University Of South Carolina At Columbia

NSF Awards · FY 2026 · 2026-10

The project aims to serve the national need of supporting and retaining science teachers by exploring how the professional learning (PL) of District Science Coordinators (DSCs) impacts, if at all, the effectiveness and retention of new science teachers in high-need schools. There are a variety of factors influencing why new teachers stay or leave teaching; one factor not investigated is the role of DSCs in supporting teachers. With well-prepared DSCs, new science teachers could be better supported to teach and stay in high-need settings with students who often do not have positive or rigorous experiences in science. Thus, in time, this project could contribute to improved success of students in science which could translate into an increase in the STEM talent pool. This study is designed to generate new knowledge about how DSCs provide support for and aid in the effectiveness and retention of new teachers in high-need schools. This project at Clemson University and the University of Georgia partners with the National Science Education Leadership Association (NSELA) and districts across the nation. The project’s goal is to determine how different levels of PL among DSCs impacts new teachers in their first five years of teaching. The study aims to examine the PL of DSCs in terms of both required professional development and free-choice learning. The latter refers to PL that is chosen by DSCs, such as reading journal articles, watching educational videos, visiting museums and parks, and attending professional conferences. The project intends to gather and analyze qualitative and quantitative data to understand how the PL of a DSC contributes to the effectiveness and retention of new science teachers. Among the analytic approaches to be used are a two-cycle coding process to examine the impact of PL on DSCs and the subsequent impact on teachers, with the first cycle using holistic coding and the second using organization or hierarchical outlining. Another analytic approach to be used is multiple linear regression modeling regarding the relationship between variables and to support explanation of observed variance. These are just two of sixteen analytic approaches to be used to examine the two project research questions. The findings from this project are expected to contribute to the understanding of the selection, quantity, and quality of PL of DSCs as well as if there is any impact on new teachers associated with the PL of the DSCs. This project builds upon current synergies across the country to cultivate teacher leadership - but with a focus on DSCs – and could suggest what additional studies are needed in this area. The results are to be shared with a wide audience, through traditional and novel formats, including usage of well-established social media outlets used by the PIs and presentation of findings at state and regional levels targeted to reach administrators with responsibility for the hiring and support to DSCs. This will provide the opportunity for others to benefit from and build upon this project’s findings to further improve K-12 STEM education. This Track 4: Noyce Research project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K-12 STEM teachers in high-need school districts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-09

Pathfinding is essential for making effective decisions about how to move through complex spaces, from robots navigating crowded warehouses to synthesizing molecules through sequences of reactions. A significant roadblock to solving these pathfinding problems is that the goal may only be describable at a high level. For example, in chemical synthesis, scientists may know the properties a therapeutic molecule should have, but not know of any molecule that has such properties. This project seeks to remove this roadblock by creating novel artificial intelligence (AI) algorithms to solve pathfinding problems based on high-level goal descriptions. This could lead to the discovery of new goal configurations and reduce the time and cost required to solve pathfinding problems. The objective of this project is to address high-level goal specification and goal reaching for pathfinding problems, where a high-level goal specification defines a set of goal states without explicitly specifying any state in the set. The ability to find paths based on high-level goal specifications becomes necessary when properties of a goal state can be specified, but states that satisfy these properties are not known. This project will develop novel domain-independent AI algorithms that combine machine learning, heuristic search, and formal logic to reach goals specified with answer set programming, an expressive logic programming language. Deep reinforcement learning will be used to train a deep neural network heuristic function that estimates the distance between a given state and a high-level goal specification defined using answer set programming. To learn from failures, a neuro-symbolic algorithm that refines the heuristic function using symbolic constraints extracted from failure cases will be developed. The project will also contribute to education and training of students in AI by providing hands-on experience, exposure to real-world applications, and engagement with current research challenges that build both practical skills and deeper conceptual understanding. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

Despite advances in additive manufacturing of 3D porous lattices, printing resolution remains below the length scales where the nano-size effect, and the resulting “smaller-is-stronger” behavior can be exploited. This EArly-concept Grant for Exploratory Research (EAGER) award supports research to develop new scalable nanofabrication knowledge through a bottom-up strategy in which nanoscale architecture is programmed by well-controlled material self-organization rather than a toolpath. The objective is to enable lightweight, damage-tolerant porous polymer lattices whose performance is governed jointly by architecture and nanoscale feature size. If successful, this work will expand the manufacturing toolkit for next-generation nano-architected materials with impact across energy technologies, protective systems, and resilient structures. The project includes training for students in integrated polymer synthesis and physics, liquid-crystal science, nanofabrication, nanoscale characterization, and micromechanical testing, and will expand participation in STEM through undergraduate research and pre-college outreach. This research will pursue a high-risk, high-reward bottom-up nano-manufacturing strategy that uses 3D defect networks that spontaneously form in chiral blue phase liquid crystals as nanoscale templates for polymer assembly, enabling the fabrication of porous nanolattice architected materials. The central hypothesis is that these soft disclination “blueprints” can be transduced into solid, shape-defined polymer networks, providing a nanofabrication route that can surpass conventional top-down manufacturing. The approach relies on precisely controlled photopolymerization of reactive monomers within a non-reactive blue phase liquid crystal host, driving polymerization-induced phase separation and preferential formation of crosslinked polymer within locally disordered disclination cores with a theoretical core diameter of approximately 10 nm. Two integrated research goals will establish foundational process-structure-property relationships. First, the project will explore how monomer structure and photopolymerization kinetics govern phase separation, selective localization within defect cores, and structural retention, and how solvent-mediated template removal impacts nanolattice integrity. Second, thermodynamic control parameters of the blue phase liquid crystal host, including chirality, temperature, and surface anchoring, will be tuned to program lattice topology and length scales. Finally, this study will quantify how architecture and nanoscale dimensions control macroscopic mechanical response using in situ scanning electron microscope micropillar compression. 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 · 2026-05

