University Of South Carolina At Columbia

NSF Awards · FY 2025 · 2025-02

The current generation of Artificial Intelligence (AI) models has not only revolutionized real-world applications like conversations. These models have also transformed AI itself, as they can serve as the basis, or foundation, for more specialized AI tasks if given sufficient additional training. However, building and training these models and specializing them for challenging tasks like planning is hard due to barriers including limited computing resources, limited non-proprietary knowledge, and even limitations on the basic assumptions used to build the models. This effort investigate an alternative path: it creates and trains a specialized AI model, using innovations on current methods, that can be applied to problems involving planning and chains of tasks. The resulting model has the potential to outperform and be more efficient and more understandable at these types of tasks than the current generation of general-purpose Large Language Models. The project offers a unique demonstration opportunity to overcome resource barriers and democratize knowledge, especially for underserved communities. The model development process, resulting AI infrastructure, and accompanying outreach activities will engage research communities from a wide array of academic disciplines at multiple universities including minority-serving institutions. AI foundation models, optimized primarily for next-token prediction, excel in generating coherent and contextually relevant text, making them effective for natural language and conversational agents. However, these models exhibit significant limitations when applied to tasks from real-world applications requiring sequential decision making, reasoning, and other planning-like tasks. Examples include business processes, guidelines, instructions, programs, and workflows. Previous work on this topic using foundation models has primarily focused on using or fine-tuning pre-trained, off-the-shelf models with limited success. This project will instead investigate a different approach, creating a comprehensive, yet compact, foundation model for planning-like tasks from scratch. This model will be based on innovative approaches for tokenization, training, and other steps that will enable the model to specialize in advanced planning. The project will follow a transparent, bottom-up methodology for building this new model that will open new avenues for research, education, and real-world applications. 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.

NIH Research Projects · FY 2025 · 2025-01

Project Summary Wildland-urban interface (WUI) fire is a term commonly used to describe a transition zone where wildfire meets urban development. WUI fires are becoming more frequent, intense, larger, and harder to contain. The combustion of vegetation and structures within the WUI releases gases, smoke, and ash, and various types of contaminants, including among other metal-bearing nanomaterials (NMs). Fire ash spread by wind and water contaminating air, water resources, and food resources. Therefore, WUI fire ash poses significant health hazards to humans. We collected 90 WUI fire ash samples from two burned sites following the 2020 California fire season and from the city of Lahaina following the 2023 Maui (Hawaii) fire. Our investigation of the metal-bearing NMs in the California fire ashes demonstrated the presence of various types of metal oxide NMs such as chromium, arsenic and titanium oxides which tend to be reduced in the fire environment. Our preliminary screening suggests that WUI fire ash can cause cardiac malformations in zebrafish embryos. Further studies in mouse embryonic cushion mesenchymal cells showed that the WUI fire ash adversely affects cell growth and cell viability. The overarching goal of the proposed research is to gain a comprehensive understanding of the transformations of metal oxides NMs in the WUI fire environment; and how these transformations impact the toxicity of metal oxides NMs which could contribute to congenital heart disease. The overall hypothesis of the proposed research is that metal oxides nanomaterials are transformed (get reduced and/or oxidized) in the WUI fire environment increasing their ability to induce congenital heart defects. In Aim 1, we will test the hypothesis that the fire-transformed metal oxide nanomaterials result in higher cardiac malformations compared to untransformed metal oxide nanomaterials. We will perform a large screen and identify WUI fire ashes that can cause cardiac malformations in zebrafish embryos. Next, we will use selected WUI fire ashes found to be teratogenic in our initial zebrafish screen and characterize frequency and spectrum of cardiac malformations in mice. Also, we will determine if WUI fire ashes result in abnormal EMT and/or cardiac mesenchymal cell remodeling in mice. In Aim 2, we will characterize and quantify the transformations of metal oxide nanomaterials as a function of fire conditions and nanomaterial properties. Metal oxide NMs will be thermally transformed in the presence of cellulose as a source of organic fuel to generate ash under controlled laboratory conditions. We will test the impact on cardiac malformations of individual thermally transformed metal oxide NMs in controlled laboratory experiments. Successful completion of this project will lead to better understanding of the transformations of metal oxides NMs in the WUI fire environment and how these transformations impact the toxicity of metal oxides NMs which may have detrimental effect on cardiac development.

NSF Awards · FY 2025 · 2025-01

Artificial intelligence has achieved remarkable success in recent years, largely driven by advancements in foundation models, which leverage complex neural networks trained on vast amounts of data in order to perform a variety of tasks, such as question answering, text summarization, and image generation. This project seeks to extend the success of foundation models to sequential decision-making, where an agent--a programmable entity---interacts with an environment, seeking to accomplish a task by taking a series of actions over time, with each action influenced by the outcomes of previous actions. Sequential decision-making commonly arises in situations characterized by uncertainty, limited resources, or dynamic conditions, where each decision can have an impact on future actions. The objective is to select a sequence of actions that maximizes profits, rewards, utilities, or some other well-defined objective. Adapting foundation models for sequential decision-making is challenging, because high-quality data is often lacking and it requires recognizing task-specific structures and optimizing long-term objectives, where minor differences can drastically change optimal solutions. This project will develop novel methods for overcoming these challenges to significantly increase the applicability of foundation models for a wide range of sequential decision-making applications, such as smart manufacturing, multi-agent systems, and human-machine interaction. This project will develop novel techniques and methods to effectively adapt foundation models to multimodal sequential decision-making. The proposed research will be conducted and evaluated on three thrusts with progressively increasing problem complexity. Thrust 1 studies sequential decision-making problems in textual modalities where the decision-maker only needs to look one step into the future when evaluating the consequences of a proposed action, referred to as contextual bandits. The investigators will develop new techniques such as reward-aware text summarization and mixing foundation model-based and online-learned decision rules that leverage foundation models to warm-start the agent while avoiding being locked into pretrained parameters to improve the performance in the long run. Thrust 2 studies sequential decision-making problems that involve long decision horizons (the full reinforcement-learning problem) and are multimodal. The investigators will develop additional techniques that leverage foundation models for multimodal and hierarchical reinforcement learning. Thrust 3 extends the techniques to the cooperative multi-agent setting, where the foundation models are leveraged to facilitate both centralized and decentralized inter-agent communication, which is crucial for multi-agent coordination. In and outside the classroom, this project will conduct a series of educational and outreach activities, including development of course materials related to foundation models and sequential decision-making, undergraduate research mentoring, and public outreach in local 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.

