanism of change on MH using mixed methods; 3) understanding social determinant of health (SDH) on implementation. The candidate will apply knowledge and skills gained through training to conduct a pilot randomized control trial (RCT) using an effectiveness-implementation design in Kenya. The candidate will leverage existing partnerships with AMPATH and Moi Teaching and Referral Hospital (MTRH) in Kenya. She will also build on collaborative preliminary work with AMPATH/MTRH that showed promising proof-of-concept for ‘Learn, Act, Engage, Dedicate’ (LEAD), a 5-session task-shifted behavioral activation intervention with motivational interviewing and masculinity discussion strategies for fathers in Kenya. Proof-of-concept findings with nine fathers and families were promising with high participant satisfaction and improvements in father depression, parenting, and child MH. This supports pursuit of a pilot RCT, proposed here, using an effectiveness- implementation Hybrid Type I design to explore preliminary effectiveness and its implementation. Specifically, the candidate will conduct a pilot RCT with fathers randomized to either LEAD or a waitlist control group to (Aim1) explore change in fathers’ MH; (Aim 2) explore drivers of change in father MH, father parenting, and child MH (or non-response); and (Aim 3) explore the feasibility and acceptability of implementing a task-shifted MH treatment for fathers in a low-resource setting. Results will inform the development of an R01 proposal for a Hybrid trial to assess clinical outcomes of father and child MH and the impact of implementation strategies. Together, the training, research, and partnerships in the proposed award will support testing of a scalable MH treatment delivered by peer-father counselors with potential for engaging men and improving father and child MH in a low-resource setting, contributing to the NIMH’s goal of maximizing public health impact of research.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Conduct problem (CP) symptoms in early childhood, including aggression and defiance, are associated with adverse social, emotional, and behavioral outcomes extending into adolescence and adulthood. The presence of callous-unemotional (CU) behaviors among children with CPs exacerbates these negative outcomes and reduces the effectiveness of evidence-based treatments. Given the severe negative trajectories and high societal costs associated with early CP and CU, it is important to accurately identify young children exhibiting CU behaviors to inform early intervention efforts for children with CPs. Traditional measures of CU behaviors exhibit some inconsistencies, necessitating a more nuanced multimodal approach. This study utilizes multiple units of analysis, including physiological measures (i.e., heart rate variability), parent/teacher ratings, and observational coding, to assess levels of CU among young children with and without CPs. Emotional development in early childhood, encompassing empathy acquisition, is shaped by direct interactions with parents and environmental observations. Parents play a crucial role in socializing their children's emotional experiences through supportive or non-supportive reactions, discussions, and emotional expressiveness. Parental emotion socialization, particularly emotion talk, is considered a crucial factor influencing child outcomes. Positive emotion socialization is linked to favorable outcomes, including less severe symptoms of CP and lower levels of CU behaviors, highlighting the importance of parental influence. While cross-sectional research establishes the impact of parental emotion socialization on child empathy and CP severity, the current study aims to investigate the longitudinal association (across the course of one year), specifically examining whether it can buffer the negative effects of CU on later CP severity. The study involves secondary data analysis from a sample of children with and without CPs (n=323), exploring multiple measures of CU behaviors across three timepoints. Aim 1 seeks to determine if physiological functioning, observed empathy, and parent/teacher ratings of CU behaviors converge into a single latent factor. Aim 2 will examine intra- and interindividual variability in the CU latent factor at baseline and over time, and its predictability of CP severity on year later. Lastly, Aim 3 focuses on parental emotion socialization and its potential impact on the link between CU behaviors and later CP severity.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Type-2 diabetes (T2D) is rising at an alarming rate globally and in the United States (US). This rapid increase in the T2D burden has a particular impact on cities, where more than half the global populations currently live and where 3 out of 4 people with T2D reside. In response to this growing global challenge, the World Health Organization (WHO) has emphasized (a) the need for a sustained improvement in the detection, treatment, and control of T2D, and (b) a rapid implementation of the WHO’s evidence-based HEARTS module for tackling chronic diseases. However, currently, in most countries (including Bangladesh and the US), effective adoption of the WHO HEARTS module into routine urban primary care has been limited. These include suboptimal delivery mechanisms, poor uptake, a weak monitoring system, and inadequate capacities. To address this, we will evaluate a community-to-facility integrated strategy to implement the WHO HEARTS-Diabetes (D) module in the existing urban service delivery system in Bangladesh. First, we will develop and optimize a community-to-facility integrated strategy for adopting the WHO HEARTS-D module using Implementation Mapping. Guided by this approach, we will conduct mixed methods assessments to: (a) identify contextual factors, and (b) assess the implementation behavior of providers that may influence T2D care in cities. We will then develop and optimize a suitable implementation strategy that can achieve high coverage, access, and utilization of chronic T2D care through iterative cycles of mixed methods qualitative assessments, implementation, and outcome measurements. For this aim, study staff will select the key stakeholders, primary care providers, and community health workers as participants, based in 3 wards in Sylhet city of Bangladesh. Second, we will evaluate the impacts of the optimized community-to-facility integrated strategy on implementation outcomes. We will conduct a 2-arm, type 2, hybrid implementation-effectiveness randomized trial. The study will involve 20 municipal wards as clusters from Sylhet city (10 in each arm). This study compares the following strategies: (a) a community-to- facility integrated strategy for implementing the WHO HEARTS-D module and (b) a facility-only usual service delivery. We will evaluate the implementation process by relevant outcomes based on the RE-AIM framework components: reach, effectiveness, implementation, and maintenance. Third, we will compare the effectiveness of this strategy on T2D status. In a study sample of 5,000 randomly selected participants, we will compare improvements in the prevalence of controlled T2D, treatment uptake and adherence to glucose-lowering therapy, T2D complications, and awareness among participants in both study arms from baseline to end-line. Our study should guide policymakers into effective implementation and sustainment of the WHO HEARTS-D module that can be: (a) embedded within local organizational structures, and (b) adapted to the US and other similar global contexts.

