George Washington University

NIH Research Projects · FY 2026 · 2025-01

Project Summary As one type of endocrine therapy (ET) drugs, aromatase inhibitors (AIs) are the first line of treatment for ER+ breast cancer; however over 20% of patients eventually develop AI resistance during treatment. Changes in the estrogen receptor 1 (ESR1) gene, including mutations, are one of the major mechanisms contributing to AI resistance, for which there is currently no treatment. Thus, it is urgent to develop a novel approach to overcome AI resistance caused by ESR1 mutations. We recently identified an R-loop located at a transcriptional enhancer of ESR1 that regulates ESR1 expression, providing a novel approach to overcome AI resistance by targeting R- loop resolution. And-1 is an HMG box domain-containing protein involving in DNA replication and repair. And-1 is highly expressed in tumors but not in normal tissues, and is essential for tumor cell growth and proliferation. Multiple independent studies have identified And-1 as an excellent target gene for cancer therapy. Our preliminary studies indicated that And-1 is critical for R-loop resolution. Using both high-throughput and in silico drug screens, we successfully identified potent And-1 inhibitors. The major objectives of this application are to: (1) determine a novel molecular mechanism by which And-1 promotes R-loop resolution using in vitro and in vivo assays, and (2) assess the effects of And-1 inhibitors on AI resistance using AI resistant patient-derived xenograft (PDX) and syngeneic breast cancer models. The completion of proposed studies will not only elucidate a new mechanism regulating R-loop resolution in ER+ breast cancer, but also provide an innovative therapeutic strategy to treat AI-resistant breast cancer patients.

NSF Awards · FY 2025 · 2025-01

This conference will support the development of an updated North American Roadmap, first produced at the Gender Summit - North America held in 2013, with a worldwide perspective based on the latest research, policy advances and outcomes to advance excellence in research and innovation. The network of the Gender Summit platforms (Europe, Africa, Latin America & the Caribbean, Asia Pacific, North America, and the newly created GS-India) provides a unique opportunity to implement the recommendations for short-, medium- and long-term impact. The 2013 summit resulted in a Roadmap for Action on Diversity Fueling Excellence in Research and Innovation which included a range of specific, evidence-based actions and identified the relevant key actor organizations who had the power to bring about the improvements needed. The Roadmap is expected to mobilize relevant science actors and stakeholder organizations to consider social and cultural contextual factors in their STEM research and innovation agendas. While social and cultural contextual factors have been recognized in research and innovations in health-related fields, it has not yet been fully recognized as important other research areas such as: Artificial Intelligence (AI), Machine Learning, Data Science, Energy Transition, Sustainability, Advanced Materials, Big Science and other fields which impact societal wellbeing. Other goals of the new Roadmap include elevating the importance that social and cultural contextual factors can have on the replicability of studies, and to elevate support for meta-analytic and systematic review which combines multiple research results ultimately strengthening evidence-based policy making. This conference will strengthen partnerships between academia, industry and government and help inform public policy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This project aims to develop the mathematical foundations for a digital twin (DT) system for individuals with autism spectrum disorder (ASD), focusing on dynamic modeling, prediction, uncertainty quantification, and treatment or intervention recommendation through DT-based optimization. ASD is characterized by challenges in social interaction, communication, and behavior, such as difficulties in forming relationships, understanding nonverbal cues, speech development, repetitive behaviors, and sensory sensitivities. The project will create a unified system integrating clinical and neuro-developmental data, analyzed using a DT healthcare paradigm. The DT technology will enable individualized models, and its predictive capabilities will allow healthcare providers to anticipate progression and adjust treatment or intervention proactively. Additionally, the continuous feedback loop from real-time data will enhance therapeutic outcomes. The developed methods and theories will have broader applicability to other medical areas, improving healthcare efficiency, reducing system burdens, and informing public health strategies. This will ultimately enhance care and promote community well-being. The project will also develop quality cyberinfrastructure to share algorithms, data, and open-source software with the community. Furthermore, the investigators plan to expand scientific impacts through collaborating with medical experts and industry scientists, training undergraduate and graduate students, and integrating research findings into course development. The project will develop a DT framework by modeling brain activities with a unified data structure, linked to behavioral characteristics and interventions aligned with individuals' neuro-developmental processes. This system will integrate multimodal and multi-source data related to human health and development. It will establish foundational models for training and generating synthetic data from DT models, enabling personalized predictions of progression and uncertainty quantification through novel interdisciplinary approaches. The DT system consists of four research modules: (1) Develop computational models based on conditional variational auto-encoders (CVAE) and longitudinal CVAE to analyze brain activities, integrate diverse imaging data, and model neurodevelopmental processes. (2) Create a novel bilevel formulation for multi-distribution fine-tuning techniques on pretrained foundational models and a fast algorithm to learn from heterogeneous data sources to predict ASD outcomes. (3) Develop a model-free conformal prediction procedure to ensemble predictions from multiple models obtained with different modalities and progression simulations, integrating various types of uncertainties into one framework. (4) Develop a DT-based reinforcement learning framework to recommend personalized treatment/intervention plans that significantly improve online learning efficiency and clinical outcomes. The project will address challenges such as multimodality and multi-source data, high-dimensional features, dynamic progression of ASD symptoms, brain functional connectivity, and the need for personalized intervention or treatment recommendations and uncertainty quantification. This project is jointly funded by the Division of Mathematical Sciences, the OAC Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program, and the CBET Engineering of Biomedical Systems 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.

