Clemson University

NSF Awards · FY 2024 · 2024-10

The EMPOWERS (Evaluating Mentoring Practices for Optimal Work-life balance in Education and Research in STEM graduate studies) program is an innovative, four-year, multi-dimensional approach that aims to enhance the holistic mentoring environment for faculty and graduate students. EMPOWERS was developed to respond to the emerging crisis in graduate education, which is a result of ineffective mentoring, high levels of distress in graduate students, a lack of inclusion, and a lack of career and professional development. In fact, the majority of faculty are not developed or trained in how to be an effective mentor during their graduate studies; this often becomes on-the-job training in academic positions. While effective mentoring can yield many positive benefits for the graduate student and faculty member, ineffective mentoring can lead to high rates of attrition for faculty and students, mental health concerns, and reduced well-being. This National Science Foundation Innovations in Graduate Education (IGE) Track 2 award to the Clemson University EMPOWERS program will address these issues through two innovative and distinct goals: 1) Promote holistic mentorship, which will include mentorship training, mental health and wellness, inclusion, and career and professional development; and 2) Affect systemic change at the department, college, and University levels through capacity building and policy development related to holistic mentoring. Ultimately, these two goals will lead to advances in the knowledge of effective mentoring practices while employing holistic mentor training at the university level. Grounded in Ecological Systems Theory and the Cultural Framework for Institutional Change, EMPOWERS provides a novel approach to this problem by providing holistic mentor training to both graduate students and faculty members. This holistic mentor training will build upon existing curricula to address mental health, well-being, inclusion, career, and professional development, while developing mentoring plans for graduate students. Development of additional curricula will include an emphasis on responsible and ethical research conduct. The project will also determine graduate student and faculty perspectives on needed policy changes related to holistic mentoring, and how to best implement these changes at the department, college, or University levels through qualitative interviews and focus groups as well as quantitative surveys. This work will lead to the development of broad university policy changes to embed mentoring systemically, both at the graduate student and faculty levels. EMPOWERS team members from the Engineering and Science Education Department and the Graduate School will use the data from this study to build a national model of holistic mentoring. The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader 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 2024 · 2024-10

The manufacturing industry has recently transformed into an intelligent and interconnected ecosystem, fueling many emerging services - such as connected healthcare, smart transportation, and modern manufacturing - to benefit people's daily life. As a key enabler to this new paradigm, smart computing devices (e.g., Industrial Internet of Things) leverage their computing capability and wireless connectivity to form a hierarchical service functionality, providing the ability to perform real-time data analytics, to detect anomalies, and to make predictions to improve the efficiency and quality of manufacturing. Although this paradigm is promising, environmental sustainability issues are becoming increasingly urgent, especially during industry expansion. The substantial increase of new device production and adoption inevitably leads to higher greenhouse-gas emissions, contributing to global warming, which in turn results in economic losses for industries. This project develops a framework, termed sustainable revitalization, to reduce the greenhouse-gas emissions by migrating current devices to their most suitable locations in the service hierarchy for continuing service. As such, computing devices maximize their lifespan by moving around inside a system at each device-updating stage while minimizing their environmental impact through greenhouse-gas emissions by avoiding frequent production and disposal processes during industry expansion. This project also seeks to improve the scientific training of undergraduates and students in the field of environmental science and engineering, computation optimization, and communication, preparing them with the cross-disciplinary skills needed to succeed in the modern workforce. This project introduces lifecycle sustainability into computing-system design and maintenance. By adopting an adapted approach to life-cycle assessment and by constructing new metrics for environmental sustainability as an optimization objective, this project addresses greenhouse-gas emissions during industry expansion. Specifically, this project lays a foundation by establishing a robust system boundary to comprehend the greenhouse-gas emissions of computing devices across various lifecycle stages, thus informing subsequent sustainable-revitalization efforts. To find optimal pathways for device migration, this project pioneers inventory analysis by integrating demand-based migration and compatibility-optimized computation strategies. From a system-design perspective, having these strategies within the system boundary facilitates examining the strategies' effectiveness. Based on the optimization strategies of migrated devices, this project introduces collaborative on-device learning that dynamically adapts smart devices to new scenarios while upholding environmental sustainability and addressing communication challenges through a novel physical-level parallel inclusive communication. This new methodology will help fully utilize obsolete computing and communication devices to meet environmental-sustainability demands. 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

Cyber infrastructure and artificial intelligence (AI) are core components of smart manufacturing in South Carolina (SC), West Virginia (WV), and the United States. To drive radical transformation of industry, factories must securely expand beyond their physical boundaries. These Future Factories (FF) consume and create interdisciplinary knowledge along with the ability to forge innovative technological transformations. This project introduces a novel future cyber-manufacturing paradigm – the Factory-to-Factory (F2F) network framework. Geared towards automation, F2F networks require interoperability of stakeholders and efficient understanding systems for data, information, and knowledge. This collaboration between academia – led by the University of South Carolina and West Virginia University - and industry will produce advanced smart manufacturing technologies and an educated upskilled workforce in SC and WV. Furthermore, it will create a blueprint model for future manufacturing technologies that can be integrated with a F2F network to increase small-scale and industrial manufacturing capabilities across the US. To expand our workforce infrastructure, we will establish a lifelong learning pipeline for smart manufacturing ranging from K-12 education, higher education, to professional development support for scholars and industrial professionals. Particularly, we will create online learning resources and STEM-oriented smart manufacturing summer programs for K-12 students and provide internships for college and graduate students through our industrial partners. This project will adapt, enhance, and integrate informational technologies (IT) and operational technologies (OT) such as real-time secured sensing, high performance computing, wireless communications, and AI, to support process optimization among distributed smart manufacturing systems for F2F. Convergence and true progress can only be achieved by fusing expert knowledge of manufacturing processes with newly emerged hardware and software technologies. The project focuses on manufacturing knowledge stemming from: (1) autonomous feature extraction and recognition from product ‘manufacturing DNAs’ as a novel manufacturing knowledge representation among distributed systems, (2) architecture of interactive cyber spaces that combines cross-platform simulation results within product lifecycles, (3) data-driven control theories during process monitoring leading to rapid autonomous decision-making in replacement of manual input/output modules, and (4) robust business models and operations research (information service-oriented) concerning autonomous Key Performance Indicator (KPI) decomposition among distributed sub-systems and rapid feedback control loops. This will build a foundation for real-time production information sharing and control platforms and facilitate manufacturing knowledge generation and utilization among networked systems to address manufacturing management challenges. Furthermore, it enables human interventions and interoperations in the development and decision-making process of these highly collaborative networked smart manufacturing systems. This project will showcase several novel cyber manufacturing implementations and establish a roadmap towards a universal digital F2F standard. 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