NSF Awards · FY 2024 · 2024-09

Designing and building mechanically robust, multi-functional materials is a challenging problem in engineering. However, naturally occurring biological materials typically perform multiple functions and are robust to environmental disturbances throughout their development. In contrast to most human-engineered materials, biological materials are often formed through self-assembly, a process that occurs when large-scale emergent structures form not from overarching designs, but instead from physical interactions between cells and other structures. One of the most important organs in nature is the plant leaf, which is the site of almost all terrestrial carbon fixation globally. Despite being seemingly planar, leaves are three-dimensional organs composed of multiple, porous tissues that develop from tightly compacted, undifferentiated cells. This award supports research to create a three-dimensional model of leaf development to recapitulate the structural diversity among real leaves and test how this structural variation influences leaf performance. In so doing, this project will create a "virtual leaf" platform for future studies of leaf function and advance the development of self-assembling, biomimetic materials, as well as mentoring and educating high school, undergraduate, and graduate students. Building stable, porous materials with tunable and targeted properties through self-assembly has the potential to transform material science and engineering. This project uses the plant leaf as a model of self-assembly because it is composed of multiple tissues organized in three dimensions (3D) that vary in properties ranging from completely confluent to highly porous, even though each of these tissues develop in unison from undifferentiated, identical cells. This research will (1) develop new image analysis methods to characterize the 3D cell and tissue structural diversity of leaves to guide modeling, (2) develop a 3D computational model of leaf development incorporating all major leaf tissues, and (3) test how structural variation at the cell and tissue levels influence the mechanical and functional properties of leaves. Together these aims will illuminate approaches for engineering multifunctional materials with tunable properties and structural heterogeneity. 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 2024 · 2024-09

The National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) is a highly competitive, federal fellowship program. GRFP helps ensure the vitality and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based master's and doctoral degrees in science, technology, engineering, and mathematics (STEM) and in STEM education. The GRFP provides three years of financial support for the graduate education of individuals who have demonstrated their potential for significant research achievements in STEM and STEM education. This award supports the NSF Graduate Fellows pursuing graduate education at this GRFP institution. 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 2024 · 2024-08

Biodiversity faces threats from fluctuating salinity levels, influenced by climate shifts and human activities such as water extraction, agricultural runoff, and pollution. These changes pose significant challenges to aquatic ecosystems, stressing and endangering resident species. However, some organisms exhibit remarkable resilience to varying salinity levels through physiological adaptability and genetic mechanisms. How do these species thrive in fluctuating salinity conditions, and what can studying their evolutionary, genetic, and physiological processes reveal about resilience and adaptation in changing environments? This project will investigate these questions using microscopic invertebrates—monogonont rotifers, whilst nurturing a new generation of scientific experts in this ecologically important yet often understudied group of invertebrates. This project will support a postdoctoral associate, a Ph.D. student, two M.S. students, and 15 undergraduates, primarily recruited from underrepresented groups. Additionally, 40 undergraduate students will participate in hands-on research activities through two summer programs. Researchers will engage the public through collaborations with universities, university museums, and the Tennessee Aquarium, promoting a broader understanding and appreciation of the challenges biodiversity faces in a changing world through the lens of rotifer evolutionary ecology. Monogonont rotifers play critical roles in aquatic ecosystems by influencing bioturbation, bacterial denitrification, and the transfer of energy from producers to larger consumers. Their short generation times and varying salinity tolerances within the same clade, make them ideal subjects for studying ecological, physiological, and evolutionary processes related to salinity tolerance. This project will: (1) enhance understanding of rotifer taxonomy and ecological distribution by sampling across various salinities using an integrated taxonomic approach. Despite extensive documentation of marine biodiversity in the United States, there is no formal survey of saltwater rotifers from the Southeast U.S. Focusing on this area is particularly relevant due to significantly increasing coastal flooding, sea-level rise and salinification interacting with dense human populations; (2) investigate genomic and transcriptomic differences among salinity-tolerant species through common garden experiments, revealing differential genomic arrangements, gene expression patterns, and phenotypic responses to changing environments; (3) utilize phylogenomic methods and advanced phylogenetic comparative methods to determine ancestral habitat preferences among targeted genera, assess niche conservatism impacts on current species distributions, and analyze rates of adaptation for salinity-tolerant traits. 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 2024 · 2024-08