NSF Awards · FY 2025 · 2025-01

Organic matter, proteins, and biological organisms tend to stick to surfaces that are submerged for long periods. This layer formation process is called “fouling,” and the things that stick are called “foulants.” Membranes used for water treatment experience fouling, leading to frequent maintenance and reduced efficiency. In heat exchangers, fouling decreases thermal efficiency, increasing energy consumption and operational costs. In marine environments, biofouling on ship hulls increases drag, leading to higher fuel consumption. To address the operational challenges and energy inefficiencies posed by fouling, this project aims to develop high-performance antifouling coatings to help prevent fouling from occurring before it becomes a problem. The team will explore the effectiveness of using a type of polymer called aromatic polyamide brushes, which will be modified with chemical side chains of various functions, as antifouling coatings. The innovative approach leverages the inherent stability and robustness of the underlying aromatic polyamide structures, enhanced with antifouling functionalities, to provide durable and effective surface coatings. Developing new antifouling polymer chemistries that can be coated on large substrates using mild synthesis conditions (i.e., open to air, fast reaction times, lower pressure, etc.), exhibit a sustained resistance to multiple foulants, and have robust physical and chemical stability in submerged environments, will bring significant economic benefits to industries in which fouling plagues system performance and longevity. This research project will also develop an outreach program by leveraging an existing NSF-funded research experience for undergraduates site at George Washington University. The program will expose undergraduate students to K-12 lesson development and, simultaneously, will provide high school students and teachers with learning opportunities in the surface chemistry and engineering fields. Antifouling surface coatings that resist the adsorption of organic matter and bacterial cells are desired in many applications, including water filtration membranes, heat exchangers, and ship hulls. However, current antifouling polymers based on aliphatic backbones often lack long-term stability in submerged environments. This project focuses on the antifouling behavior of a novel class of aromatic polyamide brushes. The overall goal of this proposal is to elucidate the underlying mechanisms of the novel antifouling properties of side-chain functionalized aromatic polyamide brushes to inform the design of high-performance antifouling coatings. The specific objectives are: 1) Synthesize aromatic polyamide brushes with well-controlled grafting density, thickness, and side-chain functionalization; 2) Combine experimental characterization and computational simulation to fundamentally understand antifouling mechanisms of aromatic polyamide brushes; and 3) Develop material design guidelines for aromatic polyamide brushes on various substrates using an iterative process that integrates chemical synthesis, performance characterization, and molecular simulations. The development of this novel class of antifouling polymer brushes, using a simple, versatile, and scalable synthetic procedure, will aid in transferring polymer brushes from small-scale laboratory systems to real-world applications. The development of more realistic molecular models and computational methods to search for fouling locations will aid the structural design and optimization of aromatic polyamide brushes for a range of anti-fouling applications. This project will train the next generation of engineering and chemistry students by engaging them in cutting-edge, collaborative research. In addition, a series of activities will be developed to educate K-12 students about the chemistry and engineering related to antifouling surfaces by incorporating education workshops into existing courses. Working with local high school STEM teachers, these activities will be integrated into classrooms to stimulate students’ interest in pursuing a STEM career. 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 · 2024-12

Project Summary Malaria remains a major public health problem with an estimated ¼ billion clinical cases and mortality to a total of 627,000 in 2022. More than 90% of the global malaria burden is due to infection by Plasmodium falciparum and P. vivax, often co-endemic in Asia, Pacific, Central and South America, with growing evidence in parts of Africa. The P. falciparum malaria vaccine RTS,S/AS01, approved by the WHO in 2021 and R21/Matrix-M approved in 2023 are significant steps toward malaria control. However, both vaccines exhibit partially protective efficacy of short duration, and emphasize the need for additional tools such as more effective and improved vaccines to interrupt malaria transmission. It is widely recognized that combination vaccines targeting antigenically distinct life cycle stages (sporozoites, erythrocytic asexual parasites and male and female gametocytes) in P. falciparum and P. vivax will provide effective immune protection against infection and reduce transmission. The primary focus of the proposed studies is to develop combination vaccines targeting infection by sporozoites and mosquito transmission by gametocytes of P. falciparum and P. vivax. P. falciparum circumsporozoite protein (PfCSP) is the leading pre-erythrocytic vaccine (PEV) candidate. In addition to Pfs25, pre-clinical studies and limited phase 1 trials have identified Pfs230 as another strong candidate for the development of a transmission-blocking vaccine (TBV). Orthologous antigens (PvCSP and Pvs25) in P. vivax have also been identified as strong PEV and TBV candidates. The mRNA-LNP approach shown to be relatively safe and effective offers the flexibility to combine several antigens in a multivalent vaccine. Our published studies on effective immunogenicity of mRNA-LNP vaccines encoding PfCSP and Pfs25 provide the basis and a solid foundation to develop proposed mRNA-LNP combination vaccines. In specific aim 1 we will evaluate protective efficacy of Pfs25 and Pfs230 TBV antigens, individually or in combination with PfCSP. The potency of PfCSP mRNA-LNP will be compared to RTS,S/AS01 as a benchmark. Immunogenicity of Pvs25 and chPvCSP (a chimeric P. vivax CSP representing all three allelic types) combination will be assessed in studies in specific aim 2. Studies in specific aim 3 will evaluate a combination of TBVs (aim 3a) and PEVs (aim 3b) of both Plasmodium species. Finally, immunogenicity of select combinations of TBVs and PEVs will be validated in rhesus macaques in specific aim 4. Vaccine combinations targeting different stages in: (i) P. falciparum and P. vivax, and (ii) validated in mice and nonhuman primates, will serve as a guide for designing future clinical trials aimed at reducing disease burden and interrupting malaria transmission to achieve the long-term goal of global malaria elimination.