NSF Awards · FY 2025 · 2025-01

The frequency of wildfires has increased rapidly in recent decades and is projected to increase in the future. Among the many impacts of wildfires on society and the environment, there is growing evidence that wildfires alter forest vegetation and soil in ways that enhance the spread of debris and chemical contaminants in the environment. This leads to increased contamination of lakes, rivers, and groundwater. Unfortunately, when wildfire contaminants enter drinking water supplies, they can react with disinfectants used to treat drinking water and form harmful disinfection by-products (DPBs). The goal of this project is to address this gap in our understanding of how wildfire impacts the formation of DBPs through a combination of laboratory and field experiments. Researchers will characterize the organic and inorganic material present in forests during wildfires and assess how these compounds affect the formation of DBPs when released into water bodies. The successful completion of this research will benefit society by advancing fundamental knowledge about the impacts of wildfires on water disinfection processes and water quality. The findings of this research will be shared with stakeholders to implement effective mitigation strategies for wildfires. Further benefits to society will be achieved by training undergraduate and graduate students at the University of South Carolina to increase the STEM workforce. Combustion processes during wildfire alter the properties of organic matter and metals, leading to enhanced formation of DBPs during drinking water treatment of impacted waters. However, the relationships between the severity of wildfires and the transformation and mobility of metals, the levels and composition of dissolved organic matter (DOM), and enhanced formation of DBPs in drinking water sources remain unclear. To address these knowledge gaps, investigators will conduct a combined field and laboratory study to: (1) Characterize the wildfire-induced metal transformations in field-collected ashes, soils, and forest litter under laboratory-controlled experiments; (2) Determine the concentrations of metals and DOM levels and composition in simulated runoff and streams from the burned area; and (3) Investigate the formation of DBPs through controlled laboratory chlorination and chloramination of ash, soil, and water leachates. This research will improve the fundamental understanding of the real-world implications of wildfires by conducting planned moderate- and high-severity burns that allow for the collection of “before and after” fire samples. Both watershed-scale studies and laboratory experiments combining nontarget and target analysis of metals and organic matter will be used to create realistic predictions of real-world fires. Together, these results may facilitate the discovery of unexpected organic and inorganic compounds in the fire ash, soil, and water samples post wildfire 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 2026 · 2025-01

Project Summary Traumatic brain injury (TBI) is a significant health care problem, affecting over 2.5 million people in the United States annually. Most cases are classified as mild or concussive TBI, caused by a rapid acceleration to the skull that may lead to short-term loss of consciousness and memory, as well as long-term disability. Prevention of TBI is therefore a critical consideration in contact sports, military operations, motor vehicle use and other activities. Accurate quantification of the locations, magnitudes, and orientations of brain deformation (strain) during TBI is a precursor to a better understanding of the onset and secondary cascades of injury. A key approach to understanding the risk of TBI and assessing preventative measures is computational modeling, which simulates the external forces applied to the head and the associated biomechanical response of the brain. The development of accurate computational models of TBI is challenging, however, requiring the collection of experimental data to calibrate and validate the models. Recently, our team has developed two complementary approaches to acquiring measurements of brain biomechanics under loading. One approach employs dynamic magnetic resonance imaging (MRI) in living human subjects undergoing both mild head accelerations. The other approach employs sonomicrometry to track the motion of markers in cadaveric brain specimens at concussion- level accelerations. The output of these experimental datasets has been limited to either 1) dense 3D brain strain data at non-injurious head motion in vivo, or 2) spatially sparse brain displacement data at injurious loading in cadavers. As a result, there remains two unknowns in our understanding of brain deformation during head impact: 1) the actual magnitude of strain at concussive accelerations, and 2) the accuracy of local strain distributions and if the strain patterns change with increases in impact severity. To address these fundamental deficiencies in our knowledge of brain biomechanics, we propose to integrate these two previously disparate but complementary ends of the experimental TBI spectrum to better enable prediction of the biomechanical response of the living brain during concussion. We will perform three aims: 1) Optimize dynamic MRI techniques for increased loading in phantoms and cadavers; 2) Perform multiscale biomechanical characterization of cadaveric specimens using both MRI and sonomicrometry techniques; 3) Demonstrate application of combined MRI and sonomicrometry data for improved injury prediction by evaluating publicly available computational models and applying them to sports impact databases. This work will provide a novel and complete characterization of the intracranial biomechanics of TBI. The combined use of cadavers and phantoms will enable a cross-validation of our ability to predict high acceleration response based on low acceleration measurements. By the end of the project, we will have fully characterized phantom and cadaveric data that will be made publicly available for other researchers to use and, by combining the acquired data with existing in vivo data, developed new tools to predict the response of the living human brain during concussion-level loading and improve injury prediction models.

NIH Research Projects · FY 2026 · 2024-12

PROJECT SUMMARY/ABSRACT Measuring mobile screen use accurately has proven difficult due to limitations in current methods. Direct observation can be costly, invasive, and unfeasible for longitudinal assessment of mobile screen use, while self- and proxy-report struggle to sufficiently capture the intermittent bouts of screen use characteristic of child screen use behavior. Current objective measures of screen time, passive sensing applications, are unable to determine who is using the device, which is of particular concern when children are sharing devices with parents and siblings. A strategy to address this critical shortcoming of objective measures of screen time is using built-in sensors on mobile devices to determine who exactly is using the device. Mobile device sensors (i.e., accelerometer, gyroscope, magnetometer, orientation, touch) have been widely used in behavioral biometric authentication to identify the user while the user is interacting with the device. However, this technology has not yet been applied to mobile screen use measurement to advance our current measures of mobile screen use. To address this, Aim 1 will train machine learning models using mobile device sensor data (accelerometer, gyroscope, magnetometer) to distinguish a target child, through which the most salient features for child biometric authentication will be identified. Aim 2 will then evaluate model performance when machine learning models are tested on a different sample of children during a structured screen time protocol. To accomplish these aims, this study will complement and extend a current R01 (R01DK129215) that has an overarching aim to strengthen the measurement of 24-hour movement behaviors in children using raw sensor data (i.e., heart rate, accelerometry) from consumer wearable devices. This project will leverage sensor data already collected as part of the sedentary block of the R01 protocol to address Aim 1 and will recruit a novel sample of children from the free-living portion of the current R01 to address Aim 2. We will use the sensor- tracking application, SensorLog, on the iPads and data from the built-in accelerometer, gyroscope, and magnetometer sensors will be recorded continuously while the child interacts with the device. This project will use advanced analytic techniques, including machine learning, to address these aims. Findings from the proposed project will open a window of opportunity in child screen time research and offer an innovative approach to objective screen time measurement. Long-term, the goal of this project is to improve the longitudinal monitoring of screen time, which can then inform evidence-based guidelines for screen time in children. As an NIH F31 predoctoral trainee completing this project, the following skills will be accrued: implementing a validation study in children, using advanced analytical techniques, and disseminating research findings in peer-reviewed publications and scientific presentations through a cross-disciplinary lens, all of which will be instrumental in becoming an independent and productive research scientist.

