Mississippi State University

NSF Awards · FY 2025 · 2025-06

The construction industry is estimated to make up $400 billion in the US economy, employing over 5.5 million workers. However, construction work often suffers from low efficiency, where reworks due to defects, quality deviations or construction errors result in over almost $75 billion in wasted costs. Clearly, robotics and automation holds the key for the future of construction in order to deliver building projects in a way that is more accurate and efficient compared to conventional labor-intensive methods. This project proposes a framework for construction robots to scan and create schematic maps for highly dynamic and rapidly changing construction environments. Building accurate maps of construction sites is of great importance from both a robotics perspective and a construction management perspective. From a robotics perspective, semantic map building empowers robots with the capability to navigate and understand complex, dynamic workspaces. From a construction management perspective, having updated maps of the jobsite offers improved visibility in automating and scheduling construction projects and the capability to run construction work simulations and “what-if” scenarios. This project involves a confluence of research from robotics, machine learning, architecture, and civil and construction engineering. The overarching research goal is to achieve long-horizon map building, motion planning, and simulation in dynamic, unstructured construction sites through a merging of Building Information Modeling (BIM) technology from the construction industry with 3D mapping technology in robotics. The research tasks involve developing incremental semantic map building algorithms for construction environments, using the generated BIM maps to assist robot motion planning on jobsites, and training time-dependent neural rendering models to recreate and replay construction events for robot simulation purposes. The project aims to achieve broader impacts through annual cross-disciplinary workshops, development of a Construct-Gazebo simulator, open-source tutorial software, and an interactive BattleCrane challenge. 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

Complex systems—like transportation networks, power grids, and social platforms—often behave in surprisingly similar ways, even though they arise from very different fields. For example, the flow of traffic and the distribution of electricity both follow fundamental rules, such as conservation laws and flow dynamics. Despite these shared principles, research into these systems tends to remain siloed within specific disciplines, missing opportunities to leverage common insights. This project seeks to bridge these gaps by creating graph-based computational tools that uncover shared patterns in diverse system behaviors, enabling scientists to reuse knowledge across fields and reduce redundant efforts. Beyond studying individual systems, the research explores the dynamics of interconnected systems, where interactions between networks can produce unexpected and complex outcomes. For instance, controlling a disease outbreak might fail unless social awareness and behavioral changes are also addressed. Similarly, adding a new road to ease traffic could unintentionally make congestion worse. Another example involves electric vehicles (EVs): their simultaneous charging near busy highways can overload local power grids. By examining these interactions holistically, the project aims to develop tools that predict and prevent cascading failures, optimize resources, and strengthen system resilience. A strong emphasis is also placed on interdisciplinary education and outreach to prepare the next generation of researchers to tackle challenges in managing interconnected systems, benefiting society at large. This research develops a unified framework to analyze general graph dynamics and interconnected networks through three primary objectives. First, to develop a theoretical framework by deriving a generalized, interpretable framework for graph dynamics using advanced methods such as sparse symbolic dynamics to extract governing equations, probabilistic methods to model uncertainties in dynamic behaviors and uncover hidden patterns, and optimization techniques leveraging shared physical principles like conservation laws and flow dynamics. Second, to model interconnected systems by investigating how dynamics in one network, such as disease-spreading networks, affect others, like social awareness networks. To address the non-linear and often counterintuitive behaviors of interconnected systems, the project will create high-fidelity digital twin simulations that integrate domain-specific knowledge with probabilistic models. Established techniques like Lyapunov exponents will also be adapted for networked systems to enable early prediction and precise intervention. Third, to conduct community engagement and outreach by promoting awareness of the societal benefits of this research by introducing new interdisciplinary topics, connecting a variety of application domains, and ensuring the research outcomes have a broad and lasting impact across fields. Together, these efforts lay the groundwork for advancing our understanding and management of complex behaviors in interconnected systems, offering solutions to real-world challenges while fostering interdisciplinary collaboration. 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 · 2025-04

This project supports a two-day meeting for leaders and researchers from the National Science Foundation (NSF) CyberTraining and Strengthening the Cyberinfrastructure Professionals Ecosystem (SCIPE) programs. The event will be held alongside the meeting for the NSF Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program. It will serve as a platform for participants to advance the mission of the CyberTraining and SCIPE programs by exchanging progress and experiences in training Cyberinfrastructure (CI) professionals, disseminating best practices in training CI professionals among the CyberTraining and SCIPE communities, emphasizing the importance of these programs to colleagues from the CSSI program; and creating opportunities for collaboration to advance the training of CI professionals both within the CyberTraining and SCIPE communities and across the CSSI community. The CyberTraining/SCIPE PI meeting aims to advance the community-building efforts of previous workshops by providing a platform for principal investigators (PIs) to share technical information about their projects with peers, NSF program directors, and other stakeholders, including the CyberTraining, SCIPE, CSSI, and Computational and Data-enabled Science and Engineering (CDS&E) communities. These workshop interactions will support the continued development of CI and SCIPE professionals, who will go on to advance, deploy, and utilize the nation's CI resources and services. These professionals will accelerate research in science and engineering, furthering the impact of NSF support. This meeting will include multiple NSF programs, implement highly interactive program activities, and encourage reports from participants in small group discussions. These impacts will be assessed by various metrics in a final report, which will be disseminated through public services and circulated to participants and the broader scientific community. 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 · 2025-04

PART 1: NON-TECHNICAL SUMMARY Charged polymers, or polyelectrolytes, are ubiquitous in nature and can be found in many day-to-day products. For example, negatively charged alginates obtained from brown algae are used in food products as thickening and stabilizing agents. Likewise, positively charged chitosan can be found in wound-healing bandages as a blood coagulation agent. The combination of oppositely charged polyelectrolytes in aqueous media can form polyelectrolyte complexes (PECs). The proposed research investigates the application of electrochemical processes to control the complex formation, a phenomenon not widely reported in the literature. Furthermore, the proposed research will explore how the application of an electric field can cause the adhesion of two oppositely charged ionic gels of polyelectrolytes. The factors that control the properties of PECs, the adhesion between oppositely charged ionic gels, and the disintegration of ionic gels and complexes will be investigated by changing the nature of charged polymers, both of synthetic and natural origin. The ability to control the properties of PECs will advance their applications in many areas, including biomedical, adhesives, food, and personal care products. The electric field-induced dissociation of ionic gels can provide pathways for the degradation and reuse of polymer gels. The understanding of the electric field-induced adhesion of ionic gels can be translated into development of adhesives for demanding applications such as biomedical adhesives. The proposed research results will be widely disseminated in peer-reviewed journals and through conference presentations. The new generation workforce will be trained through active participation in cross-disciplinary research activities and coursework related to this research and overall polymer science. New K-12 students will be inspired to pursue STEM education through laboratory demonstrations. PART 2: TECHNICAL SUMMARY This project aims to understand how natural and synthetic polyelectrolytes respond to an applied electric field in both solution and gel form. The underlying factors that dictate i) the electroformation of complexes of oppositely charged polyelectrolytes and their electrodeposition on electrodes through electrochemical processes and ii) the electric field-induced dissociation of ionic gels and adhesion between two oppositely charged ionic gels will be assessed by using state of the art characterization techniques and theoretical frameworks. The underlying mechanisms for electroformation, electrodeposition, electroadhesion, and degradation will be understood and linked to the physicochemical properties of PEs, including their molecular weight and charges per chain. For the electroformation processes, the impact of various factors that dictate the outcome of the process and properties of the resulting PECs, such as the polymer concentration, the ratios of molecular weight and charges of oppositely charged polyelectrolytes, and environmental conditions used, (e.g. doping with salts) will be investigated. The structure of PECs will be determined by using different microscopy and scattering techniques. For the electroadhesion between oppositely charged ionic gels, how the network properties and applied field in tandem affect the adhesion strength via control of the gel-gel interface will be elucidated. The scientific outcomes of this project will be (i) a new pathway for PEC formation via electrochemical processes and (ii) design principles for adhesives through electroadhesion between oppositely charged ionic gels. Overall, a fundamental structure-property relationship for polyelectrolytes subjected to an electric field will be achieved. 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 · 2025-04