Some metals are essential nutrients for life, while some are non-essential or even harmful to living organisms. Fungi and bacteria are microorganisms that often live in a close association and play a key role in transforming and detoxifying metals in the environment. In spite of this importance, there is relatively little understanding of how the interactions between bacteria and fungi influence the transformation and/or detoxification of metals. The goal of this project is to address this knowledge gap by identifying how fungal-bacterial interactions affect metal transformation. This will be achieved through a novel multidisciplinary research approach employing advanced, state-of-the-science analytical techniques. Knowledge gained through this project will allow the engineered control of metal transformations for a wide range of applications in environmental cleanup, biorefining, production of nanoparticles, and other beneficial applications. Successful completion of this research has strong potential to benefit society through improvements in environmental remediation and industrial manufacturing. This project will improve the Nation’s STEM workforce by providing a unique training opportunity for student researchers that bridges diverse fields such as environmental engineering, microbiology, geochemistry, bioinformatics, and art. Remediation of metal contamination is a major environmental challenge because, unlike many organic pollutants, metal species cannot be degraded and can only be extracted or biotransformed to less toxic forms. While past approaches to biotransform metals have focused primarily on single microorganisms, host-microbiome interactions have shown potential to biotransform surrounding environments and improve host resiliency. However, the mechanisms for metal biotransformation by microbial host-microbiome systems are largely unknown. The overall goal of this project is to elucidate the rules of life that govern fungal microbiomes. This goal will be achieved through a specific focus on fungal microbiomes, which include a fungal host, endosymbionts (endobacteria), and symbionts (exobacteria that live extracellularly) as a model host-microbiome system. The specific research objectives of this project designed to achieve the goal are to: understand the effects of metals and metalloids on the diversity and transmission of fungal microbiomes (facultative and obligatory); and determine the role of fungal microbiomes in metal tolerance by mediating the uptake, transformation, and sorption of metal ions, nanoparticles, or other metal species. A deeply integrated multidisciplinary approach will be used to investigate physiological, genetic/genomic, and metabolic processes that govern the structure and function of fungal microbiomes in the presence of metals. This will be achieved using novel state-of-the-science isotope probing, advanced microscopy, spectroscopy, and integrated genomics, transcriptomics, and metallomics to elucidate how the microbiome influences the metabolic activity of the host towards metal ions. Successful completion of this research has strong potential to identify new genes and/or pathways for metal tolerance and biotransformation, as well as expand our mechanistic understanding of the structure and function of fungal microbiomes in nature. This knowledge has strong potential to benefit society by facilitating applications in remediation, water treatment, electronics manufacturing, antimicrobial production, medicine, and related fields. 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

Artificial intelligence (AI) is the simulation of intelligence using advanced computer algorithms on diverse large-scale datasets including finance, healthcare, consumer and market science, cybersecurity, and transportation. Recently, AI has been emerging as a prominent tool for biological data analyses including assisting with medical diagnostics as well as the detection of disease-related mutations in genetics and genomics. We aim to employ AI to improve and refine gene functional annotations as well as predict functions for previously unclassified genes in Arabidopsis, a model plant system. Given that biological systems including plant-pathogen interactions are exceedingly complex and genes to phenotype relationships require an understanding of diverse layers of biological information, we will utilize a deep learning- and network-based framework that can integrate multiple heterogeneous datasets to obtain more accurate inferences of gene functions. We will validate our computational findings using experimental approaches. Specifically, we will focus on a set of genes that are related to “Sulfur”, which is considered “the 4th major phytonutrient” using genetics and plant pathology approaches. We expect a variety of deliverables to a wide range of users including researchers both nationally and internationally, educators in high schools, systems biology and bioinformatics specialists, and the plant research community in general. This will provide biological insights, gene prioritization, and testable hypotheses to plant researchers. Moreover, we will discern the molecular mechanisms of newly identified genes in plant defense. This project will also significantly contribute towards local education and outreach priorities through a minority-oriented program, PlantGIFT (Plant Genomics Internship For Teachers. Network science and deep learning, a subtype of machine learning enable predictive modeling from large and multi-dimensional datasets and elucidate the complex relationships among various layers of –omics to predict the function(s) of the proteins. We aim to apply a hybrid method encompassing network biology and deep learning computational approach to predict gene functions for the unclassified genes as well as refine the Gene Ontology for the inadequately annotated genes. Specifically, we will generate a suite of diverse co-expression networks using transcriptome studies derived from a wide spectrum of biotic and abiotic stress treatments including plant-pathogen interactions. These co-expression networks will be integrated to transcription factor-targets and protein-protein interaction networks. Network topological features will be extracted from the above-described diverse –omics networks and integrated into the predicted function(s) for each node. Moreover, we will predict Arabidopsis genome-wide gene functions using a deep neural networks-based integrative framework that can efficiently perform network embedding on heterogeneous networks. The precision of these computationally derived gene function predictions will be independently validated through genetics- and pathology-based experimental assays. In particular, we will focus on a GO term “sulfur” and investigate the biological functions of 60 genes and a pair of regulatory transcription factors in biotic and abiotic stresses including plant defense. We will also establish PlantGIFT (Plant Genomics Internship For Teachers) to integrate research, education, and outreach for minority participation in genomics and plant sciences. In summary, this project will generate resources that will benefit the plant research community and local educators. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project serves the national interest by creating visualization and augmented/virtual reality (AR/VR) education modules in architectural engineering and construction (AEC) courses that will help improve students’ persistence in the program, self-confidence, and diversity awareness. Students will work on real world projects in teams using the visualization and interactive AR/VR modules. Social and technical interactions between students will be facilitated using virtual spaces that simulate physical meeting rooms in which students can share engineering artifacts. Student learning will be supported with an interactive building environment, interactive exercises in a 3D virtual environment, and embedded audio/videos that provide feedback on student design decisions. Students will visualize themselves as AEC professionals and increase their self-efficacy starting in K-12 or community college contexts as well as in the first year of their degree program. More advanced real-life engineering experiences will continue to reinforce self-efficacy and engagement in engineering throughout the AEC curriculum. This collaboration between a research-intensive institution, University of Nebraska-Lincoln, and an HBCU, Tennessee State University, will provide an opportunity for students to interact with other students from diverse backgrounds and produce valuable insights on the effectiveness of the education modules with different audiences. A virtual workshop series will be used to support the adoption of the modules by other AEC programs, other engineering disciplines, and high school teachers. Three education modules will be developed and implemented in courses at the collaborating institutions. An introductory module will be used for outreach to K-12 and community colleges to generate interest in AEC fields. The remaining modules will be used for first and second-year AEC students in existing courses. The design of the modules is informed by the Model of Domain Learning theoretical framework including its constructs of stages of learning and phases of interest. Modules will be developed with attention to the learning objectives of the courses and the level of AEC curriculum that is being targeted. The project seeks to answer the following overarching research questions: 1) How does a module impact students' engagement, self-efficacy, and diversity awareness? 2) How do enrollment, retention, and graduation rates change as modules are introduced into the curriculum? and 3) How do the findings to the first two questions vary by institution? Each module will provide the basis for a multi-case study where the institutional context defines each case. Rich case descriptions and cross-case analysis will guide improvements to the module, highlight module transferability barriers and affordances, and enable interpretation of findings based on data from survey instruments and interviews. This project will advance knowledge about the impact of AR/VR-based learning modules in STEM teaching and learning. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project is a planning workshop that will kick off an Advocacy Building Campaign for Engineering Education Research (ABC for EER) to promote the recognition and support of EER by universities and colleges nationwide. The ABC for EER seeks to address the problems facing the field of EER in gaining widespread acceptance and support within academia as a valued field of study. The field of EER includes educators and researchers with experience in the practices of both engineering and teaching and with expertise in education research, psychology, and social sciences. EER focuses on complex problems such as recruiting students in engineering with diverse perspectives and backgrounds, embracing new technologies and approaches to teaching engineering, and preparing diverse types of engineering students for the global challenges they will face as practicing engineers and leaders. Research by EER scholars provides evidence and knowledge to help universities and colleges address these complex problems; yet their work is often dismissed by administrators and faculty in engineering programs at universities and colleges as not being robust or relevant. This project aims to start the process of advocating for EER through a targeted campaign that will clearly articulate the value of EER to scholars outside the field, to leaders in higher education, and to policymakers who make decisions that affect higher education. This project aligns with the focus of the Engineering Education and Centers Division to promote research that creates and supports an innovative and inclusive technical workforce for the future, particularly in terms of understanding systemic and structural changes in engineering education programs, departments and colleges. This planning workshop for the Advocacy Building Campaign for Engineering Education Research (ABC for EER) aims to address the challenges facing the field of EER, which can be characterized as a disconnect between the tangible benefits and impact of EER and the metrics typically prioritized by engineering programs, colleges and universities. These challenges often result in a lack of institutional support and recognition for EER scholars, they hinder the career advancement of EER scholars and the growth of EER as a discipline, and they limit the integration of innovative educational practices within higher education. The planning workshop will convene EER scholars, stakeholders and leaders at different levels of power and influence in higher education, policymaking, advocacy, and legislation. It will be conducted as a series of three sessions structured to build trust, foster collaboration, outline critical paths for implementation, and establish evaluation metrics to assess the success of the ABC for EER. Interim steps between workshop sessions are proposed to synthesize, communicate, and gather feedback on the planning process at different time points. The workshop sessions will be guided by elements of campaign strategy to identify and articulate the value of EER to a broad audience both within and outside of academia, develop a strategic communication plan, and lay out actionable objectives for promoting EER within the higher education community. The two anticipated outcomes of this workshop series are: (1) a list of key stakeholders in the EER ecosystem to participate in the future, ongoing ABC for EER, and (2) a plan for a series of activities to successfully conduct the ABC for EER as an advocacy campaign. 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

