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NSF

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professor Skrabalak of Indiana University and her team will investigate how to precisely design and produce complex nanoparticles made from four or more elements. These materials, known as polyelemental nanoparticles, could offer unique properties that do not exist in simpler materials made of fewer elements. Such properties include improved strength, stability, and reactivity. This research could pave the way for new materials used in energy technologies, industrial catalysis, and next-generation electronics. Moreover, this project will provide immersive research experiences and professional development for graduate and undergraduate students, with a focus on scientific communication and mentoring. Public engagement activities—including interactive nanoscience exhibits and community events, will be used to enhance the visibility and accessibility of nanoscience while helping to prepare a well-equipped STEM workforce. This research will aim to establish general design principles for synthesizing high entropy alloy (HEA) nanoparticles, high entropy intermetallic (HEI) nanoparticles, and polyelemental heterostructures via thermal conversion of polyelemental core@shell nanoparticles. HEA nanoparticles—single-phase solid solutions composed of five or more elements in near-equal ratios—are expected to exhibit enhanced mechanical, chemical, and catalytic properties due to their high entropy of mixing. HEI nanoparticles, with their partially ordered multi-elemental structures, may offer distinct bonding environments that improve stability and performance in catalytic applications. The proposed strategy will involve the seed-mediated synthesis of core@shell nanoparticles followed by annealing to drive interdiffusion and phase transformation. Through three specific aims, Professor Skrabalak's team will investigate how nanoparticle composition, structure, and interactions with support materials influence the formation of HEAs, HEIs, or heterostructures. By integrating experimental studies with computational modeling, the research is expected to yield a broadly applicable framework for the rational design of compositionally complex nanoparticles. 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.

NIA - National Institute on Aging

Summary/Abstract Vascular pathology has been identified as a critical driver of Alzheimer’s disease. This proposal aims to establish a human induced pluripotent stem cell (iPSC)-derived model to study the impact of genetic and environmental factors on the development of Alzheimer’s disease-related vascular pathology. Most Alzheimer’s disease patients have cerebral amyloid angiopathy (CAA), the symptoms of which include build-up of amyloid around vessels, cerebral hemorrhages, microbleeds, and inflammation. Studies in multiple mouse models of Alzheimer’s disease have demonstrated that hemopoietic-derived brain-resident microglia and circulating monocytes and perivascular macrophages can be protective against CAA. The APOE44 genotype is the strongest genetic risk factor for Alzheimer’s disease and CAA. Despite the clear interplay between Alzheimer’s disease, CAA, and APOE genotype, the molecular mechanisms underlying the vascular changes are not fully understood. These observations motivate us to create a model using human cells to study the underlying mechanisms of microglia- vascular interactions in cells of different APOE genotypes. In prior published studies, we created a 3D human iPSC-derived vascular model in a hydrogel scaffold. It undergoes vasculogenic and angiogenic events and forms a model plexus (the VAMP model). We showed that the VAMP model can be built inside a microfluidic device to enable regulatable perfusion, mimicking blood flow. We have advanced the model by incorporating microglia (VAMP-MG) to reflect the key cell-cell interactions in CAA better. The work proposed in two specific aims will increase the utility of the VAMP-MG model in five significant ways: We will 1) build the model from a recently established collection of APOE44 versus APOE33 isogenic iPSC lines to incorporate genetic risk factors; 2) incorporate live reporters to monitor the activation states of microglia and vascular cells in real-time; 3) create the VAMP-MG model in microfluidic chips to achieve perfusable vasculature; 4) flow human plasma from Alzheimer’s disease, aged-matched healthy, or young healthy donors through the vessels to improve modeling of disease-relevant environmental factors; 5) use Ribo-Tag technology to identify transcriptomic changes in the vascular endothelial cells and the microglia. The model will be deeply characterized and validated at cellular and molecular levels. Alzheimer’s disease-relevant phenotypes, including amyloid deposition and clearance, A secretion, and inflammatory factor production, will be assessed. We will compare transcriptomic data collected from the model to published human Alzheimer’s disease brain versus healthy control brain transcriptomic data, including single nuclear RNA-seq datasets. Completing the proposed work will enhance our ability to study vascular and microglial responses to inflammatory signals in real time, generate transcriptomic data at cell type levels, and establish APOE44 versus APOE33 phenotypes in microglia-vascular models to a new level of complexity. In the future, this VAMP-MG model can potentially define key genetic and environmental factors that exacerbate or alleviate Alzheimer’s disease-vascular phenotypes.

NSF

Healthcare is rapidly changing into a multidisciplinary field. Data science and artificial intelligence (AI) have become integral for healthcare and medical services. Machine Learning (ML), a branch of AI, is broadly applicable for developing predictive models that drive research, development and healthcare practices. Unintentional bias within the datasets and computer programs used for ML creates healthcare outcomes which benefit some people more than others. This project will develop an innovative and inclusive learning and teaching ecosystem for high school students. The ecosystem consists of educational agencies and teachers, cross-disciplinary expertise from data scientists and medical clinicians, community members and college students from diverse background as mentors. Authentic cross-cultural discussions amongst community members and students will be a key component of students’ learning experience. The ecosystem will provide a data science and ML laboratory course and an annual datathon. Computer science teachers will facilitate the course, and will also receive professional development in problem-based data science approaches. Students in the course will explore student-led, inquiry-based strategies on how to navigate and visualize large healthcare sets using the same programming languages and tools that data scientists use. During the datathon, students will team with their local community members and Science, Technology, Engineering, and Mathematics (STEM) teachers to solve authentic data-driven healthcare issues which are important and personal to them. Community members will share their experiences to ensure all voices are heard. Datathon participants will be introduced to culturally-responsive methods. The project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST), which seeks to engage underrepresented students in technology-rich learning environments, including skills in data literacy, and increase students’ knowledge and interest in information and communication technology (ICT) careers. During the project period, researchers will develop and study a semester-long program that engages up to 1000 Rhode Island high school students, with an emphasis on recruiting racial minorities and young women from 12 Title 1 schools. The researchers will investigate how students engage in the program and datathon, the usability and sustainability of this program, and the enactment of the innovative learning ecosystem. The following questions will guide this study: 1) How do the data laboratory and datathon contribute to student learning and efficacy in data science, and their interest in data science and healthcare careers? 2) What are teachers’ perspectives about the usability and effectiveness, including challenges, of the materials, curriculum, and supports? 3) How do teachers take up and enact the activities and tools to support student learning and interests in data science? Researchers will collect and analyze data using mixed methods, including data from a digital learning platform, surveys, interviews, assessments, and observations. The outcome will include a novel pedagogy for teaching high school students about rapidly evolving technologies. Deliverables will consist of annual professional development for the teachers; a public website for all Rhode Island district leaders, teachers, and parents; a vetted data science and ML laboratory course; and designs of the multidisciplinary, cross-cultural datathon. These will be freely shared and promoted online, presented at professional conferences, and published as research articles in peer-reviewed literature. 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.