Mississippi State University will establish a Research Experiences for Undergraduates (REU) site focusing on Cybersecurity in Emerging Technologies, an essential area addressing the growing threats to modern technological systems. This program will allow undergraduate students to engage in impactful research projects that address real-world cybersecurity challenges in areas such as wireless communication security, privacy-preserving spectrum sharing, AI robotic systems security, explainable malware detection, and advanced network defense mechanisms. Students will work on cutting-edge topics like mitigating jamming attacks in wireless networks, designing explainable AI-based malware intrusion detection systems, and implementing dynamic IP mutation strategies to secure modern infrastructures. This REU site aims to recruit and mentor students from underrepresented groups, including those from community colleges, Historically Black Colleges and Universities (HBCUs), and primarily undergraduate institutions (PUIs). Through this initiative, students will gain hands-on research experience, technical skills, and critical thinking abilities, preparing them for impactful careers in cybersecurity and STEM fields. The program aligns with national priorities to secure digital ecosystems and promote diversity in STEM, ensuring that underrepresented groups play a vital role in addressing critical cybersecurity challenges. This REU site will host ten undergraduate students each summer for a ten-week research program centered on advancing cybersecurity knowledge and skills. Students will participate in a series of challenging research projects designed to develop innovative solutions for emerging threats in various domains. For example, students working on wireless network security will investigate jamming-resistant medium access control protocols, leveraging MATLAB simulations to analyze the effectiveness of these algorithms under different attack scenarios. Those focusing on spectrum sharing will develop machine-learning models to detect interfering and anomalous signals while ensuring privacy through homomorphic encryption techniques. Other projects will explore defenses against adversarial attacks on vision-language navigation systems in robotic platforms, equipping students with expertise in tools such as ROS and PyTorch. Additionally, students will work on explainable malware intrusion detection systems using artificial immune system theories, enhancing cybersecurity technologies and AI model explainability. Another critical area of research will involve dynamic IP mutation strategies to defend networks against cyber-attacks, with experiments conducted in software-defined networking environments using Mininet and Ryu controllers. Students will develop essential research skills throughout the program, including experimental design, data analysis, programming, and scientific communication. Mentorship by experienced faculty members and collaborative team projects will ensure students receive comprehensive training. The program also promotes professional development through weekly seminars, technical presentations, and training in responsible research practices. This REU site will prepare students to contribute to national security and economic prosperity by focusing on emerging technologies and addressing pressing cybersecurity challenges. The program aims to inspire and support students from underrepresented groups, fostering the next generation of leaders in cybersecurity research and innovation. This project is jointly funded by the Office of Advanced Cyberinfrastructure and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Farm-worker availability is declining. Autonomous agricultural machines are critical to food security and the upskilling of manual farm work. This planning project will facilitate subsequent work to improve research infrastructure. The project will accomplish this goal through the establishment of a research consortium that will facilitate jurisdiction-wide improvements in Mississippi's research infrastructure. The establishment of the research consortium subsequently will facilitate the development of STEM faculty and students in engineering, agricultural/biosystems, computer science, and agronomic disciplines. This will thereby allow the participants to research and develop autonomous solutions to agricultural problems where labor is essential but unavailable. The final, fully developed project has potential to innovate STEM-education opportunities for K-12 and post-secondary students and develop the workforce by creating high-tech jobs in agriculture. The jurisdiction and project team members will have increased physical and human capacity for research in autonomous agricultural machines. The project will support the nation in being better able to provide for national and global food needs and advancing in becoming a world leader in this type of research. Mississippi State University will lead the planning efforts to develop this project, through engagement with ten collaborating partners. These statewide partners include universities (Alcorn State, Belhaven, Delta State, Jackson State, and Mississippi Valley State), colleges (Tougaloo), and community colleges (East Mississippi, Mississippi Delta, Northwest Mississippi, and Northeast Mississippi). Autonomous agricultural machines include aerial and terrestrial robots and teams performing agricultural tasks. They are critical to food security, considering the declining availability of farm workers. The planning activities for this project include cataloging existing infrastructure to support autonomous agriculture needs across the state, catalyzing the development of a jurisdiction-wide consortium, and a series of jurisdiction-wide meetings to facilitate long-term project development. The potential intellectual contributions of the fully developed E-RISE project include integrating advanced technologies to produce worker-robot farming systems; mechanics and end-effectors for agricultural robots; communication and AI-based computation in remote, rural environments; and accurate perception and decision-making in agricultural fields. Additionally, related infrastructure and human talent will be improved across Mississippi, including enhancements to computing facilities, internet capacity, and project-specific data sharing capabilities to advance simulation for autonomous systems, creation of digital twins, and development of AI for autonomous detection and decision-making. Project plans involve hiring faculty and coordination staff to ensure progress and maintain timelines. The breadth of participants in both the planning stage and the fully developed stage of the project potentially will lead to heightened research capacity integrated with training and education across multiple institutions including universities, colleges, and community colleges, most being Historically Black Colleges and Universities or Minority-Serving Institutions. This project is funded by the NSF EPSCoR Research Incubators for STEM Excellence (E-RISE) Research Infrastructure Improvement Program. The E-RISE RII Program supports the development of sustainable research infrastructure capacity in EPSCoR jurisdictions through hypothesis-driven or problem-driven research and workforce development to improve competitiveness in a selected STEM field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This EPSCoR Research Fellowship project aims to establish a sustainable interdisciplinary collaboration between the lead researcher's renewable energy condition monitoring group at Mississippi State University and Professor Yelena Yesha’s artificial intelligence (AI) and distributed systems cybersecurity group at the University of Miami. The project focuses on revolutionizing the management and operation of distributed wind systems (DWSs). DWSs are wind turbines of varying scales increasingly deployed in rural areas to generate clean, renewable energy. By developing innovative technologies for condition monitoring, data sharing, and cybersecurity, the project conducts market-inspired research to address critical challenges facing the distributed wind industry, including high operational and maintenance costs and cybersecurity vulnerabilities. This research serves the national interest by advancing renewable energy technologies, enhancing grid reliability and resilience, and supporting rural economic development. The project’s interdisciplinary approach, which combines expertise in renewable energy, AI, and blockchain technology, will push the boundaries of multiple fields while creating practical solutions for the wind energy industry. Beyond its technical contributions, the project will provide valuable educational opportunities for students, particularly those from underrepresented groups, in cutting-edge renewable energy technologies. By fostering innovation in DWSs, this research has the potential to lower electricity costs, create jobs in rural areas, and contribute to a more sustainable and resilient national energy landscape. The project’s primary goal is to develop a comprehensive federated learning framework for the condition monitoring, data sharing, and cybersecurity of DWSs through interdisciplinary collaboration. The research focuses on three main themes: (1) Collaborative federated learning algorithms for condition monitoring to enable privacy-preserving analysis of SCADA data across DWSs; (2) Secure data sharing and auditability, leveraging blockchain technology to create a tamper-resistant ledger for operational data; and (3) Blockchain-enabled automated maintenance operations, integrating smart contracts to streamline maintenance scheduling and decision-making. The project will employ advanced techniques in AI, edge computing, and distributed systems to create a scalable, secure, and efficient monitoring solution for DWSs. Key methodologies include developing communication-efficient federated learning algorithms, implementing a permissioned blockchain network with appropriate access controls, and designing edge computing architectures for real-time data processing. The lead researcher's expertise in renewable energy condition monitoring will be complemented by Prof. Yesha’s world-class knowledge of distributed blockchain systems, secure federated learning frameworks, and AI techniques. This collaboration aims to produce innovative solutions that address the unique challenges of DWSs, including anomaly detection models, a decentralized federated learning-based blockchain platform for secure data sharing, and edge computing infrastructures for real-time monitoring. The outcomes of this research are expected to significantly reduce operational expenditures, enhance cybersecurity measures, and improve the overall reliability and performance of DWSs, contributing to their wider adoption and integration into the energy grid. 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 S-STEM Research Hub will contribute to the national need for a well-educated STEM workforce by researching factors that influence the retention and graduation of high-achieving, low-income students with demonstrated financial need. Building on a collaboration among Mississippi State University, North Dakota State University, Indiana Wesleyan University, University of North Alabama, and Texas Tech University, the project will develop a strategic alliance among Rural Serving Institutions (RSIs) to collaborate and conduct research aimed at increasing rural, low-income college students’ success in STEM majors and participation in STEM careers. In particular, the alliance seeks to build capacity to conduct research about important aspects of belonging that can develop, accommodate, and support the graduation of domestic, rural, low-income STEM students. As a result the project will inform ways that RSIs can support the growth of the STEM workforce in rural communities. Specific project activities include building and managing Rural Serving Institution Network Groups to gather and analyze data and insights from the experiences of rural, low-income students who are participating in the NSF S-STEM program. The Research Hub will also provide capacity-building and technical support for STEM faculty at RSIs to conduct education research about the role of belonging in rural student persistence, graduation, and STEM employment. Research results about interventions that better support rural, low-income student success and contribute to the social and economic well-being of the rural communities they serve will be disseminated to the broader community of Rural Serving Institutions. The overall aim of this project is to address the existing gap in rural STEM higher education research about how to support rural, low-income students, who face specific challenges in enrolling in, persisting in, and completing STEM degrees. Three project goals guide the project's efforts. First, is to conduct research through Rural Serving Institution Network Groups that gather and analyze data and insights resulting from the experiences of rural, low-income students participating in S-STEM projects. Second, is to provide capacity-building, technical support, and strategic alliances for researchers at RSIs, including Summer Institutes and Network Group facilitation, to contribute to the collective understanding of rural, low-income students’ enrollment, persistence, and completion of STEM degree programs. Third, and finally, is to disseminate research to share information about what works and what does not for STEM students who are both rural and low-income. This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. 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 Engineering Research Initiation (ERI) award supports research that aims to understand why and how sinkholes form above defective buried pipes. Sinkholes induced by defective buried pipes have become a substantial threat to community safety, resulting in fatalities, damage to buildings and infrastructure, and economic loss. However, the collapse mechanism of sinkholes above buried pipes remains unclear, making prediction of sinkhole collapses difficult. In this project, the research team will identify the most susceptible soil and water flow conditions around defective pipes leading to sinkhole initiation and develop models to predict their growth. Currently, the US has more than one million miles of pipes nearing the end of their service lives that pose a sinkhole risk to their communities. This project will expand our understanding of formation and collapse mechanisms of such sinkholes to provide predictive capabilities. The research will also be complemented by establishing a responsive and flexible educational and outreach program based on curriculum development and K-12 summer camps that can motivate young students to pursue their college education in STEM fields. The research goal of this project is to predict sinkholes caused by defective buried pipes based on understanding of progressive formation and collapse mechanisms. To achieve this goal, the objectives are to (i) identify critical hydrogeological parameters leading to initiation of soil internal erosion around defective pipes through soil erosion tests in the laboratory; (ii) model the time-dependent progressive evolution of sinkholes induced by defective buried pipes using a sinkhole simulator; and (iii) develop an analytical method to predict the collapse timing and size of sinkholes considering external loads based on time-dependent soil arching models. This project will test three hypotheses: (1) internal soil erosion around defective buried pipes initiates when the ratio of hole diameter to D85 (i.e., 85% percent fines) of soil infill surrounding the pipe reaches a threshold value related to the hydraulic gradient; (2) sinkholes in a submerged zone will be cone-shaped with an angle from the horizontal direction that equals to the surrounding soil friction angle; and 3) sinkholes above the submerged zone will be dome-shaped, and collapse of the dome will be exaggerated by external loads. This project will allow the PI to advance knowledge of the sinkhole formation and collapse around defective buried pipes and to lay a firm foundation for his long-term career focused on safety and sustainability of buried civil infrastructures. 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

