North Carolina Agricultural & Technical State University

NSF Awards · FY 2024 · 2024-09

This research project is a collaboration between two Historically Black Universities, Howard University (HU) and North Carolina A&T State University (NCATSU). Type 2 diabetes (T2D) is a progressive disease of glucose homeostasis caused by various genetic and environmental factors. The most common pathway toward T2D is a failure of insulin-secreting pancreatic beta-cells to maintain normal blood glucose concentration as the body’s insulin demand increases due to obesity and aging. Clinical studies have shown that glucose concentration gradually increases before frank diabetes and then sharply rises at the onset of the disease, supporting the hypothesis that there exists a threshold for diabetes progression in terms of glucose concentration. Glucose concentration, however, cannot be used as the threshold for progression to diabetes. This project aims to identify mathematical structures for the mechanism underlying the sharp rise of glucose at the onset of diabetes. The project will also investigate the threshold behavior of diabetes and its dependence on genetic and environmental factors. The dynamics of the sharp rise of glucose will be identified by the diabetes progression feature of model. To reveal the dynamic structure of each patient’s diabetes intervention and therapy, the PIs also find personalized parameters by parameter estimation on short timescales and use those parameter estimates to inform progression on long timescales. Personalized treatment will be captured by the parameter estimation feature. The novelty of the current study lies in this approach of both revealing the dynamic structure of progression and estimating parameters with one model, which will potentially enable the implementation of personalized treatment. Diabetes educators will be able to use the theoretical threshold to motivate patients to keep lifestyle intervention. The project will provide STEM major graduate students in each institution with excellent research experience in mathematical biology. Workshop and conference presentations and manuscript preparations will lead these graduate students to a deeper understanding of the material while also cultivating their communication skills in interdisciplinary environments. Integration of research results and assets will facilitate outreach to local high school students and lead to additional avenues to recruit domestic students to STEM education. To enhance understanding of the mechanisms underlying the threshold behavior of glucose concentration and blood glucose dynamics over the course of progression to T2D, the PIs use mathematical models, which incorporate secretory capacity of beta-cells to regulate beta-cell function and beta-mass, and insulin sensitivity to reflect body weight gain. The first part of this project is to construct a theoretical threshold that is identified by a slow manifold that plays a role of separatrix between diabetes and non-diabetes in the physiological model. The second part of the project is to validate the proposed models with a longitudinal clinical data from studies of Southwest Native Americans. Additionally, the project includes building of a data-derived slow manifold that will be used for personalized therapy. The theoretical threshold that is developed by mathematical tools and is validated with a historical clinical data set will give guidelines to design biological and clinical studies of diabetes. In particular, targeted metabolic parameters related to progression to T2D will be estimated by the theoretical threshold before implementing the experiments, so the degree of environment factor will be selected for precise experiments. This project has the potential for broad social and economic impacts and health benefits by facilitating new treatment methods for diabetes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Understanding how various environmental pollutants affect our health and well-being is crucial for creating healthier communities. This project focuses on how exposure to a mix of pollutants, such as chemicals found in everyday products and industrial waste, impacts stress levels in both individuals and neighborhoods. Unlike previous studies that look at one pollutant at a time, this research examines the combined effects of multiple pollutants, providing a more comprehensive picture of environmental health risks. Importantly, this study also explores how social, economic, and lifestyle factors influence who gets exposed to these pollutants and how they affect different communities. For example, people living in certain areas or with certain socioeconomic backgrounds might be more vulnerable to harmful exposures. By using publicly available health and environmental data, the project aims to develop better ways to assess and manage the risks associated with multiple pollutants. The results are being disseminated to help policymakers and public health officials create more effective strategies to protect public health, especially in communities that are most at risk. The insights gained from this study not only benefit local communities but also offer valuable knowledge applicable to regions facing similar environmental challenges worldwide. Moreover, this project enhances research capabilities at North Carolina A&T State University, the nation’s largest Historically Black College and University (HBCU), by providing valuable research opportunities for students and fostering a learning environment that encourages scientific discovery and innovation. The exposome encompasses all the exposures an individual encounters throughout their life and their effects on health. Despite extensive research on single pollutants' effects, there is limited understanding of the impact of exposure to multiple pollutants simultaneously and the role of social, economic, and behavioral factors in exposure risk and stress-related outcomes. This interdisciplinary DISES-EX project aims to investigate how these factors influence exposure to and effects of multipollutant mixtures, including PFAS, metals, PAH, and PCBs, on individual and neighborhood stress. By leveraging publicly available health, environmental, and socioeconomic data, the project analyzes complex interactions between multiple pollutants and contextual factors shaping exposure patterns and health outcomes. Advanced statistical techniques are used to understand the relationships between these pollutants and stress within an integrated socio-environmental system. The study seeks to provide a comprehensive understanding of the collective impact of these pollutants on stress and to address current deficits in multipollutant modeling by incorporating social, economic, and behavioral frameworks. The findings offer valuable insights for researchers and policymakers, promoting a holistic approach to community health and well-being. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The growing dependence of organizations on cloud cyberinfrastructure (CI), coupled with the intrinsic on-demand and elastic nature of the cloud CI, have widened the attack surface and made it an attractive target to rapidly evolving cyber threats. The development of fairness-aware Artificial Intelligence (AI) and machine learning (ML) based security solutions can make cloud CI more resilient and trustworthy. However, a key pillar of a successful secure cloud adoption necessitates scientific research workforce training. This project aims to train the future research workforce to develop and use AI-based cloud CI cybersecurity solutions that are fair, ethical, and unbiased. In addition, the project aims to instill the ability of the workforce to adapt and evolve these AI based cybersecurity solutions for cloud CI to improve their trustworthiness and resiliency, as new adversary models are discovered. The technical innovations of this project address the growing needs for a fairness-aware AI-skilled secure cloud CI research workforce in two-fold. First, the project will develop and integrate seven advanced experiential learning modules, referred to as AI4SecureCI, for secure cloud CI using fair and explainable AI concepts into undergraduate and graduate curriculum, training around 500 diverse participants including faculty and students directly. The developed AI4SecureCI modules will include the concepts of network security, authorization and automated access control, online malware detection, classifying malware threats, adversarial attacks and defenses, bias and fairness, and explainable AI, relevant to cloud CI. These modules will include the (1) lecture materials to provide conceptual knowledge for AI4SecureCI, and (2) hands-on lab exercises to provide practical experience. To support hands-on labs and enable wider adoption of the modules, the team will utilize ready-to-use datasets created from their own cloud CI security research and public security datasets, and free-tier cloud services such as AWS Educate. Second, the project will ensure broader adoption, via student boot-camps and series of faculty workshops of developed advanced AI4SecureCI and computational data-driven methods, into underrepresented groups of CI users and contributors to foster research advancements for evolving cloud CI security threat vectors. The advances made under this project, both in terms of research, modules developed, as well as training material will be made publicly available on a project website. The team will collaborate closely with the NSF ACCESS program to enhance the dissemination of knowledge and expertise within the CI community by incorporating the AI4SecureCI modules into the ACCESS Knowledge Base. "" This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