PROJECT SUMMARY In the United States, the prevalence of opioid use and opioid use disorder (OUD) more than doubled in recent years. There has been an increase in opioid-related overdose deaths with nearly 92,000 reported in 2020. This has led to an unprecedented current crisis of OUD and overdose deaths resulting from indiscriminate use of opiates. While opioids are potent analgesics and provide relief from pain, they are also prone to be addictive. This crisis is further worsened due to the availability of illicit and more potent synthetic opiates like Fentanyl. The current FDA-approved drugs for OUD are both inadequate and have adverse effects. Therefore novel mechanism-based drug discovery approaches are urgently required for OUD and also to prevent overdose deaths resulting from respiratory depression. The heritability of substance use disorder is estimated to be greater than 50% based on twin, family, and adoption studies, and yet few modulating genes have been evaluated. Mu-opioid receptor (MOR) signaling is the major pathway responsible for both pain relief and euphoric effects of opioids. It is important to note that morphine inhibits nuclear translocation of TFEB, a master regulator of the autophagy-lysosome pathway (ALP), thereby reducing autophagic activity. Interestingly, we also found that MOR is colocalized with TFEB in the subcellular neuronal membranes, and most importantly also physically interact with each other as shown by coimmunoprecipitations. Also, opioids induce significant damage to neurons with reduced synaptic plasticity, and TFEB is known to protect against neurodegeneration in vivo in the brain, especially the dopaminergic neurons. Even more important, the most frequent cause of overdose death due to opioids is opioid-induced respiratory depression (OIRD) as well as damage to the lung tissue. Interestingly, TFEB overexpression can decrease inflammation and mitochondrial damage in the lung tissue thereby protecting against acute lung injury. Based on this overwhelming evidence we hypothesize that “As a master regulator of ALP, TFEB plays a pivotal role in the mitigation of opioid tolerance and dependence by enhancing synaptic plasticity in the brain”. In specific aim 1, we will use SH-SY5Y cells and striatal primary neurons to verify whether TFEB overexpression or siRNA-mediated knockdown alters morphine-, fentanyl-, DAMGO, and Methadone-induced MOR desensitization, internalization, and stability. Specific aim 2 is an in vivo study designed to assess whether TFEB or its activator TPI-132 influences MOR agonist-induced analgesia, dependence, tolerance, respiratory depression, and withdrawal symptoms using flag-TFEB, TFEB-/- mice and wild-type mice after sub- chronic exposure to morphine and fentanyl. To increase rigor, we have included two cell types, multiple opioids, different time points, doses. If TFEB indeed mitigates opioid addiction and tolerance, TPI-132 that can activate TFEB and autophagy may be developed as novel and excellent therapy for OUD and overdose deaths

NSF Awards · FY 2024 · 2024-08

Many minority-serving institutions (MSIs) encounter challenges to establishing and maintaining a robust research enterprise, including a lack of training in proposal writing, difficulties in starting research partnerships, and limited involvement of MSI students in research endeavors. To address these obstacles, this project will organize four workshops to aimed at training aspiring principal investigators (PIs) from MSIs in the southern and southeastern US states, preparing participants for submitting proposals to the CISE MSI program in the 2025 and 2026 competitions. The events will provide a platform for participants to meet at large, share research insights, identify challenges and opportunities, and initiate potential research collaborations. Broader-impact aspects of the project comprise the broadening participation of over 60 MSI scholars as well as the fostering of MSI collaborations across nine US states. The workshops will pursue four major objectives: 1) Community building - the strengthening of relationships among data science (DS), artificial intelligence (AI), and extended reality (XR) to boost broader data-intensive research communities at MSIs; 2) Cross-learning: the facilitating of knowledge sharing, the exchanging of best practices, and the training of aspiring PIs from MSIs to leverage success and enhance proposal development skills; 3) Mentoring - the providing of each research team with experienced coaching; 4) Collaboration development - the fostering of new collaborations, the exploring of future opportunities to advance research initiatives, and the preparing of collaborative proposals towards CISE MSI-focused programs. 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 2024 · 2024-08