NIBIB - National Institute of Biomedical Imaging and Bioengineering

Project Summary Allogeneic cell therapies offer transformative potential in clinical medicine, providing universal treatments with donor-derived immune cells and mitigating the variability of autogenic therapies. Among immune cell types, natural killer (NK) cells and neutrophils stand out for their therapeutic potential. NK cells can effectively target tumors and virally infected cells without patient-specific matching, while neutrophils, which do not require ABO matching, show promise in treating infectious diseases and inflammatory conditions. However, widespread adoption of allogeneic therapies is limited by challenges in cell cryopreservation, as current methods often compromise cell viability and function due to osmotic stress and toxicity during CPA loading. To address these limitations, we propose veloporation, a novel mechanoporation technique leveraging viscoelastic stretching to enable rapid, high-throughput CPA loading while minimizing toxicity. Unlike traditional CPA-loading methods, which expose cells to two primary injury mechanisms—molecular toxicity from prolonged CPA exposure and osmotic stress from excessive volumetric excursions—veloporation is the first approach to load CPAs almost instantaneously without altering cell volume. By alleviating both injury mechanisms simultaneously, veloporation preserves immune cell phenotype and functionality post-thaw, offering a transformative solution to the critical barrier of effective cell storage. The goal of this project is to develop and validate veloporation for CPA loading (velCPA) into immune cells, with a focus on NK cells and neutrophils for use in adoptive cell therapy (ACT). Aim 1 develops and optimizes the veloporation device for small immune cells. Aim 2 evaluates the ability of veloporation to load membrane-permeable and membrane-impermeable CPAs, minimizing toxicity and maximizing cell viability compared to traditional methods. Aim 3 assesses the post-thaw functionality of veloporated cells, including viability, immune activity, and potential for CPA co-delivery with therapeutic agents. By advancing cryopreservation techniques, this project addresses a critical barrier to the scalability of allogeneic cell therapies. It has the potential to enable the development of stable, off-the-shelf immune cell products for diverse clinical applications. While here we focus on NK cells and neutrophils, this technology could be adapted for the cryopreservation of other cell types used in regenerative medicine and immunotherapy, such as T cells or stem cells. The versatility and scalability of veloporation make it a promising platform technology with far-reaching implications for cellular therapies and biobanking.

NSF

This project aims to serve the national interest by developing STEM courses that will prepare students to apply knowledge from mathematics courses to other disciplines. Undergraduate students are increasingly asked to make connections and articulate problems from disciplines outside of mathematics in quantitative terms. College faculty also need to understand and meet the changing educational needs of students, and most institutions have limited resources to address this critical challenge. The National Consortium for Synergistic Undergraduate Mathematics via Multi-institutional Interdisciplinary Teaching Partnerships (SUMMIT-P) has been working since 2016 to address these issues by forming and strengthening faculty partnerships across disciplines and institutions. These partnerships have reduced the separation between disciplines, resulting in undergraduate courses that make explicit connections between mathematics and numerous other disciplines. This Level 2 IUSE Institutional and Community Transformation project, led by West Virginia University, Virginia Commonwealth University, and Western Michigan University, includes approximately 40 new institutions that will adapt the SUMMIT-P model to form local faculty teams. The SUMMIT-P team plans to study the experiences of students in these revised courses while also collaborating with national professional societies to ensure the long-term sustainability of the consortium’s work, significantly expanding the scope and positive impact of SUMMIT-P to thousands of additional college students. The goal of this project is to assist institutions with the adaptation of a known model for developing and implementing cross-disciplinary STEM courses. The project will also work to broadly disseminate the SUMMIT-P model to institutions beyond the intended 40 project participants. The project will support the development of sustainable collaborations that aim to minimize traditional disciplinary silos. Additionally, the proposed work will advance understanding of the student experience in these interdisciplinary courses, using previous student outcomes in SUMMIT-P courses combined with new data focused on long-term student impact. The project’s research plan is designed to advance understanding of how choices made by institution-based teams affect positive impact of the change process, as well as the institutional qualities and resources that are critical to lasting change. Additionally, the research team plans to study the effectiveness of faculty partnerships formed using SUMMIT-P change processes in creating significant and lasting curricular change that results in positive long-term student impact. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. 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