A class of chemical reactions called multi-step multi-electron conversion reactions may help improve a variety of energy storage technologies including next generation batteries. However, controlling these chemical reactions is challenging. This CAREER project will conduct experiments to analyze these reactions in a lithium-sulfur battery as a model system. The results will show how the chemistry and material properties of such systems affect their performance. The outcomes will help improve the predictability and efficiency of battery materials and improve resource utilization. The project’s insights will advance foundational knowledge in electrochemistry and materials science. The project will integrate research and education by engaging students in hands-on discovery and by developing new instructional materials based on the research. The activities will expand the future science and engineering workforce and contribute to long-term technological leadership in energy and materials innovation. The primary goal of this project is to elucidate the fundamental redox mechanisms underlying multistep, multielectron conversion reactions by establishing the principles governing the dynamic interplay between chemical evolution and multiscale structural transformations of materials. Using lithium-sulfur batteries as a model system, the project will address critical knowledge gaps and determine how coupled chemical and structural dynamics influence the thermodynamic, kinetic, and transport landscape of the redox system. Specifically, it will (i) investigate how atomic-scale motifs in active materials affect the redox barriers in solid-to-liquid reactions; (ii) establish a generalizable framework linking phase transitions, chemical speciation, and redox barriers within confined environments; (iii) deconvolute the interplay between mass transport and phase transformations to clarify overpotential signatures of liquid-to-solid conversions; and (iv) elucidate how molecular interactions between redox intermediates and local environment control key reaction barriers. These objectives will be pursued using integrated operando electrochemical–spectroscopic methodologies to directly correlate material structure, local chemical environment, and electrochemical behavior. The resulting insights will provide a fundamental, generalizable framework for interpreting complex electrochemical mechanisms and establishing structure–function relationships critical for the rational design of conversion-based redox systems, with principles broadly applicable to electrochemical processes involving coupled redox and phase transformations. 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 · 2026-04

This project requests support for US-based doctoral students to attend the IEEE International Conference on Healthcare Informatics (ICHI2026) in Minneapolis, MN, USA. ICHI2026 provides a supportive scientific forum for students focusing on computing, health- and bioinformatics. It provides a forum for expert and peer critique of students' research with the goal of improving their science. Student participants will also have the opportunity to receive networking support and career advice from internationally recognized experts. This proposal supports doctoral students by providing a Student Doctoral Consortium focused on encouraging students to learn from peers and experts from multiple perspectives to understand how innovative computing, AI and informatics, combine to make the most impact in the area of health. In addition, student participation in rapid talks and roundtable sessions at the conference enables students to explore their science and see different career paths for researchers in this area. The ICHI2026 conference exposes participants to different scientific disciplinary approaches, supports networking with conference attendees, and is designed to support the development of the next generation of scholars in health-, bioinformatics and biotechnology. This travel support will strengthen the national AI and biotech workforce pipeline by providing intensive, mentored professional development for doctoral researchers who will become the next generation of faculty, R&D scientists, and innovation leaders. The Consortium’s focused training in problem framing, evaluation, and communication across disciplines increases participants’ capacity to conduct high-impact interdisciplinary research and to translate AI advances into biotech and health outcomes. Overall, the travel support brings together students with experts who might not otherwise engage with one another and engage in multidisciplinary science in the area of health. 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 · 2026-04

Converting renewable or waste materials to useful products is important for energy security and U.S. manufacturing. Upgrading plant-based materials and plastic wastes often requires converting small molecules into larger molecules. The larger molecules can be used in various products, including aviation fuel. The required reactions use catalysts to speed up product formation and suppress byproduct formation. Unfortunately, many catalysts do not perform well. Their short lifespan also limits their practical use. Recent studies show that dissolving small-molecule reactants in solvents improves catalyst performance. However, the reasons for the improvements are not well understood. This project will combine laboratory experiments and computer modeling to determine how solvent properties influence key reactions. It will establish design principles for use of solvents or other chemicals to improve catalyst efficiency. The outcomes will enable more efficient production of chemical products from domestic sources. The research will be carried out by a team of researchers who will learn to communicate and work with a cross-disciplinary team. Students at graduate, undergraduate, and high school levels will be trained in research skills. Solvents are widely known to affect rates and selectivity in heterogeneous catalysis. Changes in solvation environment can increase the rate of aldol condensation from lighter carbonyl compounds. This also decreases condensation rates for heavier molecules, leading to improved resistance to deactivation from carbonaceous deposits. The project will develop a framework for understanding these changes in relative reaction rates (i.e., selectivities) for aldol condensation on TiO2 catalysts. Experimental kinetic studies will benchmark computational models, which in turn will suggest new solvent combinations to further improve selectivity. Solvation effects will be probed using vapor phase condensation within catalyst mesopores. Controlling the extent of pore condensation will enable kinetics measurements in the presence and absence of a solvating environment. This methodology facilitates comparisons between experiment and theory by providing experimental information on how the addition of a solvent environment perturbs surface chemistry at the gas-catalyst interface. In the first phase, researchers will study acetaldehyde self-aldol condensation and develop models for a relatively simple reaction that is strongly impacted by solvents. These models will be expanded to include mixed reactant systems, including both acetaldehyde/acetone and acetaldehyde/ethanol systems. Mixed systems have been shown to exhibit complex reaction kinetics due to changes in surface intermediate populations. These studies are therefore designed to determine the sensitivity of solvent effects to surface intermediate concentrations. Finally, a similar approach will be used to investigate how catalyst materials can be designed to exhibit transfer of the promotional solvent effects to the catalyst surface. The material modifications will be achieved by depositing ligands with different chemical functionalities within the TiO2 mesopores. Cumulatively, the goal of this research is to better understand the role of solvent functional groups — and the impacts of tethering those groups within catalyst pores — in directing the reactivity of oxygenates. Because solvent effects are ubiquitous in catalysis, the proposed research will help develop methods to effectively model them and, in turn, predict the effects of solvent design. 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 · 2026-03