Scientific Cyberinfrastructure (CI) is evolving to become Internet of Things-driven, and relies on machine learning (ML) models for advanced data analysis and predictive modeling. These ML models handle serious societal responsibilities such as flood modeling and hurricane prediction. However, the leakage of these models can cause serious issues, ranging from national security and cybersecurity to intellectual property loss. This project implements a secure ML inference solution to prevent safety- and security-critical ML models from leaking to attackers. It raises awareness of ML model extraction attacks in device-driven scientific Cis. It also broadens the impacts of CI security by enabling new functionalities and having more mission-critical ML models safely and securely deployed in CIs. This project aims to advance the security and privacy of on-device ML models tailored for scientific studies using Internet of Things-based CIs. It consists of two primary tasks. First, the project presents a novel runtime detection and prevention mechanism for ML model extraction attacks. It employs multi-level instrumentation techniques for CI applications and extracts patterns related to ML functions. It re-defines memory regions for various ML tasks and allows ML developers to customize security policies to control access to model-related data. Second, the project implements a comprehensive assessment mechanism for on-device ML model security. It measures the feasibility of a potential model extraction attack with a newly designed model extraction dependency graph, and dynamically runs penetration-based model extraction attacks against potentially vulnerable applications to confirm the existence of such attacks. This project integrates these techniques and tools into device-driven CIs across various existing scientific domains, and envisions to significantly reduce the attack surfaces of ML models deployed in these CIs. 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 2024 · 2024-08

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is the development of secure communication protocols for robust and accurate vehicle position, navigation, and timing (PNT) where Global Positioning System (GPS) or Global Navigation Satellite System (GNSS) are not available. This will ensure safe navigation of small airborne, marine, or ground vehicles operated by government and commercial sectors, when GPS signals are compromised or unavailable. With this in mind, the objective of this PFI-TT is to develop and demonstrate a novel system as an alternative to GPS. Non-GPS guidance is achieved using four ground transceiver stations along with a Location Information System (LIS) onboard the drone. Importantly, this system will be highly secure (to interference) and spectrally efficient (as it transmits below the noise floor). The market for such systems is valued at $800 M in 2022, projected to grow at >25%, and expected to be $8B by 2032. Given the need for such technologies, their commercialization into popular product is nearly assured. Even more, this project will prepare next-generation minority students and a diverse workforce with deep technical skills, business opportunities, and commercialization training. The project brings forward innovations developed to enable unspoofable remote guidance of small airborne and ground vehicles, called unspoofable Assured-Position, Navigation, and Timing (A-PNT). The following innovations are introduced: (1) New transceivers that are ultra-wideband and enable reception and transmission in a highly secure manner, (2) Back-end beamforming circuits that will operate across a large bandwidth, for the first time, (3) Low cost and low-weight transceivers to be carried on the palm of a hand, and (4) New technology brought forward by our industry collaborator and new algorithms to carry out geolocation triangulation that provide the vehicle’s location using the signals from the transceivers. Four of these transmitters will send their ground location using below the noise level along with their time stamp. The transceivers and the transmitter’s ground location are used to determine their distance from the transmitters. The system is based on a new high accuracy MEMS Clock Ensemble as compared to available technology. The vehicle will send the extracted geolocation back to the ground for iterative improvements. 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 2024 · 2024-08

This project aims to create an AI-powered framework that helps designers automatically evaluate the environmental impact of their designs and optimize them for sustainability. This research overcomes several key challenges in sustainable design practices, including the scarcity of lifecycle inventory (LCI) data essential for lifecycle assessment (LCA), the lack of accurate tools for including product lifespan uncertainties in LCA prediction, and the absence of an inclusive decision-making framework that integrates environmental and technical considerations. This project develops advanced AI algorithms such as graph attention networks (GATs) and reinforcement learning (RL) to advance design science and national manufacturing prosperity. It augments designers’ abilities to quickly update design alternatives that align with sustainability practices and provides manufacturers with faster and more accurate tools to assess the environmental impacts of their products, aiding in the development of software packages to measure corporate social responsibility. This framework supports sustainable development within engineering design and extends its applications to decision-making throughout the product lifespan and supply chain. Furthermore, this work aligns with national interests by promoting scientific progress, supporting sustainable manufacturing, and advancing STEM education, particularly through undergraduate research experiences and K-12 activities. The objective of this project is to establish a cutting-edge AI-driven framework for sustainable design that cohesively integrates advanced eco-design algorithms with product LCA and sustainability evaluation, setting a new standard in optimizing design alternatives. To achieve this objective, this research will implement several key innovations: (i) develop novel GAT algorithms to extract LCI data by analyzing similarities with existing designs that possess known LCI data, thereby addressing the issue of data scarcity essential for LCA; (ii) create advanced algorithms inspired by Markov process simulations to model the heterogeneity and stochasticity of product lifespans, thereby significantly improving the accuracy of LCA predictions; and (iii) establish a decision-making framework based on a novel non-lexicographic multi-criteria RL algorithm to facilitate the simultaneous evaluation of environmental and technical considerations and produce Pareto-optimal design alternatives. This integrative approach will combine the data extraction capabilities of GAT algorithms and the LCA prediction accuracy provided by Markov process simulations to transform sustainable design practices. This researched framework will be rigorously evaluated through its application in designing consumer electronics, including smartphones, tablets, and laptops. This collaborative project between the University of Florida and Florida International University combines expertise in engineering design, lifecycle assessment, circular economy, optimization, and artificial intelligence. Educational initiatives will include the organization of industry and academic webinars and workshops, provision of timely training for students, and promotion of STEM education with an emphasis on underrepresented groups. 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 · 2024-07