To feed the world’s growing population, food production must increase by an estimated 70% by 2050. Achieving food security by minimizing supply fluctuations and adjusting to the growth in food demand presents many challenges that will require major adjustments in current agricultural practices, most importantly in controlled-environment agriculture (CEA). Current CEA facilities consume substantial energy, hence making this technology energy-hungry and preventing their wider adoption. This interdisciplinary Cyber-Physical Systems (CPS) project intends to build a networked CPS together with advanced data analytics and integrated renewable energy and energy storage aiming at reducing the dependence on utility grid and hence energy cost, while optimizing crop production efficiency. This project led by Clemson University brings together a team from agricultural sciences, control systems and computing/data science to create a networked system for CEA, with the goal of improving crop growth and yield while minimizing the energy cost; it enables self-adaptation and autonomy of CEA and advances the frontier of core CPS research. The research results will be integrated into the undergraduate and graduate curriculum development at the institutions involved with students trained on interdisciplinary research and education. The PIs’ partnership with K-12 schools and CEA growers will be leveraged to educate students, mostly from underrepresented groups, and practicing engineers on the development and deployment of CPS technologies in CEAs. This project builds a novel system for multi-scale, cooperative and autonomous sensing, control and renewable energy management to address several fundamental challenges of complex CEA systems, a key step towards fully autonomous and net-zero-energy CEA. The hierarchical structure of this project exploits inter-dependencies of crop physiology, energy systems and environment to advance research in CEA systems aiming at enhancing their resilience. This project outcomes enable a paradigm shift in a number of areas including: (1) integration of photosynthesis models with real-time biophysical measurements for optimizing environmental parameters; (2) automatic monitoring of the crop growth and environmental conditions using advanced AI-guided image and sensor data analytics; (3) automated robot-assisted data collection using novel control approaches for optimal distribution of mobile manipulators over large CEAs with safety guarantees; (4) devising novel stochastic control tools to manipulate environmental parameters to facilitate photosynthesis for each crop species and growth stage. The tight interaction of controllable physical systems with autonomous biological systems and the environment provides an intriguing problem space that can be also useful for a broad range of other cyber-physical systems. 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

Virtual reality (VR) is an exciting technology, but many people cannot use it because it makes them feel sick. This problem, called cybersickness, causes symptoms like nausea, headaches, dizziness, and eye strain, similar to motion sickness in a car. Cybersickness happens when what we see in VR does not match what our body feels in the real world. For example, a person might feel like they're moving in VR but know they are sitting still in real life. This project explores a new way to reduce cybersickness. It explores if people feel less sick when given simple tasks to do while in VR, like listening to music or solving puzzles. These activities may distract users from feeling sick, like how listening to music can help with car sickness. The goal is to help people use VR for longer without discomfort. This project could make VR more accessible for education, entertainment, and job training. It may also encourage and enable more fields to use VR technology.   The project aims to study how periodic distraction tasks affect cybersickness in VR. The project will look at realistic and abstract tasks, how demanding they are mentally, and how long they should last. The project has four main goals: 1) Test how tasks affect cybersickness in different sickness inducing situations in VR. 2) Study how emotions and task length impacts cybersickness and mental workload. 3) Create an artificial intelligence system to automatically distract users at the right times. 4) Use this research to teach college students about VR, cybersickness, and user-centric VR design. The project will involve them in hands-on projects. The team will also work with outreach organizations to introduce VR concepts to a diverse group of students. All the findings, tools, and data from this project will be made available to other researchers and VR developers. This will help create a shared resource of information on using distraction tasks to reduce cybersickness in VR. The project aims to provide practical ways to make VR experiences more comfortable. This could lead to more people being able to enjoy and benefit from this technology. 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 will address key challenges in the life-cycle design of wind-sensitive cable-stayed bridges. Medium- and long-span cable-stayed bridges are gaining momentum in use, given their capability to span distances from 300 to 1000 meters. However, US coastal regions experience hurricanes and other extreme wind events that affect the performance and safety of cable-stayed bridges during construction, service, and long-term conditions. Taking advantage of the digital revolution and the continuous improvement of computer-aided simulations and data-driven design methods, this research will recast the design method for cable-stayed bridges currently used in the bridge industry based on heuristic experience-based design strategies that have relied only on wind tunnel testing or in-situ performance evaluation. A new simulation-based, multi-model, aero-structural design optimization methodology will seek material reduction while keeping the bridge’s required life-cycle performance and safety levels, thus achieving the desired reduction in carbon footprint. This research will synergistically combine education and outreach activities at a minority-serving institution, including curriculum development, training demonstrations in wind tunnel testing, and student tours to local bridge construction sites. This award contributes to the National Science Foundation (NSF) role in the National Windstorm Impact Reduction Program. Data generated from this project will be archived and made publicly available in the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) DesignSafe Date Depot (https://www.DesignSafe-ci.org). This research will develop a novel computational methodology for the aero-structural design optimization of cable-stayed bridges considering multiple phases of their life-cycle under extreme wind loading. The overarching goal is the development of a holistic design methodology that permits further exploring the effects of deck shape modifications on the life-cycle performance of the bridge under extreme winds to achieve a sustainable and cost-effective design while improving the life-cycle aeroelastic performance. The research will synthesize the state-of-the-art capabilities of bridge aerodynamics, linear and nonlinear aeroelasticity models, computational fluid dynamic simulations, machine learning, finite element modeling-based multi-model design, and optimization algorithms. The research objectives include (i) develop linear and nonlinear wind-resistant performance models for the life-cycle design of bridges, (ii) develop multi-fidelity aeroelastic surrogates for the shape-dependent emulation of fluid-structure interaction parameters, and (iii) formulate efficient multi-model surrogate-based aero-structural design optimization strategies. The research will address the aeroelastic life-cycle performance of a cable-stayed bridge when changing the bridge deck cross-section and other key design variables and the life-cycle aero-structural optimum design of a cable-stayed bridge for a particular location, local climate, and project specifications. 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

