Clemson University

NSF Awards · FY 2024 · 2024-09

Disk features like gaps and cavities are commonly attributed to planets. But other mechanisms can create similar features, e.g. the radiation pressure instability, resulting from fundamental interactions of radiation, gas, and dust. Moreover, even when planets are the root cause of the disk’s features, such instabilities can be triggered. That effect needs to be taken into account to infer planetary properties using the features. Researchers at Clemson University, in the EPSCoR state of South Carolina, are funded to study these physical systems with PEnGUIn, an open-source, aggressively optimized hydrodynamics code that runs on Graphics Processing Units (GPUs). They aim to produce a unified picture of the co-evolution of super-Earth systems and their natal disks. The project will also design and carry out interdisciplinary projects with art students, giving scientists and artists a chance to learn from each other. It will also enhance the Emerging Scholar program at Clemson, promoting college-culture in the underprivileged areas of South Carolina. The dynamics of radiation-gas-dust interaction in low-viscosity protoplanetary disks, with or without planets, still has many unknowns. Accurate simulations of these systems will require either high resolution to track complex gas flow in the disks, or tracking long term evolution over millions of years, or both. This project develops a more physically-motivated thermal treatment to replace the commonly assumed isothermal equation of state or simple thermal relaxation prescription. The researchers will study whether planet-shepherded rings remain stable against the Rossby wave instability. They will study whether super-Earths, the most abundant type of known planets, can open large cavities in disks; if so, the rarer giant planets will not need to be invoked to explain cavities. They will also study whether planets may drive disk evolution by quantifying the amount of planet-induced disk transport and whether gapped disks may turn into transition disks. Working with art students in both fine arts and in digital arts, art-pieces will be produced that can be used to engage the public. As part of the Emerging Scholar program, the PI will help design and teach summer classes for high school students and provide them with research opportunities within this project. 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

Research in the 1960s revealed that Earth’s outer shell is broken into a dozen or so relatively rigid plates that represent the top of a convecting system in Earth’s deep interior. Motions between and within these tectonic plates create mountain ranges, volcanoes, sedimentary basins, and other major geologic surface features. These features represent vertical relief that, under the force of gravity, is then subject to erosion, landsliding, and other forms of downslope movement of mass. Earth’s topography is thus controlled by the balance between tectonic processes that build relief, and erosional processes that remove and redistribute relief. Conversely, the evolution of topography affects the forces within tectonic plates, influencing subsequent faulting and volcanic activity, and leading to feedbacks over a range of spatial and temporal scales. On million-year timescales, sedimentary basins create natural resource deposits (such as oil and gas reservoirs), and chemical reactions associated with erosion can remove carbon dioxide from the atmosphere, directly influencing Earth’s climate and habitability. On human timescales, the creation of vertical relief promotes landsliding and far-reaching sediment distribution, which is often associated with interacting geohazards including earthquakes, tsunamis, and volcanic eruptions. Building on prior, previously independent work modeling Earth’s interior and surface processes separately, this project develops new computational methods to simulate and advance our knowledge of the dynamic interplay between Earth’s surface and interior and makes these methods available to the scientific community. The computational methods derived through this project have direct societal relevance to studying geohazards and resource exploration. All software developed through this award follows established software engineering practices, is openly available to the public, and is fully documented. Community training activities are used to engage other scientists and promote the adoption of the new methods developed by this project. A major research challenge in the geosciences is understanding how the Earth’s surface and its interior interact to shape one another. Because much of the relevant interactions are inaccessible due to their space or time extents (or both), computer simulations serve as an essential tool for studying interactions in coupled geologic systems. Yet, numerical models have traditionally treated the Earth’s surface and its interior as independent domains. None of the widely used, open-source software packages for simulating mantle convection, long-term tectonics, or short-term tectonics have incorporated surface processes until very recently. Similarly, software for the simulation of surface processes has generally been driven by prescribing vertical uplift rates, even though it is clear that these uplift rates depend on, and thus must be coupled to, erosion rates. This project couples two widely used community codes: (i) ASPECT, a package originally intended for the simulation of mantle dynamics but more recently also used extensively for modeling of long-term processes in tectonic plates, with active development towards incorporating physics (such as compressible elasticity) necessary to capture shorter term processes; and (ii) Landlab, an environment that includes and facilitates the description of surface processes. Since their inception, these codes have transformed the level of complexity of simulations in their respective domains and have gained large user bases. Both codes are backed by large NSF-funded centers: the Computational Infrastructure for Geodynamics (CIG) in the case of ASPECT, and the Community Surface Dynamics Modeling System (CSDMS) in the case of Landlab. The software and workflows developed through this project enable scientific communities that are typically siloed, studying either Earth’s surface or its interior, to initiate new studies of coupled processes with direct societal relevance, including geohazards and resource exploration. Model use cases implemented by the project demonstrate the coupling on different spatial and temporal scales, which can be used by domain scientists to initiate independent research projects. Project training materials are incorporated into long-standing training programs associated with ASPECT (e.g., annual hackathons) and Landlab (e.g., CSDMS clinics), as well as online videos, interactive web visualizations, and at various community meetings and workshops. Finally, a major part of the development effort is parallelizing Landlab, which improves its performance over a wide range of applications, including modeling short time-scale processes such as volcanic eruption cycles, landslides and flooding. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the National Discovery Cloud for Climate initiative within the Directorate for Computer and Information Science and Engineering and by the Geosciences Directorate’s Research, Innovation, Synergies, and Education and Earth Sciences divisions. 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 Rapid, inexpensive detection of biomarkers at the point of care is vital for many clinical purposes. However, limitations in current detection platforms have prevented the sensitive detection of many protein and small molecule biomarkers, forcing clinicians to rely on either potentially inaccurate empirical diagnosis or expensive lab tests to make critical treatment decisions. Sensitive detection of nucleic acid targets has been readily achieved by exploiting Watson-Crick base pairing to amplify signals (e.g., PCR), but there has been a lack of innovation for detection of low concentration antigens and small molecules at the point of care. Nature, on the other hand, has evolved intricate mechanisms for rapidly amplifying protein signals in vivo via post-translational modification and protein-based signaling networks that could be adapted for biomarker detection. Towards the goal of developing novel, rapid, ultrasensitive diagnostics, the central hypothesis of this project is that in vitro, protein-based signaling networks incorporating self-amplifying motifs can detect clinical biomarkers. Specifically, we plan to incorporate three mechanisms of protein signaling networks with potential for diagnostics: split enzyme reconstitution, autocatalytic positive feedback loops, and small molecule biosensors. We have previously demonstrated the in vitro use of split adenylate cyclase for small molecule detection via the simultaneous binding of two proteins (i.e. a sandwich assay in solution), bringing two halves of adenylate cyclase together and producing cAMP. Aim 1 (K99) will incorporate binding domains for detection of clinically relevant biomarker targets and investigate detection in human sample matrices. We will test different binding domains fused to split adenylate cyclase fragments to detect Hepatitis C Core Antigen (HCVcAg). Towards the development of a point-of-care diagnostic, Aim 2 (K99) will optimize reaction components for lyophilization and long-term storage and develop a hand-held point-of-care reader to detect the shift in fluorescence produced by the FRET-based cAMP biosensor. Lab-bench validation of all components will be performed using HCVcAg spiked into commercially purchased pooled blood samples. Aim 3 (R00) will investigate the use of split adenylate cyclase and cAMP receptor protein to create an autocatalytic feedback loop in vitro. This work would result in the first known in vitro protein signaling network with bistable switch-like sensing, a significant advancement for the diagnostics field. If successful, this system would be broadly applicable for protein and small molecule detection and could be used to detect a wide range of target analytes with known binding domains. As such, future work includes the incorporation of binding domains to detect other high value protein and small molecule analytes currently unable to be rapidly detected at the point of care and the clinical translation of the described diagnostic device with pilot clinical trials in collaboration with both domestic and foreign clinical partners.