While the research on securing IoT software and systems has made significant progress in recent years, educational offerings in this area have not kept pace. This lag can be attributed to several factors, which include a lack of wide access to IoT software and the essential infrastructure needed to develop cybersecurity curricula and teaching materials, and a lack of active learning platforms for students such as IoT specific Capture-the-Flag (CTF) systems. Moreover, existing CTF platforms have many pedagogical, functional, and inclusiveness limitations. To mitigate current educational shortcomings, this project will design and host a next generation CTF platform. It will have profound broader impacts, including: (1) enhancing the education and training of the next generation of cybersecurity researchers in topics related to IoT software and systems security; (2) preparing educators and practitioners who will have deep theoretical understanding and practical skills in IoT software and systems security; and (3) involving partner institutions in the project that will utilize the project's outcomes to enhance their cybersecurity curricula. This project will advance the state of knowledge in IoT software and systems security education pedagogy and platforms. The key intellectual merits include the following. (1) Student-centered pedagogy in software and systems security education that will involve students in designing CTF and defensive challenges, while tracking and supporting students' progress by automating the feedback process. (2) Inclusive pedagogy in software and systems security education. (3) Development of the PwnIoT.Academy, a next generation student-centered CTF platform. (4) Development of IoT CTF and defensive challenges for different architectures and software platforms. (5) Collection of extensive data on student learning, which will enable a better understanding of the capabilities of the platform as well as identification of persisting challenges to cybersecurity education and workforce development. This project is supported by the Secure and Trustworthy Cyberspace (SaTC) program, which funds proposals that address cybersecurity and privacy, and in this case cybersecurity education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy. 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.