The emerging Human-Robot Collaboration (HRC) is reshaping the traditional construction processes, introducing new uncertainties and complexities, and leading to unforeseen interactions and safety concerns. In traditional construction contexts, research into cognitive risk factors such as mental fatigue, stress, and attention has been a focal point for ensuring worker safety. However, as we shift into the realm of HRC in construction where novel challenges and safety and health risks surrounding the construction workforce have emerged, there's a noticeable gap in understanding and explaining how these cognitive risk factors manifest and interplay. The existing literature on human cognitive risk factors in HRC is very limited, overlooking the complexities and roles of the human component in HRC and offering inconsistent insights on key cognitive aspects. The impacts of HRC on worker safety pertaining to mental fatigue, stress, and attention remain largely unexplored, presenting an urgent and critical need for research and intervention. Neglecting the understanding and analysis of cognitive risk factors in HRC can lead to unforeseen safety hazards and health issues and further hinder the successful adoption and efficiency of HRC in construction. Our long-term goal is to accelerate and pioneer the development of human- centric approaches to achieving safe and efficient collaborations between humans and robots in the construction sector. This includes a profound emphasis on the analysis of human cognitive factors to bolster construction occupational safety and health, leading cutting-edge workforce development strategies. The overall objective of this application is to determine the impact of levels of human-robot collaborations on workers’ (male and female) cognitive risk factors to enhance safety in HRC. Two specific aims are proposed: (1) Determine the impact of the autonomy levels of HRC on construction workers’ mental fatigue, attention, and stress; (2) Determine the impact of the involvement of HRC in construction on male and female workers’ cognitive risk factors and determine the difference (if there is). We will employ Virtual Reality to simulate construction environments that encompass distinct levels of robot autonomy (including no robot). Participants, comprising both males and females, will be equipped with wearable technologies that will continuously capture data (eye movements, electroencephalography, heart rate, and skin temperature) pertaining to their cognitive states during the tasks. Both within- subject and between-subject experimental designs will be utilized to gain comprehensive insights. The expected outcomes are that we will have determined the influence of robot autonomy levels on construction workers' cognitive states and further have gained insights into the differential impacts of HRC on male and female workers. The expected outcomes will contribute to the r2p initiative by not only offering strong evidence to enhance safety measures and promote effective HRC implementations but also positing that the strategic integration of robotics in construction can potentially foster a more inclusive construction environment and alleviate workforce shortages. The outcomes are expected to contribute to the strategic goals ‘improve workplace safety to reduce traumatic injuries’, ‘promote safe and healthy work design and well- being’ and ‘reduce occupational musculoskeletal disorders’ and benefit the health and safety cross-sectors of healthy work design and well-being and also musculoskeletal health (e.g., robots do the tasks with high musculoskeletal risks).