NSF Awards · FY 2024 · 2024-08

The propulsion of floating objects via self-generated surface tension nonuniformities, also known as Marangoni surfing, represents a fascinating phenomenon observed in the world of living organisms while also bearing promising potential for robotic applications. For example, in nature, this mode of locomotion is employed by water-walking insects for speedy movement in emergency situations and by certain bacterial swarms for rapid interfacial migration toward nutrient-rich regions for further colonization. In recent years, Marangoni surfers of various sizes have been engineered to perform a wide array of tasks, including environmental sensing and monitoring, microfluidic manipulation, and interfacial self-assembly. The goal of this project is to investigate the motion of Marangoni surfers at spherical interfaces, which are difficult to generate on Earth but achievable in zero gravity aboard the International Space Station (ISS). The propulsion of these interfacial surfers will be studied, with a specific focus on the importance of both the global interfacial curvature of the spherical water droplet and the local interface curvature around the surfers. Additionally, this project will have broader societal impacts through its integrated educational initiatives, which include outreach to underrepresented middle and high school students, research mentorship of community college and graduate students, and curriculum development. The principal objective of this project is to investigate the individual and collective hydrodynamics of Marangoni surfers that self-propel on spherical interfaces. This research aims to generate new knowledge by establishing a computational-experimental framework that includes both ISS- and ground-based measurements. The framework is designed to capture the complex interactions between the motion of active particles, the transport of released species, and the effects of interface curvature and confinement. Notably, performing experiments on a levitating spherical drop in microgravity allows us to probe the importance of interface curvature on particle motion and assembly while simultaneously eliminating the local gravitationally-induced interface curvature effects from around the active particle that have been shown to play an oversized role in inter-particle interactions. The insights gained from this project are expected to define the foundational principles for designing self-propelled surfers optimized for curved interfaces, potentially leading to transformative advancements in robotics and microfluidics. Also, the results of this research will enhance our understanding of self-assembly processes, facilitating the rapid production of small-scale structured materials. Moreover, this study will shed light on the role of Marangoni stresses in the colonization of antibiotic-resistant bacteria at fluidic interfaces, offering new strategies for tackling infectious diseases by elucidating bacterial colonization and survival mechanisms in adverse conditions. 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

In Summer 2024, the Argonne National Laboratory (ANL) will launch Aurora, which will be the world’s most powerful public supercomputer. Despite the centrality of supercomputer development to maintaining U.S. global leadership position in the advancement of science and technology and substantial taxpayer investment, the public rarely has the opportunity to stand in the thrumming, chilly machine room among the rows and rows of towers of compute nodes that comprise a supercomputer. As a result, questions such as, “How do you build a supercomputer?” “What does a supercomputer do?”, “What do scientists and other staff do all day at the supercomputing center?”, “What does the supercomputer do that affects me?”, and “Why does the US need the fastest supercomputers?” don’t have concrete answers. This project is designed to answer those questions and deepen public understanding of massive supercomputing infrastructures for generating new forms of knowledge. The researchers have been invited by the ANL Division Director to do on-site and remote observational and interview research with individuals and processes related to Aurora, including Argonne employees working on Aurora, scientists running projects on Aurora, and the technical documentation that makes Aurora possible. The project will proceed in two related phases: The first phase will focus on the everyday practices of supercomputing and how a big science project is used and maintained through the work of scientists, operations staff, and technical documentation processes; the second phase will move from analysis of the everyday practices of supercomputing to higher-level examinations of how the global competition for supercomputing leadership has been shaped by, and also shapes, the U.S.’s identity as a leader in scientific and technological advancement. The project is designed to increase public awareness of the many hidden elements of supercomputing while also developing opportunities for undergraduate and graduate students at Portland State University and Clemson University to work with the U.S. National Laboratory system and learn about the world of public supercomputing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The broader impact of this Partnerships for Innovation - Technology Translation (PFI-TT) project includes a novel technique that will enable pharmaceutical companies to significantly reduce their time and financial cost in their efforts to improve drug quality and yield, production cost, and development of new drugs, especially drugs for rare diseases. This solution will result in more effective, affordable, and equitable healthcare services. In particular, the unique technology features (e.g., simple sample preparation and the measurement of easily understood cell properties (such as cell size and shape), and a wide range of potential applications) will reduce educational barriers and attract students who lack intensive and specialized training or prior preparation. The project will train future entrepreneurs and build and strengthen industry-university partnerships. The project develops a label-free and non-invasive method for rapid measurement and selection of high-performing, highly stable Chinese hamster ovary (CHO) cells. This project involves the creation of an automated microwave system for comprehensive single cell measurement; the characterization of a variety of CHO cells; the development of machine-learning techniques for microwave feature extraction; and the identification of stable, high-producing CHO cells based on cell microwave properties. The microwave cell properties, features, and markers will fill significant knowledge gaps for measurements related to CHO performance. The low-cost system will fill a critical technology gap in biomanufacturing. Additionally, the project will enable graduate students to learn and experience the full cycle of market needs identification, technology conceptualization, prototype implementation, and performance evaluation as well as the development of close collaborations with partners to transition basic scientific discoveries to practical use. Such experience is key for a student to mature as a technology pioneer and leader. 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