This project aims to serve the national interest by developing STEM courses that will prepare students to apply knowledge from mathematics courses to other disciplines. Undergraduate students are increasingly asked to make connections and articulate problems from disciplines outside of mathematics in quantitative terms. College faculty also need to understand and meet the changing educational needs of students, and most institutions have limited resources to address this critical challenge. The National Consortium for Synergistic Undergraduate Mathematics via Multi-institutional Interdisciplinary Teaching Partnerships (SUMMIT-P) has been working since 2016 to address these issues by forming and strengthening faculty partnerships across disciplines and institutions. These partnerships have reduced the separation between disciplines, resulting in undergraduate courses that make explicit connections between mathematics and numerous other disciplines. This Level 2 IUSE Institutional and Community Transformation project, led by West Virginia University, Virginia Commonwealth University, and Western Michigan University, includes approximately 40 new institutions that will adapt the SUMMIT-P model to form local faculty teams. The SUMMIT-P team plans to study the experiences of students in these revised courses while also collaborating with national professional societies to ensure the long-term sustainability of the consortium’s work, significantly expanding the scope and positive impact of SUMMIT-P to thousands of additional college students. The goal of this project is to assist institutions with the adaptation of a known model for developing and implementing cross-disciplinary STEM courses. The project will also work to broadly disseminate the SUMMIT-P model to institutions beyond the intended 40 project participants. The project will support the development of sustainable collaborations that aim to minimize traditional disciplinary silos. Additionally, the proposed work will advance understanding of the student experience in these interdisciplinary courses, using previous student outcomes in SUMMIT-P courses combined with new data focused on long-term student impact. The project’s research plan is designed to advance understanding of how choices made by institution-based teams affect positive impact of the change process, as well as the institutional qualities and resources that are critical to lasting change. Additionally, the research team plans to study the effectiveness of faculty partnerships formed using SUMMIT-P change processes in creating significant and lasting curricular change that results in positive long-term student impact. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. 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

This project aims to serve the national interest by developing STEM courses that will prepare students to apply knowledge from mathematics courses to other disciplines. Undergraduate students are increasingly asked to make connections and articulate problems from disciplines outside of mathematics in quantitative terms. College faculty also need to understand and meet the changing educational needs of students, and most institutions have limited resources to address this critical challenge. The National Consortium for Synergistic Undergraduate Mathematics via Multi-institutional Interdisciplinary Teaching Partnerships (SUMMIT-P) has been working since 2016 to address these issues by forming and strengthening faculty partnerships across disciplines and institutions. These partnerships have reduced the separation between disciplines, resulting in undergraduate courses that make explicit connections between mathematics and numerous other disciplines. This Level 2 IUSE Institutional and Community Transformation project, led by West Virginia University, Virginia Commonwealth University, and Western Michigan University, includes approximately 40 new institutions that will adapt the SUMMIT-P model to form local faculty teams. The SUMMIT-P team plans to study the experiences of students in these revised courses while also collaborating with national professional societies to ensure the long-term sustainability of the consortium’s work, significantly expanding the scope and positive impact of SUMMIT-P to thousands of additional college students. The goal of this project is to assist institutions with the adaptation of a known model for developing and implementing cross-disciplinary STEM courses. The project will also work to broadly disseminate the SUMMIT-P model to institutions beyond the intended 40 project participants. The project will support the development of sustainable collaborations that aim to minimize traditional disciplinary silos. Additionally, the proposed work will advance understanding of the student experience in these interdisciplinary courses, using previous student outcomes in SUMMIT-P courses combined with new data focused on long-term student impact. The project’s research plan is designed to advance understanding of how choices made by institution-based teams affect positive impact of the change process, as well as the institutional qualities and resources that are critical to lasting change. Additionally, the research team plans to study the effectiveness of faculty partnerships formed using SUMMIT-P change processes in creating significant and lasting curricular change that results in positive long-term student impact. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. 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

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at the University of Puerto Rico Mayaguez. A total of 39 scholars pursuing bachelor's degrees in civil, computer, and electrical engineering will receive scholarships averaging $15,000 for up to five years. Scholars will also receive faculty and peer mentoring, and the project will build strong scholar cohorts through career-focused workshops and seminars centered on professional skills development. Additional activities for scholars include opportunities to engage in research, internships, and a co-op program. The overall goal of this Track 2 Scholarships in STEM project is to increase STEM degree completion of low-income, high-achieving undergraduates with demonstrated financial need. There is a significant national need to grow the STEM workforce and nurture key talent that will ensure economic competitiveness and provide domestic leadership across critical sectors. This project directly speaks to this need by growing the engineering workforce, which will strengthen sectors including AI, microelectronics, resilient infrastructure, and other key areas of need. The project will be assessed by an experienced evaluator and the data generated will contribute to the knowledge base on effective strategies to support talented, low-income students in STEM. This project is funded by NSF's Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NCATS - National Center for Advancing Translational Sciences

Human induced pluripotent stem cells (hiPSCs) are a foundational technology for regenerative medicine, disease modeling, and cell-based therapies. However, the clinical translation of hiPSC-derived therapeutics is constrained by unresolved challenges in manufacturing scalability, product consistency, and long-term storage. Current production methods, such as static culture or stirred-tank bioreactors, either lack throughput or impose damaging shear forces that compromise cell viability and stemness. Additionally, existing cryopreservation strategies are not optimized for the large-volume required for centralized manufacturing and distributed clinical deployment. To address these bottlenecks, we propose to develop a modular, low-shear, perfusion-based biomanufacturing platform coupled with controlled-rate cryopreservation for scalable and reproducible expansion of hiPSCs. This R21 will establish the technical feasibility and foundational workflows for a generalizable manufacturing system suitable for diverse therapeutic applications. In Aim 1, we will engineer and optimize a multilayer, perfusion-based bioreactor system to support hiPSC expansion at clinical scales. We will define operational parameters, evaluate multiple hiPSC lines, and develop protocols for efficient harvesting and redeployment. In Aim 2, we will implement long-term culture strategies to preserve genomic stability and pluripotency across multiple passages and optimize cryopreservation workflows using cryobags and controlled-rate freezing. Post-thaw cells will be assessed for viability, genomic integrity, and differentiation potential. Successful completion of this project will yield the first integrated platform for low-shear, current Good Manufacturing Practice (cGMP)-compatible expansion and cryostorage of hiPSC-derived therapeutic products. This innovation will reduce production variability, enhance scalability, and enable rapid deployment of hiPSC-based therapies across a range of disease indications. The resulting system will serve as a flexible and translational foundation for future commercial partnerships and larger-scale studies, consistent with the high- risk, high-reward goals of the R21 mechanism.