NSF Awards · FY 2024 · 2024-09

Civil and construction engineering students may encounter various construction components, including structural elements, materials, equipment, and operations, in their everyday lives, such as when walking in an urban environment. These unstructured observations can offer great learning opportunities; however, without expert support, it is unlikely that students will effectively learn from such observations on their own. If educators were physically available during students' everyday activities, they could direct students’ attention to the main construction components and explain their observations in real-time. However, since this is not feasible in the real world, this project aims to design, develop, and test a transformative learning system that uses Artificial Intelligence (AI) as an on-demand educator. The envisioned AI-enhanced learning system relies on a digital platform in the form of a mobile application. When students face a construction project, they can look at the project through their smartphones using the mobile app, which will help students learn from their observations by 1) directing their attention to the main construction components they encounter in their everyday life or formal site visits, 2) explaining the observations, 3) linking the observations to students’ formal engineering education materials available on web-based learning management systems, and 4) generating automated reports about students’ observations and performance for instructors to help them adjust the course activities accordingly. To promote equity and accessibility in education, the mobile app will be designed to operate on the most basic and affordable smartphones and will use color palettes compatible with the needs of users with color vision deficiency (CVD), along with subtitles and audio narrations. The envisioned AI-enhanced learning system will be designed based on the Activity Learning Theory, which asserts that the human mind is an integral part of environmental interactions and positions activity—whether sensory, mental, or physical—as a precursor to learning. The AI-enhanced platform will be designed based on human-centered principles and will operate using a novel hybrid image-audio processing system that can efficiently and effectively recognize and classify various construction components. In addition to integrating imagery and audio data through this novel hybrid approach, the project will introduce two major technological innovations in audio processing and sound recognition. First, the hybrid use of collected audio and imagery data will improve the overall performance of the system by capturing a more comprehensive range of construction components and operations. Second, by using innovative audio processing and signal source separation algorithms, the need for multiple microphones will be eliminated, enabling the entire system to be encapsulated in a single device (i.e., a student’s smartphone) with the ability to sense and analyze audio signals from distances of up to 100 feet. Throughout this project, the proposed AI-enhanced teaching and learning approach will be implemented in multiple undergraduate construction engineering courses to empirically evaluate its effectiveness on students’ learning processes and outcomes, as well as the perceptions of both students and educators regarding this innovation as a formal pedagogical method. Although the AI-enhanced learning platform will be developed in the context of construction engineering, the proposed learning method and the intellectual merit of this project can be transferred to other disciplines. This project will also assess the broader applicability of the proposed innovation. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. 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

With the advent of Industry 4.0, cyberinfrastructure (CI) technologies have been increasingly reshaping the construction management and safety (CMS) industry. The Internet of Things (IoT) revolutionizes data acquisition by seamlessly connecting devices and sensors on construction sites. Machine Learning (ML) and Deep Learning (DL) dramatically transform data analysis, enabling predictive insights and decision-making. Robotics technology enhances construction automation, significantly improving efficiency and worker safety. Furthermore, advancements in cybersecurity are crucial for protecting against the increasing threats of cybercrime, securing data integrity across construction operations. Despite the transformative potential of these advanced CI technologies, the CMS industry remains one of the least digitized sectors globally. This lag in digital adoption can be largely attributed to a lack of exposure to these technologies and a deficiency in the necessary skillsets required for their effective implementation. Recognizing this gap, this project is plans to develop CI training modules tailored specifically to the CMS domain. The overarching aim is to equip trainees and students with practical, industry-specific skills. It will encompass four key training modules: integration of IoT, ML/DL, robotics, and cybersecurity within the CMS field, covering essential aspects such as hardware, software, networking, and understanding cyber-attacks and defenses. Key tasks of this project include a) developing and conducting in-person ConstructionCI training sessions each summer of the project period, b) creating interactive online training modules that will be available permanently under the Creative Commons Attribution (CC-BY) license, c) integrating these training materials into both undergraduate and graduate curricula at the home institution of the investigators. The anticipated outcome of this project is to equip students with advanced ConstructionCI skills, fostering innovation and driving the digital transformation within the construction industry, thus preparing them for the future demands of this evolving sector. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The broader impact of this I-Corps project is the development of an unattended production line to produce different kebabs with various meat, seafood, and vegetable combinations. In 2021, U.S. domestic per capita red meat and chicken consumption was 208 pounds, of which chicken (46%), beef (28%), and pork (24%) comprised the greatest fractions. The U.S. also has the largest seafood market globally, with $70 billion consumed in 2017. Shrimp had the greatest retail supermarket sales ($4,921 million) in 2020–2021. However, the U.S. meat and seafood industries face challenges in enhancing profitability due to international market competition and overproduction. Moreover, the outbreak of COVID-19 revealed a sharp decline in the labor force for the entire food industry. For U.S. meat and seafood processors to stay profitable and competitive, an increased level of automation is needed for value-added production. Currently, a kebab production line needs 5-10 operators for the manual preparation process. Realizing an automated process can save labor costs. Moreover, with appropriate adjustments, this automation system can be applied to all U.S. meat and seafood species. 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 an automated handling and feeding process to automate the skewering operations for meat cubes, vegetable slices, and shrimp that would be difficult to realize via commercially available production means. Currently, most skewering machines need manual placement and alignment of food items during processing. Fully automated skewering machines have high error rates and high manufacturing costs and cannot handle and process shrimp. The newly developed system is the first unattended kebab production line to process shrimp, and the first design using a horizontal skewering operation to produce meat and vegetable kebabs and shrimp and vegetable kebabs. The solution can be freely integrated with skewering machines, which have the potential to benefit the U.S. meat and seafood processors by minimizing labor dependence and costs. 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

