University Of South Carolina At Columbia

NSF Awards · FY 2025 · 2025-07

Sprays are formed by breaking down a liquid into tiny droplets, enabling a wide range of practical applications from fuel injection in engines to efficient cooling systems. It is important to be able to predict how droplets in sprays deform and move while interacting with each other and the surrounding airflow. However, accurately predicting the behavior of large numbers of droplets in sprays is challenging due to the immense computational power required. By using advanced computer simulations to create detailed datasets and applying machine learning, this project will improve predictions of spray behavior. These improvements could help scientists and engineers design more efficient engines, better cooling technologies, and innovative solutions to reduce environmental pollution. The project also supports education by engaging high school students in science and engineering careers through workshops at engineering summer camps. The project will employ the Basilisk multiphase flow solver to perform high-fidelity simulations of droplet swarms in gas flows, generating a comprehensive dataset across a wide range of key parameters, including droplet volume fraction, Reynolds, and Weber numbers. A novel machine-learning model, based on a graph convolutional network, will be developed to capture how individual droplets and their interactions influence deformation and forces. This model ensures consistency with physical principles, such as maintaining symmetry in motion and orientation. The project will also use SHapley Additive exPlanations (SHAP) analysis to interpret the machine-learning model, identify the most important factors, and create simpler models for faster predictions. The resulting models will be made publicly available, enabling researchers to improve spray simulations in applications like combustion and spray cooling. This award is expected to advance computational fluid dynamics and provide practical benefits for many industrial 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 · 2025-07

The multiscale nature of the brain is arguably the most significant impediment to understanding its inner workings. This project aims to establish a modeling framework for addressing pressing neuroscience-related questions, and to demonstrate this capability by tackling clinically relevant research questions related to atypical neural activity in autism. This project is expected to have lasting societal benefits by developing analytical methods further empowering the study of the brain and the myriad of psychiatric, neurological, neurodevelopmental, and neurodegenerative conditions. Proposed modeling efforts will fuel synergistic ongoing collaborative projects on biologically-inspired learning systems, further sustaining the growth of artificial intelligence and deep learning. This research also integrates with educational objectives through a series of didactic videos and a one-week workshop. The brain is a tremendously complex system operating at multiple spatiotemporal scales. No framework has proved versatile and powerful enough to integrate the heterogeneous sources of information required to understand this system across scales. This project proposes a novel model-driven approach to address these gaps. Specifically, it will implement a multiscale forward model linking cellular mechanisms with whole-brain dynamics, integrating spiking neural networks and neural masses using co-simulation (Aim 1). It innovates by conceptualizing and implementing a framework informing macroscopic analyses (e.g., EEG) from microscale mechanisms constrained by mesoscale organizational principles. Further, it will leverage deep Bayesian learning for model parameter inference from simulated EEG, demonstrating that physiologically relevant latent variables can be studied from experimental data using multiscale, integrative modeling (Aim 2). The power of this model-driven approach will be illustrated using EEG recorded in genetically defined groups to investigate whether the imbalance in excitatory/inhibitory ratio in autism is associated with a breakdown in long-range effective connectivity, a use case of crucial relevance for understanding neural mechanisms and their dysregulation in autism (Aim 3). By using deep Bayesian learning for multiscale inference, this project is expected to alleviate scalability issues with traditional approaches and have a transformative effect on computational neuroscience methods and neuroscience data analysis. 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 · 2025-07

For many years, new crop varieties with improved traits like grain yield and disease resistance were developed through conventional plant breeding programs. In recent years, successful transgenic efforts were adopted to add beneficial DNA from other species into plants for research and/or crop improvement, leading to increased agricultural productivity in the United States. For example, most row crops, such as soybean and corn, possess beneficial transgenic DNA that makes them resistant to insect pests and/or herbicide spray, thereby vastly improving the crop yield for food and feedstock security. Developing and releasing transgenic crops requires a large amount of time, effort, and costs, in addition to going through safety trials and deregulation processes. This causes an almost decade-long lag between scoping the challenge to improve a crop trait and making it ready for adoption by farmers. Therefore, to accelerate crop improvement by conventional plant breeding-based approaches and avoid regulatory and social/global-level reluctance to adopt transgenic crops due to the presence of foreign DNA in a genetically modified crop plant, the team proposes an intragenic approach by adjusting, combining and transferring the desired DNA element sourced from the same plant species for improving crop traits. This intragenic approach faces significantly less regulatory burden, avoids consumer reluctance and will deliver faster technology transfer from the laboratory to the field. The research develops new biotechnology that will impact the bioeconomy and improve production of critical food crops. Transposable Elements (TEs) are mobile DNA fragments that naturally reshuffle parts of the genomes. Researchers have developed genetic tools to control the TE activity, insertion site, cargo size (user-defined sequences delivered by the TE), and the timing of TE insertion. Such genome engineering delivers a cargo DNA responsible for improving traits, e.g. disease resistance, by inserting it at a targeted position in the plant genome but flanked by TE sequences. However, such engineering leads to the insertion at one to many off-target sites in the genome. Often, these insertions tend to undergo silencing or become non-functional after a few generations. Such transgenic approaches have sourced TEs from another species. To avoid the transgenic approach, the project team proposed a new intragenic technology based on TEs sourced from the same plant species and engineered to deliver large custom cargo DNA also sourced from the same species. They also proposed to improve precise insertion at a desired location in the genome without additional off-target insertions. This new technology, called Transposase-Assisted Homology-Independent Targeted Insertion (TAHITI), will be experimented on rice plants. The team will examine the rate of targeted insertion and the off-target rates and selectively regulate the activity of the related endogenous TEs. They also plan to identify active TEs in the economically important crops maize and soybean for future adoption of intragenic approaches in these crops. 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 · 2025-07