Climate extremes in global breadbasket regions trigger ripple effects on global food security and American multinational food industries. While individual extreme events like droughts and heatwaves negatively affect crop yields, the most severe effects result from compound extreme events, posing significant challenges for climate adaptation. Quantifying these extreme climate shocks is of great interest to industries, insurance companies, and governments. This study aims to investigate the interconnected climate extremes of droughts and heatwaves, which can co-evolve over time (temporal compounding) or occur simultaneously in different breadbasket regions (spatial compounding). Although the risks of individual extremes have been studied, an integrated risk assessment of temporal and spatial compounding extremes on global breadbaskets and supply regions is lacking. Spatial compounding events can lead to significant economic impacts on American industries and strain interconnected supply chains. This GOALI project involves a university-industry partnership to investigate the risks of temporal and spatial compounding climate extremes on global breadbasket regions and PepsiCo's supply chain source regions. The project objective are to: (a) quantify the potential risk of drought, heatwaves, and compound drought and heatwave events on crop yields for breadbasket regions, (b) quantify the spatial compounding risk of extreme events simultaneously occurring over multiple (coupled) breadbasket regions, (c) investigate the potential impact of climate change on the evolution and spatial synchronization of drought, heatwave, and compound events for the breadbasket regions, and (d) develop seasonal prediction models for spatially compounding extreme events and associated risk on crop yields over the supply regions for PepsiCo. The research results will have a positive societal impact by reducing the risk of extreme events on global food security and enhancing the competitiveness of American multinational food industries globally. The collaborative framework will drive innovations and foster new talent, such as graduate students and postdocs, by creating a skilled workforce capable of addressing real-world challenges. Agriculture-related stakeholders, including business units, industries, and farmers who outsource agricultural products, can use the created tools to mitigate the risks of climate extremes, enhance environmental sustainability, promote social equity and economic stability for farmers, and strengthen global cooperation between American industries and international partners. 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

Viral infections by the Highly Pathogenic Avian Influenza virus (HPAI) A/H5N1 have historically affected wild birds with costly outbreaks periodically spreading in poultry flocks. With the emergence of HPAI clade 2.3.4.4b, new avian species and, even more concerning, terrestrial mammal infections have been reported, including an outbreak in dairy cattle, thereby creating an unprecedented and urgent health risk. The Food and Drug Administration reported that samples of pasteurized milk taken from grocery stores in the United States have tested positive for H5N1. Dairy farmers are key stakeholders, as their biosecurity strategies can minimize risks to the food supply. To do so, however, they need clear and actionable instructional messages. As the situation evolves dynamically and a potential crisis unfolds, the research team assesses and tests the efficacy of biosecurity recommendation messages. Specifically, the researchers (1) monitor real-time H5N1 crisis message recommendations in both social and traditional media to identify crisis constraints and misinformation; (2) assess the perceptions of dairy farmers and other national and state stakeholders who are responsible for identifying effective biosecurity strategies for managing the risk; and (3) provide feedback and recommendations to practitioners based on principles of instructional risk communication as articulated in the IDEA model. The IDEA model’s primary assumption is that instructional risk and crisis messages are most effective when they include a balance of internalization (affective learning), explanation (cognitive learning), recommendations for action (behavioral learning), along with considerations of how the messages can best be distributed. The intention of the research is to produce findings generalizable to biosecurity threats beyond this particular avian influenza virus. 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

From beverage marks on the kitchen table to salt deposits on windows of the family car, we are all familiar with these stains that plague our homes and vehicles. However, such residues are not in random nor uniform patterns, and they often appear as circular spots with a bright center and a dark rim. A closer inspection of this phenomenon reveals that the accumulation of the particles at the edge is facilitated by the fluid flow inside the droplet, which is, in turn, driven by the liquid leaving the free surface of the drop due to evaporation. Apart from its everyday relevance, the evaporation-driven deposition of solutes offers a simple, inexpensive method for assembling complex miniature structures. This method can also be used as a diagnostic tool for forensic analysis, disease examination, and biodetection. The goal of this award is to examine the motion of particles suspended in evaporating colloidal droplets that rest on a substrate, and to link the transport of the colloids to where they finally reside when the droplet is desiccated. The planned studies are coupled with a range of educational activities that involve outreach to underrepresented middle and high school students, mentorship of diverse community college and graduate students, and curriculum development. This award aims to investigate the transport and aggregation of nonvolatile particles in drying colloidal sessile drops with the ultimate goal of understanding the mechanisms underlying various deposition patterns. Specifically, high-fidelity numerical simulations and theoretical analyses will be used to study the (i) role of the liquid-gas interface in the evolution of the evaporative self-assembly process, (ii) impact of shape and polydispersity on transport and deposition of solutes, and (iii) sensitivity of particle transport and deposition to droplet geometry and shielding effect. The findings of this award will narrow the gap in our current knowledge and fundamental understanding of the evaporation-induced transport, self-assembly, and deposition of particles initially suspended in a sessile drop of a simple liquid. They will also establish a transformative physics-based regime map for deposition patterns that can serve as an engineering guideline for tailoring the system parameters in order to elicit the desired self-assembled structure for a variety of high-tech applications. The map can also be utilized as a diagnostic tool for pinpointing the properties of the suspended particles and the nature of their interactions with themselves, with the liquid-gas interface, and with the substrate. 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