This project establishes the Mississippi Research Alliance (MRA) as a nexus for the state’s research ecosystem, leveraging existing strengths, expanding networks, and creating new opportunities to enhance Mississippi’s research and development (R&D) competitiveness. With a focus on overcoming barriers to access and sustainability, MRA will mobilize the entire jurisdiction to bridge critical gaps, enhance physical infrastructure essential for academic-led R&D, and encourage partnerships that connect comprehensive research universities with Emerging Research Institutions, state agencies, and public and private organizations. MRA creates an inclusive R&D ecosystem by broadening opportunities, lowering entry barriers and ensuring a diverse group of researchers can contribute to scientific progress. The project bolsters the state’s technology-driven research enterprise by fostering a collaborative, multidisciplinary R&D ecosystem that can attract top scientific talent. Ultimately, this project will drive economic growth through scientific and technological advancements, making significant strides towards a knowledge-based economy that will improve the quality of life for Mississippi residents. This project, led by Mississippi State University in partnership with Mississippi Valley State University, The University of Mississippi, and The University of Southern Mississippi, is overseen by the Mississippi EPSCoR jurisdictional steering committee (JSC). MRA’s vision is to be a transformative force in the Mississippi research and innovation ecosystem. MRA will forge strategic partnerships that harness and enhance existing human and physical assets and coordinate new investments to position Mississippi as a leader in science and technology, fostering an ecosystem where innovation thrives through collaboration. MRA efforts are divided into three cores: Strategic Governance, Sustainable Shared Instrumentation, and Administrative. The Strategic Governance Core will expand the JSC to enhance statewide resource coordination, accelerating economic growth grounded in scientific research and development. The Sustainable Shared Instrumentation Core will improve the impact and longevity of core facilities within the state by addressing existing barriers to access and sustainability and promoting the use of these facilities across Mississippi. The Administrative Core will cultivate and expand interdisciplinary team networks that foster effective collaboration and resource sharing to facilitate seamless knowledge exchange throughout the R&D ecosystem. This core will also use an expanded networking strategy to engage key individuals, groups, and organizations to amplify collaborative efforts supporting the MRA’s mission. These distinct but highly integrated cores will collectively address infrastructure gaps and integrate essential components to foster a thriving ecosystem. This will ensure Mississippi’s continued progress, competitiveness, and capacity to tackle the complex challenges facing our state and Nation. This project is funded by the NSF EPSCoR Collaborations for Optimizing Research Ecosystems (E-CORE) RII Program. The E-CORE RII program supports jurisdictions in building capacity in one or more targeted research infrastructure cores that underlie the jurisdiction’s research ecosystem. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The frequency of different extreme environmental events poses a significant threat to the health of people, livestock, and agriculture by causing economic disruption that affects local communities. For instance, heat and drought are expected to reduce soybean yields by 40%, and these effects are predicted to be particularly severe in most Southern US soybean-growing areas. To address this challenge, this project brings together a team of scientists, educators, social and economic researchers, extension specialists, and outreach professionals. They will assess the impact of changes in the environment on soybean yields, from the cellular level to the whole plant, as well as on associated microbial communities, utilizing advanced technologies and artificial intelligence. The team will also develop solutions to boost soybean yields. The impacts of this project will be evaluated by social and economic scientists. The project also focuses on education and workforce development. An important goal of this project is to train and support students, teachers, and early-career researchers. The project provides training for teachers and students and will reach thousands of K-12 students annually. The results of the project will also benefit other crops and improve food security both in the US and globally. By integrating innovative research with education and community outreach, the team will build a sustainable future for agriculture and positively impact affected communities. To combat the decline of soybean yield, this initiative conducts extensive research from "single cell to field-based phenomics." The team includes 11 STEM experts, social and economic scientists, extension specialists, and outreach professionals. The project aims to enhance soybean resilience to heat and drought through five key strategies by generating single-cell level data, using advanced sensing to collect detailed morphological and physiological data, evaluating soil chemistry, root structures, and microbial communities, utilizing network science and Artificial Intelligence to find novel RNA markers and beneficial microbes, and testing selected markers and microbes in field conditions. The project aims to enhance sustainable soybean production by understanding stress responses from cellular to field levels. It integrates data from diverse scientific disciplines to develop precision agriculture solutions and assess their impacts. Moreover, the project employs a comprehensive, multipronged approach with eight programs to train a STEM workforce. Overall, the project aims to advance knowledge from single-cell -omics to phenomics, develop strategies that integrate data from various scientific fields and technologies, provide precision agriculture solutions using cultured microbes in field conditions, and assess the impact on a range of communities. Moreover, it will broaden the pipeline for individuals to enter STEM research careers. 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

Drought has large societal impacts in West Africa, however there is little known about the natural variability in rainfall through time because of the lack of paleoclimate reconstructions from this region. In addition, climate model projections of drought in the future are inconsistent, which is a challenge for drought risk management. Thus there is a strong need for reconstructions of past drought in order to characterize the natural variability and how this variability may have changed with human-caused climate change. This project will utilize field schools in Ghana and Senegal to collect samples and contribute to dendrochronology capacity building in West Africa. The goal of this project is to collect samples from Senegal and Ghana from four tree species that have been shown to be useful for paleoclimate reconstruction. The sample collection will be through a yearly field school funded by the project, and will include training of local partners. The data will be used to reconstruct hydroclimate through time, and will be incorporated into the West Sub-Saharan Drought Atlas gridded drought reconstruction. The project will estimate hydrological and agropastoral drought and investigate drought variability on seasonal and centennial timescales at high resolution. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This award focuses on obtaining a better understanding of the structure of the atomic nucleus and working towards a predictive model of the atomic nucleus as a function of proton number, neutron number, and energy. The objective of the research program is to determine properties of medium-mass nuclei (mass numbers from ~30 to ~80) and ranging from stable nuclear systems to exotic, neutron-rich nuclear systems. Each of the nuclei to be studied has predictions of exhibiting multiple shapes, long-lived nuclear states called isomers, and/or they are relevant to other rare nuclear phenomena such as neutrinoless double-beta decay or double-gamma decay. Through experimentally quantifying nuclear properties directly related to these phenomena, the research addresses the question, “How do the rich patterns observed in the structure and reactions of nuclei emerge from the interactions between neutrons and protons?”, as outlined by the broader nuclear physics community in “A New Era of Discovery: The 2023 Long Range Plan for Nuclear Science.” The PI and graduate students in the group will perform experiments at US based facilities such as the Facility for Rare Isotope Beams, the High Intensity Gamma-Ray Source, and John D. Fox Laboratory at Florida State University, and build on the PI’s experience with experimental nuclear physics techniques and detection systems employed at each laboratory. The objective of this project is to determine properties of excited nuclear states and their decays, such as excitation energies, lifetimes, branching ratios, and transition strengths, in medium mass nuclei ranging from stability to extremely exotic. This information is targeted for nuclei that are predicted to exhibit multiple shapes, i.e., so-called shape coexistence, and/or show effects of shell evolution caused by the emergence of effects from intruder orbitals, altered shell gaps, and cross-shell excitations. Comparisons of results with modern shell-model calculations will aid in the determination of the underlying nuclear configurations and serve as a stringent test of theoretical predictions. By studying 72Ge, 72Ga, and a host of neutron-rich nuclei approaching N = 28 and N = 50 using state of the art nuclear detection systems, a thorough approach is taken to characterizing this region of medium-mass nuclei where shape coexistence and intruder orbital effects are predicted to occur yet remain to be fully quantified. This research will also have an important impact on the education of young scientific researchers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2024 · 2024-08