PROJECT SUMMARY Clinician burnout is a significant concern in Emergency Departments, and with increasing workload, shift work requirements, staffing shortages, and other stress-inducing activities, clinician well-being can be significantly compromised. Burnout is usually preceded or accompanied by prolonged periods of chronic stress, which can impair cognitive function, increase risk of heart disease, diabetes, sleep disruptions, insomnia, increase risk for suicide and substance abuse. Beyond personal well-being, burnout has harmful consequences on patient care and safety, including less time spent between clinician and patient, increased miscommunications, and a potential for increased handoffs. Most researchers have focused on identifying the factors leading to clinician burnout and its impact on clinician well-being, patients, health systems, community, and society. However, there is a lack of research on monitoring clinician well-being and using a mixed-methods approach to develop system- level interventions to prevent burnout. Moreover, there is a research opportunity to employ mathematical modeling for planning clinician shift schedules that consider clinician preferences, clinician well-being, patient safety, patient flow, and other constraints of ED departmental staffing. The purpose of this proposal is to create a framework for redesigning ED nurse and physician shift schedules, integrating clinician preferences and physiological parameters to improve clinician well-being and patient outcomes. The first aim “Analyzing staffing schedules, collecting clinician preferences and monitoring well-being and its indicators” will inform how current schedule planning can be improved. The team will gather and analyze current schedules, operational and safety data, clinician shift preferences, and well-being (burnout and fatigue) using validated surveys (e.g., burnout) and physiological markers (e.g., cortisol) that will both be collected on-site. This multimodal data will help identify associations between clinician stress, fatigue, workload, well-being, patient safety, and patient flow in the ED. These relationships will help inform the importance of specific shift design parameters when developing clinician shift schedules in the ED. The second aim “Incorporating operational factors, clinician preferences, and indicators of well-being into a modeling framework of ED shift schedule redesign” will reduce perceived workflow imbalance and will lower stress levels at the planning stage. By incorporating data sources and relationships between each factor, the shift model can generate clinician functional schedules while aiming to improve clinician well-being, patient flow, and patient safety. To benchmark the effectiveness of proposed staffing schedules, the team will use their recently developed computer-based simulation model representative of the ED at the partner hospital to assess how clinician well-being, patient flow, and patient safety are affected. Additionally, we will gather feedback from clinical leadership and ED providers across the health system to comprehend their likes and dislikes.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT Atherosclerosis is a disease caused by cholesterol accumulation in the arterial intima. Since atherosclerosis causes heart attacks and strokes, this disease is considered to be the leading cause of death globally. Hence, finding more effective treatments for atherosclerosis may reduce deaths that are caused by heart attacks and strokes. Increasing the removal of cholesterol from arterial intimal cells may be a possible approach for treating atherosclerosis. One type of arterial intimal cell that has been proposed to greatly influence atherosclerosis progression is vascular smooth muscle cells. These types of cells can migrate from the arterial media into the intima and begin internalizing intimal cholesterol. However, once vascular smooth muscle cells become cholesterol-filled, it is thought that they lose the capacity to remove excess cholesterol through downregulation of the ABC- transporters, ABCA1 and ABCG1. These two transporters remove intracellular cholesterol from cells by participating in cholesterol efflux. ABCA1 and ABCG1 do not efflux cholesterol identically though, as they interact with various cholesterol acceptors differently to mediate cellular cholesterol efflux. Furthermore, it is not currently known whether ABCA1 and/or ABCG1 expression in vascular smooth muscle cells is atheroprotective, and if increasing expression of these transporters may protect against atherosclerosis. The goal of this proposal is to test whether ABCA1 and ABCG1 expression in vascular smooth muscle cells is atheroprotective. This proposal has two Aims, with both Aims utilizing cell culture systems and atherogenic mice. One Aim will robustly analyze the potential atheroprotective impact of ABCA1 expression in vascular smooth muscle cells by using vascular smooth muscle cell-specific overexpression and knockout models. The second Aim will rigorously assess the potential atheroprotective impact of vascular smooth muscle cell ABCG1 expression by employing models that are either ABCG1 deficient or overexpress ABCG1. The long-term objective of this proposal is to discover vascular smooth muscle cell ABC-transporter expression as being atheroprotective. Succeeding in both Aims would accomplish this objective and demonstrate proof-of- principle that increasing ABCA1 and ABCG1 expression precisely in vascular smooth muscle cells may be a novel strategy for treating atherosclerosis.

NIH Research Projects · FY 2024 · 2024-09

Project Summary Emergency department clinicians (ECs) regularly experience among the highest rates of psychological distress. Manifesting in poor well-being and burnout, EC psychological distress can lead to adverse outcomes for patient care, institutions (e.g., costly attrition), and individual ECs (e.g., illness and death). On-going surveys conducted by the research team since 2020 have collected validated measures of an EC well-being index and burnout score for three types of ECs (attendings, Advanced Practice Clinicians, and residents) at seven emergency department locations at a large academic health system in upstate South Carolina. Locations span urban to rural settings. Our interdisciplinary team proposes to link key contextual factors of institutional stressors (including staffing strain and patient congestion) with coincident contextual factors (including external pandemic status and individual demographic characteristics) to measure and visualize well- being and burnout. Aim 1 will quantify the relationships between stressors and context with well-being and burnout. Generalized linear mixed effects models will consider contributors of each predictor on both outcomes, and factor analysis will compare the relative contribution of each predictor. Machine learning methods will be used to develop a predictive model to evaluate future burnout risk. In Aim 2, we will develop a visualization framework to display predictors and corresponding real-time predictions of well-being. Iterative refinement will improve the visualization channels, user performance, and support accessibility. Mixed-method validation with EC leadership will involve interviews to probe interpretation of data insights with the graphical framework and eye-tracking to evaluate performance and speed. Consistent with the exploratory nature of an R21, the visualization will be developed with EC-informed hypothetical values. A follow-on study will be proposed to integrate the work both aims and to implement and evaluate the use of the visualization to improve interpretation in practice. We anticipate that this research will both spur further work to develop decision- support tools to address modifiable factors to improve well-being and have immediate impact in understanding contributors to EC well-being and burnout. This work seeks to improve EC experience to interrupt the negative feedback loop that drives adverse outcomes in patient care, EC turnover, and institutional goals to ultimately improve EC well-being and patient care.