Robotic swarms have attracted much attention in recent years due to their vast potential applications. In particular, there has been a growing interest in aquatic robots, either swimming underwater or surfing at the air-water interface. By using large numbers of individuals working in tandem through local communication, a swarm of underwater swimmers or interfacial surfers can augment their collective intelligence while maintaining relatively simplistic designs. Harnessing this unique, joint ability leads to achieving superior functionalities, which makes aquatic robots very appealing for a myriad of practical applications, including surveillance, monitoring of invasive species, tracking weather and sea conditions, pollution management, etc. The principal objective of this project is to examine the hydrodynamics of aquatic robots locomoting in orderly ensembles and to identify the collective behaviors that emerge from the flow-mediated interactions among the robots in those formations. The planned research studies in this project are coupled with a range of educational activities that involve outreach to middle and high school students, engagement with the general public, mentorship of community college and graduate students, and curriculum development. This project aims to obtain an in-depth understanding of many-body hydrodynamic interactions in the collective motion of robotic swimmers and surfers at high Reynolds numbers. The design of robots chosen for the studies is motivated by species in nature that have mastered their respective terrains. The swimmers mimic the general form of a fish, with the tail flapping providing the thrust, while the surfers take inspiration from water-walking insects. The investigations will be conducted using a synergistic application of high-fidelity numerical simulations and laboratory experiments. Validated simulations allow for exploring an extensive range of flow regimes and combinations of relative positions between the robots. Coupled with reinforcement learning algorithms, they also enable searching for optimal strategies for collective locomotion. The unsteady flows generated by the motion of robots in the experiments will be captured via time-resolved, volumetric particle tracking velocimetry. The fundamental knowledge gained during this project is expected to directly contribute to the design and implementation of future aquatic robots capable of functioning alongside each other with a high degree of coordination, similar to the behaviors exhibited by fish in schools and birds in flocks. 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

Technology that helps users with complex tasks is advancing rapidly and promises to transform human performance in multiple aspects of life and work. Recent examples of such technology that have captured public attention are semi-autonomous vehicles and artificial intelligence. However, a closer look shows that this technology does not always benefit human performance equally. Automated technology seems to only assist some people, and for some automated technology can make performance worse. The goal of this project is to understand why and when automated technology helps or hurts a person's performance. Failing to understand why potentially transformative technology is not helpful for everyone risks creating a new digital divide. If such a divide occurs, some users would get all the benefits while others would be harmed. In addition to the research, this project includes events to enhance public awareness of the research and training of students with diverse backgrounds. This project aims to gain a better understanding of how individual differences in cognitive abilities, specifically working memory and attention control, relate to human performance with automated systems. Given recent findings on the role of attention control, as well as new measures of attention control, the project examines which cognitive tasks or processes are replaced in a human-autonomy team. Additionally, this project aims to use the concept of attention control to derive automation design principles. Such design recommendations may improve human performance with automated systems for users of all attention control levels. Both goals will capitalize on the new theoretical understanding of attention control and newly developed and scientifically validated measures of attention control. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The health of U.S. forests is of paramount importance for economic security, but rapid environmental change is placing unprecedented stresses on trees that may lead to catastrophic forest loss. Forecasting these changes is a priority for management, but scientists and managers alike require new tools for understanding how near-term stresses influence tree survival, a process often delayed by months or years. In this interdisciplinary project, ecologists, biochemists, and modelers are applying a new laboratory technology related to chemical fingerprinting to understand how immediate changes in leaf chemistry may determine whether a tree lives or dies in response to drought. This approach is novel because it ‘peeks inside’ a leaf to measure how it handles stress in real-time, but performed in natural forest settings to take advantage of long-term datasets of tree growth and survival in two iconic U.S. National Parks. In this way, this project is developing early-warning markers of tree mortality risk, with the ultimate goal of providing accurate forecasts of forest health under changing environmental conditions. The project will provide training to two postdoctoral fellows, three PhD students, two REU students, and additional undergraduate students at Clemson and Denver, and expose all personnel to cross- institutional and governmental (NPS) research collaborations. Researchers are testing new hypotheses that provide a mechanistic framework for linking stress-related metabolomics to whole-plant physiology and forest demography. The main hypothesis is that energetic constraints induced by light competition significantly alter the leaf metabolome composition of drought-stressed saplings toward those compounds that maximize the efficacy of antioxidant and osmoregulant activity at a relatively low cost of biosynthesis. Such constraints potentially decouple growth-survival and growth-fecundity relationships in forest demography. Researchers will first couple measurements of leaf physiology and metabolome composition in controlled drought-response experiments of eight different tree species to that of the same species along moisture gradients in Great Smoky Mountain and Rocky Mountain National Parks. Forest simulation models will incorporate effects of drought and light on tree recruitment, growth, and survival, and integrate demographic outcomes conveyed by metabolomics profiling. A transformative aspect of this activity is the identification of ‘stasis’ populations that are demographically stable but experiencing significant drought stress. Such populations indicate disequilibrial dynamics in species distribution models, and provide critical information for forecasting future forest composition. 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

Program Director/Principal Investigator (Last, First, Middle): Anker, Jeffrey N. Abstract Our goal is to fabricate and validate a novel immunosensor which attaches to implanted medical devices to detect and monitor local infection biomarker concentrations using standard-of-care X-ray imaging. While implanted medical devices improve patient quality of life and extend life expectancy, they both reduce the number of bacteria needed to cause an infection and provide a haven for growth biofilms that tolerate both antibiotics and host immune system. Thus, over half of the 2 million hospital-acquired infections in the US are associated with implanted medical devices, with 90,000 annual attributed deaths and staggering costs. Early detection is key to treatment as it allows interventions before tissue damage or progression to systemic infection and sepsis. Likewise, during treatment it is important to monitor infections for eradication to ensure success of device replacements. However, systemic blood biomarkers are ineffective at early stages or during antibiotic treatment when the infection is localized near the device. Fluid aspiration is performed when needed, but not appropriate for screening or repeated monitoring as it is invasive, painful, can cause inflammation, and for areas such as hip is performed by a radiologist under fluoroscopy. By contrast, our device will be attached during implantation or surgical irrigation & debridement and noninvasively read with X- rays which are already acquired as the standard of care during followup and emergency visits. Previously, we demonstrated proof-of-concept for X-ray readout of radiodense dials for mechanical strain in cadaveric and sheep models, and pH measurements in cadaveric and a rat peritonitis models. Here, we will extend the concept for the first time to an antigen-responsive immunosensor, and specifically to detecting alpha- defensin an infection-specific biomarker released by activated neutrophils. To do this we will have to control the synthesis conditions, optimize device shape for sensitive readout at the clinical alpha-defensin threshold, and add mechanical gain mechanisms to the gauge. We will also study sensor stability over 1 month and more. We will develop a prototype and fasteners that attach it to a hip prosthesis and peritoneal dialysis tube. We will characterize the hip-attached sensor in human cadaver models and the peritoneal dialysis tube- attached sensor in a live rat model. The proposed research is significant because it develops a noninvasive method to detect, monitor, and study infection biomarkers in situ with the ultimate potential for reducing morbidity, mortality and associated cost from implant infections. Additionally, the approach can be generalized to a wide range of other antigens on implanted devices or injected sensors which would broaden what we can detect with X-rays and enable new tools for biomedical research and clinical practice. PHS 398/2590 (Rev. 06/09) Page Continuation Format Page