NHLBI - National Heart Lung and Blood Institute

Project Summary/Abstract Cardiac fibrosis compromises left ventricular (LV) function and worsens clinical outcomes in heart failure (HF). Currently, there are no clinical therapies that target fibrosis in the failing heart. Platelet-derived growth factor (PDGF), a central mediator of fibrotic responses, acts via two receptors - PDGFRα and PDGFRβ. Our pilot studies using explant cultures, flow sorting, single cell RNA sequencing (scRNAseq), and in vivo mouse models have uncovered a novel stem cell antigen(Sca)-1-expressing PDGFRα+ multipotent cardiac fibroblast population that sources myofibroblasts, interacts with tissue macrophages, and drives fibrotic and inflammatory responses during adverse LV remodeling. During HF, these cells decrease PDGFRα expression and instead upregulate PDGFRβ. The role of PDGFRβ in cardiac fibroblast biology in HF is unknown. We propose the novel hypothesis that a heretofore unrecognized Sca-1+PDGFRβ expressing cardiac fibroblast with mesenchymal stem cell features (termed cFMSCs) are key drivers of a PDGFRβ-dependent immunofibrotic axis in HF. Three Aims will test this hypothesis. In Aim 1, using a murine coronary ligation model, in vitro studies of cell function, scRNAseq, and inducible cardiac fibroblast-specific and Sca1+ cell-specific PDGFRβ-deletion mouse models, we will comprehensively define the importance of PDGFRβ in cFMSC cell function in ischemic HF. We will isolate Sca1+PDGFRα+ cFMSCs and assess PDGFR β-dependent myofibroblast differentiation, multipotency, downstream signaling and immuno-fibrotic secretome, and cFMSC-macrophage interactions. scRNAseq of sorted cells will assess cFMSC subpopulations and changes in transcriptomic profiles. In Aim 2, we will establish the pathogenetic role of cFMSC-localized PDGFRβ in chronic HF, by using inducible cardiac fibroblast-specific PDGFRβ knockout mice, deleting cFMSC PDGFRβ during chronic HF, and assessing late effects on LV remodeling, fibrosis, tissue macrophages, and inflammatory profiles. To establish necessity of cFMSC PDGFRβ signaling, cardiac fibroblast PDGFRβ loss will be induced by tamoxifen in PDGFRβf/f-Tcf21MerCreMer mice 4 w post- MI and LV remodeling, fibrosis, and tissue macrophage and inflammatory profiles will be assessed 6 w Iater. The effects of sustained cardiac fibroblast PDGFRβ activation on LV remodeling will be assessed using PDGFRβ[S]D849V-Tcf21MerCreMer mice. To establish sufficiency of cFMSC PDGFRβ signaling, we will perform intramyocardial adoptive transfer of sorted PDGFRβ-deficient and -competent Sca1+PDGFRα+ cFMSCs from HF mice into naïve mice and assess late LV remodeling and fibrosis. In Aim 3, we will test potentially translatable therapies to antagonize cFMSC PDGFRβ signaling in chronic HF, including CP-67345, a specific small molecule PDGFRβ inhibitor, fibroblast-targeted nanoparticle delivery of PDGFRβ siRNA, and anti-PDGFRβ neutralizing antibody. We will measure the effects of these therapies on LV remodeling, fibrosis, cFMSC abundance, cardiac macrophages, and tissue inflammation. By conclusively defining the role of cFMSCs and fibroblast PDGFRβ in pathological LV remodeling, these studies will provide novel perspectives on the immunofibrotic response in HF.

NSF

With support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professor Zhou of the University of Nevada, Las Vegas is developing a state-of-the-art experimental platform for ion-neutral collision studies, focusing on molecules prevalent in the interstellar medium (ISM). This platform aims to replicate ISM conditions in a laboratory setting, covering a broad temperature range (10-5000 K) while ensuring high resolution, sensitivity, and minimal background. Accurately reproducing astrochemical reactions in the lab requires precise control over reactants, a challenge that is particularly complex for molecules containing carbon (C), hydrogen (H), oxygen (O), and nitrogen (N). To overcome this challenge, Professor Zhou and his students will demonstrate precise manipulation and broad tunability of collision energy using a merged beam configuration, where the neutral beam originates from a cryogenic buffer gas cell, and the ion beam is generated within a ring-shaped ion trap. Their work could lead to the establishment of a forefront astrochemistry facility, advancing our understanding of the molecular mechanisms shaping the universe. As the first experimental platform at the Nevada Center for Astrophysics (NCfA), it will expand NCfA’s mission into laboratory astrophysics, drive cutting-edge research, enhance STEM education, and attract top talent, further elevating the institution’s research profile. The proposed method centers on the ability to transport ions at the same velocity as neutral species, enabling low-energy collisions in a moving frame. Unlike pulsed beam experiments, this approach utilizes a ring-shaped RF trap to confine the ion beam, making it conceptually closer to static hybrid trap setups, where ion traps overlap with magneto-optical traps. In this configuration, the axial secular frequency of the trapping potential is significantly higher than the circular frequency of ion motion. This key feature distinguishes it from high-energy ion beams in storage rings. As a result, this method offers precise control over ion velocities, ranging from stationary to several thousand meters per second, enabling the study of collisions across a wide energy spectrum with high resolution. The system’s fine control over ion velocity makes it particularly well-suited for interfacing with supersonic or buffer gas-cooled molecular beams, allowing for precise manipulation of collision dynamics. Furthermore, the proposed approach enables extended interrogation periods lasting over a second. This prolonged interaction time is critical for studying slow ion-neutral reactions, allowing for the observation of detailed reaction dynamics that would otherwise be missed in shorter timescales. Enhanced duration not only provides greater control and precision but also significantly improves resolution in analyzing reaction mechanisms. 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