Nearly two-thirds of all hospital-related infections are associated with biofilms, and Staphylococcus epidermidis biofilms are responsible for 40% of infections in hip and knee replacements. The first step in biofilm formation is bacterial attachment to a surface. This process is mediated by components on the cell wall and the implant surface. Understanding the mechanism of bacterial surface attachment and methods to prevent it could lead to novel and innovative approaches for preventing biofilms. The extracellular autolysin protein (AtlE) strongly binds with polystyrene and serum-coated surfaces, and inhibiting this binding reduces surface attachment and biofilm formation. Moreover, the R2ab subdomain of AtlE, binds not only polystyrene but also staphylococcal cell wall components. While polystyrene is a valuable model for studying surface attachment, it is brittle and unsuitable for biomedical implants. This project will extend prior investigations to a more clinically relevant surface, poly- methylmethacrylate (PMMA), a material used in orthopedic and dental implants (bone cement). Prior work has shown that nanoparticles are a valuable model for studying many aspects of protein-surface interaction. Their colloidal stability and high surface-to-volume ratio enable studies of protein behavior when nanoparticles are present. By examining protein binding to PMMA nanoparticles, the structural and biophysical determinants that influence bacterial attachment during biofilm formation will be identified. Strong preliminary data demonstrate that structural rules for these protein-surface interactions can be determined and manipulated to reduce bacterial attachment and subsequent biofilm formation. In Aim 1, these “rules of PMMA surface attachment” will be established using biophysical experiments. The structure and orientation of extracellular S. epidermidis proteins on PMMA surfaces and serum-coated PMMA surfaces will be determined. In Aim 2, a novel biomaterial surface functionalization strategy will be developed that reduces protein binding and could dramatically slow biofilm formation on PMMA surfaces. This strategy will be developed using PMMA nanoparticles and tested on commercially available bone cement in an in vitro biofilm reactor. Finally, in Aim 3, the R2ab domain will be used to localize photothermally active nanoparticles to biofilms in an in vivo animal model, testing whether targeted near-infrared photothermal therapy is a viable treatment for biofilms. Two models will be tested, including a wound model and an osteomyelitis (bone infection) model. The goal of this basic and preclinical research is to understand how S. epidermidis surface proteins lead to biofilms on medically relevant surfaces. The mechanistic details of biofilm formation on PMMA surfaces will improve the understanding of how biofilms form; the new surface treatments will establish a practical approach for slowing down biofilm development; and the targeted nanoparticles will lead to more practical strategies for treating established biofilms. Each aspect of this project addresses a significant challenge in the biofilm field with an innovative and biophysically motivated approach. Ultimately, this research will lead to a more favorable outcome for patients facing a biofilm infection.

NSF Awards · FY 2024 · 2024-08

This collaborative project between seven institutions establishes the Mississippi Nano-bio and ImmunoEngineering Consortium (NIEC) to enhance biomaterials research, education, and workforce development in Mississippi. Partnering institutions include Alcorn State University, Jackson State University, Mississippi State University, Tougaloo College, the University of Mississippi, the University of Mississippi Medical Center, and the University of Southern Mississippi. NIEC will develop new materials, test their interactions with biological systems, and evaluate their effectiveness in treating diseases, with a focus on addressing health disparities in Mississippi and the region, ensuring long-term benefits to the state, region, and country. The project's goals include synthesizing and characterizing novel biomaterials appropriate for safe clinical use, educating and retaining a diverse group of scientists and engineers in Mississippi, and securing sustained funding to advance this work. By building a comprehensive research network, promoting inclusivity, and supporting local biotech startups, NIEC seeks to impact science, healthcare, and the state's economy, creating high-tech, well-paying jobs in Mississippi and fostering economic growth. The project aims to create a robust pipeline of next-generation materials by fostering a collaborative, multidisciplinary research team centered around three research focus areas (RFAs): (i) developing biomimetic materials to modulate nano-immuno interactions via protein corona engineering, (ii) designing polymer nanocarriers for efficient nucleic acid complexation and release, and (iii) developing pathogen resilient bioinspired polymeric scaffolds for tissue regeneration. Leveraging NIEC's expertise in nanomaterials synthesis, physicochemical characterization, and computational modeling, these RFAs will form an integrated design loop that will enhance understanding of the interface between biological systems and nanomaterials, establishing generalizable structure-property-function relationships that support the safe and effective translation of innovative biomaterials into clinical applications. In addition to the three biomaterials RFAs, an evaluation of state policies and regulations influencing the growth of the biotech industry in Mississippi will be conducted. This project will measurably impact the preparation of a diverse research-ready workforce that can foster the economic development in MS through (i) leveraging existing undergraduate, graduate, postdoctoral, and earlier career faculty training; (ii) strategically hiring faculty, providing seed grants, and offering mentoring for junior faculty and postdoctoral researchers; and (iii) promoting the development of intellectual property and its commercialization. This project is funded by the NSF EPSCoR Research Incubators for STEM Excellence (E-RISE) Research Infrastructure Improvement Program. The E-RISE RII program supports the development and implementation of sustainable broad networks of individuals, institutions, and organizations that will transform the science, technology, engineering and mathematics (STEM) research capacity and competitiveness in a jurisdiction within a field of research aligned with the jurisdiction's science and technology priorities. 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