NSF Awards · FY 2024 · 2024-07

The ENVR 2024 Workshop: Spatial Data Science for the Environment is the biannual meeting of the Section on Statistics and the Environment (ENVR) of the American Statistical Association (ASA). It will be held in Boulder, Colorado, on October 3-5, 2024. The purpose of the ENVR workshop is to encourage the exchange and mutual understanding of current research ideas in environmental statistics and to provide motivation and direction for further research progress. The goals of the workshop are to (i) facilitate interdisciplinary research, (ii) present state-of-the-art methods, and (iii) develop the next generation workforce by hosting a poster presentation and a short course. This workshop offers new researchers an opportunity to participate in the meeting and interact closely with leaders in the field, in a manner not possible at larger conferences. The program of the forthcoming Workshop on Spatial Data Science for the Environment features the following topics: (i) Advances in statistical computing for massive spatial data; (ii) Novel applications in environmental science; (iii) Recent developments in statistical methodologies in modeling complex space-time data. This award supports the participation of graduate students, early-career researchers, and members of groups underrepresented in statistics and environmental sciences. The ENVR website can be found here: https://community.amstat.org/envr/home; the dedicated workshop website can be found here: https://community.amstat.org/envr/events/workshops/envr2024workshop 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 · 2024-07

The overall objective of this K01 is to propel the continued upward trajectory of the career development of Dr. Rahemi as an independent researcher. Her research focus is on decision making for persons with Alzheimer’s disease and related dementias (ADRD) and their caregivers and the role of advance care planning (ACP), using large datasets and data science. This K01 will position her to achieve her career goal of supporting ADRD-related decision making for end-of-life care among older adults, regardless of their background. Research: The specific aims are 1) to identify variation across cognitive function levels in the relationship between ACP and healthcare use and 2) to describe predictors of caregivers’ perception of patient care quality. To address these aims, she will obtain training in data science, including advanced statistics, explainable artificial intelligence (XAI), and state-of-the-science ADRD and aging. The Health and Retirement Study (HRS), including HRS post-mortem proxy interviews and HRS-linked Medicare data, will be used. This project will provide evidence on nuanced interactions between a mix of variables in end-of-life care for older adults with or at risk of ADRD and their caregivers—groups vastly overlooked in end-of-life research. Candidate: Dr. Rahemi is an Assistant Professor of Nursing at Clemson University. She has substantial training in quantitative research using secondary data analytic approaches (e.g., HRS dataset analyses and training) to investigate ACP and healthcare use among populations with ADRD. Building upon her Carolina Center on Alzheimer’s Disease and Minority Research pilot project using HRS data, this K will allow her to model complex determinants of differences in ACP and end-of-life care. Two training goals will support her success in becoming an independent researcher: 1) develop expertise in current research on aging, ADRD, and ACP and 2) gain skills in advanced statistics, XAI, and secondary analysis of large datasets in interdisciplinary aging research. Mentors/Environment: This career development plan includes experiential and hands-on research training on related projects led by Drs. Demiris and Jarrín. Dr. Rahemi and her mentors have developed a 3-pronged strategy to address her career goals and training needs: 1) a robust and interdisciplinary team of mentors and collaborators who will guide her research and career development; 2) an innovative research project integrated with her training goals that are scientifically relevant and rigorous; 3) an assembly of coursework, workshops, and seminars offered by artificial intelligence (AI) programs, the Alzheimer's Association Interdisciplinary Summer Research Institute, the National Institute on Aging, and the Hartford Institutes for Geriatric Nursing, complemented by professional interactions that build upon existing resources at Clemson University and affiliated institutes. This project will provide training, mentorship, and research experiences foundational to Dr. Rahemi’s career development as an independent investigator improving ACP for all older adults living with ADRD and their caregivers.

NSF Awards · FY 2024 · 2024-06

Discrete graphics processing units (GPUs) are crucial for providing the computing power of today's data centers and high performance computing systems, enabling significant advancements in many disciplines such as climate modeling, nuclear energy, drug design, social networks, deep learning, and artificial intelligence. Programming for GPUs has been laborious and error-prone due to the need to manage data migration between discrete host and GPU memories. As deep learning models and social networks become increasingly larger, and scientific workloads more data-intensive, it is imperative to relieve programmers of such tasks and make GPU programming more productive and portable. Unified Memory (UM) technologies have been developed to meet this need. Nevertheless, current UM technologies cause significant or prohibitive performance degradation. This award establishes a foundation for efficient and intelligent unified memory design, closing the prohibitive performance gap and harnessing the power of advanced GPU accelerators. It leads to reductions in time-to-production and time-to-completion of various scientific simulations and deep learning workloads, enabling them to scale up to larger problem sizes with ease. The award includes rich education, outreach, and broadening participation activities, offering in-class and out-of-class experiences, as well as team-based undergraduate research and engaged learning spanning multiple semesters. It recruits and supports students from underrepresented groups, including people with disabilities, fostering their inclusion and belonging. The comprehensive framework, ACCess Pattern ORienteD (ACCORD), includes abstraction methodologies, cost models, and techniques to enable efficient UM algorithms and systems for various workloads and problem sizes. Its key innovation lies in the abstraction of access patterns, which uses metrics obtainable at the system level to capture the spatial distribution and temporal repetition patterns of massively parallel memory accesses. This abstraction empowers the quantitative assessment of their interaction with UM designs and guides the optimization of algorithms and UM techniques to eliminate performance bottlenecks and optimize data movement effectively. The research objectives include: (1) devising the abstraction of access patterns for UM-based GPU-accelerated systems, (2) developing quantitative methods to analyze the cost of various access patterns and their interaction with UM techniques, (3) designing access pattern-oriented UM techniques for online deployment, and (4) integrating ACCORD into real-world UM systems to support various applications. This project is jointly funded by the Software and Hardware Foundations (SHF) core program at the Division of Computing and Communication Foundations (CCF) and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-06