Though advanced fiber-reinforced polymer matrix composites have been under development for decades, their use in load-bearing structures is far more recent. The reason for such reticence in their use is readily seen in news stories: structural failure of composite components, catastrophic collapse of deep-sea submersibles, including the recent Titan disaster, etc. Composite components are subject to damage that affects strength, fatigue resistance, and durability— issues that continue to this day. As a first step in addressing these challenges, the primary research objective of this award is to develop an experimental-computational framework for a methodic characterization of damage magnitude as well as mechanisms in the emerging field of mesostructured composites. Knowledge developed from this effort would look to enable the development of more accurate damage evolution models while reducing the time-intensive design and certification timeline of lightweight, damage-tolerant, and energy-efficient composites for transportation, defense, and energy applications. In addition, an educational module based on this research will be incorporated into the courses for undergraduate and graduate students to prepare them with critical skills in the mechanics of composite materials. In this research project, a combined digital image correlation (DIC) and discontinuous finite element method (DFEM) approach will be developed to determine damage and fracture parameters from experiments for a range of mesostructured composites. A DFEM will be formulated to solve an inverted form of the boundary value problem with spatially varying material properties and damage as the unknown, where full-field displacements and strains obtained using DIC will be used as input, to reconstruct the full-field evolution of material properties and damage. Deep neural network models will be used to assess the influence of material mesostructure on damage evolution by using the datasets produced by the DIC-DFEM approach to predict full-field damage evolution. Experimental validation will be performed on additively manufactured mesostructured composites. This research has the potential to significantly advance the field of experimental/computational mechanics for design and characterization of spatially heterogeneous materials and, in the process, provide a capability to drastically reduce the number of experiments required to characterize damage evolution and determine damage laws. 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 · 2025-07

Wireless signals have long been a cornerstone for many communication-based applications, from mobile cellular systems to navigation. This project aims to use wireless signals in a new direction, i.e., computation, particularly for machines interacting with each other wirelessly, with the ultimate goal of faster computation for fast decision-making, learning, and coordination. To this end, the project plans to use the wireless medium as a computational substrate, i.e., over-the-air computation (OAC), to perform underlying calculations in various applications by harnessing the additive nature of electromagnetic waveforms in the wireless medium. The project plans to answer the fundamental research questions on the reliability, scalability, and feasibility of OAC with three interrelated research thrusts. The foundations of OAC for large-scale systems will be established by designing and advancing (1) reliable OAC schemes that are robust against distortion in wireless channels, impairments, and noise through non-coherent waveforms compatible with continuous functions, new error-correction codes based on modulo-lattice modulation and modulation-on-zeros, and analytical over-the-air computable function expressions based on Kolmogorov-Arnold superposition theorem; (2) scalable OAC frameworks for decentralized optimization, consensus for networked robotics and control systems under latency constraints, and distributed machine learning architectures over wireless networks; (3) methods and procedures that providing insights into the limits of OAC in practice by considering time-frequency-phase synchronization, calibration errors, and the dynamic nature of wireless networks such as mobility. This project’s goal is to improve the understanding of low-latency computation over wireless networks to enable intelligence over wireless systems, real-time learning with the fresh data available at wirelessly connected devices, and faster autonomous systems. The outcomes of this project is expected to be beneficial to multidisciplinary industries such as networked robotics, Internet-of-Things, and vehicular networks. The societal impacts include noticeable latency improvements in computation-heavy applications that affect daily life, ranging from smart cities to environment monitoring and cost reduction through better spectrum utilization. The project integrates research and education through a comprehensive action plan while training engineers and researchers on relevant problems in the areas of communications, computer science, and mathematics. 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 · 2025-07

Overheating of electronic equipment often leads to device failure. As electronic devices are made smaller, removing heat becomes a bigger challenge for designers. A microchannel heat sink is a small device containing many narrow tunnels that can be used to remove heat. Cooling liquid flows through the tunnels and efficiently carries away heat from hot electronic parts. Microchannel heat sinks are especially promising because they have a lot of surface area in a small space. Furthermore, they can use two-phase boiling inside the microchannels to increase the rate of heat removal, as it takes a lot of heat to boil the cooling liquid. However, adopting two-phase boiling in microchannel heat sinks has proven difficult because vapor bubbles that form during boiling can disrupt flow through the microchannels. To address this issue, the team will use both computational and experimental models to understand how flow is disrupted and use the results to design methods to suppress the disruptions. The results from the project will benefit myriad applications, including electric vehicle batteries, power systems, and space thermal control systems. The project will also provide STEM outreach, improved heat transfer courses, and hands-on undergraduate research experiences at a primarily undergraduate institution. A major challenge for microchannel heat sink boiling is instability due to vapor bubble reversal, which leads to pressure and temperature oscillation. The project will suppress flow boiling instability in a diverging counterflow microchannel heat sink by removing the vapor bubbles along the flow direction using secondary flow through interconnectors. It is hypothesized that the pressure distribution along the flow direction will induce secondary flow in a diverging microchannel heat sink, facilitating the collapse of any trapped elongated vapor bubbles. As a result of vapor bubble clearing, the heated surface will be prevented from dry-out and operate at higher heat flux. The project’s numerical task includes developing a three-dimensional computational fluid dynamics model that will provide a detailed understanding of secondary flow controls through interconnectors and bubble dynamics of flow boiling by pressure, temperature, and flow contour. The experimental framework will consist of a flow loop, a test section, a power supply system, and a data acquisition system, and the parameters tested will include critical heat flux, surface superheat, pressure drop, and boiling images. A high-speed camera will be used for flow visualization and will provide details of bubble dynamics and secondary flow behavior. 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 · 2025-07