NIH Research Projects · FY 2025 · 2024-09

ABSTRACT—Cell State Network-Directed Therapy Drug resistance is a significant challenge in cancer therapy and has historically been addressed from “one step behind”, whereby drug(s) become progressively ineffective, resistance mechanism(s) are studied, and then different drug(s) are used. While genetic and other resistance pathways are well-appreciated, so-called cell state plasticity is increasingly understood to be a major determinant of resistance. Cell state is typically defined by a transcriptome pattern, and plasticity is mediated largely by dynamic epigenetic transitions that mimic developmental or tissue homeostasis programs. The different cell states along with the transitions between them comprise a cell state network. Because different cell states can have unique drug sensitivities and resistances, and cells can change state dynamically through the network, cell state networks provide flexible resistance mechanisms. This proposal seeks to design therapies based on cell state network dynamics, such that drug sensitive states are promoted while limiting transitions to drug-resistant states. However, an agnostic experimental screening approach for drug combinations is extremely challenging, even considering just two drugs and the hundreds of FDA-approved anti-cancer compounds, not to mention inter-patient heterogeneity, doses, and timing/sequence. Computational models that predict how tumor cell populations respond to drug combinations could help fill this gap, but so far this has been a challenging problem, even for modern machine learning methods. Our preliminary work shows combining knowledge of cell state transition network dynamics and single drug dose responses could enable computational prediction of how varied drug combinations influence cell population growth dynamics, and how a sufficient and attainable set of data enables unique inference of cell state networks. These motivate our proposal to predict and test “cell state network-directed” therapies, with particular focus on glioblastoma multiforme (GBM), a brain tumor with poor survival and few treatment options. We propose four Aims to study 3 different patient-derived xenograft models of GBM with in vivo-like tumor-chip systems: Aim 1. Develop Glioma Cell State Network Models; Aim 2. Determine How Microenvironmental Factors Alter Cell State Networks; Aim 3. Determine How Glioma Cell State Networks Respond to Single Drugs; and Aim 4. Evaluate Model-Predicted Combination Therapies in Cell Culture and Tumor Chips. We will combine computational modeling with state-of-the-art single cell RNAseq, spatial transcriptomics, and flow cytometry to characterize cell state networks in gliomas and their dynamics, and how they respond to a variety of glioma-relevant chemotherapy drugs. We will then use these models to propose efficacious regimens that not only control growth but favorably modulate cell state transitions, and test them using in-vivo like tumor chips. Furthermore, we will study how spatial arrangements of cell types and co-cultures with primary neurons influence glioma behavior. Although the application is GBM, cell state networks are a universal feature of cancer so findings here may have widespread significance across human cancers.

NSF Awards · FY 2024 · 2024-09

There is consistent industry demand for innovative, globally engaged engineers skilled in biomedical design and innovation. The National Science Foundation supports research in the professional formation of engineers, recognizing that “engineering research and education are critical building blocks for the nation's future prosperity”. Engineering breakthroughs that address global health challenges require students to gain essential global competencies in engineering and health education. One widely successful way students develop global competencies is through international service-learning opportunities. This project will develop a new way for engineering students to gain global competencies that is based on a hybrid model of service-learning, meaning the students will be working together in-person and using video communication platforms to connect virtually. The researchers will develop a new global short course that integrates problem-based service-learning. Over a two-year period, teams of American and Tanzanian engineering students will complete the short course while being placed in hospitals located in medically underserved areas across South Carolina and across Tanzania. By linking international academic and healthcare institutions, this hybrid model will provide students who are unable or unwilling to travel abroad a means of participation while allowing researchers to explore how students gain global competencies in the new hybrid model of service-learning. The Biomedical Engineering Design collaboration at Clemson University in South Carolina and Arusha Technical College in Tanzania focuses on innovation of biomedical technologies in diverse healthcare innovation ecosystems. This collaborative partnership enables international educational opportunities for engineering students and is centered on expanding the global experiences and mindsets in the undergraduate engineering curricula at both academic institutions. Grounded in Community of Practice theory, which views learning as being both situational and participatory, the researchers will develop a new global short-course that integrates problem-based learning using best practices in biomedical engineering design and incorporates existing training materials readily available from the American Society for Engineering Education. The researchers hypothesize that students can gain global competencies through the hybrid practice-oriented learning regardless of geographic location and pose the central research questions for this proposal: How do international practice-oriented learning experiences for biomedical engineering design broaden participation and opportunity access? How do these experiences develop global competencies of engineering students? The project has two specific goals: Goal 1: Develop and test a practice-oriented learning experience to infuse undergraduate engineering education with global competencies defined within the Consortium of Universities for Global Health (CUGH) framework. Goal 2: Describe the similarities and differences in how students with different types of engagement develop cross-cultural, practical engineering, and engineering design skills. The researchers will use a collective case study approach and mixed methods research design to analyze the new hybrid model and understand how international practice-oriented learning experiences in diverse healthcare innovation ecosystems support opportunity access for a broader cohort of students and enrich the attitude of engineers. By establishing a common framework encompassing both countries during the design thinking portion of the course, students will be prepared to make global connections and develop global competencies in the local practical immersion component. This research will advance understanding of different ways that students gain global competencies and how it influences their attitudes and perceptions and approaches for technical problem-solving in engineering and engineering design. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2024 · 2024-09

Project Summary Low-back pain is the primary cause of disability across the world, and over 80% of workplace injuries also occur to the lower back. Wearable robotic technologies, such as back-support exoskeletons (BSE), provide on-body assistance to support and assist the trunk. BSEs are rapidly emerging as a potential intervention to reduce the risk of occupational musculoskeletal disorders. However, user expectations for intimate body-worn devices such as exoskeletons are likely to vary significantly across diverse user-groups representing different sex, age, and racial characteristics. Current design/testing approaches that do not include diverse user perspectives in these early stages of design and development of health interventions such as wearable robotics technologies can lead to ethical challenges in equitable accessibility and technology acceptance. The ethical question addressed in this proposal is whether user perceptions, technology acceptance, and health effects of exoskeletons significantly differ across race, as this is currently undocumented in the literature. Our hypothesis is that important race related differences among men and women in physical characteristics like anthropometry and strength, and psychosocial factors influencing technology adoption, are likely to produce differences in user-acceptance and health benefits of BSEs. We postulate that limited participant selection/representation in current exoskeleton study designs is thus a critical ethical concern that may lead to health disparities if unaddressed. A repeated-measures mixed-methods study, with a total of 80 participants representing men and women of different racial groups, is proposed. Participants will trial two different BSEs and complete a standardized battery of tasks. Our first specific aim is to document and analyze user-preferences and health effects of BSEs for different racial groups. We will characterize race-related differences in physical design preferences, device usability, opinions about BSE usefulness, and physical, psychological, and socio-cultural factors promoting/inhibiting BSE use. We will also quantify racial differences in health effects of BSEs using biomechanical measures of device effectiveness in trunk (primary) and hip/knee (secondary) body regions. Our second specific aim is to explore race-related differences in factors that predict user acceptance of BSEs, using a decision tree analysis to predict exoskeleton acceptance for each racial group. Outcomes from this study will help document the ethical concerns in current exoskeleton design and evaluation methods. Detailed data on anthropometric fit and user-preferences, evidence of their correlations with the subjective and objective assessments of exoskeleton effectiveness, and feedback from our diverse stakeholder group (advisory board) will be broadly disseminated, to guide future inclusive design and use of exoskeletons.