This project brings together education researchers, high school science teachers, research scientists, and community-based organizations as co-design teams to modify science curriculum materials to be justice- and community-oriented. Building on existing partnerships between education researchers and 11 science teachers in two districts in Illinois, project teams will engage in cycles of curriculum analysis and adaptation over the course of 3 years. These professional learning cycles will develop pedagogically relevant content expertise, such as deepened understanding of locally relevant science phenomena, as well as infrastructure for community-engaged science instruction. This kind of science instruction provides relevant entry points into science by providing opportunities for students to address their communities' needs, potentially encouraging students to pursue careers in science. Each year, 2,250 students will experience at least one adapted unit, resulting in over 6,500 individual encounters with such an orientation to science over the lifespan of the project. Moreover, by working with science departments for 3 years to support adaptations of the materials they already use, teachers will develop skills for analyzing and adapting curricula beyond the lifespan of the project, impacting additional cohorts of students. The project will develop and disseminate frameworks, tools, and sample adapted units to support similar partnerships across the US. This study will examine two Illinois districts as comparative cases, strategically selected because of the contextual variation in the levers and entry points for teacher professional learning at each site. This intentional structure enables collection of empirical support for the effectiveness of core elements of the professional learning co-design program on teacher and student learning. Measured learning outcomes include: (a) teachers' frequency of use of NGSS-aligned science pedagogies; (b) development of teachers' critical consciousness; (c) teachers' self-efficacy for culturally sustaining pedagogies in science; and (d) students' epistemologies for science and critical science agency. The research will illuminate variant and invariant elements of the professional learning model, providing theoretical generalizability informing how the model could scale in additional contexts. Finally, the study will examine how the research team builds capacity, both internally and amongst co-design teams, for enacting the professional learning program objectives. These analyses will provide principles and recommendations for establishing and sustaining such co-design teams in other contexts. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed 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

Learning materials that align with students’ lived experiences and that are relevant to the challenges and issues that confront their communities have been shown to improve student engagement and learning outcomes. Yet, students in rural spaces often find themselves taught through lessons and curricular materials not directly relevant to, or seemingly counter to, their lived experience. This project seeks to co-develop and implement a locally relevant and integrated computer science curriculum that centers the lived experiences of rural Hawaiian students and communities. It is hypothesized that understanding and engaging local educational needs creates opportunities for students to learn in ways that make schooling meaningful and relevant to their experiences, culture, community, identity. These impacts will increase interest, belonging and pursuit of STEM coursework and careers. Using a mixed methods approach, researchers and educators will work together to iteratively co-design and evaluate curricular materials that center Hawaiian students’ experiences and sense of community to support youth in developing strong connections to STEM learning that foster their future engagement and interest in STEM. Before, during, and after co-design activities, the research team will directly observe interactions amongst teachers and students during instructional time. As implementation, design, and refinement of the curriculum proceeds, the research team will leverage interview, focus group, and observational data collection from multiple stakeholders (i.e., school administrators, staff, teachers, students, and parents of students), to develop survey items. These items reflect distinct perspectives on what schooling and school success are and should be, the perceived value of and interest in future opportunities associated with Computer Science, s well as the level of empowerment and inclusion experienced. The surveys will be administered 3 times per year. The team will initially conduct exploratory factor analyses on the responses to establish a tentative factor structure that links the meanings for items held by stakeholders within the community to broader concepts. As those components stabilize within groups across the three deployments of the first full academic year (Fall, Y1-Spring Y2), analyses will move to a confirmatory factor analytic framework to validate the within-group measurement structure, moving next to assessing measurement invariance between groups and across time. The results of this study will allow the team to develop an evidence-based, transferable model of curriculum development that centers the experiences of local students to meet the educational needs of rural communities. Further, the team will develop locally responsive measures of educational success, student sense of belonging, and agency in schooling to gauge its development in context over three years of middle school and to ascertain impacts of materials developed using this model for rural curricular development. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.  This project is also supported through the Computer Science for All: Research and RPPs 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.

NEI - National Eye Institute

Abstract Age-related macular degeneration (AMD) and related macular dystrophies (MDs) are leading causes of adult blindness with limited treatment options. AMD/MDs can present in two forms, geographic atrophy/GA (dry form) and choroidal neovascularization/CNV (wet form). There is strong evidence linking sterile inflammation to AMD/MD pathogenesis and two complement pathway inhibitors are already approved by FDA for treating GA in the dry form of AMD. However, due to limited therapeutic impact and adverse effects of complement inhibitors and other approved drugs for both dry AMD and wet AMD, there is a significant need for novel therapies for AMD/MDs. Our recently published data and preliminary studies identified secretory phospholipase A2-IIA (sPLA2-IIA), a pro-inflammatory enzyme, as a key molecular player in AMD/MD pathogenesis. AMD/MD primarily affect the retinal pigment epithelium (RPE) cells in the eye and patient-derived induced pluripotent stem cell- RPE (iRPE) from AMD and 2 distinct MDs showed elevated levels of sPLA2-IIA. Furthermore, AMD/MD iRPE cultures and AMD donor eyes showed elevated sPLA2-IIA levels in drusen, a pathological hallmark of early AMD/MD that is the key driver of later stage pathologies in AMD/MDs. Notably, pharmacological modulation of sPLA2-IIA activity in AMD and MD iRPE cultures led to reduced drusen. In addition, directly linking elevated sPLA2-IIA activity to AMD/MD pathology, sPLA2-IIA overexpression led to AMD-associated pathological alterations (drusen, Bruch’s membrane thickening, RPE thinning, CNV and visual deficits) in C57BL/6J mice. Altogether, these studies provide a strong rationale for targeting sPLA2-IIA activity in AMD/MDs. Toward this goal, we propose to develop proteolysis-targeting chimeras (PROTAC) compounds for specific inhibition of sPLA2-IIA in AMD/MDs. In initial experiments, we have synthesized a ‘lead‘ PROTAC (UR-00059) that can induce degradation of sPLA2-IIA in iRPE cells with half-maximal degradation concentration DC50 of 295.5 nM. The following milestone-driven aims will allow us to develop an effective PROTAC-based therapy targeting sPLA2-IIA for AMD/MDs. Aim 1: Optimize UR-00059 structure and activity and characterize the target engagement in vivo; Aim 2: Conduct in vivo efficacy studies and non-GLP absorption, distribution, metabolism, and toxicology of UR-00059; Aim 3: Perform IND enabling studies and obtain FDA approval for human testing. Ultimately, the proposed studies will develop a novel PROTAC-based therapy for targeting inflammation, drusen and consequently late stage pathologies of AMD and related MDs.