The aim of this research program is to study the 3D structure of atomic nuclei and their constituents (protons and neutrons) to unravel the mysteries of the strong nuclear force that binds them and generates their mass. This work will address several key topics in the 2023 NSAC long-range plan for nuclear science. To achieve these goals, the program will use world-leading electron-scattering facilities at Jefferson Lab (JLab) in Virginia. By combining the excitement of cutting-edge discovery science with a direct path to applications in energy, medicine, and defense, this program also contributes to recruitment of the next generation into STEM and workforce development that can directly affect the economy and everyday lives. The program will focus on the study of exclusive lepton-antilepton production using the framework of generalized parton distributions. Here, Compton scattering is sensitive to the distribution of quarks while quarkonium (e.g., J/psi) production is sensitive to gluons – the force carriers of the strong interaction. The PI and the postdoc will complete the analysis of the first-ever measurement of time-like Compton scattering on the proton (JLab experiment E12-12-001), and prepare for measurements of such processes at the EIC, providing essential input for future detector development. The PI and the graduate student will use JLab data to establish the feasibility of a similar measurement on the neutron, which would provide complementary information. If the results are promising, the analysis will lead to the first-ever publication on this process. The PI and the undergraduate students will carry out a series of projects aimed at the study of J/psi production on protons and nuclei at the EIC, which will for the first time make it possible to directly map out the gluon field. Measuring both Compton scattering and J/psi production will make it possible to compare the distributions of quarks and gluons (matter and force), while a comparison of J/psi production on the free and bound proton, as well as a nucleus as a whole, can tell us how the strong interaction is modified inside a nucleus. 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 · 2025-07

PROJECT SUMMARY Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized in part by difficulties with social communication. Social impairments are evidenced across the lifespan in ASD and often become more apparent throughout early childhood. Interestingly, robust behavioral indicators of ASD prior to 12 months of age have yet to be uncovered. This may be due to a focus on core social features that have not fully emerged within the first year. To gain a mechanistic understanding of ASD that can inform detection and intervention before 12 months, researchers must look beyond social behaviors. The contributions of infant physiology to social-interactive behaviors have been well-documented in typical development, but the role of this developmental context for predicting social-communication outcomes, including those associated with ASD, is understudied. Infant physiological regulation, indexed by respiratory sinus arrythmia (RSA), is predictive of in-the-moment parent- infant dyadic coordination and later social communication. Dyadic coordination directly relates to the emergence of the infant’s capacity for more complex and reciprocal social interactions around 6- to 9-months of age. This transition marks the emergence of social communication skills, thereby providing evidence for a cascade wherein physiology supports coordination which begets social communication. While understudied, evidence suggests that this dynamic developmental context is disrupted in ASD. By leveraging data from the first-ever study to utilize head-mounted eye tracking to capture the dynamics of social interactions in infants at elevated likelihood for ASD, the current proposal will therefore examine the longitudinal associations between these factors in a group of infants at elevated and low likelihood for ASD. Specifically, the study aims are to (1) compare dyadic coordination using dynamic systems modeling at 4- and 8-months-old, (2) to identify the role of infant physiology on dyadic coordination, and (3) to examine relationships between RSA and dyadic coordination on 18-month social communication. Results have the potential to elucidate a novel mechanistic understanding of the early development of social communication skills. This research will be implemented within the outstanding training environment at the University of South Carolina with full support from the primary mentor, Dr. Jessica Bradshaw, and an exceptional interdisciplinary team of Co-Sponsors. The proposed training plan focuses on (1) advanced training in translational and clinical research in the infant period in ASD, (2) development of dynamic systems modeling skills that can be applied to both behavioral and physiological data, (3) developing expertise in validating and disseminating a novel RSA tool, and (4) refining essential professional development skills to support the transition into a tenure-track research career. The proposed research and related training experiences will provide the fellow the necessary skillset to develop a programmatic line of translational research that is focused on identifying both social and physiological mechanisms underlying the development of ASD that can inform novel methods for diagnosis and intervention.

NSF Awards · FY 2025 · 2025-06

This award supports U.S.-based participants in attending the workshop "Connections between Commutative and Non-commutative Harmonic Analysis," to be held at the International Centre for Mathematical Sciences (ICMS) in Edinburgh, UK, during the week of September 1–5, 2025. The workshop aims to bring together senior and early-career researchers working in commutative and non-commutative harmonic analysis. Although these fields share deep connections, their communities rarely have the opportunity to meet at conferences of this scale. The workshop will provide a unique environment to present recent advances and exchange expertise. This workshop will focus on four central topics in harmonic analysis: matrix weights, Fourier multipliers on (non-abelian) groups, vector-valued harmonic analysis, and martingale-related techniques. It will begin with four plenary mini-courses on these themes, delivered by leading experts. The workshop will also feature invited long and short talks by researchers working around these core topics. In addition, there will be contributed talks and a poster session to provide more opportunities for graduate students and early-career researchers to present their work. More information can be found at https://www.icms.org.uk/CommutativeandNonCommutativeHarmonicAnalysis 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 · 2025-06

Fourier analysis is a foundational area of mathematics with widespread applications. One of its key ideas is to understand a signal through both its time-domain and frequency-domain representations. In theoretical computer science and learning theory, signals often arise in discrete settings with rich structure—for example, as functions on the Hamming cube (i.e., the space of binary strings). Such functions often exhibit low complexity when analyzed through their Fourier expansion or frequency components. Tackling questions in these domains requires a deep understanding of these structured, low-complexity functions, for which Fourier analytic tools have proven useful. As quantum computing rapidly advances, the natural setting shifts from classical bits to qubits, where signals are represented not by functions but by operators that are non-commutative in nature. This introduces new challenges that demand extensions of classical Fourier analysis tools to the quantum setting. Addressing these challenges is the goal of this project. The project will include efforts in training both undergraduate and graduate students through PI’s mentoring role and in serving the research community through organizing conferences and workshops. This study investigates quantum analogues of low-complexity functions on the Hamming cube, where complexity is measured, for example, by influence, degree, or the number of variables on which a function depends. A central challenge in this context arises when the dimension tends to infinity. Although Fourier analytic tools have proven highly effective for such problems in the classical setting—illustrated by foundational results on Boolean functions by Kahn, Kalai, and Linial, as well as Talagrand, and by dimension-free inequalities originating from the early work of Littlewood, Bohnenblust and Hille—they do not readily extend to the quantum realm due to inherent non-commutativity and the absence of an underlying classical space. The principal investigator will therefore develop tools to address challenges from the high-dimensional regime and non-commutativity, thereby strengthening the connections between harmonic analysis, functional analysis, quantum information theory, and related areas. 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 · 2025-06