NIH Research Projects · FY 2026 · 2024-12

Summary Silent mating-type Information Regulation Two homology 1 (SIRT1) is the founding member of the Class III NAD+-dependent histone deacetylases and plays critical roles in a variety of cellular processes, including aging, homeostasis, metabolism, and stress response. Importantly, increasing evidence shows that, by deacetylating both histone and non-histone proteins, SIRT1 is involved in the initiation and progression of cancer. The proposed project is significant because despite the surge of interest in SIRT1 over the past two decades, there are still many knowledge gaps of exactly how SIRT1 works in tumorigenesis. The objective of this resubmission application is to advance our understanding of the novel function of SIRT1 and its implication in cancer. Aims 1 and 2 are designed to define the molecular mechanisms of SIRT1 in regulating centrosomal and ciliary biogenesis through the deacetylation of Polo-Like Kinase 2 (PLK2) and Coiled-Coil Domain- Containing protein 66 (CCDC66). A series of posttranslational modification events will be delineated to dissect how SIRT1 coordinates with cellular regulatory machineries and signaling pathways to tempo-spatially control centrosomal and ciliary biogenesis and function. In Aim 3, we will explore the therapeutic potential of manipulating SIRT1 activity and signaling to repress the in vitro and in vivo growth of sonic hedgehog (SHH) subtype medulloblastoma, a cancer subtype in which abnormal centrosomal/ciliary biogenesis and function are highly implicated. The results of this work will accelerate the fundamental understanding of an under-explored yet critical function of SIRT1 in cancer. More importantly, the expected outcome will yield new insight into the development of novel therapeutic approaches for difficult-to-treat cancers, such as SHH-medulloblastoma.

NSF Awards · FY 2024 · 2024-12

This NSF support will enable 54 U.S.-based students, early career engineers, and faculty members around the country to attend the 10th Thermal and Fluids Engineering Conference (TFEC), thereby leading to their significant intellectual benefit and professional development. The TFEC, organized by the American Society of Thermal and Fluids Engineers (ASTFE), is growing to be one of the leading platforms for presenting and discussing research advances in thermal and fluids engineering. Since its inception in 2015, TFEC has been organized in a progressively student-centric fashion by seeking firsthand input from the student attendees. In line with the students' top priority, the 10th TFEC will invite 14 plenary, keynote, and special talk speakers and facilitate their interaction with the students. This support will also enable the students to realize their second priority: networking and socialization. The field of thermal and fluids engineering impacts a vast spectrum of our industry and society. New advances in this field prompted by the attendance and collaborations at the conference through this support would help improve and optimize numerous applications of societal benefit such as gas and wind turbines, fuel cells, electric vehicles, electronic devices, freshwater production, power plants, industrial processes, and medicine among others. In addition, programming at the conference will aid in enhancing the professional development and career selection skills of both the current and the future workforce. 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 · 2024-12

Arctic infrastructure is rapidly changing under the impact of climate change, socioeconomic and political shifts. Its existence varies by different regions and countries. In some places it is outdated and inadequate, while maintaining or upgrading poses significant challenges. In other places there is a need in new roads, airstrips, Internet and utilities. And for some places and communities’ concerns exist about negative impacts of infrastructure on cultures and ecosystems. This issue is especially important among Indigenous Peoples as infrastructure development has traditionally served interests of external groups to exploit resources without local approval. As the Arctic undergoes transformations, and climate-induced damages, such as flooding and erosion accelerated by thawing permafrost and sea level rise, understanding where and what infrastructure demands the most attention becomes highly important for building resilient and sustainable infrastructure. Development of this understanding requires collaborative efforts to learn from past mistakes, ensure just transitions, and construct Arctic infrastructure that fosters a more equitable and sustainable future. The project brings together researchers working in the Arctic (e.g., Alaska, Norway, Greenland, and Canada) and Indigenous community members (in particular, residents of the Yukon-Kuskokwim Delta Region) to collaboratively identify the challenges and good practices of co-production of place-specific and theoretically grounded knowledge of Arctic infrastructure. The project will promote transdisciplinary research inclusive of diverse ways of knowing, experiencing and perceiving the world. This project aims to formulate locally and regionally relevant questions for future research on just infrastructures through the following objectives: 1) Map existing expertise developed by academia, Indigenous Peoples, communities and other stakeholders and learn from each other about infrastructure planning, managing, maintaining and adapting to climate conditions and how it involves/excludes stake-, rights- and knowledge holders; 2) Identify prospects for knowledge convergence and co-production on studies of different forms of infrastructure in the Arctic; 3) Start creating a self-sustaining experts network that could be utilized for decision making, research and other purposes related to critical infrastructure planning, construction, maintenance, and removal of abandoned objects. The project will begin development of a knowledge platform to help decision-makers assess solutions designed to facilitate the equitable and just development of critical infrastructure. The project provides learning opportunities by including Indigenous knowledge holders and early-career scholars in planning, implementing, and reporting research projects. 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