The K99/R00 Award will support my transition to an independent researcher focused on using repetitive Transcranial Magnetic Stimulation (rTMS) to enhance post-stroke aphasia recovery. The majority of non-invasive brain stimulation (NIBS) studies, including those using rTMS, apply facilitatory stimulation to the left Inferior Frontal Gyrus (IFG) or inhibitory stimulation to its right homologue. While IFG is an important hub, the language system is widespread and complex, and its damage induce impairments that vary significantly across individuals. For instance, post-stroke aphasia often leads to anomia and semantic errors due to disruption in lexicosemantic processes. I propose to use a novel and promising target for stimulation – the Anterior Temporal Lobe (ATL) – given that evidence suggests that it plays a crucial role in lexicosemantic processing and hence may be pivotal in individuals with such impairment. Additionally, the theory of transcallosal interhemispheric imbalance hypothesize that brain lesions cause hyperactivation of the undamaged hemisphere and hypoactivation of the damaged one, justifying left hemispheric (LH) facilitation and right hemispheric (RH) inhibition. However, this model does not fully explain the complex involvement of the RH in post-stroke aphasia recovery. Furthermore, while rTMS shows promise in improving post-stroke language impairments, individual responses vary, necessitating a better understanding of factors influencing its efficacity. To address these gaps, I propose a three-arm randomized clinical trial involving 60 individuals with post-stroke aphasia. The study will compare facilitatory rTMS targeting the left ATL, inhibitory rTMS targeting the right ATL, and sham stimulation. By tailoring rTMS targets to participants’ clinical profiles, we aim to improve language processing outcomes (Aim 1). I will investigate how RH inhibition versus LH facilitation affects language recovery. Using resting-state and task-based functional Magnetic Resonance Imaging (fMRI), I will explore the mechanisms underlying each stimulation type and assess the relevance of the theory of transcallosal interhemispheric imbalance (Aim 2). I will also study how lesion localization and neural disconnections influence responses to brain stimulation using lesion- and connectome- symptom mapping (LSM/CSM) techniques (Aim 3). This research builds on my Ph.D. work, which focused on tailoring rTMS to specific post-stroke language impairments and extends it to address lexicosemantic impairments. The K99 phase will primarily develop my expertise in fMRI and LSM/CSM methodologies, supporting my progression to independent scientist in the R00 phase and enabling future investigations into NIBS for aphasia recovery, improving outcomes and understanding the underlying mechanisms of these stimulations.

NIH Research Projects · FY 2026 · 2026-03

PROJECT SUMMARY/ABSTRACT Early language skills are predictive of later social, cognitive, and interpersonal development and are often disrupted in the younger siblings of autistic children, who are at elevated likelihood for social communication impairments. Evidence suggests that discrete features of early vocalizations – including volubility and canonical proportion – are related to language development and often differ for siblings of autistic children. The proposed research investigates how early vocal features develop and interact with the emergence of more advanced social communication skills in this population and seeks to optimize these measures for translation into clinically meaningful assessments. The developmental progression of vocal features prior to 12 months of age has been relatively well-established; however, little is known about how these features develop across the second and third years of life, a time of rapid expansion of language and social communication skills. Furthermore, no studies have examined the association of vocal features with developmental outcomes three years of age, a time when diagnostic stability of autism and other social communication diagnoses is maximally balanced with the opportunity to capitalize on early intervention efforts leading to optimal outcomes. To fill these gaps in knowledge, Aim 1 will contrast the trajectories of early vocal features in infants at elevated likelihood for social communication impairments to those of infants without elevated likelihood from 12 to 36 months of age. Aim 2 will then determine the association of vocal feature trajectories with preschool outcomes at 36 months of age in infants at elevated likelihood for social communication impairments. Finally, Aim 3 will evaluate the representativeness of vocal features obtained from brief language samples compared to those obtained from daylong recordings, providing critical insight into the potential for these brief samples to serve as clinically translatable methods for assessing vocal development. The long-term goal of this research program is to identify vocal features as targets for early intervention that are easily assessed and reliably linked to later social communication skills. The candidate has assembled an expert team of mentors that will guide her career development in order to (1) advance her expertise in predicting social communication outcomes among children with an older autistic sibling, (2) obtain training in efficient processing of daylong language samples to generate reliable measures of vocal development and optimize them for clinical translation, (3) acquire proficiency in advanced statistical analysis of longitudinal vocalization data using random effects modeling in clinical samples, and (4) develop skills needed to pursue a successful career as a tenure-track faculty focused on translational research in populations at elevated likelihood for social communication differences. Overall, this project directly aligns with the mission of NIDCD to support behavioral research in disordered processes of speech and language as it characterizes the trajectories and developmental consequences of vocal development in children at elevated likelihood for social communication disorders, ultimately advancing translational efforts to effectively detect and intervene on these conditions.

NSF Awards · FY 2026 · 2026-03

Osteoporosis is a disease that weakens bones and makes them susceptible to fracture. It affects millions of people in the U.S., especially the elderly. Bone morphogenetic protein-2 (BMP-2) is a protein that can stimulate bone growth, enhance healing of damaged bone, and reduce pain in osteoporosis patients. However, its use is limited by complications such as bone overgrowth and tumor formation. An alternative is to use short amino acid chains called peptides that mimic the actions of BMP-2 without its complications. This project will explore how a peptide’s molecular conformation affects its biological activity. The project will employ machine learning to connect a peptide’s amino acids and molecular conformation with its ability to stimulate bone formation. The team will produce a database of peptides and their structures and then test them for therapeutic activity and bone regeneration. The outcomes will help identify effective peptides that can be used safely to treat damaged bone. The project will also develop an undergraduate course project and a workshop for high school teachers on machine learning in biotechnology. The hypothesis of this proposal is that the orders of magnitude lower osteogenic activity of the knuckle epitope peptide of bone morphogenetic-2 (BMP2-KEP) is rooted in configurational differences between its native state on the protein and its free state. The BMP2-KEP activity is much lower in the free state due to the collapsed state of the peptide structure. This hypothesis will be tested by building an ML-driven Quantitative Structure-Activity Relationships (QSARs) model for discovering new osteogenic peptides using a database of configurational properties of modified knuckle epitope of BMP2-KEP sequences. The sequences are predicted by mesoscale simulation followed by validation of the QSARs by experimental evaluation of the predicted peptides in a biomimetic tissue model to assess osteogenesis and immunogenicity. The project will also deliver the ML-predicted and experimentally tested peptides to mesenchymal stem cells safely and effectively by conjugation to nanogels. The outcomes of this project will advance knowledge of configuration-mimetic peptides and will help understand relations between peptide configuration and biological activity. The project will promote applications of therapeutic peptides that lead to clinical breakthroughs in regeneration of injured tissues and bone. 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 · 2026-03