Project Summary Half of those living with HIV in the United States are over 50 years of age. As people living with HIV (PWH) live longer with the disease, the tradeoff is that they live with multiple comorbidities. One of the more common comorbidities is depression. There is a need to find innovative and accessible nonpharmacological interventions that can help older people living with HIV/AIDS to manage depressive symptoms that may also affect their treatment outcomes. Tai chi/Qigong (TCQ) is a series of slow, low-impact meditative movements that integrate breath work, meditation, and stances; and may improve depressive symptoms. We propose testing the efficacy of a remotely delivered standardized TCQ intervention that has shown to be acceptable and feasible with a population of older people living with HIV/AIDS. Thus, this study proposes three aims: (i) to determine whether a remotely delivered TCQ intervention is directly efficacious in improving depressive symptoms compared to a health education control group among older PWH (50 years of age or older); (ii) to determine whether a remotely delivered TCQ intervention indirectly improves depressive symptoms via biological, psychological, and behavioral mechanisms compared to a health education control group among older PWH and (iii) to determine whether the direct and indirect associations between a TCQ intervention and depressive symptoms is moderated by gender among older PWH. Participants (n=326) will be recruited using social media sites (e.g., WebMD, google, Facebook) throughout the United States, and from at least two contact registries of older PWH. For the registries, research staff will contact participants via phone. All assessments will be conducted via Zoom videoconferencing or phone. Participants will be randomized to 1 of 2 conditions: the TCQ intervention, or a health education control group condition. Both the TCQ and the health education control condition will be delivered via Zoom to participants in the form of live, synchronous classes. We will assess the efficacy of TCQ by looking at instruments that measure depression; and potential mechanisms such as sleep, fatigue, emotional regulation, and heart variability at baseline, 3-month, and 9-month post-intervention. Blood will also be collected via Quest Diagnostics at baseline and at 9-month to collect viral load and an inflammatory marker (C-reactive protein). Data will be described using descriptive techniques. Efficacy of the TCQ treatment will be evaluated using a random effects analysis of covariance (ANCOVA) model. We will follow the Baron & Kenny’s method for mediation analysis to assess if the effect of TCQ intervention on depressive symptoms is mediated by biological (heart variability, using HF power values expected to increase), psychological (emotional regulation, anxiety, stress), and behavioral (fatigue, sleep) variables.

NSF Awards · FY 2024 · 2024-07

The broader impact of this I-Corps project is the development of advanced technologies to enhance magnetic resonance imaging systems. This innovation aims to significantly improve the safety and quality of imaging, particularly benefiting patients with metal-based medical implants. By reducing the dependency on high refractive indices materials and simplifying the system architecture, the technology can lower operational costs, making high-quality diagnostic imaging more accessible and safer. This advance promises to address critical gaps in current medical diagnostic capabilities, opening high-quality imaging to a broader market, including resource-limited and rural areas. Preliminary market surveys indicate a strong demand for safer, more efficient imaging solutions, highlighting the technology's potential to transform healthcare diagnostics and improve patient outcomes. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a dual-layer design tailored for magnetic resonance imaging systems. The technology finely tunes and manipulates electromagnetic waves, enabling in situ adjustments to adapt to different imaging configurations seamlessly. Initial research has demonstrated that this design can enhance image quality and safety in high magnetic field systems, providing a significant improvement over current technologies. Experimental setups have validated the feasibility of the solution, indicating its potential to achieve high diagnostic accuracy while shortening scan times and ensuring patient safety. 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 · 2024-07

PROJECT SUMMARY/ABSTRACT The World Trade Center (WTC) General Responders (GRs) carry a significant mental health (MH) burden from the exposure to intense psychological trauma after the WTC collapse. PTSD and depression are two of the top ten certifiable mental health conditions that the 9/11 GRs are burdened with. Members of the WTC Health Program (WTCHP), including GRs, FDNY and Survivors, have increasingly moved to Florida representing close to six percent of the cohort. They are away from the NY Metropolitan Area WTCHP, creating a need for localized, accessible care. Additionally, preliminary findings from our ongoing pilot study on the WTCHP GRs residing in Florida, indicate a large majority of the participants identify as Latinx, creating a need to provide accessible services in the language of their choice, addressing health equity. The findings also indicate that the majority prefer individual care and prefer, or are open to, remotely- delivered services. While many studies have identified the MH burden that the responders carry, there have been no controlled studies of psychosocial interventions to reduce PTSD, depression, anxiety, and sleep symptoms among multiethnic GRs. There is a critical need for developing or testing interventions appropriate for the GRs. Preliminary evidence suggests that remotely-delivered and clinician-supported interventions can reduce adverse MH symptoms as part of low-cost approaches with scalability potential in adults with significant MH conditions. The goal of this proposed U01 2-year study from Principal Investigators at a Hispanic Serving Institution (HSI), is to assess the feasibility, acceptability, and efficacy of Clinician-Supported PTSD Coach, originally developed for English speaking military veterans but never tested with responders, for reducing symptoms of PTSD, depression, anxiety, and sleep disturbance among English and Spanish speaking WTC GRs. PTSD Coach is a mobile app available at no cost for Apple and Android mobile devices. PTSD Coach is a self-managed (SM) application but given the need for more personalized, synchronous, and attentive services, the clinician-supported (CS) PTSD Coach intervention was developed. Studies of CS PTSD Coach with English-speaking veterans, reported reductions in PTSD symptoms. Collectively, these preliminary data suggest CS PTSD Coach is a promising intervention for the GRs in Florida. Findings from this study with a known cohort in Florida will inform the deployment of an expanded trial nationally on CS PTSD Coach with WTC GRs.