Project Summary: Cardiovascular disease is the cause of 1 out of every 4 deaths in the United States, with >800,000 people suffering from myocardial infarction each year. Human pluripotent stem cell derived cardiomyocytes (hPSC-CMs) hold remarkable promise for treating infarcted hearts. To accelerate clinical translation of hPSC-CMs, our lab developed nanowired human cardiac organoids composed of hPSC-CMs, human cardiac fibroblasts, endothelial cells, stromal cells, and electrically conductive silicon nanowires (e- SiNWs). Our in vivo data showed that the organoids with/without e-SiNWs (1E6: 1x106 cells/rat) robustly engrafted in Ischemia/Reperfusion (I/R) injured athymic rat hearts, with an engraftment rate of ~30% 1-week post-implantation. The unwired organoids showed comparable functional recovery (~39% Fractional Shortening (FS) recovery) to previous studies that injected 10-fold greater dissociated hPSC-CMs/rat (1E7 cells/rat). Further, the nanowired organoids illustrated superior functional recovery (~69% FS recovery). Despite the progress, current cardiac organoids are prepared through spontaneous assembly of hPSC-CMs and primary cells, leading to undefined human leukocyte antigen (HLA) expressions and immune rejection after implantation. To alleviate allogenic rejection, key genes involved in immune activation/suppression (e.g., B2M necessary for HLA-A/B/C expression, HLA-E/CD47) have been knocked-out/in to hPSCs to derive hypoimmunogenic hPSCs. Notably, islets derived from hypoimmunogenic hPSCs have been shown to effectively evade host immune surveillance post-implantation and functionally recover diabetes in a humanized mouse model. Unlike hypoimmunogenic hPSC islets, there have been few studies on the derivation and validation of hypoimmunogenic hPSC cardiac cells to date. Further, effects of hypoimmunogenic gene editing on therapeutic efficacies of hPSC cardiac cells have not been assessed. In addition, the effects of e-SiNWs on the hypoimmunogenic cardiac cells has yet to be explored. The goal of the proposal is to test if hypoimmunogenic gene editing of hPSCs will affect therapeutic efficacies of nanowired hPSC isogenic cardiac organoids. The central hypotheses of the proposal are hypoimmunogenic gene editing does not significantly affect therapeutic efficacy of nanowired cardiac organoids. The innovations of the proposal include, for the first time, we will 1) determine the effects of hypoimmunogenic gene editing on structures/functions of hPSC cardiac organoids, and 2) illustrate the synergy between biomaterials and hypoimmune on regenerative medicine. Accordingly, we will pursue 3 Aims: 1) Determine the effects of hypoimmunogenic gene editing on hPSC cardiac cells, 2) Determine the effects of e-SiNWs on hypoimmunogenic hPSC isogenic cardiac organoids, and 3) Determine therapeutic efficacy and hypoimmunogenicity of the nanowired hPSC cardiac organoids optimized in Aim 2 in vivo. The proposed studies will lay down the foundation to develop readily available, “off-the-shelf” hPSC cardiac organoids to treat infarcted patients.

NSF Awards · FY 2024 · 2024-06

This project explores a proof-of-concept and feasibility evaluation to inform the future development of a centralized data repository to support the privacy research community. The repository will enable tracking and systematic study of privacy harms. Current incident reporting systems are designed to track the occurrence of large-scale data breaches, but there is currently no centralized reporting system to effectively track other types of privacy violations (e.g., online harassment, cyber abuse) that negatively impact end-users. Without access to this information, it is difficult to quantify / qualify how and to what extent different online platforms propagate privacy breaches, as well as how to redesign such systems to be more secure and trustworthy. Therefore, this planning effort aims to (1) solicit the opinions of privacy experts on the design of the repository; (2) prototype the repository and solicit feedback from experts piloting it; and (3) build on these learnings to develop a plan to develop a centralized privacy incident repository. This will ultimately enable researchers to work together to (1) identify and prioritize privacy harms and the factors associated with the incidents; (2) understand how various populations are impacted by these harms; and (3) develop and evaluate potential interventions. This repository is envisioned to support the protection of vulnerable end-users who are disproportionately threatened and harmed by digital privacy violations, addressing the recent R&D budget priority from the White House and the Office of Science and Technology Policy focused on reducing inequities. By identifying evolving privacy risks, we also work towards two other budget priorities -- advancing trustworthy AI technology and maintaining global security and stability. 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 · 2024-04

Flame retardants (FRs) are a ubiquitous group of chemicals used in furniture, car seats, and children's products and can leach into indoor dust, resulting in chronic exposures. Epidemiological and experimental evidence shows that developmental FR exposures result in short- and long-term adverse health outcomes, but the knowledge gap remains- what targets do they attack and how do they drive adverse outcomes? Using zebrafish, our preliminary data on a brominated FR, tetrabromobisphenol A (TBBPA) shows that TBBPA exposures during early developmental windows (cleavage, blastula, early gastrula) results in developmental delays, mortality and downstream defects in dorsoventral patterning. These early windows are marked by a rapid maternal to zygotic transition (MZT) and zygotic genome activation (ZGA) when maternally loaded mRNA degrade, and zygotic genome is activated. Our mRNA-sequencing data reveals that TBBPA inhibits ZGA and targets chromatin remodeling. Since the latter process is regulated by histone acetylation H3K27ac and catalyzed by P300 protein, we used a P300 activator CfPB and showed that co-exposures with TBBPA and P300 activator mitigated the TBBPA-induced phenotypes. Molecular docking showed strong binding affinities between TBBPA and P300. Based on these, our overarching goal is to understand the diversity ofTBBPA-induced epigenetic and genetic modifications and how they modulate embryonic development. Our oldective is to determine how TBBPA disrupts ZGA through histone modifiers and the downstream consequences on chromatin remodeling and gene expression. The central hypothesis is that TBBPA inhibits P300 activity and limits zygotic transcription by inhibiting H3K27 acetylation and chromatin remodeling of zygotic genome. This hypothesis will be tested through two specific aims. Within aim 1, we will conduct P300 activity assays and quantify of global and genelevel H3K27ac using Western Blots and ChIP-seq. In aim 2, we will use ATAC-seq and nascent RNA-seq to examine chromatin accessibility and nascent RNA transcription. We will also integrate the multiome data from both aims into a gene regulatory model for systems level analyses of pathways that are impacted by TBBPA during ZGA. Based on the sequencing outcomes, we will select specific targets and use qPCR to quantify gene expression across environmentally relevant TBBPA concentrations. The grant is innovative, since it is the first work to systemically assess how an environmental toxicant impacts ZGA through histone modifications and leverages state of the art technologies, including nascent RNA seq, to describe a novel adverse outcome pathway (AOP) for TBBPA early life exposures that spans multiple biological levels from epigenetic regulation (acetylation), chromatin biology, co-regulated genes, signaling networks and organism development. The work is significant since it will reveal how TBBPA directly or indirectly targets proteins or gene products and their regulatory regions that modify onset of zygotic transcription and can be further used in mechanistic or epidemiological studies to evaluate disease mechanisms. Project Summary/Abstract