Human adaptability is highly dependent on technology. However, the current knowledge of early technological development derives almost exclusively from stone tools and fossil bones found in the archaeological record. Tools made from organic materials (i.e., wood) are intrinsically perishable and as such are almost entirely absent in the early record of human material culture, leading to an incomplete picture of human technology's origins. This study investigates non-human primate species that use wooden tools as an analogue for the possible behavioral diversity of ancient human ancestors. Organic tools used by non-human primates, are analyzed to identify the resulting modifications. These changes are compared with those observed in wood that was naturally altered. The research employs novel technologies such as machine learning and experiments with robots to identify and standardize diagnostic modifications in ancient fossil wood that show similar damage patterns found in modern primate tools. The resulting methods are applied to fossilized wood remains dated to 4-2 million years ago to investigate wooden tool use in early human ancestors. The goal of this study is two-fold: (1) to develop a method to identify evidence of percussive tool use in wood (organic tools), and (2) to establish whether there is evidence of percussive organic tool use in fossil wood remains dated between 4.0-2.0 Mya. To attain these goals, researchers collect, document, and analyze organic tools used by non-human primates. Additionally, controlled experiments with a percussive robot are carried out to investigate the effect of various variables (e.g., species, moisture content, etc.) on the formation of percussive traces on wood. Researchers document a variety of natural damage patterns (e.g., due to fungus or insect activity, or due to taphonomic processes). The information is analyzed to create a catalogue of natural and artificial damage patterns. Machine learning models analyze these patterns, distinguishing between natural and tool-use induced damage. Researchers then collect and document fossil wood specimens. Their antiquity is stablished by dating thin sectioned fossil wood (uranium/lead dating), as well as the associated strata (paleomagnetic and tephrochronology analyses). The damage catalog and the AI models are applied to identify patterns of percussive use in the fossilized wood. The study offers new insights into early human behavior and the origins of human 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 award provides support for the 50th Knots in Washington conference that will take place at George Washington University in Washington, DC, December 6-8, 2024. Knot theory is not only a central part of mathematics, but it also has deep connections to physics, chemistry and biology. The series covers cutting edge topics from knot theory and its ramifications. Organizers will strongly encourage students and junior researchers, as well as members of under-represented groups to participate in this event and engage in formal and informal research collaborations. An example of a chain of breakthrough in modern knot theory includes: Thurston Geometrization of 3-dimensional manifolds (Perelman theorem), Jones link polynomial (quantum knot invariants, e.g. HOMFLYPT and Kauffman polynomials), Vassiliev (finite type) invariants, Khovanov homology, Ozsvath--Szabo, Heegaard--Floer homology, and Witten Conjecture on skein modules of closed 3-manifolds. All of these topics were covered at Knots in Washington conferences as they were emerging and before they became a part of mainstream research. The series continues to ensure coverage of current trends in knot theory and low dimensional topology. More information is available at the conference website: https://blogs.gwu.edu/ccas-knotsinwashington/. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

The mission of the Research and Engagement for Action on Climate and Health (REACH) Center is to bridge big data to health-protective solutions for climate-sensitive risk factors. Specifically, the Center leverages the power of novel geospatial datasets and research co-generation with governmental and non governmental partners to conduct research at the intersection of climate and health, from local to global scales. The Center forms a new multi-institutional partnership that leverages world class strengths in public health, medicine, and public policy at George Washington University; Earth and atmospheric sciences at George Mason University and Howard University; and research translation at Environmental Defense Fund. The Center’s ground-breaking partnership cultivates a multi-disciplinary, collaborative research enterprise that generates new knowledge and accelerates research translation. Our ideal location in the National Capital Region, hosted at the only school of public health in Washington, DC, gives us unique opportunities to collaborate with the local and federal government and a plethora of civil society organizations engaged in environmental and health policy development. The REACH Center will generate new knowledge along the spectrum of discovery to applied research, particularly leveraging the strengths of geospatial datasets to address research questions that can guide health-protective actions at a range of administrative scales. The Center will catalyze collaborations between investigators and governmental and non-governmental stakeholders to understand the complex interactions between the Earth system and health and design approaches for protecting public health from environmental hazards. The Center will achieve its mission through Administrative, Developmental, Community Engagement, and Exposure Assessment Cores. The Center’s Developmental Core will develop new research infrastructure that fosters research exploring health-protective solutions for environmental risk factors through pilot awards, student fellowships, educational and networking opportunities, and interaction between investigators and governmental and non governmental partners. The Community Engagement Core will provide a bridge between academic research and a community of potential users of the findings to drive health-protective solutions forward. The Exposure Assessment Core will lead the Center’s efforts to make geospatial environmental data more accessible, interoperable, and interpretable for relevant research conducted by researchers from various disciplines.

NIH Research Projects · FY 2024 · 2024-09

Project Summary If successful, the proposed study will provide accessible and scalable methods with measurable and validated accuracy to assess key indicators of an older adult’s health related to body composition phenotypes and associated physical function. The approach uses inexpensive and widely available commodity optical scanners as a novel imaging modality with the potential to practically reduce healthcare costs and disparities. The study meets NIA strategic priorities by developing improved approaches for the early detection and diagnosis of disabling illnesses and age-related debilitating conditions (C1) and in providing a foundation for developing interventions for treating, preventing, or mitigating the impact of age-related conditions (C3). Early screening and diagnosis of body composition and physical function combined with timely, dietary, exercise, and/or pharmacological interventions can mitigate the risk of functional decline and negative health outcomes in individuals with sarcopenic obesity. Simple anthropometric measures are easy to perform but have poor diagnostic accuracy and are inconsistently associated with morbidity and long-term physical function. While other modalities (e.g., dual-energy X-ray absorptiometry (DEXA), CT, MRI) may have higher diagnostic accuracy, they are impractical for widescale integration into clinical practice. There are relatively inexpensive systems and mobile apps that use 3D body shape from optical scanners for predicting body composition. While their validity is sufficient for consumer-oriented applications, these prediction algorithms may not be applicable for clinical use on older adults ─ they were not trained on data from this population. These systems also do not predict both muscle mass and physical function which are critical in the diagnosis of sarcopenia and obesity. To address these limitations, our team has previously developed highly accurate prediction algorithms using optical body scanning technology (R21HL124443, R01DK129809). These promising results merit us to further test and validate our system to translate such technologies into routine clinical and home-based care. We propose collecting data in an observational cross-sectional study of participants recruited from community- dwelling older adults. Participants will undergo: (i) 3D optical body scans to determine body shape; (ii) DEXA to assess body composition; (iii) D3-creatine dilution tests to determine total muscle mass; and (iv) validated physical function assessments. This data will be used to train artificial intelligence algorithms to predict body composition and physical function. We will investigate the usability of the approach for clinicians and for older adults using mobile platforms. We anticipate that the next step in this line of research is to conduct a cohort study that demonstrates the predictive nature on adverse outcomes in participants with sarcopenic obesity, or in using this system as part of a clinical trial. Additionally, we envision transitioning this technology to real-world settings by developing a prototype system as part of a Small Business Technology Transfer program.