NSF Awards · FY 2024 · 2024-07

This project aims to explore how family involvement in everyday activities can spark children's interest in science, technology, engineering, and math (STEM), with a focus on Latino families and how cooking together can be a fun and natural way for children to learn about STEM concepts. This research is important because it can: 1. help others understand how families can best support their children's interest in STEM, 2. provide valuable insights into how family culture and interactions shape children's STEM identity, especially for underrepresented groups like Latino children, and 3. lead to new and effective ways to encourage children to pursue STEM careers. The study also aims to improve children's ability to think critically and analyze information, encourage healthier eating habits through family cooking activities, and contribute to a more diverse workforce in STEM fields. This project offers an engaging approach to promoting STEM education for underrepresented children by utilizing the power of family and familiar activities. The project is designed for the postdoctoral researcher to develop a deeper understanding of STEM education while enhancing skills in research, communication, leadership, and management. Diversity within the STEM workforce is essential for a wide range of ideas and viewpoints that foster innovation and productivity within organizations. However, minoritized groups, including Latinos, are significantly underrepresented in STEM. The goal of this study is to better understand the development of STEM identities of Latino youth by way of exploration, inquiry, and mastery of STEM related skills through a culturally driven STEM program for Latino families. By way of a quasi-experimental design, over a 5-week period, the project is designed to evaluate the effects of culturally affirming, at-home, co-cooking activities on children’s STEM identity, career aspirations, and diet quality. The goal of utilizing STEM tools, a family activity guide, and a reflective journal for children is to incorporate STEM concepts into the weekly co-cooking activities which are planned to highlight specific scientific phenomena during the cooking process. In addition, this study intends to capture caregivers’ and children’s lived experiences pertaining to cultural family cooking routines, family STEM conversations, and caregiver perceptions of children’s STEM identity through semi-structured interviews and audiovisual data collected through recordings of co-cooking experiences. The determination of best practices to enhance children’s STEM identities and to inspire future career aspirations via authentic and informal learning opportunities within Latino households are anticipated study outcomes. The project is designed to enhance the skills of the postdoctoral fellow in study design, research methods, and utilization of theories of change in furtherance of a career path in academic research; and to advance subject matter expertise related to children’s STEM identity, culturally affirming STEM practices, parent-child interactions, and children’s STEM career aspirations. This project is funded by the STEM Education Postdoctoral Research Fellowship (STEM Ed PRF) program that aims to enhance the research knowledge, skills, and practices of recent doctorates in STEM, STEM education, education, and related disciplines to advance their preparation to engage in fundamental and applied research that advances knowledge within the field. 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 2024 · 2024-07

The goal of this project is to broaden graduate student participation at the International Conference on Parallel Processing (ICPP 2024) conference (to be held in Gotland, Sweden, from August 12th to 15th, 2024 ) by providing travel awards to help pay for travel to and from the conference venue, lodging, and conference registration fees. Funding of this proposal will not only enhance scientific discovery in areas of parallel processing, but also broaden the impact of the parallel processing field by increasing graduate student participation. ICPP has a history of attracting high quality submissions from researchers around the world. Travel awards will provide graduate students the opportunity to engage with leading experts in parallel computing by presenting their results at the conference proceedings, workshop proceedings and poster sessions. In addition, attendance will create opportunities to network with their peers and solicit feedback from senior members of the research community. This networking may lead to future research collaborations and career advancement opportunities. The International Conference on Parallel Processing (ICPP) is one of the oldest continuously running computer science conferences in parallel computing in the world. ICPP 2024 will be the 53rd year of this conference series and will be soliciting refereed conference papers, posters, and workshops along six tracks, including, Algorithms, Applications, Architecture, Multidisciplinary, Performance and Software. This travel award will prioritize graduate students who are coauthors of an accepted paper or poster, who are members of underrepresented groups, and who do not have alternative funding sources to attend ICPP 2023. We will use travel award application materials to gauge relevance of current and future research work to the conference topics, importance of attending the conference, and career plans related to parallel processing. Each travel award application will be reviewed by a selection committee of at least three members. This selection committee will be led by the ICPP 2024 Poster and Student Program Chairs and will include additional members invited from the ICPP 2023 organizing and program committees and senior members of the parallel processing community. To further broaden participation at the conference, we will engage with societies and institutes which serve and support students from underrepresented groups. 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 · 2024-06