NIAID - National Institute of Allergy and Infectious Diseases

PROJECT SUMMARY Zika virus (ZIKV) is a re-emerging mosquito-borne flavivirus of significant public health concern. To date there are no effective licensed antiviral treatments or a vaccine. Therefore, elucidating the molecular biology of these viruses and the interactions with the host cell are foundational to identifying and developing effective treatment options. The single-stranded positive-sense RNA genome of ZIKV mimics a cellular mRNA. Specifically, the viral RNA encodes a single open reading frame, has structured untranslated regions (UTRs) adjacent to the open reading frame, and contains a N5’-methyl guanosine cap. Unlike mRNAs however, flaviviruses lack a poly(A) tail. From a multiple sequence alignment of all mosquito-borne flaviviruses we identified conserved regions containing 3-6 tandem adenosines in the 3’ UTR. These A-rich regions are localized in single-stranded regions within and between the XRN-1 resistant pseudoknots (xrRNA), dumbbell and 3’ stem loop (3’SL) RNA structures. In preliminary experiments we investigated the role of the A-rich region between xrRNA2 and the pseudo- dumbbell RNA structure (pre-pseudo/Y-dumbbell). Specifically, we generated three different mutations in a ZIKV Renilla luciferase reporter replicon and infectious clone. These mutants revealed that the pre-YDB A-rich region had a role in translation in both mammalian and mosquito cell lines. Different mass spectrometry studies have shown that the cellular poly(A) binding protein (PABP) interacts with the ZIKV 3’ UTR. Additionally, PABP was previously shown to interact with a region in the dengue virus 3’ UTR that harbors an A-rich motif. In preliminary studies we find that depletion of PABP1 decreased ZIKV, but not cellular, protein levels and viral titers without affecting cell viability. We therefore hypothesize that A-rich regions in the ZIKV 3’ UTR function to recruit cellular RNA binding proteins such as PABP1 to promote distinct steps in the virus infectious cycle. In Aim 1 we will mutate the A-rich regions in the infectious clone and a subgenomic luciferase reporter replicon to investigate the function of select A-rich regions on translation, replication, and viral fitness in mammalian and mosquito cells. In Aim 2, we will investigate the role of PABP1 on ZIKV gene expression and determine if PABP1 and other cellular RNA binding proteins bind A-rich regions in the ZIKV 3’ UTR. Overall, this study will advance our understanding of how flavivirus 3’ UTRs interact with the host to promote distinct steps in the infectious cycle in two vastly different hosts. Understanding how specific sequences in RNA genome function could lead to the therapeutic advancement namely the development of an attenuated vaccine ZIKV strain. Moreover, defining similar and unique RNA-protein interactions between mammalian and mosquito hosts could inform future vector control strategies.

NSF

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at the University of Indianapolis. A total of 18 scholars pursuing a bachelor's degree in mathematics or engineering will receive scholarships averaging $15,000 per year for up to five years. Scholars will receive faculty and peer mentoring, and the project will build strong scholar cohorts through community-building cohort activities, targeted academic support, and comprehensive career development. The overall goal of this Track 1 Scholarships in STEM project is to increase STEM degree completion of academically talented, low-income undergraduates with demonstrated financial need. There is a significant national need to grow the STEM workforce and nurture key talent that will ensure economic competitiveness and provide domestic leadership across critical sectors. This project directly speaks to this need by supporting STEM student success, which will strengthen the workforce in key areas of need. The project will be assessed by an experienced evaluator that will evaluate the effects of coordinated cohort-building, academic support, and career preparation services on the success of low-income students in STEM disciplines. The data generated will contribute to the knowledge base regarding effective strategies to support talented, low-income students in STEM. This project is funded by NSF's Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of academically talented, low-income students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

EPSCoR states and territories make significant contributions to the national and global STEM research enterprise. This project aims to further coordinate and enhance these contributions through fostering collaboration and knowledge sharing across NSF EPSCoR investments, resulting in increased research and innovation capacity within EPSCoR states and territories. Led by the American Association for the Advancement of Science (AAAS), project goals span three major areas. First, AAAS will host two national EPSCoR Summits. Second, AAAS will develop, maintain, and coordinate an electronic EPSCoR Hub to disseminate relevant research capacity material and facilitate collaboration across all twenty-eight EPSCoR jurisdictions and beyond. Third, AAAS will host interactive workshops for project personnel and prospective awardees, and also manage a blog series to share discoveries, resources, and best practices. This project aims to serve national interest by strengthening and expanding the research capacity and collaborations of organizations within and across NSF EPSCoR jurisdictions. The outcomes of these efforts will support national priorities of economic growth, national security, and sustained U.S. leadership in science, technology, engineering, and mathematics (STEM). Utilizing a Networked Improvement Community framework, this project will focus on synthesizing and disseminating knowledge, strategies, policies, and practices that directly enhance the research capacity of organizations and individuals in EPSCoR jurisdictions. Project activities will include a study and review of the needs and challenges of EPSCoR jurisdictions, implementation plan for leveraging prior findings and improvement science to address these challenges, and collaborative structure for developing and disseminating interventions that address identified potential barriers to research capacity building in EPSCoR jurisdictions. This project will convene key stakeholders from EPSCoR jurisdictions and beyond, including EPSCoR Research Infrastructure Improvement principal investigators and project personnel, industry partners, and academic leaders across all disciplines and regions. Project outcomes have potential to expand the research capacity across jurisdictions, facilitate the sharing and dissemination of research, data, and promising practices, and increase participation across all disciplines, institution types, and organizations involved in the STEM enterprise. By supporting EPSCoR jurisdictions through the knowledge sharing of evidence-based research and promising practices, this project will bolster innovation, research capacity, education, and workforce development across these regions and the nation. This project is funded by the U.S. National Science Foundation Established Program to Stimulate Competitive Research (EPSCoR). EPSCoR pursues a mission to enhance the research competitiveness of targeted jurisdictions by strengthening STEM capacity and capability through a broad portfolio of investments from talent development to local infrastructure. 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