Abstract With over 1.2 million people living with HIV (PWH) and a high prevalence of substance use disorder (SUD) among PWH—6.5 times higher than in the general United States (U.S.) population—syndemic HIV/SUD presents a barrier to the nation’s goal of ending HIV in the U.S. by 2030 (i.e., Ending the HIV Epidemic: A Plan for America initiative). The prevalence of SUD among PWH varies significantly across local communities in the U.S., reflecting differences in social and economic conditions and public health policies. To our knowledge, excluding alcohol-related research, no study has systematically examined the prevalence and correlates of SUD among PWH in a representative population. Addressing this critical gap is essential for developing targeted, effective interventions and policies. Additionally, without a comprehensive understanding of the local context and its impact on the HIV/SUD syndemic with regard to emerging hotspots, the HIV epidemic will continue to intersect with and amplify challenges related to SUD, stymying progress toward achieving national health goals. Despite the recognized critical role of social determinants of health (SDoH) in shaping HIV/SUD trajectories, few studies have focused on the complex interplay between substance use, HIV, and SDoH. This gap persists due to several key challenges, including: 1) limited longitudinal data resources for PWH; 2) the challenge of integrating individual- and aggregated community-level data; 3) the need for advanced analytical methods to address the high dimensionality of SDoH amidst dynamic, evolving patterns of HIV and SUD; and 4) the lack of cohesive, multidisciplinary frameworks that bring together expertise across epidemiology, biostatistics, social science, and public health. Leveraging our multidisciplinary team and preliminary HIV and SUD work in South Carolina, we will examine the HIV/SUD syndemic in the state by integrating the PWH cohort with the Agency for Healthcare Research and Quality's database on SDoH using our innovative Big Data approach. We will develop a novel dynamic spatial model that accounts for multivariate outcomes to understand dynamic co-incidence patterns of HIV and SUD by substance type at the county level (Aim 1); apply advanced machine learning techniques to identify SDoH that contribute to HIV and SUD patterns and may be amenable to targeted interventions (Aim 2); and develop predictive models to identify future high-risk hotspots for co-occurring HIV and SUD (Aim 3), enabling evidence-based, proactive resource allocation and intervention planning. The project will provide critical insights into community-based factors linked to poorer treatment access and outcomes for HIV and SUD, guide targeted policy and intervention strategies, and create a scalable framework for addressing future public health challenges. By leveraging diverse data sources and innovative modeling, this research has the potential to transform the prevention and management of HIV and SUD not only in South Carolina but also nationally by creating a predictive modeling tool that can be applied in other states and public health systems.

NSF Awards · FY 2026 · 2026-02

NON-TECHNICAL SUMMARY This project is building an improved understanding of how magnetic nanomaterials behave when they are placed in a magnetic field that oscillates back and forth in time. Also known as magnetic relaxation, these behaviors depend on the properties of the material, including what elements comprise the material, as well as what geometric shape the material is formed into. Intellectual merit: By changing the ratio of the elements included, as well as the length and diameter of rod-shaped magnetic particles, this project is delivering new knowledge about how these core properties affect the time-varying magnetism response in these materials. This new understanding could ultimately enable the use of these materials in technologies that can identify, image, and treat cancer and other diseases, improve detection of magnetic effects and materials in industrial applications such as advanced manufacturing, and consumer applications such as home and food health and safety. Broader Impacts: A future workforce is being trained to discover and implement technology that can improve the health and well-being of U.S. citizens. Finally, this project is explaining why these materials are so important to audiences of all ages and interests across the state of South Carolina, which already manufactures many goods whose continued improvement depends on an understanding of the raw materials that enter these factories. TECHNICAL SUMMARY Dynamic relaxation in magnetic nanostructures, especially suspended in fluids, is very difficult to measure at frequencies higher than 100’s of kHz, and as a result, researchers in this field are left using an over-simplified ratios to combine physical (Brownian) particle relaxation with magnetic (Neel) relaxation in these particles. Intellectual Merit: By measuring relaxation over a wider range of frequencies and particle number, this project will deliver an improved expression that more accurately combines these relaxation contributions. In addition, by varying both the composition of the magnetic material, and the aspect ratio of cylindrical nanorods, the contribution of particle magnetism (i.e., magnetization, anisotropy, and uniformity) will be separated from that arising from interactions between the particles, e.g. in a fluid, delivering engineers information needed to properly design and specify magnetism and size for nanostructured magnetic systems to be used in applications ranging from medical imaging, diagnostics, and treatment, to industrial optimization of manufacturing precision, relying on magnetic sensing and measurement. Broader Impacts: A future workforce skilled in magnetic nanomaterials and measurement is being trained to work in these factories and clinics to improve the health and economic security of U.S. citizens. 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 · 2026-02

Project Summary Investigating and Addressing Modifiable Factors in the HIV Care Continuum for People with HIV (PWH) affected by Substance Use and Mental Health Social determinants of health like poverty, and unstable housing, combine synergistically with comorbidities like substance use (SU) + mental health (MH) as a syndemic to disproportionately burden disadvantaged populations people living with HIV (PWH). Substance use and mental health comorbidities are associated HIV Continuum of Care Outcomes (HCC) like delayed entry into care, lower retention in care, reduced ART adherence, poor VL suppression, and higher mortality for PWH. For the US to end the HIV epidemic (EHE) by 2030, the underlying mechanisms of SRD- driven health disparities on viral suppression and HCC outcomes among all PWH experiencing substance use and mental health syndemic must be elucidated and addressed. The lack of suitable comprehensive longitudinal data to examine substance use, and mental health impact on dynamic changes in HCC outcomes limits our ability to end the HIV epidemic. Defining and describing the impact of substance use and mental health on HCC outcomes requires examining the complex interactions of sociocultural, economic, environmental, and geographic contexts influencing these interactions. To address the knowledge gaps on modifiable factors related to the intersection between SU+MH, we propose using real-world multiple linked datasets, including enhanced HIV/AIDS surveillance (e-HARS), Electronic Health Records (EHR), Department of Mental Health data, Department of Alcohol and Other Drugs of Abuse (DAODAS) data, corrections data administrative claims, and other relevant public data sources, to investigate the disparities in SU, MH recognition, treatments, and HCC outcomes using data science. We will use qualitative methods to examine interpersonal and intra-individual factors to identify modifiable factors for moderating the effects of the intersection of SU+MH on viral suppression and the HCC. The specific aims are to: 1. Examine and visualize the longitudinal patterns/trends, heterogeneity, and disparities arising from SU on viral suppression and other HCC outcomes among PWH in SC; 2. Determine the interactive effect of SU+MH on viral suppression and other HCC outcomes; and 3. Understand experiences and impact of SU+MH on viral suppression and other HCC outcomes among PWH population in SC using focus group discussions/in-depth interviews.