NIH Research Projects · FY 2025 · 2024-09

The Common Fund (CF) research initiative has generated a wealth of data that can provide vital context, origin, and, in some instances, quantitative inferences for biomarkers. However, the systematic harmonization and organization of biomarker data, as well as their connections to CF data, remain in an early stage and is currently the focus of the year-long Common Fund Data Ecosystem (CFDE) Biomarker-Partnership project that aims to develop a working biomarker data model. The proposed BiomarkerKB project aims to refine and populate the biomarker data model through a close and dynamic external partnership with the NCI-supported Early Detection Research Network (EDRN) with built-in community input mechanisms. The initial focus is on refining our current biomarker data model using EDRN's cancer biomarker data and knowledge. Initially focusing on cancer will allow us to limit our scope while retaining the ability to evaluate a variety of data types and therefore ensure extensibility of the model as new data types and technologies emerge. The Minimal Viable Product (MVP) will include persistent biomarker identifiers, linked data, connections to recognized standards and ontologies, downloads/APIs, and data access through interfaces for biomarker explorations. This data model will serve as the cornerstone for AI-ready datasets, machine learning-based biomarker prediction models, and biomarker knowledge graphs. The scientific use case the project proposes to support is the ability to explore molecular biomarker-related knowledge for most prevalent cancers at a systems level, categorized by biological functions through mapping to key ontologies, pathways, biomolecular data (glycans, proteins, genes, metabolites) and Electronic Health Record (EHR) terms and tests. Example biomarkers (including non-molecular ones that are of interest to Data Coordinating Centers (DCCs)) for other diseases will also be considered to ensure the comprehensiveness and robustness of the data model. This project promises to enrich our understanding of the translational health record and intervention space, revolutionizing the way we approach diverse diseases, clinical assays, molecular mechanisms, and disease classifications. The potential benefits extend to our partners in the EDRN and the broader CFDE community, underscoring the real-world significance of biomarkers across the medical and research landscape.

NSF Awards · FY 2024 · 2024-09

Despite ongoing efforts to broaden participation in engineering in the United States, Black men remain significantly underrepresented, with only 2.8% of engineering bachelor's degrees awarded to them in 2020. These statistics indicate that there is a disconnect between cultural, institutional, or academic factors in engineering education settings and the expectations and experiences of Black men resulting in this lack of representation. Moreover, within both engineering education and professional engineering work contexts, complex projects are formulated and executed by teams. Given the critical role of teamwork in engineering in both industrial and academic settings, understanding the social interactions between Black men and their peers within these teams is vital. Consequently, this project will investigate the experience of Black men in undergraduate engineering student teams. The project aims to produce results that will be used broadly to support Black men’s sense of belonging and enhance their academic and professional success in engineering. To address these issues, this project focuses on two research questions: 1) What are the experiences of Black men on student project teams? and, 2) How do Black men perceive their participation in decision-making processes within these teams? This project will expand the research available to instructors, researchers, decision makers, and policy makers to support Black men in engineering from an asset-based perspective. To achieve the goals of this project, this mixed-methods qualitative study will use Interpretative Phenomenological Analysis (IPA) and Photovoice methods. These phenomenological and participatory methods enable the prioritization of the voice of Black male engineering students in constructing study findings and co-constructing future scholarly work with student-driven strategies for increasing a sense of belonging and academic success. This project will address three key gaps in the current literature. First, in the past 5 years only one research study has explored the experiences of Black men on student project teams. Second, there is a lack of research on how Black men participate in decision-making processes on student led teams. This is critical because researchers have suggested there is a strong connection between identity production processes and the construction of engineering judgments among team members. By cross-fertilizing these literatures, the research team will investigate the ways that Black male experiences illustrate how identity processes directly impact engineering work practices among undergraduates. Third, this study will adopt an assets-based approach, focusing on the positive aspects of Black men's experiences in engineering rather than individual deficiencies. The participatory aspect of the photovoice methods will facilitate the development of student-driven strategies that have the potential to foster positive cultural change at the institutional level. The research may result in tangible recommendations for supporting and retaining Black men in engineering fields nationwide. To broadly share the student-driven strategies co-created with study participants, the project will include co-creation of a photovoice exhibit to share participants’ strategies, resources, and experiences. Disseminating project findings through this photovoice exhibit will make the research accessible to a wider audience, including community stakeholders, students from other institutions and disciplines, university researchers, administrators, and the general public. 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