Nanoparticles are increasingly used in medicine, materials, and agriculture. These nanoparticles come into contact with humans during manufacturing or during use by consumers. In any biological system, proteins adsorb on the surface of nanoparticles forming a coating of proteins on the surface of the nanoparticle, often referred to as a protein “corona.” The specific proteins that adsorb on the nanoparticle surface determine the subsequent interactions of the nanoparticles with cells. Understanding the nanoparticle properties that influence the protein corona is essential for determining the toxicity associated with human exposure to nanoparticles and developing new nanomedicines and nanosensors. This research aims to predict protein-nanoparticle interactions based on nanoparticle and protein properties using machine learning combined with mechanistic biophysical experiments. Understanding protein-nanoparticle interactions is vital for industrial and environmental nanoparticle exposures, as well as for therapeutic and diagnostic applications. In addition, this research provides an ideal training platform for students to address fundamental questions of nanoscience using machine learning, providing training relevant to future academic or industry jobs. This research project aims to predict which proteins will adsorb on the surface of nanoparticles and train students in a highly interdisciplinary environment. The research team will first characterize the protein corona as a function of nanoparticle properties and develop a machine-learning workflow for prediction. The team will vary nanoparticle core composition, ligand, diameter, zeta potential, surface area, and hydrophobicity to sample a wide parameter space. Proteomics will be used to characterize the adsorption of serum proteins on the nanoparticles. The team will utilize a set of controlled protein features and biophysical assays (isothermal titration calorimetry and nuclear magnetic resonance) to test the predictions from machine learning. Well-defined protein classes will be used to determine whether corona behavior follows expected predictions made by machine learning. The team will then extend these studies by probing the robustness of machine learning predictions. Challenging mixtures of proteins will be tested, and the observed nanoparticle coronas will be compared to predictions obtained using optimized algorithms. The outcomes of this research will include the proteomics data (shared through ProteomeXchange), machine learning algorithms (shared on GitHub), and a template for recruiting and mentoring undergraduate researchers. TThe ability to predict protein-nanoparticle interactions based on nanoparticle properties will promote the development of nanoparticles for a range of applications and help to determine safe exposure limits. 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

Network dynamics manifest themselves across a wide spectrum of domains, from the propagation of rumors over social networks and the transmission of diseases through human interactions, to the transportation of goods via traffic routes, as well as the circulation of blood through the brain's intricate vasculature. However, despite shared similarities in their underlying physical laws, practitioners in each field have traditionally worked in isolation in the past. This siloed approach has led to a duplication of research efforts, and a lack of understanding of the complex interplay among different network dynamics, such as the dissemination of rumors could amplify the spread of infectious diseases. This project's novelties lie in creating a unified framework to analyze dynamic behaviors across various fields, and developing a comprehensive supporting infrastructure. Through a comparative study spanning multiple domains, this project offers a schema for "analogy learning" and "interplay learning" in interdisciplinary research, serving as an exemplar for exploring the synergies and interconnections among different networks. It opens new avenues for investigating the intricate effects among multiple disciplines. The project's impacts are not limited to individual scenarios such as epidemiology, circuits, transportation, and banking systems; they will also enhance our understanding of coupled dynamics, including the co-evolution of epidemics and rumors, traffic and power systems, and banks and supply chains, among others. Consequently, it will enhance our comprehension of the robustness and vulnerability of interconnected societal infrastructure as a whole. Moreover, the project will foster new avenues for increased dialogue and knowledge exchange across various disciplines, promoting novel breakthroughs. The project outlines a plan to advance the field of network dynamics through the development of a unified computational framework and an experimental platform. It addresses the fragmentation in network dynamics research by integrating various network types and bridging multiple knowledge domains. The central methodologies of this endeavor involve treating each distinct domain of network dynamics as a specific instance within a unified framework, standardizing methodologies by identifying common patterns across diverse network dynamics, and devising a systematic framework that encompasses the structural, routing, and diffusion dimensions of networks. This approach fosters interdisciplinary cooperation, drawing insights from various fields. The project will analyze both similarities and differences among these dynamics, revealing generalizations through shared principles observed in both virtual dynamics (such as information dynamics) and physical dynamics (such as traffic dynamics), as well as homogeneous and heterogeneous dynamics. To engage the community in this emerging topic, the approach begins with theoretical framework prototypes, drawing on concepts from computational fluid dynamics and electrical network theory. Furthermore, the project aims to build upon prior work on open datasets and benchmarks, including the XFlow library and the COPE-ID platform, thereby establishing new platforms for network dynamics. This research advances the technical aspects of network dynamics by constructing a convenient working platform while promoting interdisciplinary collaboration and dialogue, consequently encouraging further interdisciplinary research. Additionally, its open-source nature aims to broadly benefit educational outreach and influence various societal sectors, making significant contributions to network management and research. 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 · 2024-06

The Noyce Track 1 project aims to serve the national need of enhancing STEM education in rural areas, targeting the shortage of certified educators in chemistry, physics, and mathematics. In collaboration with the Louisville Municipal School District, Mississippi State University (MSU) is launching an initiative to recruit, train, and place top-notch STEM educators in high-need schools across Northern Mississippi. Learning from past projects, this project focuses on innovative training of future teachers to address the challenge of keeping certified STEM teachers in rural schools, particularly impacting minority students. By encouraging excellence in STEM subjects, promoting original research, and fostering cross-disciplinary skills, this project aims to prepare educators for the unique challenges of rural high-need schools. As Mississippi’s largest university, MSU leverages its resources for comprehensive teacher training, aiming to make a significant impact on student outcomes. This project extends its influence beyond Northern Mississippi, aspiring to contribute to improved statewide mathematics and science education. Drawing from past successes and insights, this project seeks to be a model for other southeastern universities, addressing the nationwide challenge of recruiting and retaining STEM educators for rural communities. This project at Mississippi State University includes partnerships with the Louisville Municipal School District. Project goals include the recruitment, training, and placement of highly qualified teachers in high-need schools in Northern Mississippi, with a focus on majors in physics, chemistry, and mathematics. Over the next five years, this project seeks to produce up to six new STEM teachers annually, leveraging theoretical foundations that prioritize pre-service teacher training and interdisciplinary thinking. Building upon successful NSF projects at MSU, this project's intellectual merit lies in its innovative approach to STEM teacher preparation, aiming to increase teacher production by approximately twenty percent and impact up to one thousand secondary students annually. Beyond Northern Mississippi, this project aspires to serve as a model for Southeastern universities facing similar challenges in recruiting and retaining STEM educators for rural communities, contributing valuable insights to STEM educator recruitment and retention strategies. This Track 1: Scholarships and Stipends project is supported through the Robert Noyce Teacher Scholarship Program (NOYCE). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K-12 STEM teachers in high-need school districts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2024 · 2023-09