NSF Awards · FY 2026 · 2026-02

About a third of the world’s gross domestic product passes through a catalytic reactor at some point. This includes textiles, petroleum products, fertilizer, specialty chemicals, pesticides, fragrances, and pharmaceuticals. Manufacturing these products often involves catalysts composed of metal nanoparticles containing platinum or palladium. The nanoparticles are anchored to a stable porous support, such as aluminum oxide or carbon. The materials must be accurately characterized, and the nanoparticle chemical composition and structure must be known. Characterization at the nanoparticle surface is most important because reactions take place there. Ideally, catalysts should be studied under reaction conditions at elevated temperatures. This Major Research Instrumentation award will support the acquisition of a Near Ambient Pressure X-ray Photoelectron Spectrometer (NAP-XPS) at the University of South Carolina. It will be used to study nanoparticle surfaces under reaction conditions. The new instrument will enable state-of-the-art development of new catalyst systems for myriad advanced manufacturing processes that rely on catalytic reactions. Research that will be enabled with the NAP-XPS includes studies of the active site for hydrogenation and hydroformylation over Cu-based metal-organic frameworks, upgrading of waste biomass, transition and reducible metal catalysis, and supported bimetallic catalysts in hydrogenation reactions. Other materials science research projects involve dynamic processes occurring on novel materials, such as crystalline macrocycles, quantum dots, and spongy multi-metallic nanoparticles. The impact of the NAP-XPS instrument will reach far beyond the university's campus and will support academic and industrial research and education in the southeast region of the U.S., where it will be the only such instrument. At present fifteen universities and six private companies, mainly in the southeast, are external users of the XPS facility. The instrument will be heavily utilized in the University of South Carolina's NSF Industry/University Cooperative Research Center, the Center for Rational Catalyst Synthesis. Educational outreach includes programs for students at undergraduate and graduate levels. 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 · 2026-02

PROJECT SUMMARY The initiating factor for opioid use disorder often stems from comorbid neuropsychiatric conditions including anxiety, depression, and post-traumatic stress disorder. Experience of extreme or traumatic stress is often the underlying factor responsible for these neuropsychiatric conditions and, thus, investigation into the neural mechanisms by which stress impacts the brain is essential for determining the mechanisms responsible for the initiation of substance use disorders. Our preliminary data demonstrates that stress exposure leads to increased cytokine release and heightened activity of the noradrenergic locus coeruleus (LC), a key region responsible for the integration of stress signaling that projects norepinephrine to numerous downstream brain areas. The prelimbic cortex of the medial prefrontal cortex (PrL) is one such region that receives over 90% of its noradrenergic innervation from the LC and plays a major role in drug seeking and reward-related behaviors. Thus, the overall goal of this project is to establish the circuit mechanisms of stress-related norepinephrine release in the PrL and the role of these projections in oral oxycodone seeking behaviors. These experiments will use cutting edge techniques including chemogenetics, in vivo electrophysiology, and fiber photometry with GRAB sensors to monitor neuronal activity and transmitter release across this circuit in response to stress and drug stimuli. Three main experiments have been designed to address the hypothesis that stress-evoked increases in neuroimmune activity in the LC initiate neuronal activation and downstream NE release to mediate stress- induced drug seeking behavior. First, chemogenetic suppression of neuroimmune activity in the LC will be paired with in vivo electrophysiology during stress to monitor the impact of microglial reactivity on neuronal activity within this region (Aim1, K99). The second experiment will utilize GRABNE sensors in the PrL to determine the time-course of norepinephrine release in response to stress cues. These studies will also use adrenergic receptor antagonists microinjected into the PrL during stress-cue reinstatement to determine the mechanisms by which NE is acting in this region to impact drug seeking behaviors (Aim2, K99). The final experiment will use translationally relevant compounds to determine if the reversal of stress-induced neuroimmune reactivity can prevent the deleterious effects observed as a consequence of stress (Aim3, R00). Taken together, these studies will expand our understanding of the circuit mechanisms responsible for stress-related opioid seeking behaviors and determine the therapeutic potential of clinically available pharmaceuticals all while providing extensive training in innovative preclinical techniques. The combined technical training and career development opportunities supported by this application will facilitate further independent projects designed to address unanswered questions regarding the neural mechanisms responsible for opioid use to develop novel treatment targets for comorbid stress and substance use disorders.

NSF Awards · FY 2026 · 2026-01

This Research Infrastructure Improvement EPSCoR Research Fellows project provides a fellowship to an associate professor and training for a graduate student at the University of South Carolina. This work is conducted in collaboration with Dr. Nan Li at Los Alamos National Laboratory (LANL). Through the fellowship, the PI will investigate the fundamental deformation mechanisms responsible for the exceptional strength and ductility in heterogeneous lamella-structured (HL) metals. The project will develop an experimentally validated multiscale crystal plasticity finite element (CPFE) model to explore the deformation physics of HL metals across multiple material and structural length scales. Complementary micromechanical testing and microstructural characterization at LANL will validate and refine the model. The results of this research will support the development of advanced structural metals for automotive and aerospace applications and the advanced manufacturing sector. Moreover, it will contribute to workforce development by educating a new generation of engineers in the design and processing of high-performance materials. This project focuses on advancing the understanding of deformation mechanisms in HL metals to address the long-standing strength–ductility trade-off in structural materials. The scope includes the development of predictive, multiscale modeling tools validated by cutting-edge micromechanical experiments. The project’s intellectual contribution lies in uncovering how strain gradients and internal back-stress at lamellar interfaces enhance both strength and ductility in HL metals. It will establish a mechanistic framework that links microstructural architecture to macroscopic mechanical performance, offering new design principles for advanced structural materials. This project will enhance research infrastructure through faculty professional development in multiscale modeling and materials mechanics, hands-on training for graduate students in advanced testing and simulation techniques and expanded use of LANL’s world-class facilities. Research activities are closely linked to student training, curriculum enhancement in materials science and manufacturing, and the development of a highly skilled STEM workforce in South Carolina. The fellowship will also strengthen institutional partnerships with LANL and promote long-term collaborative research and educational opportunities. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows, which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research institutions. 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 · 2025-11