NSF Awards · FY 2024 · 2024-11

Predicting sediment fate and transport over floodplains remains a challenge notwithstanding recent advances in the study of floodplain hydrology and sedimentation. This is in part associated with difficulties in modeling flow over complex topography, and in part due to the lack of a framework to quantify transport, erosion and deposition of sediment in the silt/clay range. The most recent Congaree River flood caused by hurricane Helene between September 27th and October 5th, 2024, represents a unique opportunity to characterize floodplain sedimentation over the ~100 square kilometer forested floodplain of the Congaree National Park, SC, and link it to hydraulic characteristics of a major flood. Characterizing how the spatial distribution of floodplain deposits after a major flood may vary with hydraulic characteristics of main channel flow has direct applications to determine fluxes of fine sediment and nutrients for ecosystem management and develop fate and transport models for particles and contaminants. This project will provide research opportunity to two early career principal investigators and will also provide training and mentorship for both undergraduate and graduate students. The investigators will compile a dataset quantifying the spatial distribution of floodplain sedimentation rates and grain sizes after a major flood in a natural setting. The integration of floodplain sampling with data collected at U.S. Geological Survey gages will allow the team to identify links between floodplain sedimentation patterns and hydraulic characteristics of main channel flow. In floodplain sedimentation models, flow hydraulics is generally simplified as a step function, that is, flow above and below bankfull, and sediment is described in terms of two grain sizes, bed material and wash load. It has been long recognized that floodplain sedimentation is not as simple, due to uneven development of natural levees, complex floodplain topography and a network of floodplain channels and connected depressions carrying water and sediment into the floodplain interior. The field work in this project will provide critical evidence to constrain the role of floodplain channels in the dispersal of water, bed material (sand for the Congaree River) grain sizes, as well as and wash load (mud), at floodplain scale. This will help advance our understanding of floodplain hydrology and sedimentation. This project is jointly funded by the Division of Earth Sciences 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 2024 · 2024-11

Non-technical Summary: Supported through the Solid State and Materials Chemistry Program within the Division of Materials Research, principal investigator Prof. Natalia Shustova and her team at the University of South Carolina at Columbia focus on developing stimuli-responsive well-defined materials, consisting of metal cations and organic ligands. The main advantage of these metal-organic frameworks (MOFs) is that their fundamental properties can be controlled externally using light and heat to modulate properties, enabling a close mimic of the complexity observed in biological systems. This work paves the way for applications in areas such as optoelectronic devices, precision-controlled drug delivery, artificial muscles, light- or heat-activated molecular machines, and encryption systems among others. In addition to materials chemistry research, the research group, in collaboration with other groups at the University of South Carolina, initiated and is committed to advancing the Carolinian Women in Science (Wi-Sci) Supportive Network. The primary aim of Wi-Sci is to build and expand a support network for women, especially with a focus on female scientists belonging to underrepresented minority groups at Carolinian higher education institutions, including historically black colleges and universities. This Wi-Sci Program combines educational and research opportunities to support female students in STEM disciplines. Technical Summary: Rapid and reversible switching between two discrete states in the solid state is a cornerstone for the technological development of, for example, on-demand-activated drug delivery platforms, photochromic heterogeneous catalysts, molecular motors, recyclable and healable materials, artificial muscles, and multilevel anticounterfeiting and information encryption systems. Therefore, the focus of this program, supported through the Solid State and Materials Chemistry Program in the Division of Materials Research, is to establish fundamental synthetic principles that would enable the control of the rate of photophysical material response. At the same time, introducing a second (orthogonal) external stimulus as a variable will allow for precise or multivariable control of material properties, enabling a closer mimicry of the complexity characteristic of biological systems. Thus, another part of this project is to develop a synthetic approach for the integration of two types of photochromic molecules within the same platform, for which photophysical properties can be controlled orthogonally. This project also integrates the research and educational opportunities for high school, undergraduate, and graduate students with a focus on underrepresented minority groups. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project aims to serve the national interest by (1) improving programs preparing novice college mathematics instructors and (2) establishing leadership development for faculty who are the Providers of teaching-focused professional development (TPD) for those novices. Extensive educational research has identified evidence-based instructional practices that support undergraduates' persistence and learning in science, technology, engineering, and mathematics (STEM). For undergraduates to benefit from advancements in instructional practices, novice instructors (e.g., graduate students) need opportunities to develop expertise in those practices. For novice instructors to develop that expertise, Providers (i.e., those who facilitate TPD for instructors) themselves need opportunities to develop expertise in teaching about teaching. Providers face daunting challenges: no curricular packages (e.g., textbook, assessment items) exist for teaching graduate students how to teach mathematics. This effort builds upon previous work addressing these needs through workshops for Providers and creating a library of individual activities for TPD. Experienced Providers will assemble lessons from the library of activities, create assessments of learning about teaching, and teach new Providers about use of these packages. An innovation in the project is attention to a particular group of Providers, whose ambitions include scholarly work related to the development of novice instructors. These Provider-Scholars will be the next generation of leaders in this field. Greater Provider skill will improve instruction by novices and boost learning opportunities and outcomes for undergraduates. The goals of the project are (1) to develop curricular packages for learning about teaching college mathematics which will be piloted by Providers and (2) to build new research-based understanding of the knowledge, skills, dispositions, and communities Providers develop as they grow professionally into Provider-Scholars and Stewards (i.e., Provider-Scholars who also have leadership roles). Project research and evaluation will use a mixed-methods convergent design so complementary data are collected concurrently or, as appropriate, sequentially. This approach combines the strengths of quantitative data collection and analysis (e.g., large sample, repeated measures) with those of qualitative methods (e.g., participant voices, rich detail). In particular, the exploratory research questions are: (RQ1) What is the nature of Provider-Scholar knowledge, skills, and dispositions for engaging in scholarly work as Stewards? (RQ2) What is the nature of Steward, Provider-Scholar, and Provider engagement in the work and community growth? Project evaluation questions are: (EQ1) To what extent is project exploratory research implemented as planned? (EQ2) To what extent is the project succeeding in developing and piloting starter packages and Provider orientation with target communities? (EQ3) How can the project do better in supporting the professional community, including stewardship and leadership capacity development? The project intends to build professional community through collaborative working groups of experienced Provider-Scholars and education researchers. Mathematics graduate students (94% of whom have teaching related responsibilities while in graduate school) will benefit from the strengthening of TPD programs achieved by equipping new Providers with “starter packages” of resources informed by research findings about student-responsive teaching and learning. A robust community of Providers whose scholarly activity is about TPD will seed the next generation of leaders. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its 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.