Project Abstract/Summary More than 75% of the data generated from mass spectrometry (MS) - based omics experiments are wasted due to inefficiency of existing algorithmic methods that deduce peptides. The peptides that do get identified by existing computational methods usually come from abundant proteins – and hence recent calls by scientists to study overlooked proteins are gaining traction. These non-abundant and overlooked proteins might have the same (or more) importance in human systems biology health, and disease. Yet, all downstream analysis and conclusions – related to human health – are based on suboptimal and incomplete peptide deductions indicating formal investigation is warranted and urgently needed. In the recent decade, advances in machine-learning (ML) models have provided a critical step and have made it possible to develop more accurate and deeper pipelines for MS data analysis. Our preliminary work and experiments suggest that the limited training search-space exhibited by labelled spectral libraries makes robustness, and generalizability of existing ML models highly susceptible and may not effectively work for real- world data. The overall objective of my research lab using this MIRA mechanism is to design and develop robust, reliable, and generalizable machine-learning models for peptide deduction from MS data from omics experiments. Our proposed work fills four key knowledge gaps in development of ML models pursued via this MIRA grant that, if filled, will lead to superior computational techniques capable of inferring both abundant and non-abundant peptides. Our general strategy will involve design and development of generative models, self-learning models, biologically inspired models, and methods to infer uncertainty quantification. In addition, we will strive to focus on two key gaps in adaptation of ML models that will be filled via developing ML-ready workflows and developing easy-to-use software infrastructure that can be used by scientists. All this effort via MIRA grant mechanism will fill a critical gap in our understanding and ability to deduce peptide (that are novel) and will contribute a fundamental tool for studying complex communities in proteomics, and meta-proteomics data. At the end of this grant funding cycle, it is our expectation that we will have designed and developed highly accurate ML peptide deduction engine capable of end-to-end analysis of the MS based omics data– that is robust, generalizable, and more accurate than their algorithmic counterpart. Our proposed work will facilitate reproducibility by developing ML models that perform well – irrespective of underlying MS data quality or completeness – will be a highly impactful outcome. This proposed work will also serve as the foundation for analysis of more complex data sets related to meta-proteomics, and proteogenomics as one of our long-term goals that we hope to achieve using this MIRA grant mechanism.

NIH Research Projects · FY 2026 · 2024-05

With successes in cancer treatment becoming more common, treating patients with metastatic melanoma (MM) is still grim. The advent of immune checkpoint inhibition (ICI) therapy, particularly, has revolutionized the promise of generating more durable responses; however, the 5-year survival rate for patients with MM remains only ~23%. Due to therapy resistance and persistence of tumor-initiating cell (TIC) subpopulations, there is a vital need for predictive biomarkers of therapeutic response and novel therapeutic targets to augment current treatments for MM. Our laboratory has recently pioneered investigations on the signature features of the human MM cell glycome and how these discrete glycans and glycan-binding factors impact melanoma progression. The MM glycome is characterized by an abundance of cell surface i-linear poly-N- acetyllactosamines (linear poly-LacNAc) via loss of I-branching poly-LacNAc enzyme GCNT2 and elevations in LacNAc-binding lectin, galectin (Gal)-8. In fact, low melanoma cell GCNT2/I-branching corresponds with human MM progression, whereas detection of Gal-8 in serum directly correlates with a diagnosis of melanoma. Exciting, published data show that elevations in MM cell Gal-8 and Gal-8-binding linear poly-LacNAcs directly result in expression a key TIC factor, nerve growth factor receptor (NGFR)/CD271, a known driver of therapy resistance and MM progression. Engagement of this Gal-8 – Gal-8 ligand axis enhances TIC potential, pAKT activity, and in vivo tumor-forming activity of human MM cells. Our guiding hypothesis is that the MM glycome helps govern the malignant traits and capacity for melanomas to metastasize. Expression of Gal-8 and its glycoprotein ligands bearing linear poly-LacNAcs may help predict which melanomas will metastasize and even provide new opportunities to treat patients with MM. In this grant, we will conduct molecular characterization of the MM cell Gal-8 – ligand axis, probe the role of Gal-8 in melanoma growth and metastasis, and determine whether melanoma-intrinsic or host Gal-8 impacts melanoma immunity and alters anti-tumor responses to ICI therapy. The Specific Aims are: 1.) To examine the functional role of the Gal-8 – ligand axis in melanoma progression, and 2.) To analyze the growth- and metastasis-promoting roles of Gal-8 in melanoma. We are implementing: 1.) a strong investigator team consisting of glyco-analytics experts, clinical investigators, melanoma pathologists, and galectin biology experts; 2.) primary human melanoma tissues and stage-specific melanoma patient serum samples; 3.) innovative Gal-8 ligand/glycan assessment methods; and 4.) pre-clinical mouse and MM cell Gal-8-deficient models to impart the most impactful comprehensive data in this project. Results from this transformative proposal will illuminate how MM glycome regulates MM TIC factor, NGFR, and melanoma growth and metastasis, implicating Gal-8 and linear poly-LacNAcs as biomarkers or therapeutic targets of MM progression.