7. Abstract Organophosphate (OP) anticholinesterases, e.g., nerve agents and some insecticides, present a threat to the civilian population via terrorist activity or accidents. These OP compounds or metabolites are potent and persistent inhibitors of central and peripheral nervous system acetylcholinesterase (AChE). High dose OP exposures can lead to seizures, respiratory failure and death. Survivors may suffer from brain damage and behavioral deficits. One of these OP insecticides, phorate, is very acutely toxic making it a potential threat. Unlike the nerve agents, phorate requires bioactivation to its anticholinesterase metabolites (oxons) which results in a delay (4-5 h) in toxic signs (tremors, salivation, and seizure-like behavior). The current therapy (US) for severe OP poisoning includes atropine plus the oxime AChE reactivator 2-PAM. A major limitation of 2- PAM is its relatively short plasma half-life and inability to cross the blood-brain barrier (BBB) and protect the brain. Our laboratories have invented and patented a series of oximes that have shown survival efficacy and attenuation of seizure-like behavior and neuropathology following exposure of rats to high levels of nerve agent surrogates and one OP insecticidal metabolite (paraoxon). The delay in toxic signs following phorate challenge makes the timing of oxime administration and oxime plasma half-life important in establishing an effective therapeutic regimen. Our lead novel oximes have demonstrated longer plasma half-lives compared to 2-PAM which should be beneficial combating OPs that have delays in initiation of the cholinergic crisis. Some of our novel oximes can also effectively reactivate butyrylcholinesterase (BChE) inhibited by the anticholinesterase metabolites of OPs including phorate. BChE is prominent in serum and is inhibited by circulating oxons. An oxime that can effectively reactivate BChE, creating a pseudo-catalytic bio-scavenger of circulating oxons, could prevent or attenuate OP-induced toxicity. The Aims of this project are: 1) Characterize the temporal relationship between ChE inhibition and the phorate toxidrome, and the survival efficacy provided by 3 novel oximes; 2) Demonstrate in vitro (rat and human) and in vivo (male and female rats) with a down- selected lead novel oxime the ability to reactivate phorate inhibited BChE and enhance survival. The challenge dosage of phorate will be lethal (LD99) to rats receiving atropine only. A novel oxime or 2-PAM will be administered at the initiation of seizure-like behavior and at one earlier time point prior to initiation of cholinergic crisis. In addition, oxime-mediated reactivation of OP-inhibited BChE will allow a demonstration that pseudo-catalytic BChE-mediated destruction of OP can attenuate toxicity. A selective BChE inhibitor will be used to confirm the significance of BChE reactivation, as evidenced by a reduction of AChE inhibition and lethality in oxime-treated rats having functional BChE (rats not receiving the BChE inhibitor). The data from this translational project will be used to further develop an oxime into a more effective therapeutic for poisoning by insecticidal OPs, like phorate, that display a delayed toxidrome because they require bioactivation.

NIH Research Projects · FY 2024 · 2023-09

Project Summary Squamous cell carcinoma of the head and neck (SCCHN) is the 6th commonest cancer in the world, leading to >300,000 deaths annually worldwide. The extracapsular extension (ECE) of the tumor in the lymph nodes is a significantly high-risk feature in head and neck cancers. Several studies demonstrate that ECE results in worse survival outcomes. Pathologic confirmation, such as neck dissection, is currently required as the gold standard for clinical identification of ECE. However, if it is ECE positive, postoperative chemoradiotherapy has to be considered. As a result, neck dissection followed by chemoradiation increases the toxicity, especially late toxicity such as fibrosis, and lymphedema can get worse due to the late toxicity. If we can detect ECE during preoperative evaluation, we can select those patients for chemoradiation before surgery. Thus, predicting ECE becomes a piece of critical information for clinicians planning treatment. The biggest obstacles to adopting AI/ML algorithms in the clinic are concerns about security, reliability, and transparency. If an algorithm could be developed to not only provide an accurate prediction of ECE (and/or clinical staging), but also to provide transparent and clinically understandable communication of how the predictive conclusion was reached, then clinicians would more readily adopt a tool utilizing that algorithm to be an ally. The purpose of this proposal is to optimize and pathologically validate AI/ML approaches for prognoses and diagnoses of ECE from medical images on head and neck cancer patients, which could be further implemented as a tool for diagnostic assistance and precision medicine. This translational research project focuses on filling the gap of AI/ML transparency and interpretability in the automated detection of ECE in head and neck cancers. In addition, our interpretable ML algorithm will not require the pre-annotated lymph node areas, which is a quite cost-effective technique by eliminating the time-consuming lymph node annotation process. We propose the following aims: 1) Validate and interpret the image-based ECE diagnosis model and its association with head and neck cancer anatomic organ sites and HPV status. 2) Optimize the cost-effective image-based ECE diagnosis model considering clinical markers within pathology, PETCT, and MRI results for clinical implementation. We will validate our model based on the dataset collected from both our team and existing data collected from The Cancer Image Archive. This proposal aligns with the mission of Oral & Salivary Cancer Biology Program and the NIDCR Special Interest in Supporting Dental, Oral, and Craniofacial Research Using Bioinformatic, Computational, and Data Science Approaches (NOT-DE-20-006). This study will provide the preliminary results of our future research on the precision treatment of high-risk head and neck cancer patients. We plan to submit an NIDCR R01 proposal in Year 2 for the precision treatment of high-risk head and neck cancer.

NIH Research Projects · FY 2025 · 2021-08

PROJECT SUMMARY Voltage-gated sodium channels (NaVs) are essential for action potentials in excitable cells located throughout the body (central nervous system, smooth muscle, heart and skeletal muscle). Loss of, improper, or untimely function, can each cause or contribute to disease. Many individual point mutations in the genes of NaV or accessory proteins have been associated with disease; some of which can be life threatening. Many disease associated mutations are located at or are near NaV accessory protein binding sites; therefore significant effort has been put forth by many investigators to characterize the mechanisms that underlie ion channel gating modification and in physiology and disease. It is well established that Ca2+ alters NaV function, and the Ca2+ sensing protein Calmodulin (CaM) has a prominent role in this process. Structural investigations have identified several distinct CaM-NaV interactions. However, the posited physiological function and interpretation of data are controversial. Early studies relied on measuring NaV function in the absence or presence of Ca2+ and generated seemingly disparate results. Subsequent investigation revealed the mechanism(s) of Ca2+driven NaV modification are complex and involve multiple accessory proteins, thereby rendering much of the data ambiguous. Recently, I identified a high-affinity interaction between CaM and part of NaV that is directly responsible for inactivating NaV conduction. I was able to utilize my in-depth structural characterization to impair the interaction without conferring additional modification to NaV function. This is a notable accomplishment given this part of the channel undergoes rapid conformational change during each functional cycle. Because of this, I could for the first time clearly attribute modified NaV function to reduced CaM binding. My data demonstrate that channels with this reduced CaM interaction require longer to recover from the inactivated state. Considering my structure / function findings with available literature suggest a paradigm of CaM Facilitated Recovery from Inactivation (CFRI). As demonstrated in my recent papers and preliminary data, CaM engages several NaV isoforms with high affinity, suggesting a universal model of regulation. My findings are in direct conflict with other reports that posit models of CaM Dependent Inactivation (CDI) and [Ca2+] insensitivity. These opposing models arise from knowledge gaps regarding (i) the kinetic rates of CaM interactions and (ii) the precise role of each CaM interaction in an excitable cell that contains oscillating [Ca2+]. My proposal addresses these knowledge gaps by uniquely combining structural biology, stopped-flow kinetics, and electrophysiology to dissect the roles of the CaM-NaV interactions in excitable cells. I will then explore if I can alter the kinetics of specific interactions by engineering a small molecule probe. This work will test CFRI (physiology and disease), as well as explore novel strategies for treating NaV channelopathies.