NIH Research Projects · FY 2025 · 2024-03

Project Summary Aspergillus fumigatus (Af) is the most common and life-threatening airborne opportunistic fungal pathogen. Investigations into Af pathogenesis have focused primarily on mono-species infections. The impact of co- infecting microbes on Af physiology during infection remains an understudied but critically important research area. Using cystic fibrosis (CF) infection as a model, my recent work has focused on characterizing Af physiology in the presence and absence of the coinfecting bacterial pathogen, Pseudomonas aeruginosa (Pa), using multi- omics approaches combined with reverse genetics. I have uncovered two major mechanisms of Af and Pa interkingdom communication, mediated through the toxic, microbial secondary metabolites gliotoxin (produced by Af) and hydrogen cyanide (produced by Pa). The proposed project builds logically on this work and aims to uncover the mechanistic basis underlying the physiological shifts which occur in both organisms upon coculture in synthetic CF sputum media. This work will also test if clinical isolates of Af and Pa maintain these secondary metabolite response networks after chronic human infection and test to what extent polymicrobial interactions impact Af physiology during human infection using advanced sequencing technologies. The overall goal of this application is two-fold: 1) to define the specific molecular basis for interactions that occur between Af and Pa (Specific Aim 1); and 2) to expand on these findings to determine the impact of microbial interactions and the host environment on Af physiology during human CF infection (Specific Aim 2). This work is likely to yield important discoveries that will aid in our understanding of both fungal physiology and the underlying mechanisms of interkingdom microbial interactions within chronic polymicrobial infections.

NIH Research Projects · FY 2025 · 2024-02

PROJECT SUMMARY The complex life cycle processes of encystation and excystation in Entamoeba represent potential targets for therapeutic intervention but have not been studied directly in the human pathogen Entamoeba histolytica because of an inability to produce cysts in a laboratory setting. Instead, scientists have been forced to rely on studies with the distantly related reptile pathogen Entamoeba invadens. Our lab has established a method for reproducible encystation of E. histolytica in laboratory culture and in this project we propose to develop reproducible, high efficiency excystation as well. E. histolytica causes amoebic dysentery in ~100 million people each year worldwide, although as many as 1 billion people may be infected each year as only ~10% of infections result in symptomatic disease. The inability to study encystation and excystation in the laboratory has greatly hindered investigation of the life cycle and infection process. The long-term goal of our research program is to determine how E. histolytica senses and adapts to different environments it encounters during infection. Encystation and excystation are the two key adaptations, allowing Entamoeba to continue the disease process through dissemination of infectious cysts. Having established a reproducible system for encystation of E. histolytica in culture, we are now able to do the same for excystation, thus allowing us to study these critical processes in the lab to pursue an understanding of how E. histolytica senses and responds to environmental cues that signal conversion from motile trophozoite to infectious cyst and back. As part of our long-term goal, the overall objective of this proposal is to develop a reproducible method for high efficiency excystation of E. histolytica in vitro cysts. To this end, we will use two approaches: (1) increase excystation efficiency with standard in vitro cysts; and (2) produce in vitro cysts that exhibit high efficiency excystation. We will apply these two approaches iteratively, such that we will identify the optimal combination of conditions for both encystation and excystation that leads to reproducible, high efficiency excystation. This research will have a significant impact by laying an important foundation for future studies on excystation of the human pathogen such that we can identify and characterize the roles of key genes required for this essential disease process.

NIH Research Projects · FY 2025 · 2024-02

7. Project Summary Millions of people throughout the world are exposed to arsenic, through their drinking water and food, at concentrations above the current US EPA standard. Epidemiological studies demonstrate that exposure is associated with changes locomotor activity, muscular strength, and neuropathy of the peripheral nervous system. One important functions of motor neurons in the peripheral nervous system is to synthesize and secrete the neurotransmitter acetylcholine to regulate skeletal muscle contraction. Studies have assessed changes in brain acetylcholine levels in response to arsenic exposure, but its levels in motor neurons have not been investigated. We conducted preliminary studies exposing human induced pluripotent stem (iPS) cells to arsenic for up to 28 days during their differentiation into mature motor neurons. Exposure to arsenic reduced transcript levels of stage specific motor neuron markers, reduced neurite length, but increased the number of neurites. Additional data from these studies suggest that neurotransmitter vesicular transport is impaired. The work in this proposal will ascertain the mechanisms by which arsenic can impair motor neuron formation and can alter cholinergic neurotransmitter production and function. In the first aim, we will pulse expose iPS cells to human-relevant concentrations of arsenic during their differentiation into cholinergic motor neurons, with the goal of assessing the dose-response, time course, and stage specificity of arsenic. In the second aim, we will explore changes in neurotransmitter uptake and release is a mechanism responsible for the aberrant differentiation. These studies will further our understanding of how arsenic impairs cellular differentiation and may suggest a mechanism for the neuropathy and muscle weakness noted in human epidemiological studies.