This I-Corps project focuses on the development of a stress monitoring device for women of reproductive age. Pregnancy and the postpartum period are times of major physical and emotional change, often bringing higher levels of stress that can lead to serious health problems such as preterm birth, low birth weight, and developmental delays in children. Currently, stress is usually measured through surveys or conversations with healthcare providers, which rely on self-reporting and may miss early warning signs. To address this need, the solution provides a simple wearable device that continuously monitors key health signals such as heart rate, sleep, movement, and skin responses. The device connects to a mobile application that uses artificial intelligence to analyze the data and provide real-time insights, alerts, and personalized recommendations to both the user and their care team. Approximately one in five women in the United States experiences mental health challenges during or after pregnancy, and maternal mortality has more than doubled in the past two decades—often due to stress-related cardiovascular and mental health conditions. By enabling earlier detection and targeted support, this solution helps users manage their health more effectively, improves outcomes for mothers and infants, and reduces costly complications for healthcare systems and employers. 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 wearable device integrated with a suite of advanced physiological sensors, including continuous glucose monitors, blood pressure cuffs, electrodermal activity sensors, photoplethysmography sensors for heart rate variability, accelerometers for physical activity tracking, and sleep monitors. The device is designed to capture a broad range of biometric data, enabling the creation of a comprehensive stress profile. Artificial intelligence algorithms analyze these multimodal data streams in real-time, offering predictive insights and personalized, actionable recommendations regarding stress levels. For instance, activity readings detect acute stress responses, while heart rate variability patterns provide early indicators of chronic stress. These physiological metrics are contextualized with data on health-related social needs—such as access to care, financial strain, and work-life demands—to deliver a holistic, individualized view of maternal well-being. The broader societal and commercial impact includes addressing the rising rates of cardiometabolic and mental health-related maternal morbidity and mortality. By adopting this technology, users gain earlier awareness of their health risks, personalized strategies to manage stress, and better connections to resources, empowering them to improve their overall health outcomes during pregnancy and postpartum. 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 · 2025-06

Abstract Deer mice (genus Peromyscus) are the most abundant mammals in North America. In biomedical research, their most prominent use is in the field of infectious diseases, because they are the natural reservoir of infectious agents such as Borrelia burgdorferi which causes Lyme disease, for Hantaviruses, Sin Nombre Virus, and SARS-CoV-2 that caused the COVID-19 pandemic. The University of South Carolina operates, for more than 40 years, the Peromyscus Genetic Stock Center (PGSC) that is charged with the mission of maintaining different stocks of Peromyscus, supplying them to outside investigators and exploiting deer mouse- related research. The present proposal addresses 3 major unmet needs of the Peromyscus community of researchers that impede the use of deer mice as a model and are related to the poor breeding program that does not enable rapid availability if deer mice to users, the lack of Peromyscus-specific antibodies, and the lack of readily access to breeding records for pedigree analyses. Here, we request funds to enhance the utility of deer mice as a model of relevance to NIAID’s interests by (1) strengthening the breeding capacities of the PGSC, (2) by developing specialized immunological reagents such as Peromyscus-specific antibodies, and (3) by curating our electronic databases and rendering them easily accessible to outside users. Plans for the project’s sustainability have been developed, and it is anticipated that upon completion it will be supported fully by the income generated.

NIH Research Projects · FY 2025 · 2025-05

PROJECT SUMMARY/ABSTRACT Atypical sensory reactivity is pervasive and highly impairing in autism spectrum disorder (ASD). Sensory reactivity contributes to core and associated ASD features, such as impaired social communication and elevated social fear, which are evident in infancy and predictive of later anxiety. Increasing theoretical and empirical data suggest that individuals with ASD may exhibit autonomic nervous system (ANS) hyperarousal at baseline (i.e., at rest), implying an elevated biological state primed for threat even during non-threatening situations. While ANS dysfunction has been posited as an underlying neurobiological mechanism contributing to specific features of the ASD phenotype, research has not examined the presence, onset, developmental trajectory, or developmental consequences of ANS function in infants at an elevated likelihood of ASD. Thus, this study proposes a longitudinal study of ANS function with data collected at 6, 9, 12, 24, and 36 months in infants at elevated likelihood for ASD given an older sibling diagnosed with ASD (EL-ASIBs) in comparison to low-likelihood infants to investigate ANS dysfunction as a potential mechanism of heterogeneity in core and associated features of ASD, including sensory processing, social communication, and social fear. The specific aims are to (1) identify the presence, onset, and developmental trajectory of baseline ANS dysfunction; (2) determine the developmental trajectory of ANS responsivity to sensory input and its relationship to baseline ANS (dys)function; and (3) identify how baseline ANS dysfunction and responsivity to sensory input predict social communication and social fear across infancy. This work will have tremendous impact by increasing our understanding of the neurobiological mechanistic underpinnings and biological pathways leading to ASD symptom progression that is essential to identify targets and timing of treatment for ASD.

NIH Research Projects · FY 2026 · 2025-05

PROJECT SUMMARY______________________________________________________________________ Early maternal-offspring interactions, such as the transfer of IgA through breast milk, are key in shaping mucosal immunity and microbiota composition. Correlative studies suggest that individuals who were formula-fed are at a higher risk of gluten sensitivities, underscoring the importance of maternal influence in early life. Maternal factors may play a role the in the prevention of gluten sensitivity by promoting the development of immune tolerance towards dietary antigens during the neonatal period. Recently, our lab has described two mouse models of spontaneous gluten sensitive SI enteropathy (JH-/- and CD19-/-). Here, we provide pilot data demonstrating that IgA-/- mice are uniquely susceptible to gluten sensitive enteropathy that is associated with defects in tolerogenic iNKT cell phenotypes. We also demonstrate that gluten sensitivity in our model is a transmissible (i.e. microbiota-dependent) phenotype, and that a lack of early life exposure to maternal IgA results in chronic defects in tolerogenic iNKT cells. Our overarching hypothesis is that maternal IgA is essential for the development of tolerogenic iNKT cells in the neonatal period, which suppresses gluten sensitivity later in life. The objective of Specific Aim #1 is to test whether maternal IgA prevents the development of gluten sensitivity in adult offspring. To do this, we will utilize breeding strategies of IgA-sufficient (IgA+/-) and IgA-deficient (IgA-/-) dams and then cross-foster mixed litter offspring (IgA+/-, IgA-/-) between dams. Fostered offspring will then be placed on a gluten free diet (GFD) or nutritionally matched gluten rich diet (GRD). After four weeks (when animals are 8 weeks old), SI resident immune cells and luminal contents will be assessed through high- parameter flow cytometry, 16S rRNA sequencing, histological analysis, and ATAC-scRNA sequencing on T cells and iNKT cells. The objective of Specific Aim #2 is to test whether there is a critical postnatal window during which maternal IgA reinforces a tolerogenic iNKT cell developmental program. To do this, we will colonize germfree (GF) WT and GF IgA-/- pregnant dams with WT SIM or a species of Streptococcus isolated from our colony. A timed cross-fostering approach will then be applied to determine the critical time window for tolerogenic iNKT cell development. Bacterial meta-transcriptomics, flow cytometry, and RNA sequencing will then be performed on adult offspring to explore the time-dependent impact of maternal IgA on neonatal microbial colonization and function as well as the effects on mucosal immunity and iNKT cell development. Through these aims, we will address a major gap in our knowledge regarding the effect of the maternal environment on the development of gluten sensitivity later in life. Additionally, results from our experiments will identify novel mechanisms by which maternal IgA influences immunological tolerance that could lead to novel therapeutic strategies to prevent or treat gluten-sensitivity.