The PowerCyber project aims to address the underutilization of advanced Cyberinfrastructure (CI) in the domain of power and energy engineering. Despite the significant advancements in CI, its adoption in power engineering has been limited due to a lack of quality training materials and the historical reliance on limited tools. This project seeks to fill the gap by creating an online, modular, and open-access training workshop tailored for researchers in power engineering. The project will democratize access to high-quality research training, benefit a diverse population, and foster collaborations between the power and CI communities. Also, the project is expected to equip the research workforce with an understanding of advanced CI software and hardware and help accelerate interdisciplinary research for the clean energy transition. In this project, the investigators will develop an online, modular, and openly available PowerCyber training to prepare power engineering researchers with a comprehensive understanding of advanced CI software, hardware, and emerging CI technologies. The tasks include a) developing high-quality, interactive, and on-demand training modules to cover advanced CI in software, hardware, and emerging techniques and demonstrating, by research examples, their potential to transform power engineering research; b) offering virtual PowerCyber training workshops; and c) incorporating the training materials into the curricula at the home institution of the investigator. Upon the completion of this project, we expect to have demonstrated a pilot training workshop that provides researchers with advanced CI capabilities for solving power-domain problems. This project is jointly funded by the Office of Advanced Cyberinfrastructure and the Established Program to Stimulate Competitive Research (EPSCoR). 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 · 2025-11

Non-technical abstract: Quantum magnets are promising platforms for quantum computation and future green technologies. As an emerging and dynamically evolving research area, adequate understanding of fundamental physics and design principles of these materials are still lacking. The goal of this project is to use external pressure as a control parameter to reveal complex quantum phases and advance our understanding and control ability of the emergent phases. The studies provide insights to further synthetic efforts of novel quantum materials. The integrated education and outreach activities focus on bridging the gap of STEM workforce education in Alabama by (i) directly training next generation scientists at multi-scale facilities, (ii) enhancing STEM research and education capacity in Alabama through organizing Lecture Series on Modern Synchrotron Techniques and Applications, (iii) contributing to the UAB physics-STEM Teaching and Learning Incubator program to enhance state-wide high school science teacher training, and (iv) promoting awareness in STEM career pathway and interest in scientific discovery in general public and disseminating scientific discoveries to broad audience through volunteering at McWane Science Center. Technical abstract: Intrinsic magnetic topological materials exhibit exotic topological quantum phenomena with great potential applications in future quantum computation and spintronics. Using external pressure as a tuning knob, the project targets to experimentally discover novel quantum phenomena, realize ideal magnetic Weyl state in real materials, and unravel the interplay of magnetic structure, crystal symmetry, and topological states in representative magnetic Dirac materials. The research employs a suite of cutting-edge experimental techniques, including the state-of-the-art synchrotron-based spectroscopy, scattering, and diffraction techniques, and lab-based transport and magnetization techniques combining with diamond anvil cell and piezoelectric uniaxial strain cell. The comprehensive experimental results aim to benchmark theoretical models for treatment of interplay of many-body physics and topology. The fundamental understanding of the emergent properties helps to harness the magnetic topological materials for future applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

AC motor drives are widely utilized in many on-the-move energy technologies (e.g., electric vehicles and industrial or medical robots) to control the speed or position of a mechanical scheme. The industry is constantly seeking lightweight, efficient, and reliable solutions for these high-performance AC motor drives. Recent advancements in semiconductor materials have enabled electrical engineers to design ultrafast switches and motor drive systems with higher efficiency and power density. However, the ultrafast switches can cause severe voltage stress on the motor stator winding insulation, reducing the motor lifetime and eventually leading to unexpected shutdowns. This collaborative research will address such reliability concerns through developing the technology of smart coils. Since the proposed technology avoids the conventional bulky and lossy filters, it will advance the compactness of high-performance motor drive systems. In this project, innovative educational modules will also be collaboratively developed to inspire prospective undergraduate and graduate students to pursue education and careers in applications of control theories in power electronics and motor drives. This project aims to develop the technology of smart coils for AC motor drives. The envisioned smart coil technology will make the surge impedance of the motor windings adaptively vary by the sharpness of the voltage pulses which are produced by a drive, travel along cables, and reach the motor terminals. The evolving technology of ultrafast wide bandgap semiconductor switches enhances the efficiency and power density of AC motor drives by minimizing the size of passive components and cooling apparatuses. However, the sharpness of the generated voltage pulses can induce reflected waves in the cable and overvoltages at the motor terminals. The smart coil technology can mitigate the overvoltage stress caused by the reflected wave phenomena regardless of the length of the power cables. Unlike conventional approaches for mitigating reflected wave phenomena that use bulky and lossy passive filters, the smart coil technology will enable AC motors much more compatible with the emerging wide bandgap-based fast-switching drives. This technology can also lead to more electric powertrains with high efficiency, high power density, and high reliability. 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 Noyce Track 3 project aims to serve the national need to extend the knowledge base around effective strategies for teacher and teacher leader retention and professional growth. Additionally, this project will support and develop 28 K-8 teachers (Master Teacher Fellows; MTFs) in high-need districts in South Carolina (SC) by participating in the Building Leadership among Science Teachers in Rural Regions (BLAST) Leadership Program over five years consisting of a Leadership Simulation, Summer Institute, and Summer Conferences, completing online modules designed to support their individualized learning needs and goals, implement a professional learning and leadership plan, collaborate with their principals, attend and present at National Science Teacher Association (NSTA) conferences and receive individualized coaching in their classroom. The proposed project components will enable high-achieving practicing teachers to become science teacher leaders who will lead in their high-need schools and districts by mentoring, leading professional development, and seeking out other ways they may support other science teachers. This project at Clemson University includes partnerships with WestEd’s Making Sense of Science and SC school districts. Project goals include implementation of individualized professional development and coaching over the course of five years for 28 teachers to produce science teacher leaders who can effect change within their high-need schools and school systems. This work is grounded in situated learning and the science teacher leader framework and seeks to explore the following questions: (1.) How does change happen in a high-need school system via teacher leaders?, 2. In (2.) what ways does geographic location support or hinder teacher leadership?, and (3.) What are key leverage points in high-need school systems that support rigorous science education? This project will be iteratively evaluated. Evaluation of the project will be guided by the following evaluation question(s): (1.) What is the quality of the BLAST program for MTFs? To what extent is it designed to reflect (a) best practices for professional learning for teachers, (2.) best practices for the development of science teacher leaders, and (c) best practices in science instruction? (3.) What is the effectiveness of the MTFs in terms of their ability to (a) design and implement best practices in science instruction, (b) develop and complete a professional learning plan, (c) develop and implement a leadership plan, and (d) take on leadership roles connected to science in their high-need schools and districts? and (4.) How well does the BLAST program retain MTFs in their high-need LEAs? If teachers leave the program or LEA, what prompted the decision to do so? The results of this project will be disseminated to help enhance the field. This Master Teaching Fellowships project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K-12 STEM teachers in high-need school districts. 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