NSF Awards · FY 2024 · 2024-09

Almost every galaxy has at least one supermassive black hole (SMBH) at its center. These giant black holes can grow to more than 10 billion times the mass of the Sun by pulling in gas and stars and by merging with other black holes. Such mergers rank among the most powerful cosmic events, releasing vast amounts of energy in the form of low-frequency gravitational waves. The gravitational waves produced by SMBH binaries contain clues about the conditions in the hearts of massive galaxies, and they can tell us how the SMBHs have grown over cosmic time. This proposal will enhance the scientific potential of gravitational wave measurements by using computer simulations to see how SMBH binaries interact with their surroundings and by predicting how the host galaxies will appear in ordinary telescopes. The team will start a new outreach program to raise awareness of gravitational wave science, through multi-media simulations of gas moving around black holes. These will be shown at sports events in South Carolina. The main science objective is to advance the study of binary black holes across the mass spectrum, by providing new paths to their discovery with electromagnetic and gravitational waves. The investigators will carry out numerical simulations of binary-disk interactions in realistic but unexplored regimes. In the context of massive black hole binaries, they will compute the electromagnetic signatures of binary accretion and derive community tools to help optimize time-domain observing strategies and electromagnetically constrain the massive binary black hole population. They will compute the influence of environmental gas on binary evolution and distribute digital tables of the results to support forward modeling of the stochastic gravitational wave background through population synthesis. The team will explore stellar-mass black hole binaries using hydrodynamical simulations to predict how X-ray binaries evolve in response to dynamical mass transfer. Those results will be incorporated into a community binary population synthesis code, and they will help constrain the provenance of stellar-mass black hole and neutron star binaries that merge as high-frequency gravitational wave bursts. This award advances the goals of the NSF Windows on the Universe Big Idea through research in Multi-Messenger Astrophysics. 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 water delivered to the Arctic Ocean by surrounding rivers impacts physical and biological systems in the Arctic and can influence global climate. Understanding the geological history of Arctic rivers provides insights into how the region has evolved over time scales of thousands to millions of years and can help inform future climate models. This research investigates the geoscientific birth and subsequent evolution of the Mackenzie River, the largest river in the North American Arctic. Sedimentary deposits in the Beaufort-Mackenzie Basin record past river patterns and the timing of important regional events. Results from this work will shed new light on the geologic history of the North American Arctic, including the processes that helped shape the region. This investigation will help train multiple Earth Scientists, enhance U.S.-Canadian Arctic research efforts, and improve connections between U.S. researchers and indigenous peoples in the North American Arctic. Upper Cenozoic strata in the Beaufort-Mackenzie Basin archive the evolution of high-latitude North American river systems over the last 25 million years, with implications for understanding the drivers behind drainage reorganization and changes to riverine freshwater input to the Arctic Ocean. This investigation will test the hypothesis that re-routing of ancient high-latitude river systems during the late Pliocene led to a pronounced increase in sedimentation rates in the basin. The research will: 1) Decipher the Miocene-Pleistocene sedimentary provenance history of the Beaufort-Mackenzie Basin through detrital zircon U-Pb and Nd-isotope provenance analysis. 2) Establish a chronostratigraphic framework for the sedimentary succession using Sr-isotopes that will constrain the timing of provenance changes and assist in evaluating proposed forcing mechanisms. These datasets will provide insights into when and why sediment and freshwater fluxes to the Beaufort Sea varied during the late Cenozoic, which can be used to inform geologic and paleoclimate models. 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 three-year project Research Experiences for Undergraduates (REU) Site: Human-Centered Operations Research and Engineering (H-CORE) is hosted by Clemson University. Nine undergraduate students each year will engage in research experiences that approach problems at the intersection of humans, systems, and analytics. REU projects will span a variety of high-impact, emerging, human-centered applications including disaster response, resilience, healthcare delivery, individualized training, disrupting human trafficking, and equitable access. Students will work with Operations Research (OR) and Human Factors (HF) faculty research mentors. Traditionally, OR develops mathematical models for systems and HF assesses human performance to understand how humans are integrated into the system. The professional development component includes sessions on conducting a literature review, presenting research and writing papers, collecting human-centered data and considerations for working with humans, navigating graduate school and/or job search process and other topics. By integrating OR and HF, REU students will be prepared to conduct interdisciplinary, human-centered research to address complex social and technological problems. This three-year project Research Experiences for Undergraduates (REU) Site: Human-Centered Operations Research and Engineering (H-CORE) is hosted by Clemson University. This REU site will host nine students each year during a ten-week research program. Students from a variety of disciplinary backgrounds can contribute to problems at the intersection of humans, systems, and analytics. Recruitment efforts include targeting students from biomedical engineering, computer science, human-centered computing, IE, and mathematics and from a range of universities and colleges with limited research opportunities. A brownbag meeting series will focus on critical research skills (how to think about interdisciplinary research and read papers from different areas; how to collect human-centered data and considerations for working with humans; effective research communications) and post-undergraduate job and research opportunities (how to navigate the graduate school and/or job search process; CV or resume preparation). By integrating OR and HF, REU students will be prepared to conduct interdisciplinary, human-centered research to address complex social and technological problems. This project is jointly funded by the EEC REU and the Established Program to Stimulate Competitive Research (EPSCOR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The objective of this project is to enhance the functionality of interactive theorem provers (ITPs) in mathematical reasoning by integrating advanced artificial intelligence (AI) technologies with formal methods. This endeavor, named MathScy, aims to assist in conjecture formulation, proof construction, and counterexample finding. The current limitations of ITPs include scalability, adaptability, and the depth of reasoning required to tackle complex mathematical problems. Addressing these issues, MathScy promotes the progress of science and contributes to the national interest by advancing mathematical research and supporting education. The project will utilize the latest advancements in Large Language Models (LLMs) and will be intuitive and accessible to mathematicians, guided by principles of Human-Centered AI and Explainable AI. This approach ensures that the AI tools developed are user-friendly and can provide understandable explanations of their reasoning steps, thereby benefiting the broader scientific community and society. MathScy aims to impact education, society, and science by providing accessible mathematical tools, enhancing education with new teaching methodologies, boosting industries reliant on complex modeling, and by collaborating with a range of institutions including high schools, universities and technical colleges. The project will empower scientists in various areas of theoretical and applied mathematics, fostering collaboration to address problems in Applied, Combinatorial, and Computational Algebraic Geometry, and generating, proving, or disproving conjectures. Technically, the project aims to create a compositional and modular system using fine-tuned Mixture-of-Expert LLMs for mathematical reasoning, integrated with on-demand access to external mathematical tools. The system will incorporate domain-specific human feedback and intuition into the AI-assisted discovery process, generating human-understandable explanations of the AI's reasoning steps. Additionally, the project will develop new standardized representations, datasets, and challenging problems tailored for benchmarking and advancing mathematical reasoning systems. These techniques will be evaluated on significant open problems in mathematics and theoretical computer science, focusing on areas such as Applied, Combinatorial, and Computational Algebraic Geometry. The project will improve AI’s mathematical reasoning capabilities through techniques in mathematical language processing and knowledge assimilation, enhancing conjecture discovery, proof search guidance, and counterexample identification. Leveraging AI and formal methods, this research aims to address problems in these fields and their applications to STEM sciences. This comprehensive approach together with a committee of experts will ensure that the resulting system is robust, scalable, and capable of handling complex mathematical challenges. 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 collaborative project aims to determine the characteristic features in all-sky images that correspond to distinct meso-scale auroral forms, including previously unclassified forms, to improve our understanding of multi-scale ionospheric electrodynamics. Aurora is one of the most visually captivating, yet scientifically complicated, processes in space weather. Since auroral forms can significantly perturb the upper atmosphere, which is important for satellite operations and telecommunications, it is important to achieve a more complete understanding of their behavior. Meso-scale auroral forms are known to introduce as much energy as large-scale processes to the Ionosphere-Thermosphere system’s energy budget, causing density and temperature variations, altering the conductivity profiles, and causing ground magnetic perturbations. The increased capabilities and convenience of computer vision techniques can find similarities and differences in large visual data sets more systematically than the human eye. This award will investigate meso-scale auroral forms, striving to discover new morphologies, that will propel our understanding of how M-I-T systems couple. The team will leverage the high accuracy provided by self-/semi-supervised algorithms to exhaustively scan millions of all-sky images to find morphologically distinct representations of auroral forms. The team will use visual auroral representations for K-12 education and public outreach activities. This award will support graduate and undergraduate students and an early-career woman PI. In addition, this award supports research conducted in EPSCoR states. This project will use data from the Poker Flat Incoherent Scatter Radar, the optical digital all-sky camera images, and ground-based magnetometers, which are all supported by NSF. The data will be used to find and characterize distinct auroral forms by self-/semi-supervised algorithms, and to generate three distinct databases for the space physics community. The efforts entail the creation of a Space Weather Almanac to be distributed as a part of the UAF Space Weather UnderGround (SWUG) program led by the PI. The Space Weather Almanac will be an online record of observed auroral forms by high school students. Used in combination with measurements from the semi-professional magnetometer kits distributed by the UAF-SWUG program, the space weather Almanac will demonstrate the geomagnetic effects of different auroral forms identified by students and demystify the invisible Space Weather phenomena. The science questions to be addressed are 1) How many morphologically distinct meso-scale auroral forms are there based on optical investigations? 2) What are the electrodynamic properties of morphologically distinct meso-scale auroral forms, i.e. electric field, average energy, energy flux, conductance, overhead currents, etc.? and 3) What are the occurrence sites, rates, and sequences of morphologically distinct meso-scale auroral forms during geomagnetically active periods? The project will curate and disseminate three data sets for meso-scale auroral forms: i. Optically distinct morphology clusters, ii. electrodynamic properties, iii. occurrence rate, site, and sequences. These data sets will provide a powerful resource for the community enabling more rigorous statistical analysis and event-based studies. This project is co-funded by the Magnetospheric Physics Program and the Aeronomy Program. 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