NIH Research Projects · FY 2026 · 2025-05

PROJECT SUMMARY/ABSTRACT Atypical sensory reactivity is pervasive and highly impairing in autism spectrum disorder (ASD). Sensory reactivity contributes to core and associated ASD features, such as impaired social communication and elevated social fear, which are evident in infancy and predictive of later anxiety. Increasing theoretical and empirical data suggest that individuals with ASD may exhibit autonomic nervous system (ANS) hyperarousal at baseline (i.e., at rest), implying an elevated biological state primed for threat even during non-threatening situations. While ANS dysfunction has been posited as an underlying neurobiological mechanism contributing to specific features of the ASD phenotype, research has not examined the presence, onset, developmental trajectory, or developmental consequences of ANS function in infants at an elevated likelihood of ASD. Thus, this study proposes a longitudinal study of ANS function with data collected at 6, 9, 12, 24, and 36 months in infants at elevated likelihood for ASD given an older sibling diagnosed with ASD (EL-ASIBs) in comparison to low-likelihood infants to investigate ANS dysfunction as a potential mechanism of heterogeneity in core and associated features of ASD, including sensory processing, social communication, and social fear. The specific aims are to (1) identify the presence, onset, and developmental trajectory of baseline ANS dysfunction; (2) determine the developmental trajectory of ANS responsivity to sensory input and its relationship to baseline ANS (dys)function; and (3) identify how baseline ANS dysfunction and responsivity to sensory input predict social communication and social fear across infancy. This work will have tremendous impact by increasing our understanding of the neurobiological mechanistic underpinnings and biological pathways leading to ASD symptom progression that is essential to identify targets and timing of treatment for ASD.

NIH Research Projects · FY 2025 · 2025-05

Project Summary There is great potential for promoting physical activity (PA) for chronic disease prevention and treatment through the health care sector. Research has demonstrated effectiveness in assessing patient PA levels, providing ‘exercise prescriptions’, and referring patients to evidence-based PA programs in community settings. However, implementation barriers exist, ranging from practice integration to information flow, resulting in no major health systems integrating PA as part of a comprehensive approach to patient care. In 2016, a multi-organizational partnership between a large academic healthcare system, an academic institution, and a national PA organization launched Exercise is Medicine Greenville (EIMG), a comprehensive clinic-to- community approach that involves PA assessment, prescription, and referral of patients with chronic diseases to a tailored, community-based PA program. Since 2016, EIMG has expanded to 35 Prisma Health primary care clinics and 7 community PA facilities covering >400 miles2. Despite referring >1900 patients to date, great variability exists across participating clinics in correctly identifying eligible patients and providing EIMG referrals, reducing the overall reach and efficiency of engaging patients in the community-based PA programs. Using a pragmatic, stepped wedge, cluster randomized design, we will examine the impact of implementation facilitation (IF) on improving the implementation and reach of EIMG with patients visiting participating Prisma Health primary care clinics. At six-month intervals, 24 randomly selected clinics (6 clinics per wave; 4 waves) will receive IF planning (3mos), active IF (6mos), and post-IF maintenance (min 12mos). The specific aims of this project are to: 1) determine differences in the level of implementation (i.e., delivery fidelity) and reach (i.e., number, proportion, representativeness of patients) at Prisma primary care health clinics before and after IF, 2) assess levels of patient engagement in and the effectiveness of the 12-week, community-based PA programs, and 3) evaluate the costs of IF and the effects of increased EIMG referrals to the community-based PA program on patients costs and clinical outcomes. Our mixed methods evaluation approach is guided by the RE-AIM framework to inform the assessment of implementation outcomes, and the i-PARIHS framework to describe contextual factors (i.e., determinants) influencing patient and clinic level outcomes. Through this work, we will identify successful IF strategies across heterogeneous health settings, helping us identify and address potential inequities in the types of patients that receive EIMG referrals, are engaged in the EIMG referral pathway, and enroll and complete the community-based PA program. Study findings will provide important information on improving future implementation and scalability of PA integration in large health systems, optimizing clinic-community linkages, and the cost savings related to primary and secondary prevention of cardiovascular disease-related health outcomes in the general patient population.