NIH Research Projects · FY 2026 · 2024-05

PROJECT SUMMARY/ABSTRACT Interactions between proteins and DNA control several fundamental biological processes, including gene regulation, DNA repair, replication, transcription, translation, and recombination. The PI’s previous research in instrument design, biological mass spectrometry, ion mobility spectrometry, and structural biology have enabled the study of the intra- and inter- molecular interactions and conformational landscape that drive protein, DNA, protein-protein and protein-DNA interactions in several model systems. The goal of this project is to advance mass spectrometry (MS) based techniques with complementary structural tools to better describe biomolecular complexes in their native solution environment. Coupling solution- and gas- phase separations (e.g., accessible solvent area labeling, ion-neutral reactions, and trapped ion mobility spectrometry), new dissociation methods (e.g., electron- and UV- based methods) with new developments in ultra-high, resolution MS will provide new structural biology tools capable of dissecting the molecular complexity and energy landscape of protein isoforms and protein-DNA complexes. These advanced MS tools will be used for the characterization of model systems of the non-histone chromosomal HMG protein family (e.g., high mobility group AT-hook 2 protein-HMGA2), type IA topoisomerase (TOP1) family and nucleosome dynamics (e.g., interplay of histone composition and histone post-translational modifications, PTMs). We will study HMGA2 transitions from unstructured to structured that allow HMGA2 to be involved in multiple biological processes, including DNA replication, translation, recombination, and gene regulation. We will capture the structural intermediates of the TOP1-DNA complexes associated with the catalytic cycle to search for new antibiotic candidates. We will study the influence of histone variants and PTMs in the nucleosome dynamics (e.g., partial, and full assemble); nucleosome dissociation and alteration of nucleosomal structure are crucial features by which chromatin regulates gene expression and DNA replication. The development of MS-based technologies capable of studying intrinsically disordered and dynamic biomolecular systems will significantly advance our understanding of the biological machinery associated with disease mechanisms, identification of therapeutic targets and disease prevention.

NIH Research Projects · FY 2026 · 2024-03

ABSTRACT Traumatic brain injury (TBI) causes the blood-brain barrier (BBB) dysfunction and transmigration of inflammatory immune cells into the brain, an important mechanism underlying neurovascular damage and neuroinflammation. Adhesion of leukocytes to endothelial cells is a critical step in the migration of leukocytes into injured tissues. Previously, it has been demonstrated that activation of leukocytes, especially neutrophils cause the release of nuclear and granular contents to form an extensive web-like structure of DNA called neutrophil extracellular traps (NET). However, in TBI, the mechanism of injury-induced formation of NET and its mechanistic regulatory role in thrombosis remains elusive. Moreover, it is not clear whether blocking of formation of NET provides better outcomes after TBI. Therefore, an approach to suppress the formation of NET would be a valuable therapeutic strategy and to analyze the efficacy of the therapy in the functional recovery level after TBI. Here, we hypothesize that inhibition of peptidyl arginine deiminase type 4 (PAD4), an enzyme required for NET formation, using PAD4 antagonistic peptide (PAP) will attenuate the formation of NET, NET-induced thrombosis, and promote neovascularization and functional recovery after TBI. In the first aim, we test whether PAP reduces PAD4 expression, inhibits NET formation, and promotes neovascularization. In the second specific aim, we will uncover the molecular mechanisms of the formation of NET-induced thrombosis in TBI and we will dissect the therapeutic role of PAP in depleting NET-dependent thrombosis. We will validate the role of PAD4 in the formation of NET and its role in thrombosis by CRISPR/Cas9 mediated PAD4 gene deletion in human brain microvascular endothelial cells (hBMVECs) and human neutrophil co-culture in vitro and PAD4 knockout (KO) mice (PAD4−/−) in vivo. In addition, we will study the role of different brain cells in NET formation by creating conditional knockout mice by breeding PAD4flox strain with specific brain cell Cre strains. In the third aim, we will use a cohort of behavioral tests that include sensorimotor functions, memory, and psychological stress analyses to validate the role of PAP in promoting functional recovery following TBI. Therefore, in this project, to validate the central hypothesis, these three aims target a subset of events towards unraveling a larger picture of neurovascular remodeling and functional recovery after TBI by attenuating PAD4 activity using a novel small peptide developed in the PI’s lab.