NSF Awards · FY 2024 · 2024-10

The extraordinary benefits of large generative AI models also come with a substantial risk of misuse and potential for harm. Given that roughly 3.2 billion images are uploaded daily on social networks and a rapidly growing percentage of these are AI generated, the need for robust multimodal harm prevention is more pressing now than ever. This project seeks to prevent harms associated with AI-generated vision language model content. Project techniques will be valuable in many domains and can help stakeholders in government, regulatory bodies, and policy making. This project will engage journalism and other students in the project. Project activities include undergraduate research internships and an annual AI summer camp for high school students. This project pursues three technical objectives. The first is a prompting framework for harmful content provenance in AI-generated vision language models with the use of a novel prompting method utilizing multimodal knowledge graphs, organized by who, what, when, where, and why semantic schema, and stored and optimized utilizing techniques such as joint embedding, contrastive learning, and negative sampling methods. A second objective is machine unlearning as a proactive measure to mitigate harms associated with AI-generated vision language models. The third objective is to blur segments of harmful images. The evaluation framework for the project includes automated metrics and human evaluations. The project will share open-source web codebase, datasets and demos that can be tested live. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

In recent years, global manufacturing networks experienced a variety of shocks and disturbances including COVID-19. Thus, improving network resiliency, transparency, and cybersecurity have emerged as a national priority. Smart Manufacturing technologies such as Artificial Intelligence and Machine Learning show promise in achieving these objectives, yet struggle to materialize at the manufacturing network level. Particularly small and medium-sized manufacturers struggle in their adoption of these data-driven, value added technologies due to a lack of resources and incentives. Consequently, they cannot participate in many high-value manufacturing networks that often require certain technologies and data sharing. This EArly-concept Grant for Exploratory Research (EAGER) project supports research that intends to address this challenge through a Blockchain-enabled framework that leverages secure and private Federated Learning which meets the unique requirements of defense manufacturing networks. This framework enhances the availability and integrity of critical supplies, as well as strengthens and diversifies the defense industrial base. The project’s secure and privacy-preserving data sharing and collaboration mechanisms can be applied in various domains beyond manufacturing, such as healthcare, finance, and supply chain, empowering individuals and organizations to share data securely and collaborate effectively. The results have potential to transform industry, drive economic growth, foster innovation, and enhance societal well-being. The project’s research problem stems from manufacturing networks’ inability to securely and efficiently exchange data and leverage network level Federated Learning. The project aims to increase the resiliency of distributed and dynamic manufacturing networks, specifically including small and medium-sized manufacturers, by providing access to a secure private Blockchain platform that enables decentralized, secure, and transparent communication channels. This enables manufacturing network level learning through Federated Learning while respecting data ownership and ensuring retention of competitive or controlled (raw) data and machine learning models. To achieve these goals, the project utilizes Federated Learning by integrating a private Blockchain to manage metadata, access controls, and model updates. Unlike existing approaches, the framework focuses on specific challenges and requirements of manufacturing networks. This means ensuring confidential data remains local under full control of the individual nodes while leveraging Blockchain for efficient coordination of the Federated Learning process as well as reducing overhead cost for smaller network participants that are resource constraint. The project advances the state-of-the-art in Federated Learning and Blockchain technology through efficient algorithms for model aggregation and coordination in the presence of heterogeneous data for manufacturing networks. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The project will test how the environment and geographic distance between populations affects variation in population size. The number of individuals in a population does not stay constant through time, but varies because of birth, death, and dispersal. The environment influences these processes, leading to the idea that population size will vary more in more variable environments. This project will look at how changes in population size through time is affected by different environments and disturbance. Conservation and restoration efforts will benefit greatly from being able to predict how population size will fluctuate in a changing environment. The project will help to identify populations that are at risk of extinction. The project will also provide students with hands-on research experience and will teach undergraduates how to build mathematical models. The project will assess the role of environmental variability on temporal variability in population dynamics using a combination of observational data from the National Ecological Observatory Network paired with theoretical population and community models. First, the project will test if populations in variable environments are more variable (less stable) given the context of the local community of interacting species. Next, the research will examine the potential underlying trait or phylogenetic basis for the strength of the relationship between environmental variability and population variability. Finally, the researchers will explore species population variability across species geographic ranges, providing a better understanding of population variability across space as a function of environmental variability, traits, and geographic range position. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Stand-alone power supplies, such as lithium batteries, are compact power sources for most electronic devices, however, they are unsuitable for harsh environments, needs frequent recharging, and needs regular replacement as the capacity of lithium batteries reduces 70-90% within few years of usage. Hence, there is a need for reliable stand-alone power supplies with stable power output for prolonged periods for applications in harsh-environment electronics, remote sensing, and medical implants. To address this, we propose to develop an innovative radiovoltaic battery based on metal/oxide/silicon carbide semiconductor (MOS) device that converts radiation energy from a radioactive source into electrical power without requiring an external biasing voltage (i.e. self-biased). Such batteries can power devices for decades without maintenance, making them ideal for space missions, remote sensing in isolated or harsh environments, deep-ocean equipment, and covert defense operation where conventional batteries and solar photovoltaics do not work. With biofriendly radioactive sources like tritium, radiovoltaic batteries can also power life-saving medical implants such as pacemakers. The proposed silicon carbide (SiC) semiconductor device fabrication can be retrofitted with existing silicon device facilities, reducing production cost. Radiovoltaic batteries inherently have minimal carbon footprints, they are environment friendly due to their longevity, moreover, nuclear waste can be recycled to manufacture radioisotope batteries. The project offers excellent research opportunities for graduate and undergraduate students in multidisciplinary fields. The project aims to develop a novel metal oxide/4H-SiC vertical MOS betavoltaic device capable of reaching the theoretically predicted conversion efficiency of 25% in 4H-SiC. The MOS devices will be fabricated using Y2O3 and other high-k dielectrics deposited epitaxially on high-quality 4H SiC epilayers through pulsed laser deposition. Performance of the present day radiovoltaic devices are limited by bulk and interfacial defects causing charge trapping and short minority carrier diffusion length. Therefore, the overall technical goals of the project are: i) investigate Y2O3/4H-SiC junction and interface properties to understand their role in enhancing the minority carrier diffusion length in the Ni/Y2O3/4H-SiC MOS structure; ii) explore the properties of other wide bandgap high-k dielectric (SiO2, Al2O3, HfO2) to study their passivation properties and in relative effectiveness of enhancing the betavoltaic cell properties; iii) study 4H-SiC converters with different thicknesses of epilayer to examine the effects of series resistance on the betavoltaic cell performance; iv) demonstrate betavoltaic cells with a conversion efficiency that is close to the theoretical limit. Electrical characterizations such as current-voltage and capacitance-voltage measurements will be carried out to study the junction properties, and alpha spectroscopic methods will be employed to determine the minority carrier diffusion lengths. Deep level transient spectroscopy (DLTS) will be carried out to study the bulk and interfacial defects. To understand the surface chemistry and to optimize the band offsets between the dielectrics and 4H-SiC epilayers, x-ray photoelectron spectroscopy (XPS), ultraviolet photoelectron spectroscopy (UPS), and cross-sectional high-resolution scanning electron microscopy will be carried out. These studies will be correlated with the conversion efficiency of the betavoltaic cells measured using a 63Ni beta particle source. Because of the high-quality of the 4H-SiC epilayers and efficient charge collection at zero bias, the proposed 4H-SiC betavoltaic devices are anticipated to deliver power and conversion efficiency close to that theoretically predicted for 4H-SiC. Additionally, since the 4H-SiC growth and fabrication techniques are matured, the proposed devices will reduce the cost of device production substantially than that incurred for presently available diamond radiovoltaic devices. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Most people in the USA consume disinfected drinking water. While disinfection is vitally important to prevent waterborne disease, disinfection by-products (DBPs) form as an unintended consequence. The U.S. Environmental Protection Agency currently regulates 11 DBPs in drinking water. In 2022, a new class of DBP called halocyclopentadienes (HCPDs) was discovered in chlorinated and chloraminated drinking water from three U.S. cities. These DBPs were found to be very toxic and likely to accumulate in tissues. Given these findings, more information is needed to assess the occurrence of these compounds in drinking water and identify conditions that give rise to them. The goal of this research is to conduct a national occurrence study of HCPDs across the US to uncover the factors that influence HCPD formation and investigate their genotoxicity (potential for cancer). The results will help assess whether HCPDs pose a potential risk to human health. This goal will be achieved by measuring HCPD concentrations in drinking water collected across the USA, conducting laboratory experiments to understand how HCPDs are formed, and conducting genotoxicity experiments in cells and in laboratory test animals (nematodes). Societal benefits result from a better understanding of the potential risks of these new DBPs. This information will facilitate long-term engineering solutions to enhance drinking water safety and sustainability. Additional benefits will result from science training opportunities for high school and college students to increase scientific literacy and improve the Nation’s STEM workforce. Water disinfection was the greatest public health achievement of the 20th century. However, chemical disinfection has raised a public health issue resulting from the potential for cancer and reproductive/developmental effects associated with DBPs. In 2022, HCPDs were discovered in chlorinated and chloraminated drinking water from three U.S. cities. These DBPs were found to be highly cytotoxic and likely to bioaccumulate. Thus, it is important to determine their occurrence in drinking water, along with factors that influence their formation. While the cytotoxicity of three HCPDs is known, their genotoxicity is currently unknown. The goal of this interdisciplinary research project is to address these knowledge gaps through a three-part study to: i) assess HCPD occurrence in drinking water from across the US, ii) determine important factors in HCPD formation, and iii) investigate their genotoxicity. To achieve this, HCPD DBPs will be quantified using gas chromatography-mass spectrometry. Genotoxicity will be assessed using two model organisms. Single cell gel electrophoresis using Chinese hamster ovary cells will be used to assess genotoxicity in vivo, and long amplification quantitative PCR and transgenerational assays will be used to assess genotoxicity in the nematode C. elegans. Results will advance our understanding of the potential risks of this new class of DBP and enable engineering solutions to enhance drinking water safety and sustainability. The research team will disseminate results to relevant scientific communities, as well as to the lay public and key stakeholders via established outreach programs. Graduate students will be trained to provide research experiences for undergraduate and high school students. These student research and mentoring activities will encourage participation of underserved groups in STEM. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Physical activity (PA) plays a critical role in preventing and treating chronic disease and promoting quality of life across the age spectrum. Older adults, especially racial/ethnic minorities, are a priority population for increasing PA as they experience disproportionate rates of chronic disease, are underactive, and their proportion of the US population is increasing. The USC PRC has established a long and successful history (since 1993) of applied research, practice, and training in physical activity in partnership with communities. In the 2024-2029 cycle, we will use a community-based participatory research approach to guide all activities. The specific aims of this application are to: (1) establish and maintain a PRC infrastructure to conduct applied prevention research, (2) engage the community advisory board (CAB) and other organizational partners to inform all prevention research projects, (3) build capacity to conduct prevention research, (4) communicate information about PRC activities to intended audiences, (5) conduct a dissemination and implementation (D&I) core research project to understand implementation of an evidence-based intervention (EBI) that leverages social networks to support PA change among older African American adults through churches and disseminate and translate the EBI, and (6) participate in the PRC Network and collaborate with other PRCs to increase the Network’s collective impact. Achieving these aims will contribute to the CDC PRC Program’s long-term outcome of widespread, sustained, and scaled-up use of EBIs and systems-wide public health strategies to eliminate the drivers and root causes of health disparities. The proposed core research project uses a within-site pre-post study design to study the implementation of Walk Your Heart to Health (WYHH) by AME Churches. WYHH will be integrated into an EBI shown to increase church capacity for PA and healthy eating (Faith, Activity, and Nutrition, FAN), resulting in a multi-level approach. The study’s primary focus is to study implementation outcomes. The Consolidated Framework for Implementation Research will help inform three primary questions: (1) how do contextual factors influence implementation and sustainability success or failure? (2) what barriers and facilitators to implementation exist? and (3) how can the EBI be scaled up to broader regions or populations outside the research community? Data from pastors and church implementers will come from multiple sources (surveys, interviews, etc.) and time points and will be analyzed using a matrixed multiple case study approach and rapid qualitative analysis. We will work with the CAB and other partners to ensure cultural relevance of intervention strategies and support materials in Year 1, pilot the implementation strategies and measurements in Year 2 (6 churches), conduct the implementation study in Years 3 and 4 (20 churches), and focus on translation and scale up activities in Year 5. A secondary focus is to study participant-level outcomes. Increasing PA in older adults is a Healthy People 2030 goal. Churches are vital but underutilized institutions for implementing EBIs that can contribute to reaching national priorities.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT Cancer-related health disparities in South Carolina (SC) are among the largest in the nation. The South Carolina Cancer Prevention and Control Research Network (SC-CPCRN) is currently one of nine Centers for Disease Control and Prevention (CDC) and National Cancer Institute (NCI)-funded Collaborating Centers working to reduce cancer-related health disparities among disenfranchised and medically underserved populations by advancing dissemination and implementation (D&I) science. In strong partnership with the South Carolina Cancer Alliance; federally qualified health centers; and other academic, clinical, community, and faith-based partners, the SC-CPCRN uses evidence-based approaches for the D&I of efficacious cancer prevention and control messages, guidelines, and interventions. The relevance of our D&I efforts in SC is strengthened considerably through established partnerships with our target audiences and stakeholders in both urban and rural regions, leading to improved health outcomes. Our team’s well-recognized community- engaged research approach to D&I science and community-clinical linkages increase the validity and relevance of our work, ultimately providing all stakeholders with valuable information about strategies and programming for improving health and reducing cancer-related health disparities. This work is guided by the latest D&I science and national cancer research priorities. These priorities include (1) Healthy People 2030 objectives to increase cancer screening rates, decrease mortality, and increase survival; (2) National Center for Chronic Disease Prevention and Health Promotion’s approach to the social determinants of health; (3) NCI’s emphasis on rural cancer control; and (4) Blue Ribbon Panel recommendations of the Cancer Moonshot focused on “expanding development and implementation of evidence-based interventions to reduce cancer risk and cancer-related disparities”. We will utilize existing local and regional organizational infrastructure and strong partnerships and will continue to encourage new linkages between community and clinical partners for our center-specific and cross-center D&I initiatives. Our Specific Aims are to: 1) Disseminate, implement, and evaluate efficacious, multi-level, and multi-site public health, cancer control interventions to address cancer-related health disparities; 2) Engage community and clinical partners in research, training, and technical assistance to strengthen D&I science for cancer control in SC and nationally, and translate effective community-based interventions for cancer prevention and screening into practice; and, 3) Increase the capacity of community and clinical partners in the D&I of evidence-based cancer control strategies for reaching underserved, minority, and rural populations.