NIH Research Projects · FY 2026 · 2025-05

In the United States, cardiovascular events and neuropsychiatric disorders are the two leading nonobstetric causes of maternal mortality. Postpartum cardiovascular (CV) disease (CVD) affects 10- 26% of patients and encompasses myocardial infarction, cardiomyopathy, coronary artery disease, and hypertension. Postpartum depression (PPD) is a postpartum neuropsychiatric disorder that affects up to 22% of mothers and consists of a major depressive episode. CVD and PPD are highly comorbid and significantly impacted by psychosocial stress. This relationship is particularly evident in the postpartum period where PPD patients exhibit a 70% increased risk of developing CVD in the 5 years after delivery. The clear bidirectional relationship between these two conditions points to a neurogenic mechanism induced by stress, but the effects of social stress on CVD and PPD in the postpartum are unclear. Witness Stress is a psychosocial stressor that induces robust cardiac and behavioral effects in virgin females, however our knowledge of effects in postpartum females is scarce. Mitochondria are emerging as key players in health and disease, yet little is known about postpartum mitochondrial function. To address these critical knowledge gaps, we propose to delineate the neurogenic mechanisms underlying witness stress-induced postpartum CVD and PPD phenotypes. Our central hypothesis, founded on published and preliminary data is that psychosocial stress-evoked mitochondrial dysfunction in glutamatergic cells in the mPFC directly contributes to shifts in postpartum CV and biobehavioral function. We will test this hypothesis using female rats in two specific aims. Aim 1 will determine if direct mPFC mitochondrial stabilization during witness stress is sufficient to prevent postpartum CV and behavioral dysfunction. Aim 2 will determine if indirect mitochondrial stabilization via inhibition of microglial activation prevents stressevoked outcomes. The proposed research is significant because results will reveal neurogenic mechanisms linked to systemic pathologies. Moreover, our findings will increase knowledge on an understudied critical sex-linked health period following exposure to a highly relevant social stressor and identify potential preventative treatments. The longitudinal design of the proposed studies includes CV measures using echocardiography and telemetry, beginning at the first stress exposure and continuing until weaning. These measures will track the evolution of psychosocial stress-evoked CVD across the postpartum period, providing translationally relevant information about effects of stress on CV function and morphology, and insights into therapeutically relevant interventions.

NSF Awards · FY 2025 · 2025-05

This Level 2 Institutional and Community Transformation project aims to serve the national interest by establishing networked improvement communities (NICs, scientific learning communities with a common aim) among STEM Graduate Teaching Assistants (GTAs) and the faculty members who provide GTAs' teaching professional development (Providers). In undergraduate STEM education, research has shown methods that engage students, such as active learning, can be effective. However, using active learning strategies effectively requires guidance and structure. This project strives to generate a support structure that helps GTAs implement evidence-based teaching practices with a focus on active learning by having experienced GTAs provide peer-mentoring for their less experienced peers. To equip and support the peer mentors, the project intends to develop, test, and refine a peer-mentor curriculum, facilitated by Providers and tailored to each STEM discipline. This project aims to establish and sustain two critical communities: (1) a STEM GTA NIC using peer-mentoring and (2) a STEM Provider NIC that will facilitate discipline-specific peer-mentoring. The research team will endeavor to identify how the GTA NIC and the Provider NIC become established by capturing the longitudinal changes in GTA teaching professional development via the theory of change known as Improvement Science that looks for incremental cycles of growth. This project is important because it will focus on a key undergraduate STEM teaching workforce (GTAs) by establishing a network of Providers to serve as critical agents of change for undergraduate STEM education by directly impacting GTAs' teaching. This project is significant because it can support sustainable and transformative changes in teaching undergraduate STEM courses through multiple departments using the Provider NIC. This project plans to implement a peer-mentor program across multiple STEM disciplines (biology, chemistry, communication, mathematics, physics, psychology, and statistics) at the University of South Carolina (USC) and Bowling Green State University (BGSU) to grow GTAs' effective use of active learning strategies and improve GTA teaching professional development. The rationale for using peer-mentoring comes from nine years of prior research and funding of sustainable peer-mentor programs established within mathematics departments to improve the effective use of active learning. In this program, experienced GTAs are educated in a semester-long seminar about how to mentor novice GTAs. GTA Mentors (1) regularly observe novice GTAs and provide individual formative feedback, and (2) facilitate small GTA group discussions about teaching with active learning. This model, initially funded externally, has now been internally funded and enculturated at both universities’ mathematics departments. The overarching goal of the current project is to establish peer-mentoring within other STEM disciplines with support from two new multidisciplinary NICs, one for Providers and one for GTAs. To this end, the project's three goals are to: (1) construct a network of peer-mentoring GTA teaching professional development programs in STEM departments at two universities; (2) adapt a program for each Provider to implement peer mentoring in their STEM department; and (3) revise and evaluate the effectiveness of the peer-mentor program across universities and departments using Improvement Science's Plan-Do-Study-Act cycles. Through this process, changes can be tracked, researched, and justified with a manageable scope. The main research questions this project aims to explore are (1) How do individual Providers and GTAs develop and measure the effectiveness of instruction as a part of establishing the peer-mentor program? and (2) How have the NICs transformed GTA teaching professional development through peer-mentoring with active learning? The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. 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 · 2025-04

Project Summary/Abstract Autonomic nervous system (ANS) development begins in the prenatal period and is critical for a successful transition to extrauterine life. ANS supports vital functions, like cardiac activity and thermal regulation, as well as higher-level cortical processes from birth through adulthood. Adverse neonatal conditions can impair or alter ANS function and ANS dysfunction has been associated with several neurodevelopmental impairments (NDIs), including autism spectrum disorder (ASD). The rate of ASD in infants born preterm is more than triple that of the general population, with reports of a 10-fold increase for very preterm (VPT) infants. Yet, the etiological link between preterm birth and the development of ASD remains unknown. The goals of this prospective, longitudinal study are 1) to evaluate preterm ANS dysfunction as an ASD-specific etiological mechanism in VPT infants, using complementary measures of heart rate and thermal gradients 2) to examine whether and how neonatal morbidities (severe brain injury, infection, bronchopulmonary dysplasia, retinopathy of prematurity) moderate the association between ANS dysfunction and ASD/NDI outcomes and 3) to comprehensively document emerging ASD and NDI features across the first three years of life for VPT infants, who are at increased likelihood for both ASD and NDI. This study will quantify ANS dysfunction for VPT infants across the NICU hospitalization to compare with the emergence of ASD-specific and NDI features across the first three years (months 12, 24, 36). Identification of infants who are at heightened developmental risk prior to NICU discharge is essential to guide very early intervention efforts and improve childhood outcomes.