Ninety percent of the $4.5 trillion in annual healthcare expenditures in the United States can be attributed to chronic conditions.1,2 Many of these often co-occurring conditions, including obesity, diabetes, hypertension and heart disease, can be prevented or improved by lifestyle change interventions (LCIs) such as diet and exercise.3-8 However, African Americans (AA)9-26 and rural residents regardless of race,27-34 who have higher risks of developing multiple chronic conditions (multimorbidity),35,36 often do not participate in LCIs due to barriers specific to them. They therefore do not realize the benefit of reducing multimorbidity at lower costs, with significant cost implications for payers and LCI implementers. To address these barriers and respond to the SIP25-004 Project 1 requirements, we will pursue three aims that will collectively inform policymakers and implementers on the LCI type and dose that maximize desired policy goals at the payers’ available budget level. We will focus on LCIs targeting AA or rural residents or consisting of a sufficient number of such participants. In Aim 1, we will conduct updated meta-analyses on the dose-response relationships between these LCIs and long-term (6 months+) behavior changes (physical activity, diet), improvement in cardiometabolic indicators (body mass index, blood pressure, serum sugar levels), and prevention of multimorbidity (obesity, diabetes, hypertension, heart disease). This aim will help us understand the effective LCI doses that may facilitate LCI adoption in these populations specifically. In Aim 2, we will estimate LCI implementation costs and key factors associated with these costs, either by collecting cost information directly from the published results (if available), or through direct measurement by estimating the unit costs and quantities of labor and material inputs. This aim can help identify opportunities to lower implementation costs. Finally, in Aim 3 we will combine the outcome information (from Aim 1) and the cost information (from Aim 2) to conduct a new cost-effectiveness analysis of LCIs in achieving several desired policy goals. Specifically, we will use the validated Future Elderly Model37 to project multimorbidity onset over a lifetime in a nationally representative sample of AA and rural residents and estimate the cost effectiveness of each LCI in reducing multimorbidity, increasing quality-adjusted life years, decreasing healthcare expenditures, and increasing productivity gains from improved health. Finally, to ensure continued relevance of our research, we will provide an online impact analysis dashboard for policymakers and LCI implementers to visualize cost- effectiveness at various implementation costs and LCI types and doses. Throughout, we will engage community stakeholders to help translate and disseminate our findings beyond academic audiences to encourage adoption of cost-effective LCIs. Our research will inform the optimum allocation of resources to alleviate the burden of multimorbidity and reduce cost for private, state, and federal payers as well as LCI implementers that serve AA and rural residents.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Children living in resource-limited settings face multiple adversities in their environment which can be detrimental to their growth and development. Important among these are a lack of responsive parenting practices and early learning opportunities, poor nutrition and feeding practices, and high incidence of infectious diseases including diarrhea and malaria. The developmental potential realized if lack of responsive stimulation, malnutrition, and infection in young children are all addressed is unknown. Our study will test whether early childhood development in Liberia is improved by a bundled set of interventions that promote responsive stimulation and improved feeding by the provision of eggs and dried fish (nutrient-dense animal-source food) integrated into existing infection control activities of the national health system, and whether, in combination, these stimulation and feeding interventions will be more effective than responsive stimulation alone and the provision of animal-source foods alone. Our study will build on Plan International Liberia’s longstanding community-based intervention-delivery platform. In Bomi, Lofa, Nimba, and Margibi counties, we will rigorously test the effects of a 9-month group-based bundled intervention for female and male caregivers integrated into existing health services and delivered by trained adult community members on development of children 6 to 30 months of age. Using a four-arm 2x2 factorial cluster-randomized effectiveness design, rigorous intent-to-treat longitudinal models, and context-validated measures, we will compare cognitive, language, socio-emotional, and motor development of 2160 children from 144 communities randomized to a comparison arm or one of three intervention arms. Each arm will receive the national infection control activities. Three intervention arms will also receive: 1) responsive stimulation, 2) provision of eggs and dried fish for consumption by the children, or 2) responsive stimulation + provision of eggs and dried fish. Our study has three aims. First, we will test differences in child development (cognitive, language, socio-emotional, and motor) among children receiving the national infection control intervention (Arm 1), that intervention plus responsive stimulation (Arm 2), that intervention plus eggs and dried fish (Arm 3), and all interventions combined (Arm 4). Second, we will test the impact of the interventions on intermediate outcomes of the child (e.g., anthropometry, consumption of eggs and fish, morbidity) and caregivers (e.g., early learning opportunities, responsive feeding, responsive stimulation, mental health, social support, co-parenting) and whether improvements in the intermediate outcomes mediate effects on child development. Third, we will determine the cost-effectiveness of each intervention alone and of the full integrated intervention. We hypothesize that child development will be improved in Arms 2, 3, and 4 compared to Arm 1, and in Arm 4 compared to Arms 2 and 3. This study will provide conclusive evidence of the effectiveness of a bundled intervention with implications for policy and programs aimed at improving child development.

NSF Awards · FY 2025 · 2025-09

This project uses advances in artificial intelligence, computer science, and statistics to develop the GeoRDyn toolkit to allow researchers to identify the spatial and relational complexities in more fine-grained data. This will permit new approaches to investigating longstanding questions in the field, such as if and how networks emerge and influence collective action and the conditions under which events diffuse across time and space. This project is jointly funded by the Security and Preparedness program and the Established Program to Stimulate Competitive Research (EPSCoR). Events are often observed at different temporal and spatial scales, involving complex interactions among actors at the micro, macro, and meso levels. Methodological tools commonly used in the study of collective-action events are often less dynamic and/or do not account for complex dependencies. This project develops the GeoRDyn toolkit to help researchers dissect the spatial and relational complexities in events and draw rigorous causal inferences. The toolkit incorporates recent advances in artificial intelligence (AI), computer science, and statistics. The project develops new methods and software for studying complex and dynamic processes, such as diffusion, that can be used by researchers as well as those outside of the academic community who need to understand the causes and potential implications of events. 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