NIH Research Projects · FY 2025 · 2024-09

The majority of adults with arthritis in the United States do not meet the federal physical activity guidelines of 150-300 minutes/week of moderate-to-vigorous intensity physical activity (MVPA). Adults with arthritis face numerous barriers to engaging in regular MVPA and seek specific recommendations for activities from healthcare providers. A major gap remains in identifying the minimal dose of activity necessary to see clinical improvements in arthritis-related outcomes. Lower doses of activity could lead to improvements in arthritis- related symptoms and may be a more feasible, initial goal for adults with arthritis who have difficulty engaging in activity at the recommended levels. Further, shorter bouts of MVPA could result in fewer short-term negative effects on arthritis-related symptoms as compared to higher daily durations of activity. Therefore, the purpose of this study is to identify whether a dose-response relationship exists between objectively-measured MVPA and arthritis-attributable outcomes in adults with arthritis. The first aim is to examine the effect of 3 doses of MVPA (45 min/week, 90 min/week, and 150 min/week) on arthritis-attributable outcomes (e.g., physical function, health-related quality of life, pain, and depression symptoms) in adults with arthritis at 6 and 12 months. The second aim is to examine the between-group differences in momentary levels of fatigue, pain, depressive symptoms, confidence, and happiness after engaging in MVPA, using ecological momentary assessment at 6 months. Inactive adults with arthritis (n=285) will receive a 6-month adaptive physical activity intervention. Participants will be randomized to receive one of 3 doses of MVPA: (1) 45 min/week, (2), 90 min/week, or (3) 150 min/week. MVPA goals will begin lower and gradually increase towards the 45, 90, or 150 min/week goal every 2 weeks. Participants will be encouraged to spread the MVPA out over at least 3 days. To help participants achieve goals, they will be provided with the Arthritis Foundation’s Walk With Ease Guidebook, behavioral lessons, monthly behavioral telephone coaching, and a Fitbit. If a participant achieves less than 80% of his/her goal for a given week, or does not track his/her activity for the week, the participant will be stepped up to receive additional intervention components (e.g., text message/email, biweekly or weekly coaching calls). Objectively-assessed MVPA and arthritis-attributable outcomes will be obtained at the baseline, 6-, and 12-month assessments. Momentary arthritis-attributable outcomes will be obtained through ecological momentary assessment at 6 months. Results from this randomized controlled trial will provide insight on whether adults with arthritis can experience benefits from engaging in less physical activity than what is currently recommended. Additionally, gaining a better understanding of how long and short durations of activity influence momentary symptoms will provide clinical and public health implications for prescribing specific activity goals. Key study findings will be disseminated to academic and practitioner audiences as well as additional avenues informed by our partners.

NIH Research Projects · FY 2025 · 2024-09

The " Community Action in Research to Eliminate Substance Use Disorder (CARES) Center" represents a groundbreaking effort to address the significant gaps in research related to Substance Use Disorder (SUD) treatment and care. The high prevalence of SUD, co-occurring mental illness, and barriers to care underline the urgency of our research. The CARES Center takes on the challenge of reshaping existing research paradigms and ushering in a new era of patient-centered research by placing patients with SUD at the forefront as co-developers of scientific research projects. By elevating patient perspectives and priorities, the CARES Center aims to make meaningful contributions to the advancement of equitable and responsive SUD research and care. This proposal delineates our objectives to establish the CARES Center and implement the Virtual Patient Engagement Panel (ViP Panel) to actively engage patients and other stakeholders in SUD research. The primary focus is to address the pressing need for research and innovation in SUD treatment services that reflect real-world experiences and improve the quality of care. The proposal encompasses three primary aims. Aim 1 Engagement Panel: This aim focuses on building research capacity through the establishment of the ViP Panel, consisting of patients, family members, and individuals in recovery representing a broad range of backgrounds and substance use profiles. ViP Panelists will receive training in research fundamentals, co-develop research priorities, and collaborate with investigators across the United States. This process ensures that the voices of people directly affected by SUD are central to shaping the research agenda. Aim 2: Research on Patient Experiences. This aim examines patient experiences, preferences, and barriers to SUD treatment services, with emphasis on affirming, accessible, and patient-responsive care. A mixed-methods study will be conducted in collaboration with patients and providers to compare treatment experiences across populations and settings. Findings will generate actionable evidence to strengthen treatment models and promote services that better align with patient needs. Aim 3: Pilot Research Projects. Four pilot research projects will be funded through the TRANSFORM Pilot Research mechanism, developed in collaboration with ViP Panel members. These projects will address patient-centered research priorities. The ViP Panel's consultation with researchers, input on research questions, and alignment with the community's needs will be instrumental to the success of this initiative. By project completion, we anticipate a patient-engaged evidence base for SUD treatment services, ensuring they are more responsive and inclusive of the community needs. This initiative represents a collaborative effort involving academic institutions, clinical partners, and community members, with the overarching goal of positively impacting SUD treatment services. Our ultimate aim is to enhance the mental and physical health of the community affected by substance use disorders. This study is part of the NIH’s Helping to End Addiction Long-term (HEAL) initiative to speed scientific solutions to the national opioid public health crisis. The NIH HEAL Initiative bolsters research across NIH to improve treatment for opioid misuse and addiction.

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY Diabetes affects more than 463 million people worldwide and significantly increases the risk of stroke, as well causing greater neurovascular injury in response to ischemia and compromising recovery. Cerebral vascular integrity is critical for preventing stroke and ameliorating the lasting effects of brain ischemia, should it unfortunately occur. Nonetheless, a critical barrier to our progress in reducing the morbidity and mortality of diabetics is our lack of understanding of the mechanisms that predispose them to increased stroke risk, exacerbated neurovascular injury, and impaired recovery. Mechanisms that underlie the vascular damage in diabetes vary widely, but the O-linked β-N-acetylglucosamine (O-GlcNAc) modification (O-GlcNAcylation) is significant given the perpetual state of hyperglycemia that is hallmark of the disease. O-GlcNAcylation is a ubiquitous post-translational modification that alters target protein function, activity, subcellular localization, and stability, and is executed by two enzymes: O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA), which add and remove O-GlcNAc, respectively. Acutely, O-GlcNAcylation serves as a form of stress signaling. On the other hand, chronic O-GlcNAc modifications are harmful to vascular function and have been reported in the vasculature from wide range of pathological conditions including diabetes and obesity. Importantly, it has been reported that a high fat diet augments basilar artery O-GlcNAc expression and this is associated with increased neurovascular injury after middle cerebral artery occlusion. However, it is unknown whether cerebrovascular O- GlcNAcylation, and the two O-GlcNAc enzymes, have a causal role in (i) predisposing diabetics to stroke, (ii) worsening the stroke-induced neurovascular injury, and (iii) impairing functional recovery after stroke. To address this gap in the literature, we have composed this Stephen Katz ESI R01 with the central hypothesis that deficiency of OGT will protect against pre-stroke parenchymal arteriole dysfunction, post-stroke neurovascular injury, and improve chronic recovery. On the other hand, insufficiency of OGA will exacerbate pre-stroke parenchymal arteriole dysfunction, post-stroke neurovascular injury, and worsen chronic recovery. We will test this hypothesis by executing the following approaches: In vitro we will culture primary cerebral microvascular endothelial and vascular smooth muscle cells with high glucose and palmitate. In vivo we will predominantly use a high-fat diet/low-dose streptozotocin model of diabetes, and stroke will be induced via thromboembolization of the middle cerebral artery. Measurements of cerebrovascular integrity, neurovascular injury, and behavior will be executed pre-stroke and post-stroke. Some mice will also be maintained long-term after stroke to evaluate chronic recovery. In summary, while this Stephen Katz ESI R01 application represents new research directions for our lab, our fresh insights and rigorous application (including an excellent support team of co-investigators and significant contributors) could potentially transform the fields of diabetes and stroke research by revealing cerebrovascular O-GlcNAcylation as a causal mechanism and novel therapeutic target.