NIH Research Projects · FY 2026 · 2025-04

Project Summary/Abstract Autonomic nervous system (ANS) development begins in the prenatal period and is critical for a successful transition to extrauterine life. ANS supports vital functions, like cardiac activity and thermal regulation, as well as higher-level cortical processes from birth through adulthood. Adverse neonatal conditions can impair or alter ANS function and ANS dysfunction has been associated with several neurodevelopmental impairments (NDIs), including autism spectrum disorder (ASD). The rate of ASD in infants born preterm is more than triple that of the general population, with reports of a 10-fold increase for very preterm (VPT) infants. Yet, the etiological link between preterm birth and the development of ASD remains unknown. The goals of this prospective, longitudinal study are 1) to evaluate preterm ANS dysfunction as an ASD-specific etiological mechanism in VPT infants, using complementary measures of heart rate and thermal gradients 2) to examine whether and how neonatal morbidities (severe brain injury, infection, bronchopulmonary dysplasia, retinopathy of prematurity) moderate the association between ANS dysfunction and ASD/NDI outcomes and 3) to comprehensively document emerging ASD and NDI features across the first three years of life for VPT infants, who are at increased likelihood for both ASD and NDI. This study will quantify ANS dysfunction for VPT infants across the NICU hospitalization to compare with the emergence of ASD-specific and NDI features across the first three years (months 12, 24, 36). Identification of infants who are at heightened developmental risk prior to NICU discharge is essential to guide very early intervention efforts and improve childhood outcomes.

NSF Awards · FY 2025 · 2025-04

This CAREER project seeks to improve the prediction of atmospheric nitrate by focusing on nitrogen oxide precursor emissions and oxidation pathways. Isotope observations from national atmospheric monitoring sites will be combined with the next generation of atmospheric chemistry models, that include the ability to simulate isotope compositions, to fill critical knowledge gaps regarding nitrogen oxide sources and chemistry. This information will enable the development of more effective regulatory strategies for improving air quality. The research objectives of this project are to quantify nitrate formation pathways and characterize the variability of nitrate precursor emissions across the US. The primary loss pathway of nitrogen oxide (NOx) is through its oxidation to atmospheric nitrate, mainly in the form of nitric acid (HNO3) and particulate nitrate (pNO3), with organic nitrates (RONO2) also contributing significantly to some regions. Atmospheric chemistry and transport models are often unable to accurately replicate observed concentrations of HNO3 and pNO3, including their magnitude, seasonality, and urban-rural gradients, posing significant challenges for predicting air quality responses under policy change. Stable isotope analyses of nitrogen and oxygen will be used to quantify precursor emissions and oxidation chemistry across the contiguous US. By integrating these novel isotopic measurements with 3-D chemistry transport modeling, this project will provide insights into the spatial and temporal patterns of precursor emissions and chemical formation pathways contributing to nitrate across diverse regions in the US, enhancing model predictions of nitrate. An environmental chemistry summer camp, the first of its kind in South Carolina, will be developed leveraging the existence of an established summer camp entitled the Carolina Master Scholars Adventure Series (CMSAS), designed to attract academically talented middle and high school students. The project also will support around 3-5 undergraduate and 1-2 graduate students and 1 postdoctoral scholar. 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 · 2025-04

Over 80% of the world's population lives under light-polluted skies, with indoor light exposure rising due to factors like electronic devices. Exposure to altered light environments induces circadian disruptions, leading to adverse neurophysiological and behavioral changes including anxiety disorders. Adolescents, who often have altered sleep patterns and extensive electronic device use, are particularly vulnerable to circadian disruptions. However, the neurobiological effects of light interventions on brain circuits and behaviors during pubertal development are understudied. Mammalian light perception involves specialized retinal cells that communicate light information to various brain regions, including the medial amygdala (MeA), which processes environmental cues to control emotional responses. Our long-term goal is to understand how the MeA circuit adapts to light environments during adolescence, and whether light interventions can enhance resilience and prevent anxiety disorders induced by circadian disruptions. We recently demonstrated that altered light environments during adolescence increased avoidance behaviors, increased somatostatin neuronal activity and decreased inward-rectifying potassium channels expression in the MeA. MeA neurons in turn project to the bed nucleus of the stria terminalis (BNST), a brain region involved in avoidance responses. Given the key role of somatostatin signaling in the amygdala in regulating affective behaviors, we seek to characterize the mechanism by which somatostatin neurons in the MeA adapt to light environments to regulate emotional responses and promote resilience. We will test the general hypothesis that somatostatin neurons in the MeA act as a key regulator, capable of adjusting to light environments to influence adaptive responses (resilience). Three Specific Aims are proposed to functionally dissect the role of somatostatin neurons in mediating lightinduced adaptive behaviors: In Aim 1, we will investigate whether somatostatin neurons in the MeA are required for light-induced avoidance behavior through chemogenetic manipulations and cell-type-specific ablation of somatostatin expression in adolescent mice exposed to chronic light cycles disruption. In Aim 2, we will establish the role of inward-rectifying potassium channel in regulating light-induced increase of somatostatin neuronal activity in the MeA regulating avoidance behaviors. In Aim 3, we will study MeA somatostatin cells downstream projections to uncover the circuits mediating light-induced avoidance behaviors by employing in vivo fiber photometry for neuronal activity and somatostatin release recording in the BNST of adolescent mice exposed to chronic light cycle disruption. We will also use optogenetic approach to inhibit MeA somatostatin synapsis terminals into the BNST while testing adolescent mice for light-induced avoidance behaviors. By integrating a diverse set of cutting-edge methodologies, this project will advance our understanding of the adaptive mechanisms of the MeA somatostatin circuit in response to light environments. Our goal is to provide pre-clinical data to develop safe light interventions that enhance resilience and prevent affective disorders in adolescents experiencing circadian disruptions.