NIH Research Projects · FY 2026 · 2024-02

We are a computational lab and our research is within the areas of Computational Biophysics, Bioinformatics, and modeling molecular effects associated with disease-causing mutations. We develop methods, software, and webservers to enable modeling of various processes in Molecular Biology. In the past, our primary interest was in Structural Biology, and thus the focus was on using structural information to investigate proteins, RNAs, DNAs and their assemblages with regard with their stability, interactions, dynamics, conformational and protonation changes, and effects of amino acid mutations (our lab won CAGI-6 MAPK-3 challenge in 2022, to predict folding free energy changes caused by mutations). We carry such investigations in collaborations with experimental labs. Recently we became very interested in human genetic disorders caused by missense mutations and the molecular mechanisms which cause pathogenicity. Our efforts were recognized by the community, and we were given the privilege to establish and chair the first Gordon Research Conference (2014) on “Human SNPs and disease”, which is now permanent event in GRC schedule. With this proposal we are seeking support to continue maintain and develop DelPhi (popular method for modeling electrostatics which currently has 8,000+ registered users), along with other methods and software for predicting the effect of missense mutations on folding and binding free energy changes of the corresponding macromolecules and their assemblages. Regarding DelPhi, we will focus on further development of Gaussian-based approach of treating biological macromolecules as inhomogeneous objects, which was shown to result in ensemble averaged folding and binding free energies. Regarding methods for modeling effects of missense mutations on folding and binding free energies, maintenance and further development will be done by combined first-principle and machine learning (ML) approaches. First-principle approaches have the advantage to be applicable to any case, but are not as accurate as the ML. The ML approaches are more accurate than the first-principle methods, however, they fail on cases not seen in the training database. We anticipate that combining them into a consensus algorithm for predicting the change of folding and binding free energy change will have the advantage to amplify their strengths while reducing their deficiencies. First-principle methods further development will include the Gaussian-based entropy estimation and novel method for calculation of electrostatic energy of inhomogeneous macromolecules; while for ML methods it will include new features as the Gaussian-based density and entropy estimation. We will use the above-mentioned developments along with third party methods to predict the dominant molecular effect of missense mutations associated with diseases, in collaboration with experimental labs.

NIH Research Projects · FY 2025 · 2023-09

Over 100,000 lives were lost due to drug overdose in the past year, of which 80% involved opioids. Despite the effectiveness of medications for opioid use disorder (MOUD) at reducing opioid misuse and opioid overdose, only 10% of people receive treatment. Moreover, treatment retention is low (30-50%) with half of patients experiencing an opioid use recurrence. Peer support specialists (PSSs), who are individuals with direct experience with and successful recovery from Substance Use Disorder, can offer social support and directly address treatment and recovery hurdles for individuals with Opioid Use Disorder (OUD). Our comprehensive review showed that OUD patients receiving a PSS intervention were more likely to initiate MOUD, but evidence of effectiveness for MOUD retention or opioid use remain inconclusive. Low treatment initiation and retention rates for OUD are especially concerning for rural areas, who rarely have access to clinicians who can prescribe MOUD. Given that these areas are also at a higher propensity for opioid overdose, interventions to increase OUD treatment, retention, and overdose prevention in rural and resource constrained areas with limited MOUD providers are urgently needed. Mobile health clinics (MHC) are an effective and versatile tool for timely delivery of interventions, including those for OUD treatment, to rural and resource constrained communities. However, effective intervention delivery for OUD treatment initiation, retention, and overdose prevention have not been explored in MHC settings. The goal of our proposal is to increase MOUD treatment initiation, treatment retention, and prevent overdose deaths in rural and other resource-constrained communities (via MHC) through development, testing, delivery, and evaluation of an innovative 1) PSS intervention to increase MOUD initiation and retention rates in rural and low-resource areas and 2) modeling framework to identify high OUD-risk communities for MHC delivery (based on overdose deaths prevented). Research has shown that such modeling frameworks can drastically increase the efficiency of resource allocation efforts for other diseases. The PSS intervention and modeling framework will be developed in the R61 phase (R61 Aims 1 and 2) and implemented in the R33 phase to deliver MHCs with PSS services to high OUD-risk areas (identified via modeling) in South Carolina (SC) in order to increase MOUD treatment initiation, retention, and overdose prevention. In the R33 phase, we will conduct a randomized controlled trial (RCT) to evaluate the effectiveness of the PSS intervention component (R33 Aim 1), and extend our modeling framework developed in the R61 phase in order to a) evaluate the impact and cost-effectiveness of the PSS intervention on preventing fatal overdose (R33 Aim 2a) and b) explore improvements to MHC protocols in order to increase effectiveness of MHC-based interventions for OUD (R33 Aim 2b). With opioid overdose deaths doubling over the past 2 years nationally and in SC, there are no signs that the epidemic is slowing down. Our sustainable framework has potential to prevent hundreds to thousands of opioid overdoses in SC alone and can be scaled up in other regions to save many more lives.

NIH Research Projects · FY 2025 · 2023-09

Alcohol Use Disorder (AUD) is the third leading cause of preventable deaths in U.S. and accounts for over 10% of U.S. hospital admissions. Treatment for this population often fails to address the underlying cause of the hospitalization: the AUD. Patients hospitalized with alcohol-related medical complications tend to have high-risk for recurrence of alcohol-related medical problems, high rates of hospital readmissions, and poor recovery outcomes. Methods that promote long-term recovery care are needed. Inpatient linkage to peer recovery coaching may bridge this gap in care by providing a method of continued care for AUD recovery that offers flexibility in recovery pathways, peer modeling opportunities, and strong social support. Pilot study results demonstrated the feasibility of inpatient linkage to peer recovery coaching and showed evidence of decreased alcohol consumption, increased engagement in treatment and recovery support programs, and decreased emergency department visits. However, pilot study sample size, outcomes, and methods were limited. This proposal seeks to overcome these limitations and build on these preliminary results by: using ecological momentary assessments, measuring recovery using the new NIAAA definition, examining cost-effectiveness, assessing alcohol consumption using an additional objective measure (breath alcohol content levels), and examining social support and self-efficacy as potential mechanisms of effectiveness. This proposal will rigorously test the effectiveness of an inpatient peer recovery coaching service called the RC-Link program on recovery outcomes in patients hospitalized with medical complications from AUD. The program involves a bedside introduction to a peer recovery coach during the patient’s hospitalization plus continued, recovery support for six-months. The RC-Link program provides standardized peer recovery service that is both personalized to the patients’ needs and generalized to provide socioemotional, instrumental, and informational social support during every patient encounter. Aim 1 will determine the effect of the RC-Link program on frequency of heavy drinking, biopsychosocial functioning, and remission from AUD compared to controls. Aim 2 will examine how daily changes in perceived social support and self-efficacy influence alcohol consumption and determine whether such associations differ between the RC-Link and control groups. Aim 3 will examine the cost-effectiveness of the RC-Link program; hospital utilization rates will be examined as secondary outcomes. These aims will be evaluated using a two-arm randomized controlled trial that compares the RC-Link program intervention to a control group that receives a brief intervention and connection to a peer recovery coach after the study period. Outcomes will be assessed at baseline, monthly during the 6-month study period, and 6-months post- intervention. This study has potential to advance recovery care for AUD by providing a better understanding of how long-term, inpatient-initiated peer recovery coaching influences recovery outcomes over time in this population.