Advanced AI-driven training platforms can revolutionize education, workforce development, and specialized training, such as emergency response preparation, by providing realistic, cost-effective, and interactive environments. Progress in this area has been hindered by the need for more affordable and adaptable platforms capable of creating realistic, real-time, closed-loop environments. This EArly-concept Grants for Exploratory Research (EAGER) project aims to develop research infrastructure to enhance human training through AI-driven task environments integrating humans with virtual scene simulations and multi-modal sensorimotor interactions, which holds significant societal benefits. By overcoming current technological limitations, AI-driven task environments can improve the quality and accessibility of training for various applications. The technology this award aims to develop has the potential to significantly reduce training costs, enhance learning experiences, and better prepare individuals for real-world challenges, ultimately benefiting society as a whole. Additionally, the project will introduce K-12 students to cutting-edge mixed reality and AI technologies, sparking their interest in STEM fields. This research will first develop the platform for multi-modal sensorimotor interactions to ensure the best immersive and smooth interactions between humans and virtual scene simulations. It will then establish theoretical foundations and develop efficient algorithms for a closed-loop AI-driven scene task environment. In particular, the research will encompass three interdependent thrusts: 1)Establishing an edge-assisted mixed-reality infrastructure to provide immersive environments and enable smooth sensorimotor interactions between humans and virtual scene simulations. 2)Creating a wide range of training tasks and realistic sensorimotor interactions using flexible and composable modules. 3)Developing a multi-agent reinforcement learning engine to enable dynamic virtual scene generation and adaptation based on interactions. Collectively, this project will produce an immersive mixed-reality infrastructure that ensures smooth sensorimotor interactions, supports real-time task execution, and allows for the exploration of innovative multi-agent learning algorithms. 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 award will fund a research project that studies the dynamics between migration and democracy. It explores the challenges and opportunities posed to governance, political inclusion, and cooperation. Areas of investigation include the role of émigrés in fostering resistance and the development of civil society networks. The major questions being investigated are how political migrants interact with the political landscapes of their host countries and their homeland; how these exiles contribute to political dynamics in their homeland through political remittances; and how the narrative of political emigration is leveraged to shape perceptions in both sending and receiving countries. This research spans democratization studies, international relations, migration studies, and media studies. This award will fund a research project that employs a mixed-methods approach using a combination of online surveys, semi-structured interviews, focus groups, and automated text analysis of media to collect and analyze data to study the effects of political emigration to countries on politics of the countries these emigres left behind. Survey data will be collected on at least 4,000 observations of political migrants. The two rounds of online surveys will explore their lived situations and political behavior. This approach will track shifts in their attitudes toward political engagement, political activism, dynamics of their relationships with host countries’ communities and governments, and influence on political discourse in their home country. The qualitative methods involve conducting 100 in-depth interviews with emigrants. Additionally, interviews with returnees will take place and focus groups will examine media consumption and will be held with political migrants to assess the effectiveness of narratives in international media. 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

Studies in humans have shown that early exposure to violence, as a victim or witness, have detrimental health and behavioral effects later in life. The impacts of these experiences in other primate species, however, are not well understood. This doctoral dissertation project uses a holistic and long-term approach to assess the outcomes of early life exposure to violence in a non-human primate species. Results from this study inform: (1) whether the impacts of early life violence exposure are unique to humans or shared with other members of the primate order, and (2) housing and management decisions in captive non-human primate species. This project provides field and data analysis opportunities for students and researchers at different stages of their careers. This study focuses on a non-human primate group for which historic data have shown distinct periods of increased violence. Information from existing long-term data bases will be combined with new data. Data analyzed in this study addresses questions about: (1) development (e.g., age at: weaning, material independence, sexual maturity, and first reproduction); (2) physiology and health (glucorticoids and inflammation levels); (3) behavior; and (4) social standing (e.g., rank and social network analyses). The study provides insights as to whether or not early life social adversity in the form of violence is a uniquely human experience. 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 · 2024-09

HIV-associated neurocognitive disorders (HAND) remain among the most prominent HIV co-morbidities in the era of ART-treated HIV infection. Persistent neuroinflammation is a recognized pathogenic feature of HAND, but the reason(s) for the inflammation persisting after initiation of ART remain incompletely understood. Our recent studies demonstrated that monocytes exposed to EVs carrying HIV-1 protein Nef (Nef EVs) acquire pro- inflammatory epigenetic memory that promotes exacerbated responses to inflammatory stimuli when Nef is long gone. The mechanism of this effect is similar to “trained immunity”, a concept that stipulates that an exposure to a pathogen may not only trigger an innate immune response in myeloid cells, but also induce long-lasting metabolic and epigenetic alterations causing innate inflammatory responses to be faster and more robust. We hypothesize that this effect of Nef may also target brain myeloid cells, microglia, and possibly other brain cells, astrocytes and oligodendrocytes. If contribution of this mechanism is confirmed, that would imply that even complete elimination of the virus or virus-related pathogenic factors may not be sufficient to fully abolish neuropathology in HIV-infected individuals, as brain cells will keep their pro-inflammatory status. The following Specific Aims will be pursued to test this hypothesis. SA1. To characterize epigenetic modifications induced by HIV and Nef EVs and assess their contribution to neuroinflammation. In this Aim, we will characterize epigenetic and transcriptional effects of HIV infection and Nef EV treatment in humanized mice. Results of snATAC-seq in this model will be compared to epigenetic dataset obtained from brains of PLWH. SA2. To establish how Nef EVs induce epigenetic modifications in brain cells contributing to neuroinflammation. Here, we will establish the cell type-specific mechanisms of inflammatory modifications induced by Nef EVs. This study is a continuation of the long-term collaboration between Drs. Bukrinsky and Sviridov, who discovered the training effect of Nef on myeloid cells. The proposed project is fully consistent with the R21 mechanism, as it investigates a novel phenomenon and involves high risk high reward studies.