The majority of emerging infectious diseases are zoonotic (transmitted to humans from animals). Many factors, ranging from the molecular to landscape scale, shape pathogen transmission at the interface of people, animals, and our shared environments. Toxoplasma gondii is a globally distributed zoonotic parasite that can survive in the environment for long periods of time. This parasite can cause disease in humans, domestic animals, and wildlife. Different genetic types or “genotypes” of T. gondii have varying impacts on human and animal health, with some genotypes causing severe and fatal disease. Understanding where these genotypes emerge and how they spread at the interface of people, animals, and the environment is vital for protecting human and animal health. The international Network-of-Networks (NoN) will bring together researchers and students from different disciplines and backgrounds investigating T. gondii across the world to create shared tools and research approaches that will advance understanding of zoonotic parasite transmission. As T. gondii infection impacts human and animal populations globally, this project will enhance public and animal health and well-being as well as wildlife conservation. By investing in the development of early-career researchers and fostering a culture of knowledge sharing, our approach will also contribute to the development of a STEM workforce trained in international collaboration and prepared to tackle global challenges in zoonotic disease and public and animal health. Parasite characteristics, host ecology, and landscape factors can shape T. gondii diversity as well as the emergence of novel genotypes. However, field methods, diagnostic tools, and genotype characterization approaches vary widely across international research groups and geographic regions, limiting understanding of global patterns and processes in parasite transmission and evolution. By integrating three core existing networks and strengthening emerging partnerships with networks, research groups, and consortia focused on different aspects of T. gondii biology, ecology, and epidemiology, this AccelNet Design project will build readiness to launch an international NoN to advance research on parasite transmission and ecology. Existing core networks include the International Network for Environmental Toxoplasma Studies, the Food and Environmental Parasitology Network of Canada, and the Brazilian Toxoplasmosis Research Network (Rede Toxo). The project aims to increase global collaborative research capacity for complex field, lab, and analytic approaches to understand parasite transmission among environments and hosts through 1) an in-person workshop to identify critical knowledge gaps and research needs and to create shared strategies for communication, collaboration, and partnerships among networks, 2) sharing field and lab methods and protocols among networks to facilitate standardization, 3) postdoctoral researcher and graduate student training and exchanges among network laboratories to share knowledge and strengthen STEM workforce development, and 4) harmonizing network approaches for characterizing and sharing field, diagnostic, and genotype data. Building shared field, molecular, and data management and analysis methods, this NoN will accelerate collaborative T. gondii research to advance understanding of parasite transmission at the human-animal-environment interface. Workforce development and global networking efforts will also include developing collaborative virtual and in-person outreach approaches with audiences ranging from healthcare and animal management professionals to the broader public. This project is jointly funded by the Accelerating Research through International Network-to-Network Collaborations (AccelNet) program and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Jianhan Chen of the University of Massachusetts at Amherst is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to to study the driving forces and molecular mechanisms of RNA phase transitions using coarse-grained models. Flexible biological macromolecules, such as disordered proteins and flexible RNAs, have recently been discovered to undergo spontaneous phase separation and form biomolecular condensates that play fundamental roles in a myriad of cellular functions from stress response to cellular signaling. Chen and his research group will develop a new intermediate resolution model for condensates of RNA (iConRNA) and collaborate with experimental labs to accurately calculate the phase diagram of RNAs and study how the interplay of various molecular forces control phase behaviors. These efforts will meet the urgent need for efficient computer models that can simulate the phase transitions of these dynamic molecules in studies of biomolecular condensates. Chen will integrate the latest advances in biomolecular condensates into courses at University of Massachusetts (UMass), train undergraduate and graduate students in interdisciplinary research, and contribute to broadening participation in STEM education and research through the Eureka! program at UMass. Jianhan Chen will develop a coarse-grained (CG) molecular modeling framework for efficient simulation of the spontaneous phase transitions of flexible RNAs as well as heterotypic protein/RNA phase separation. The proposed iConRNA model will represent each nucleotide using 6 or 7 beads, and includes explicit base stacking, base pairing, Debye–Hückel electrostatics, and Lennard-Jones potentials. Parameterized using atomistic simulations and experimental data, iConRNA model will be designed to recapitulate transient local and long-range structure features of model RNAs, fold several small RNAs, and correctly capture the length and sequence dependence of the phase separation of various RNAs. Specific research objectives of this proposal will be to: 1) accurately model the temperature and magnesium ion-dependence of RNA structure and phase separation; 2) elucidate how the complex interplay of intrinsic structural propensities and intermolecular interactions govern the mechanism of RNA phase separation and condensate material properties; and 3) develop a self-consistent CG framework for modeling heterotypic protein/RNA phase separation. Integrated with experimental studies, these efforts will uncover crucial new molecular details and mechanisms underlying the complex magnesium and temperature dependence of RNA phase transitions. In parallel, Chen will distribute the CG models through the open-source OpenMM package to enable the community to study biomolecular condensates in biology and materials engineering. The project will train several undergraduate and graduate students in the interdisciplinary field of computational biophysics and support efforts to broaden the participation of high-school students and the general public in STEM education and research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Complex physical systems, such as those found in material science, fluid dynamics, and life science, often involve intricate interactions between energy-conserving mechanisms and entropy-generating processes. These systems are usually modeled by reversible-irreversible thermodynamically consistent partial differential equations (RITC-PDEs). However, solving these RITC-PDEs accurately and efficiently over a long time period remains computationally challenging due to their non-equilibrium nature and the need to preserve thermodynamic properties at the discrete level. This project will develop a general computational framework to solve RITC-PDEs while maintaining their energy conservation and nonnegative entropy production for long-time dynamic simulations and predictions. The proposed research will lead to a unified computational framework for studying multiscale non-equilibrium phenomena across various scientific disciplines, along with open-source tools for the broader research community. The project will support STEM education through outreach to K-12 students and educators. Reversible-irreversible thermodynamically consistent (RITC) PDEs arise from the principles of thermodynamics. They are essential for modeling coupled processes involving energy-conserving(reversible, dispersive) and entropy-producing (irreversible, dissipative) dynamics. This project will develop high-order, accurate, efficient, easy-to-implement, and structure-preserving numerical schemes that maintain thermodynamic properties for RITC-PDEs. Specifically, the project will (a) design innovative structure-preserving discretization methods, including decoupled and high-order schemes, to accurately simulate complex RITC systems while maintaining their inherent thermodynamic consistency; (b) develop advanced time-stepping algorithms that ensure efficiency and accuracy for multiscale dynamics, by leveraging system properties such as energy budgets, entropy production, and topological changes to guide adaptive time step sizes; (c) construct a structure-preserving model order reduction framework that can generate reliable surrogate models for large-scale RITC systems; and (d) implement an open-source software package for simulating RITC-PDEs with GPU acceleration, adaptive meshing, and user-defined model inputs. Ultimately, the proposed research will lead to a unified computational framework to study multiscale non-equilibrium phenomena and contribute to the fields of numerical analysis, scientific computing, material science, fluid mechanics, and interdisciplinary modeling. 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