NIH Research Projects · FY 2025 · 2024-09

Project Summary: With the great success of combination antiretroviral therapy (cART), older adults (>50 years of age) now account for 30–50% of HIV-1 seropositive individuals in high-resource countries; a prevalence that is expected to reach approximately 73% by 2030. Critically, older HIV-1 seropositive individuals exhibit a higher frequency of neurocognitive impairments (NCI) relative to their younger counterparts. Neurological complications of HIV infection are the biggest challenge facing HIV researchers, but currently, there are no efficacious treatments for HIV-1-associated neurocognitive disorders (HAND). An innovative regenerative “dendritic spine-targeted” therapeutic approach is proposed to address the neuropathologic hallmark of HAND: synaptodendritic neuronal injury. The hypothesis is that a regenerative “dendritic spine-targeted” approach will alter the longi- tudinal trajectory of HAND (i.e., delay progression of NCI) by restoring synaptodendritic integrity via the NogoA-NgR3/PirB-RhoA signaling pathway. The specific aims are: 1) To establish efficacious “dendritic spine-targeted” therapeutics to address HIV-1-induced synaptodendritic loss and spine dysmorphol- ogy. Factorial design experiments will establish neurorestoration at the synaptic level following treatment with the promising isoflavandiol estrogen S-Equol (SE) as well as a specific estrogen β-receptor agonist (SERBA, i.e., AC-186) in sex-specified brain cell cultures. Given the well-established role of the NogoA-NgR3/PirB-RhoA signaling pathway in synaptic function and dendritic spine growth/retraction, we will employ pharmacological and molecular approaches to investigate whether this pathway mechanistically underlies neurorestoration. 2) To establish the in vivo efficacy of SERBAs to alter the longitudinal trajectory of HAND in the HIV-1 Tg rat. We will promote restoration of neurocognitive function with SERBA therapy (initially SE, a metabolite produced via the gut microbiome following ingestion of soy isoflavone daidzein). Having utilized cross-sectional studies to optimize the treatment conditions of SE to mitigate NCI, we are poised to establish the in vivo efficacy of SE to alter the progression to NCI using a longitudinal design; the factor of biological sex is integral to the experimental design. 3) To establish in vivo the neural mechanism by which SERBAs exert their therapeutic effects. Using a time-sequential longitudinal experimental design, we will examine the regenera- tion of synaptodendritic integrity in pyramidal neurons of the medial prefrontal cortex (mPFC) and medium spiny neurons (MSN) of the nucleus accumbens following SERBA therapy. Using in vivo pharmacological and molecular approaches, the NogoA-NgR3/PirB-RhoA signaling pathway will be examined to establish the underlying neural mechanism. With recently established models, methodological advances, proof-of-concept publications, and new preliminary data, we are in a unique position to critically test: 1) the efficacy of SERBAs to alter the longitudinal trajectory of NCI, and 2) the neural mechanism by which SE exerts its therapeutic effects. Notably, SE is a dietary metabolite affording high translational relevance and promising clinical utility.

NIH Research Projects · FY 2025 · 2024-09

A major problem in research and practice is that bilingual students in the U.S. are often misidentified having language and reading disorders. Both over- and under-diagnosis of disorders have serious consequences, including long-term reading difficulty and potential high school dropout. This highlights a critical need for valid, accurate diagnostic protocols to reliably identify language and reading disorders among young bilingual learners. Consequently, the long-term goal of the proposed research is to establish practical, accurate diagnostic protocols for identifying language and reading disorders among bilingual learners early in children’s development. Representing the next step toward this goal, the overall objective of this project is to generate assessment protocols that have evidence of reliability and validity for identifying language and/or reading disorders among Spanish-English bilingual learners in kindergarten to 2nd grade. Through annual assessments of children’s decoding and linguistic comprehension skills in Spanish and English, we will integrate information from static bilingual assessment, learning-based dynamic assessment, and parent/teacher report to identify accurate, reliable markers of language and reading disorders. Three specific aims will be addressed. First, we will develop an empirically derived protocol to identify word reading difficulty (WRD; also, dyslexia) among Spanish-English bilingual children across kindergarten through second grade. Second, we will develop an empirically derived protocol to identify developmental language disorder (DLD) among Spanish-English bilingual children across kindergarten through second grade. Third, we will identify how heterogeneity in reading ability influences links between word reading, oral language, and reading comprehension, measured continuously. This work is significant because it will substantially advance diagnostic practice for identifying language and word reading disorders among bilingual learners, contributing to accurate diagnoses to prevent students from falling behind their peers during the early elementary years. Early identification of bilingual students likely to have difficulty in school allows educators to allocate specialized resources and individualized instruction to maximize gains for those students. This work is innovative because it fills a critical need to establish classification protocols for both WRD and DLD with evidence of reliability and validity for young bilingual children, focuses on bilingual learners with a wide range of English and Spanish exposure, use, and proficiencies, and integrates cutting-edge dynamic and static assessment procedures (including consideration of student responsiveness to intervention) to improve the reliability and validity of diagnostic classification, and considers language and reading development simultaneously to improve identification of subgroups of children with reading difficulty.