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY/ABSTRACT There has been an increase in availability of evidence-based approaches for interventions and mental health service delivery in low-resource settings. In low- and middle-income countries this includes delivery of services by people who are not mental health specialists in primary care and community settings. However, there continues to be major gaps in delivery of evidence-based mental health services. In order to improve service delivery, we propose a quality assessment tool that can be completed by people with lived experience of mental health conditions who use services in primary care settings. When service users rate the quality of the care they receive, this feedback can be provided to health system managers and policy makers to aid in their decision making for improved mental health services. The quality assessment tool will be co-created by patients and their caregivers using health services (demand side in the healthcare system) and managers and policy makers (supply side in the healthcare system). Based on this co-creation process, service users will be able to complete assessments to rate quality indicators such as communication skills of service providers, the physical space for confidential clinical encounters, the availability of medication, referrals for psychological services, and use of patient education materials. The tool will differ from standard patient satisfaction tools that typically use subjective Likert scoring. Instead, the assessment tool will be based on a series of observations of provider behaviors, treatment and educational resources, and facility infrastructure. The quality assessment tool will be designed using the formatting of the World Health Organization Ensuring Quality in Psychological Support and Mental Health Helping Skill (EQUIP) platform, with the intention that the tool could eventually be made freely available on the platform alongside other care ratings resources. By completion of this research study, there will be an EQUIP quality assessment tool that can be completed by service users. The tool will have been piloted in two low-resource settings: Liberia and Nepal. There will also be adequate capacity building and other formative work completed to conduct a larger scale multi-site evaluation of quality improvement using this tool rated by health care service users.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT There has been an increase in availability of evidence-based approaches for interventions and mental health service delivery in low-resource settings. In low- and middle-income countries this includes delivery of services by people who are not mental health specialists in primary care and community settings. However, there continues to be major gaps in delivery of evidence-based mental health services. In order to improve service delivery, we propose a quality assessment tool that can be completed by people with lived experience of mental health conditions who use services in primary care settings. When service users rate the quality of the care they receive, this feedback can be provided to health system managers and policy makers to aid in their decision making for improved mental health services. The quality assessment tool will be co-created by patients and their caregivers using health services (demand side in the healthcare system) and managers and policy makers (supply side in the healthcare system). Based on this co-creation process, service users will be able to complete assessments to rate quality indicators such as communication skills of service providers, the physical space for confidential clinical encounters, the availability of medication, referrals for psychological services, and use of patient education materials. The tool will differ from standard patient satisfaction tools that typically use subjective Likert scoring. Instead, the assessment tool will be based on a series of observations of provider behaviors, treatment and educational resources, and facility infrastructure. The quality assessment tool will be designed using the formatting of the World Health Organization Ensuring Quality in Psychological Support and Mental Health Helping Skill (EQUIP) platform, with the intention that the tool could eventually be made freely available on the platform alongside other care ratings resources. By completion of this research study, there will be an EQUIP quality assessment tool that can be completed by service users. The tool will have been piloted in two low-resource settings: Liberia and Nepal. There will also be adequate capacity building and other formative work completed to conduct a larger scale multi-site evaluation of quality improvement using this tool rated by health care service users.

NSF Awards · FY 2024 · 2024-09

4D printing is a new and exciting manufacturing process that creates structures designed to change shape or function over time when exposed to specific stimuli. Compared to traditional 3D bioprinting, incorporating a time dimension in bioprinting is intriguing, as the dynamic 4D effect can more accurately mimic natural tissue development and better regulate cell behaviors. However, the fundamental knowledge of the relationship between living biosystems and dynamic manufacturing is still lacking and needs to be explored to uncover its full potential. This project aims to use this innovative 4D printing technology to develop soft robotic systems and establish the foundational knowledge for targeted stem cell delivery. For this purpose, a versatile 4D bioprinting approach will be created to produce custom living biosystems, expanding therapeutic options and meeting the complex demands of various medical fields. In addition, the project outlines convergent research, educational, and outreach activities, which will address the critical challenges in advanced biomanufacturing for biomedical studies. This project will investigate the manufacturing science underlying the design of reprogrammable soft robotic systems for targeted stem cell delivery within a biomimetic environment. These 4D printed soft robots will be capable of reshaping in response to stimuli, enabling cargo loading and release. Additionally, their magnetic controllability will allow precise guidance to the intended delivery site. To achieve this goal, the project will pursue two aims: Aim 1 focuses on synthesizing a multi-responsive and magnetically controllable 4D ink material for bioprinting structures. Various ink formulations will be explored to optimize printability and achieve desired properties such as stimuli-responsiveness and mechanical strength. Aim 2 involves fabricating soft robots and evaluating their performance in loading, docking, and delivering neural stem cell spheroids (neurospheres) on demand. Furthermore, the ability of these spheroids to integrate into neural networks will be assessed in vitro. The successful completion of this project will build the foundation to enable a novel biomanufacturing platform and push the boundaries of living biosystem manufacturing. 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