NSF Awards · FY 2025 · 2025-03

This CAREER project investigates how fat tissues and reproductive organs communicate and coordinate fat storage and utilization in relationship to oocyte production and nutritional status. A complex network of inter-organ communication underlies whole organism physiological responses to dietary input dynamics. There is a clear association between dietary input and reproduction, wherein suboptimal nutritional conditions, such as malnutrition and obesity, negatively impact fertility. Specifically, fat cell dysfunction associated with diet-induced obesity leads to aberrant egg or oocyte production in organisms ranging from insects to humans. However, the fundamental principles linking nutritional physiology with oocyte production are not completely understood. Using the fruit fly Drosophila melanogaster as a model organism, results from this work will uncover how ovarian and fat tissues sense, respond to, and communicate about dietary input. Overall, this study will advance understanding of the role that multi-organ nutrient sensing plays in the organismal response to dietary changes. In addition to the research goals, a Columbia, South Carolina, community outreach program centered around the nutrient sensing research theme will be developed for K-12-aged children and their caregivers. The program will consist of hands-on activities that provide practical experience with science and engineering practices, as well as exposure to pathways to careers in biological research. The leaders of the research activities will receive valuable training in effective scientific communication. In Drosophila melanogaster, direct nutrient sensing by the ovary and remote nutrient sensing by the adipose tissue regulate oogenesis; however, the different modes of amino acid sensing used are not fully understood. The overall goal of this work is to uncover the cellular and molecular mechanisms that mediate dietary protein sensing and communication. This work leverages the power of D. melanogaster genetics to perturb the amino acid response pathway, a branch of the integrated stress response that senses limitations in amino acids, in specific ovarian cell types, to assess its currently unknown role in oogenesis. Additionally, a targeted candidate approach will be taken to identify adipocyte-derived factors that function downstream of the amino acid response pathway and mechanistic Target of Rapamycin (TOR) signaling to regulate ovarian germline stem cell maintenance and ovulation, respectively. Taken together, this project will define the molecular mechanisms that govern amino acid sensing in and communication between ovarian and fat tissues. A post-doctoral fellow and both graduate and undergraduate students will participate in the research. This project is jointly funded by the BIO-IOS-Physiological Mechanisms and Biomechanics Program, the BIO-IOS-Animal Developmental Mechanisms Program, 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 · 2025-03

During conversation, people adjust their speech depending on their conversational partner. Often, speakers align with each other and become more similar in their language patterns. Such alignment is a core feature of human interaction and is believed to occur mostly without conscious awareness. Previous studies have shown that speakers often align on particular word choices with specific conversational partners and switch to a different word choice pattern when communicating with a different partner. In contrast, when speakers align their sentence structure with a particular conversational partner, they tend to maintain that sentence structure choice pattern even when talking to a different partner. This doctoral dissertation project investigates the possible reasons for the differences in partner-specific alignment at the word choice and sentence choice levels. A main hypothesis tested in this research is that word choice and sentence structure choice may serve different functions and may therefore impact communication success differently, especially in the context of joint task performance. This project also provides ample research opportunities for students and aims to increase awareness of alignment as a tool for language educators and speech therapists. This doctoral dissertation project uses openly available analysis tools to examine whether speakers align their word (lexical) and sentence structure (syntactic) choices differently depending on their conversational partner in joint task-related dialogue. Specifically, the researchers compare the speech output of participants working in pairs to complete a collaborative spot-the-differences game, either remaining with the same conversational partner or switching partners between rounds of the game. By comparing the alignment patterns of the same-partner pairs with the different-partner pairs, this study will provide insight into the mechanisms and functionality of alignment at different levels of language. 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 · 2025-02

This Research Infrastructure Improvement (RII) EPSCoR Research Fellows project would provide a fellowship to an Assistant Professor and training for a graduate student at the University of South Carolina Columbia. Cardiovascular diseases (CVDs) represent a major global health challenge, making early diagnosis critical for effective treatment. Traditional methods like enzyme-linked immunosorbent assays (ELISA) detect CVD biomarkers accurately but are limited by high costs, long processing times, complex procedures, and potential errors. To address these challenges, this project aims to develop and fundamentally characterize electrochemical aptamer-based (E-AB) sensors to rapidly and accurately identify multiple CVD-related markers at low concentrations. E-AB sensors utilize lab-synthesized DNA or RNA pieces known as aptamers, which act as keys that bind specifically to target molecules. The interactions between these aptamers and target analytes produce detectable changes in electrochemical signals. However, existing E-AB sensors face stability issues due to their reliance on large gold electrode surfaces, making reuse challenging. The proposed E-AB sensors will overcome these limitations by integrating carbon-based electrode arrays with nano-sized designs and advanced aptamer technology to enhance the sensor’s stability and performance. Students involved in this project will gain hands-on experience in designing and characterizing biosensors, enhancing their technical and measurement skills. Additionally, the Simoska Laboratory will involve undergraduate and K-12 students from diverse backgrounds and minority-serving institutions in South Carolina via research and outreach programs, inspiring them to pursue STEM careers. Given that CVDs are the leading cause of death globally, developing robust analytical sensing methods for early detection is critical. The overarching goal of this proposal is to design and fundamentally characterize chemically specific electrochemical sensors for the rapid detection, direct measurement, and continuous monitoring of physiologically relevant CVD metabolites. This work will be conducted under the mentorship of collaborators at the University of Cincinnati, a leading expert in electrochemical instrumentation and E-AB sensors. The proposed multiplexed sensors will evaluate and implement multi-analyte detection strategies using nucleic acid recognition elements at electrochemical carbon ultramicroelectrode arrays (CUAs), offering high reliability, reproducibility, and improved specificity for biomarkers. Compared to large, planar gold electrode surfaces typically used with E-AB sensors, CUAs are a versatile electrode platform for functionalization with aptamers. Additionally, the Systematic Evolution of Ligands by Enrichment (SELEX) technique will be used to tailor-design aptamers with high specificity and functionalize them with novel redox probes for CVD biomarker detection, enhancing sensor reliability. Continuous square-wave voltammetry (cSWV) will be employed to study sensor surface behavior and aptamer folding kinetics. The array-based E-AB sensors will enable rapid, sensitive, and multi-analyte detection of CVD biomarkers. The broader impacts include advanced E-AB sensing technologies for South Carolina, training students through research and courses at USC, and engaging economically disadvantaged schools in STEM outreach, fostering a diverse future in 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.