PROJECT SUMMARY Familial hypercholesterolemia (FH) affects 1 in 250 people and is characterized by impaired low-density lipoprotein (LDL) metabolism resulting in premature cardiovascular disease. CRISPR-Cas9-induced loss of function mutations in the gene encoding Angiopoietin-like 3 (ANGPTL3) has been proposed as a therapeutic strategy to permanently reduce LDL cholesterol and triglyceride levels and lower cardiovascular disease risks. However, the absence of safe and effective methods for delivering CRISPR components into hepatocytes is a major barrier. Adeno-associated viruses (AAVs) are the platform of choice for delivering gene-editing reagents but are associated with severe limitations. In addition, Cas9 immunity is highly prevalent in the human population further complicating AAV delivery. We propose to introduce gene-editing reagents into hepatocytes using nonviral delivery approaches ex vivo, then transplanting the engineered hepatocytes to replace diseased hepatocytes to treat FH. Our nonviral ex vivo strategy avoids two complications of in vivo approaches since CRISPR-Cas9 editing is only restricted to the intended target cells and enables the opportunity to maintain cells in culture until they are no longer immunogenic. But this ex vivo approach has one potential drawback: How to enhance the number of edited hepatocytes engrafted in the liver. To address this challenge, we will use a novel approach for selecting edited hepatocytes using fever medicine acetaminophen (APAP). We hypothesize that gene edited hepatocytes lacking NADPH-cytochrome P450 oxidoreductase (Cypor) will be enriched in vivo by APAP administration without permanent liver damage. In the proposed studies, we will directly compare LNPs, electroporation and AAVs for gene editing using CRISPR-Cas9 to disrupt Cypor and Angptl3. We will then replace diseased hepatocytes with gene edited hepatocytes in an established mouse model of FH (Ldlr–/– ) using APAP selection. In Aim 1, we will compare specificity and efficiency of multiplex gene editing in Cypor and Angptl3 by electroporation, LNP, and AAV-mediated delivery of CRISPR-Cas9 in primary mouse hepatocytes. For Aim 2, we will optimize transplantation and APAP-mediated selection of gene- edited hepatocytes and evaluate the long-term effects of Cypor-knockdown in Ldlr-/- mice while comparing LNP, electroporation, and AAV-mediated ex vivo delivery of Cypor-CRISPR-Cas9. In Aim 3, we will compare the effects of nonviral and AAV-mediated multiplex delivery of Cypor and Angptl3-CRISPR-Cas9 on the capacity of hepatocytes to lower plasma cholesterol and triglyceride levels and circumvent Cas9 immunity in Ldlr–/– mice subjected to transient APAP treatment. This project is the first to directly compare different nonviral approaches to AAVs for engineering hepatocytes ex vivo and evaluate the extent that edited hepatocytes clonally expand in vivo using APAP to treat FH. In addition, this study is the first to study the immunogenicity of ex vivo gene edited hepatocytes.