NIH Research Projects · FY 2025 · 2024-09

ABSTRACT Asian Americans (AAs) are the fastest growing racial group in the US. Although the prevalence of smoking across AA populations is about half of the national average, disaggregated data reveal a different picture: males of Vietnamese (24.4%), Korean (19.3%), and Filipino Americans (20.6%) report high smoking prevalence, up to two times higher than other Asian ethnic subgroups such as Chinese or Asian Indian males. New approaches are required to more effectively promote smoking cessation, such as appealing to people with the highest smoking rates. Health communication is an effective way to enhance population health outcomes, including tobacco use. Targeted health communication, developing messages that reflect unique characteristics of target audience, can be a key strategy to promote smoking cessation. The proposed research aims to assess the effectiveness of two different targeting strategies: deep (e.g., content reflecting target audience’s value systems) and surface structure (e.g., featuring similar-looking models) targeting in anti-smoking messages. The long-term goal of this research program is to inform effective and efficient intervention development that will enhance public health outcomes. The objective will be addressed in two aims: (1) Examine the acceptability of targeted anti-smoking messages among Korean, Vietnamese, and Filipino American smokers using in-depth interviews; (2) assess the effectiveness of surface and deep structure targeting strategies among Korean, Vietnamese, and Filipino American smokers using an experiment. The proposed research will use four types of anti-smoking messages: a) deep and surface structure targeted; b) deep structure only targeted; c) surface structure only targeted; and d) non-targeted messages. Study 1 will use in-depth interviews to examine how AA smokers respond to messages with different targeting strategies. Study 2 will use 2X2 between-subject online experiment to assess the effectiveness of deep and surface structure targeting strategies by observing how outcome variables differ across the conditions. We will examine the main and interaction effects of the two targeting strategies, as well as their relative effectiveness. The proposed research is innovative because this is one of the first studies to apply analytical approaches based on message effects theories to systematically manipulate specific message components to assess the effects of various targeting strategies, including their relative effectiveness. Also, this study is the first to evaluate message effects using a volumetric choice experiment that estimates individual-level price elasticity and cigarette demand, in addition to other self-reported measurements.

NIH Research Projects · FY 2025 · 2024-09

We will conduct innovative linkages between our state cancer registry, all-payer claims data, and mental health treatment sources from the South Carolina Integrated Data Warehouse to create a population-based cohort of cancer patients (including < 65 years of age) that will allow us to comprehensively examine utilization of emerging and novel targeted therapies, the joint and individual effects of treatment and co-morbidities, and multi-level contributors across multiple domains to cancer survival trajectories. While we have made significant improvements in cancer prevention and control since the 1950’s, a significant number of individuals do not receive novel treatments such as targeted therapies. Lung and colon cancer survival can be significantly improved with utilization of novel and emerging targeted therapies. Furthermore, a comprehensive (including diagnosis and all treatment courses) and national cancer surveillance system that includes younger cancer survivors (< 65 years of age) is completely impossible in our current cancer registry infrastructure. Consequently, the goal of this investigation is to examine novel targeted therapies, effects of co-morbidities, and modifiable, multi-level contributors on treatment outcomes and survival trajectories. To achieve this goal, we propose the following specific aims: 1) to examine patterns of novel targeted therapies for lung and colon cancer patients by varying access to care and their impact on survival trajectories., 2) to examine the individual and joint effects of cancer treatment and concurrent co-morbid diseases (CVD, diabetes, depression, and opioid use disorder) among cancer patients by varying access to care on survival trajectories, 3) by applying geospatial approaches to examine multi-level and system-level contributors (biological, behavioral, socio-cultural, environmental, physical environment, and health systems) to survival for lung and colon cancer patients to identify modifiable targets for intervention, and 4) disseminate and translate our research findings into multiple levels of cancer care and among a community of stakeholders. Through the extensive networks and community partners previously established by this outstanding, inter-disciplinary research team, we will partner with our community advisory panel to interpret and disseminate these findings throughout professional and lay communities in order to identify targets for future intervention at the individual, community, and policy level. The SC-Midlands Chapter of the American Cancer Society and South Carolina Cancer Alliance will be key partners in our dissemination efforts. In this way, our application is poised to make a significant impact on cancer survivorship.

NSF Awards · FY 2024 · 2024-09

This project aims to serve the national interest by enabling the "convergence" of the public, private, and education sectors to prepare learners for the "Cybersecurity Convergence" workforce (Cyber-Con2). Studies consistently show that demands for cybersecurity workers exceed supplies by large margins and the gap is still increasing. The gap is exacerbated by the rise of information technology (IT) systems and virtual activities. Moreover, the attacks on industrial control systems (ICSs) are frequently observed as plant operations and are more interconnected. The security of operational technology (OT) and ICSs is increasingly important for national security. This collaborative project intends to develop Cyber-Con2 workforce to meet the cybersecurity needs in the region and reduce the gap. The project plans to develop educational and training material in the form of virtual lab libraries for OT/ICS and IT cybersecurity. The materials will be adopted by high schools, community colleges, and universities in the Carolinas. Additionally, learners will have the option to earn industry credentials with support from the private sector. The project will modernize the Academic Cloud with new equipment for cybersecurity. The Academic Cloud is a scalable, purpose-built system for education, training, and research. The project will run an internship program conducted at businesses and governmental agencies and organize tutorials with several communities of practice (COPs) to upskill professionals and train instructors. The COPs include the Cyberinfrastructure Engineering community, the Industrial Cybersecurity COP, the community of users of FABRIC (NSF funded Adaptive Programmable Research Infrastructure for Computer Science and Science Applications project), and multiple training centers. This project is funded by the Advanced Technological Education program that focuses on the education of technicians for the advanced-technology fields that drive the Nation's economy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Pathfinding problems are a common issue in computation. Pathfinding is the search that a computer program makes for the shortest route between points. It is common is a very common issue in computation in the natural sciences and mathematics, who may have multiple potential pathways that use considerable computational power or even human intervention to test. Therefore, finding efficient solutions to pathfinding problems is crucial for technological advancement. Recent research has shown that the combination of two artificial intelligence methods, namely machine learning and heuristic search, can, in principle, solve any pathfinding problem without human assistance. This project aims to scale this approach to more complex pathfinding problems by investigating novel ways of applying machine learning to heuristic search to create an approach that learns to adapt to the problem at hand. This will result in computational approaches that can efficiently solve a wider array of real-world problems. Search algorithms have been integral to artificial intelligence. Heuristic search, specifically, has played a crucial role in problem-solving across diverse applications including robotics, chemical synthesis, quantum computing, theorem proving, and puzzles. However, the effectiveness of heuristic search is contingent on accurate heuristic functions, which estimate the cost to solve a problem from a given state. Constructing heuristic functions often requires domain-specific knowledge which may entail months or years of research, depending on the application. To address this limitation, recent research explores the use of deep neural networks and reinforcement learning to learn heuristic functions, achieving optimal or near-optimal solutions in various domains. Despite these advancements, learned heuristic functions face challenges in scaling with increased problem complexity, leading to inefficient solutions or failure in finding a solution. This project aims to enhance the scalability of heuristic search by investigating four novel applications of machine learning to heuristic search: (1) dynamic heuristic functions -- heuristic functions that can update themselves based on search instead of remaining static, (2) heuristic ensembles -- combining multiple heuristic functions in to one, (3) learning how to search -- use machine learning to learn better search algorithms, and (4) learning in bidirectional search -- use machine learning to learn better bidirectional search algorithms. The project will use puzzle solving, chemical synthesis, and theorem proving for evaluation. 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.