Studies show that preschool-age children are especially vulnerable to accelerated weight gain during the summer, with those from low-income households (≤185% poverty level or Medicaid eligible - the target population for this study) exhibiting the greatest risk of unhealthy weight gain. Despite numerous interventions designed to prevent OWOB, none target preschoolers during summer. For families from low-income households, attending center-based childcare is associated with a lower risk of developing OWOB by 1st grade compared to attending home-based care. We believe center-based childcare lowers the risk of OWOB through daily rules/routines that promote healthy behaviors. For families from low-income households, publicly funded center-based childcares (e.g., needs-based pre-K, Head Start) typically operate on an academic/school 9-month calendar (Aug-May). During summer, fewer than 30% of preschoolers attend center-based childcare. For many preschoolers from lower-income households, summer may serve as an extended period away from formal center-based childcare, because the out-of-pocket expense may prohibit attendance. This may promote unhealthy behaviors and excessive weight gain. In the majority of US states, publicly funded center-based childcare during the academic/school year for families from underserved populations is free; however, center-based childcare during the summer is an out-of-pocket expense for many of these families. Despite parents’ desire for childcare during the summer, a major reason children from low-income households do not attend center-based care during summer is cost. This creates unequal access to resources and likely exacerbates health disparities for families from low-income households. Using a structural intervention approach, our study will test the impact of providing free center-based childcare in the summer. For this R01, we will rigorously test the impact of providing free center-based childcare during the summer on weight status (i.e., BMI z-score/BIA - primary outcome) and health behaviors (physical activity, diet, sleep and screentime - secondary outcomes) of preschoolers from low-income households (≤185% of poverty level or Medicaid eligible). We will also evaluate the impact of the intervention on parental well-being and employment. Using a pragmatic randomized design, we will randomize 300 preschoolers from low-income households to either free center-based childcare during the summer or a comparison/control group. The Specific Aims are: Evaluate changes in A) preschoolers’ weight status (primary), B) preschoolers’ health behaviors (secondary) and C) parental well-being and paid employment (tertiary) between intervention and control groups; Examine changes in A) preschoolers’ health behaviors and B) parental well-being as potential mediators of changes in preschoolers’ weight status between the intervention and control groups; and Evaluate the incremental cost effectiveness of the intervention in terms of additional cost per weight status reduced and compare this to published childhood obesity cost effectiveness prevention/treatment interventions.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT The United States is experiencing a maternal health crisis. Maternal mortality and severe maternal morbidity (SMM) affect around 700 and 60,000 women, respectively, every year, and the numbers are increasing. Hidden in these averages are disparities with Black and American Indian women, women on Medicaid insurance, and women residing in rural areas bearing the disproportionate burden of maternal mortality and SMM. Over half of these maternal deaths occur in the postpartum period with maternal mortality and SMM remaining well elevated beyond 42 days postpartum. The postpartum period is a critical period for addressing maternal mortality and SMM and this has been endorsed by professional organizations emphasizing the importance of postpartum care being an ongoing process rather than a single encounter. However, use of postpartum care is deficient even for high-risk patients. As a result, maternal outcomes are suboptimal and result in excess harm. One reason for this inadequate use of recommended care is typical coverage limits on Medicaid insurance for pregnancy. Traditionally, Medicaid provides pregnancy related coverage for eligible recipients through 60 days postpartum. After that, many beneficiaries lose coverage restricting access to postpartum care. During the COVID-19 pandemic, the Families First Coronavirus Response Act halted Medicaid disenrollments since March 2020 resulting in continuous coverage for pregnant women beyond 60 days postpartum, but this expired on March 31, 2023. Meanwhile, the American Rescue Plan Act of March 2021 provided states with federal funding to extend postpartum coverage up to one year. This extended postpartum coverage created a unique opportunity to examine the impact of postpartum insurance coverage continuity. Thus, we propose to make use of a natural experiment comparing women on Medicaid (treatment group) to women on commercial (control group) insurance during their delivery hospitalization within 15 states before and after state policy adoption of postpartum Medicaid coverage extensions. We will build upon unique population-based datasets that we have amassed and combine over 25 years (2008-2026) of vital statistics birth and death records linked with maternal and newborn hospital discharge data across 15 U.S. states. Using a difference-in-differences methodology, we will examine the effect of 1-year postpartum Medicaid coverage extensions on: 1) short interpregnancy interval and preterm birth and low birth weight outcomes, 2) infant hospitalizations and ED use, 3) postpartum maternal hospitalizations, ED use, and SMM, and 4) disparities in the above outcomes by race/ethnicity, rural/urban residence, state Medicaid expansion versus non-expansion status, and state income eligibility limits for pregnant women. At the completion of this project, we will have rigorous evidence on whether continuous postpartum insurance coverage is improving maternal and newborn outcomes and mitigating disparities.

NIH Research Projects · FY 2025 · 2025-09

Carriers of the FMR1 premutation allele (FXpm) are at risk to develop Fragile X-associated Tremor/Ataxia Syndrome (FXTAS) and Fragile X-associated Neuropsychiatric Disorders (FXAND). Despite significant impacts including functional limitations, reduced quality of life, poor health, and even reduced life expectancy associated with these conditions,1–3 tremendous knowledge gaps exist that constrain accurate risk prediction and treatment. Variation across age, developmental stages, and sex has not been sufficiently quantified for FXTAS and FXAND. This lack of knowledge translates to poor understanding on whether, when, and how these FMR1-associated conditions emerge and affect individuals and families. The overall objective of this P50 Center, “Translation of the FMR1 Premutation Phenotypes Across the Lifespan,” is to advance understanding of the clinical phenotypes and underlying mechanisms associated with FXTAS and FXAND through the application of a developmental approach that examines these conditions across the lifespan. This P50 FX Center proposal addresses this critical need to characterize the FXpm phenotype across the lifespan in a non-biased sample that will identify novel mechanisms and targets for intervention. To accomplish this objective, the FX center will capitalize on strong collaborations among investigators across two projects. Project 1 - Aim: Identify the developmental trajectory of social communication deficits and their relationship to FXAND in 3 to 5-year-olds with FXpm. Determine the extent to which auditory processing and molecular-genetic variation (i.e., CGG repeat length, mRNA, FMRP, activation ratio in females) predict social communication across age (Roberts/Hogan). Project 2 - Aim: Establish the timing, developmental trajectory and prodromal language/vocal phenotypes predictive of FXTAS-related motor, cognitive, and psychiatric symptoms in FXpm women across 35 to 80 years of age. Identify how age, clinical risk factors, and molecular-genetic variation (i.e., CGG repeat length, mRNA, FMRP, activation ratio) and environmental adversity interact and influence FXTAS risk in women (Klusek).