NSF Awards · FY 2024 · 2024-09

A more diverse STEM workforce is needed to support innovation and creativity in the field. However, historically underrepresented students and low-income students are disproportionately less likely to earn undergraduate STEM degrees as compared to overrepresented students. A primary contributor to this achievement gap in graduation rates could be that historically underrepresented students are more likely to lack a sense of belonging in a STEM major. In alignment with the first goal of this solicitation, which is to conduct research in the Professional Formation of Engineers (PFE), the proposed work will evaluate the effectiveness of five teaching strategies on promoting cognitive belonging and engagement in an upper-level architectural engineering course. The teaching strategies include those that provided high structure, e.g., providing students with regular opportunities for learning the materials, and those that create an inclusive environment, e.g., in-class collaborative learning activities. These techniques will be incorporated into two offerings of the same course and evaluated using surveys, in-class observations, and interviews. If these strategies are found to be effective and eventually become widely adopted, more students will feel “I belong in engineering”, which will strengthen the engineering workforce long-term. In support of the second goal of this solicitation, which is to increase the number of researchers in this field, a mentoring and professional development plan will be implemented to develop the skills of the PI, Dr. Michelle Vigeant-Haas at Penn State University, in the engineering education research field and to grow her network. The plan includes working closely with her mentor, Dr. Karen High at Clemson University, an expert in the field, as well as a five-member advisory board composed of members from four other U.S. institutions. In addition, the PI will take relevant courses, conduct workshops, and share the results through presentations and journal publications. Belonging uncertainty contributes to the significantly lower average STEM graduation rates for historically underrepresented students. The research aim of this project is to understand how the use of high-structure and inclusive teaching strategies may impact cognitive belonging, and behavioral and social engagement in an upper-level architectural engineering class. Five instructional strategies will be incorporated into this course and be examined in the context of these three factors. A multi-method approach will be used to collect qualitative and quantitative data and a design-based method will be used to revise the strategies and measurement instruments across two course offerings. The theoretical foundation of this project consists of Vygotsky’s social constructivist theory of learning and a student engagement framework. This project will address the existing gap between these theories by exploring the impact of the proposed teaching strategies on belonging and engagement in a 400-level engineering course. The proposed work will be a significant contribution to engineering education research (EER) given most work relevant to these theories has been done in introductory math and science courses. The outcomes of this work could potentially lead to more students feeling that they belong in engineering, which will diversify the engineering workforce. The mentoring and professional development aim of this project is for the PI, Dr. Michelle Vigeant-Haas at Penn State, to develop EER skills and an EER network under the close guidance of her accomplished mentor, Dr. Karen High at Clemson University, as well as a five-member advisory board. The PI’s professional development plan includes a mentored EER experience, personal development plans, educational methods courses, and broad dissemination at both institutions, American Society for Engineering Education (ASEE) conferences, and in appropriate journals. This work will also contribute to the expansion of the number of faculty conducting EER and create opportunities for multi-institutional EER collaborations. 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

In many multi-agent Cyber-Physical Systems (CPS), the agents are self-interested in that their individual costs/rewards are not fully aligned with the network-level global objective function. A typical example is the large-scale coordinated charging of electric vehicles, where the network-level goal usually focuses on filling the overnight decrease in background power demand while individual electric vehicles only mind their own charging costs. Therefore, an opportunistic self-interested agent may be tempted to lie in information sharing to reduce its local cost. Motivated by the observation that privacy and truthfulness are closely intertwined, the project aims to investigate how to ensure both privacy and truthfulness in self-interested multi-agent CPS. The project’s novelties include the simultaneous treatment of both privacy and truthfulness in multi-agent CPS and the development of a novel framework and technical approaches for privacy-preserving and truthfulness-guaranteed multi-agent CPS. The project's impacts are closely aligned with societal goals in efficient, privacy-preserving, and truthfulness-guaranteed self-interested multi-agent CPS, and will set up the stage for achieving the goal of improved efficiency and effectiveness in large-scale CPS in sectors like energy and transportation. The project aims to address the following five tasks: 1) Leveraging the investigators’ prior results on distributed unconstraint optimization that achieve differential privacy by compromising convergence speed rather than convergence accuracy, ensure both differential privacy and accurate convergence in multi-agent optimization subject to shared inequality constraints; 2) Exploiting the fact that differential privacy can be used as a tool for truthfulness, ensure truthful behaviors of agents participating in distributed optimization; 3) Leveraging the investigators’ prior results on constraint-free Nash equilibrium seeking that achieve differential privacy by compromising convergence speed rather than convergence accuracy, ensure both differential privacy and accurate convergence in generalized Nash equilibrium seeking subject to shared inequality constraints; 4) Exploiting the fact that differential privacy can be used as a tool for truthfulness, ensure truthful behaviors of agents in generalized Nash equilibrium seeking in the partial-decision information setting; 5) Enhance the differential-privacy based approximate truthfulness to establish exact truthfulness in multi-agent optimization and Nash equilibrium seeking. The proposed algorithms and frameworks will be evaluated using both numerical simulations and experiments of coordinated charging of real electric vehicles in collaboration with Ford Motor Company. The teams is using existing undergraduate research programs and various on-going outreach opportunities to energize interests in STEM in minority middle-school girls (Clemson WISE program), high-school students (MSU HSEI program), and community-college technicians (TriCounty Technical College). 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

Abstract Two emphasis areas are proposed which are underpinned by synthetic organic chemistry. First, new synthetic methodology to access exceptionally rare nitrogen-rich compounds will be developed to expand the availability of medicinally relevant, densely functionalized heterocycles. We pioneered new methodology to synthesize the rare diazacyclobutene (DCB) heterocycle. Less than ten examples of stable DCBs where previously known. The dearth of examples has prevented their application in any biological or synthetic context. Nevertheless, preliminary biological investigations show that some DCBs exhibit potent anti-parasitic activity, suggesting that further development of the scaffold may unlock additional biological activity relevant to human disease. Accessing new examples of DCBs will enable biological evaluation and expansion of the synthetic utility of the heterocycle. Exploiting new reactivity of DCBs and related intermediates will provide new methods to synthesize a large variety of medicinally relevant, richly functionalized heterocycles through the development of cascade reactions of labile monocyclic DCB intermediates. Similarly, the intentional diversion of key reactive intermediates with Lewis Acids will access additional rare heterocycles through novel mechanisms. The second emphasis area will develop new non-microbicidal tool compounds to interrogate the polysaccharide metabolism of gut bacteria of the Bacteroides genus. The enzymatic machinery deployed by the Bacteroides genus serves as a model system to understand glycan foraging strategies deployed by the entire Bacteroidetes phylum, which represents over half of the constituents of a normal healthy microbiota). Presently, our lab has uncovered the only known chemical probes to interrogate this fundamental system governing carbohydrate utilization by prominent gut microbes. Additionally, Bacteroides spp. are associated with common anaerobic bacterial infections affecting multiple organ systems, sepsis, gut inflammatory and autoimmune diseases, and colorectal cancer. Building on our prior discovery that the natural product acarbose shuts down the Starch Utilization System (Sus) of Bacteroides spp., we will synthesize useful chemical probes to interrogate this fundamental metabolic pathway deployed by prominent gut microbes for eventual therapeutic gain. Thus, we will provide tools to clarify the mechanism of action of acarbose-induced arrest of bacterial starch metabolism by deploying synthetic analogs bearing fluorophores and photoaffinity tags. This will also provide key fundamental knowledge about the promiscuity of the probe with other Sus-like constructs. We will uncover potential downstream effects on virulence factors (i.e. biofilm formation, antibiotic resistance, and toxin production) in the organisms upon acarbose-mediated shutdown of the Sus. This work will lay important groundwork toward providing versatile tools to understand gut microbial metabolism as it pertains to human disease. We will also significantly expand synthetic access to new probe molecules capable of interrogating the Sus as well as other related enzyme suites that are leveraged by a majority of human gut microbes.