Advancements in modern technology have exponentially increased the availability and complexity of spatial and spatiotemporal data across various fields, presenting unique challenges and opportunities. This project aims to develop scalable and efficient quantile learning techniques to unlock valuable insights from large-scale, heterogeneous spatial-temporal data, overcoming limitations in handling dynamic patterns and spatial variations while accounting for uncertainty. These new analytical techniques will have wide-ranging applications, revolutionizing our understanding of spatial and temporal variations in critical areas. For example, they can help identify communities facing disproportionate risks from environmental hazards, health crises, or crime, enabling more targeted and effective interventions. By making these techniques widely accessible through public software releases, the project will empower researchers and policymakers to leverage vast amounts of spatial-temporal data and address pressing societal issues more effectively. The project will also contribute to STEM education by engaging both undergraduate and graduate students in hands-on learning and incorporating research findings into course development. The project will develop scalable and efficient quantile learning methodologies, algorithms, and theories to address challenges in analyzing large-scale spatial-temporal data through three main research aims. First, the investigators will introduce a flexible quantile spatial model framework that simultaneously captures spatial nonstationarity and heterogeneity via spatially varying coefficients. Second, they will develop a scalable distributed learning procedure using domain decomposition computing to efficiently handle large spatial datasets across complex domains, including a communication-efficient aggregation method for estimating constant coefficients to ensure optimal efficiency. Third, the research will expand analysis from 2D to 3D to tackle complex and heterogeneous dynamics of extremely large spatiotemporal data, introducing a class of quantile spatiotemporal models and developing a robust, scalable estimating procedure to meet substantial computational demands. These advancements will significantly impact multiple areas of statistics, including large-scale computing, inference, optimization, and nonparametric approximation theory. 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 US cannabis policy and retail contexts have markedly changed in the past decade. During this time, cannabis use prevalence increased in adults, particularly young adults and certain racial, ethnic, sexual, and gender minorities The disproportionate use rates may reflect evidence of cannabis industry target markets. States vary regarding their regulations for cannabis advertising (e.g., health claims, youth-targeting) and required health warnings, which may have implications for cannabis-related disparities. Advertising restrictions are important, given that product ads and/or characteristics (e.g., flavors) may appeal to specific groups, and the use of health claims, price promotions, and various marketing channels (e.g., digital media). Health warnings can effectively communicate risks and reduce substance use rates. This research aims to reduce cannabis use and related disparities by advancing the evidence regarding risk perceptions, use decisions, and their determinants, specifically cannabis regulations and industry marketing. This study is among the first to address key research gaps including limited research: a) comprehensively analyzing cannabis marketing data; b) using industry market research strategies to understand lifestyle profiles of young adults most likely to use cannabis; and c) of cannabis ad and warning messages. This mixed-methods study will address 3 aims. Aim 1 examines cannabis marketing characteristics (e.g., advertising content, media channels used, ad expenditures) over time with regard to state and local cannabis retail rules by analyzing existing longitudinal marketing data. Aim 2 assesses cannabis marketing exposure, perceptions, and use behaviors (i.e., intentions, use) over time across young adults with different lifestyle profiles by analyzing data from an existing national cohort of diverse young adults. Aim 3 explores the impact of different ad and warning messages on risk perceptions and use decisions among young adults by conducting a series of online survey-based experiments. This research protects public health and improves population-level well-being by advancing evidence regarding social determinants of cannabis use to inform regulatory efforts to reduce use among disproportionately-impacted groups. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

To advance the science on social factors associated with the well-being – defined broadly to include social, economic, physical, psychological, and relational dimensions – of new populations, in this project social scientists are convened to present their research on new populations in the United States and influences on their well-being. With sustained levels of new populations to the country, it is crucial to promote scientific knowledge on their well-being as their population is critical for the sustenance, prosperity, and health of the nation. Representing advances in this area across a variety of disciplines – including anthropology, education, criminal justice, sociology, and economics – the scholarship presented at this convening will be published in peer-reviewed journals to enhance public knowledge on the barriers and promoters to the well-being of new populations, thereby benefiting US society. Specific efforts will be undertaken to recruit scholars from under-represented backgrounds and institutions of varying research capacities, to add a diversity of perspectives to the field. Making sound decisions rooted in empirical evidence concerning new populations requires that we create spaces that amplify academic work on the subject, while also creating opportunities for the public to engage with this scholarship in consumable forms. In this conference, a collaborative effort between the George Washington University and the University of South Florida, participants present high-impact research on the well-being of new populations and take part in applied workshops on best practices for communicating that research to decision makers and the public-at-large. The intellectual merit of the conference includes its interdisciplinary and translational orientation to the application of empirical research. The translational workshops prepare a cohort of scholars with the knowledge and skills to bring empirical insights on publicly engaged scholarship on the well-being of new populations beyond the academy into decision making spaces. 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

Coastal margins along the Atlantic seaboard are experiencing significant changes due to sea level rise, causing the intrusion of saltwater into freshwater systems. This inundation and groundwater salinization has led to the emergence of “ghost forests,” characterized by stressed and dead trees, which are altering the ecology and hydrology of coastal ecosystems. This research will study the impact of sea level rise on the health of coastal forests through changes in hydrological processes, with a particular emphasis on stemflow (precipitation intercepted by trees and channeled down the trunks to the soil). Stemflow plays a crucial role in transporting nutrients and organic matter to the forest floor, as well as soil moisture recharge in near-trunk soils. The research will provide new insights into coastal forest resilience and inform strategies for mitigating the effects of sea level rise. Further, the project will develop publicly available training modules to disseminate knowledge about the flow and dynamics of water in coastal forests and provide interdisciplinary training to undergraduate and graduate students. The project will investigate the impacts of sea level rise on stemflow dynamics and associated hydrological and biogeochemical processes in coastal forests along a transect from healthy to ghost forests. Field measurements, laboratory analyses, and statistical modeling approaches will be used to understand stemflow's role in the ecohydrological responses of coastal ecosystems to changing environmental conditions. Results will inform efforts to enhance ecosystem resilience and adaptation to sea level rise by elucidating how coastal forests respond to stressors such as soil salinity, vegetation changes, and hydrological dynamics. 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.