Emerging memory technologies, which facilitate both analog and digital in-memory computations, have captured the attention of commercial and defense sectors as promising replacements for traditional von Neumann computing architectures commonly used in edge sensors. Over the past five years, numerous publications have illustrated the pivotal role of these advanced memory arrays in supporting intelligent systems capable of real-time learning and swift adaptation to changing conditions. Nonetheless, the adoption of these technologies is hampered by the limited availability of mainstream fabrication processes. Currently, TSMC is the predominant provider, which imposes significant cost barriers for research groups eager to develop and test new circuit designs. This innovative digital twin (DT) is designed to validate new designs and propel research and development in this emerging field. It promises to revolutionize design processes across various technical specifications, including frequency bands, signal-to-noise ratios, and spectrum classifiers. The DT has a scalable architecture that facilitates the integration of extensive library models for memory devices and supports these devices across a broad spectrum of material systems and operational scenarios. The potential benefits of this DT, with sensor technology and computational model for advanced receivers, extends to enhancing national security by providing significant advancements in secure communications and threat detection by improving surveillance, data processing, and decision-making capabilities. This project also seeks to improve STEM education and foster a skilled workforce by deepening students' understanding of integrated sensor systems and digital twin technology. The project incorporates a broad educational effort that creates opportunities for students to engage in cutting-edge research. The technologies developed through these student projects are poised to be highly effective across diverse environments and designed to operate on minimal energy budgets, making them economically and environmentally sustainable. This "Emerging Ideas" research effort strategically focuses on leveraging advancements in digital twin technology to fundamentally transform Intelligent Electromagnetic Sensors’ design, development, and testing processes on a Chip (iEM-SoC). This initiative significantly streamlines and enhances the sensor development process by substituting physical sensor models with digital replicas. Chip-based sensors, rapidly advancing across sectors such as autonomous aerial vehicles, industrial robotics, and consumer electronics, demand high sensor resolution, real-time response capabilities, low power consumption, and extended operational times, typically ranging from 0.5 GHz to 1-2 THz. This interdisciplinary endeavor is set to explore the feasibility of developing a Digital Twin for iEM-SoC and aims to achieve multiple objectives: it will produce a digital twin for the design and performance testing of an Intelligent RF Receiver (EM Sensor) capable of real-time operations with extremely low energy consumption; create the first digital model of an analog memory array that accounts for high-frequency effects; provide the first demonstration of a memory array operating system that enables precise segmentation for storing, accessing, and processing analog data with high accuracy (11 bits); and ultimately showcase how a digital twin can facilitate the development of a new class of EM Edge Sensors that surpass the capabilities of current design methodologies. 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

The opioid crisis is a serious public health issue, causing over 80,000 overdose deaths in the U.S. in 2021 alone, which underscores the urgent need for safer pain medications that are not addictive. The GRAM-CAROLINE project aims to transform the way drugs are designed using advanced artificial intelligence (AI) by combining scientific knowledge and medicinal chemistry. The main goal is to develop new molecules that boost the body’s natural pain relief system, providing effective pain relief without the risk of addiction linked to opioids. This project will create a new generative AI system that uses scientific rules to produce better solutions and a machine learning model that uncovers hidden scientific patterns, making the AI results more understandable and reliable. This cutting-edge approach will be used to create new pain relief medications which can then be tested in labs and iteratively improved. In addition to tackling the opioid crisis, the project aims to achieve significant scientific breakthroughs that can be applied in numerous fields, enhance interdisciplinary education, and support diversity in science and engineering by incorporating the research into university programs. The GRAM-CAROLINE project demonstrates the potential of AI to solve urgent health problems while advancing scientific understanding and benefiting society. The ongoing opioid crisis, resulting in over 80,000 overdose deaths in the U.S. in 2021 alone, highlights an urgent need for safer, non-addictive pain medications. The GRAM-CAROLINE project (Grammar-Reinforced AI Modeling with Conditional Autoencoder and Relevance-Oriented Learning for Interpretable Knowledge Extraction) aims to address this need by leveraging advanced artificial intelligence (AI) to revolutionize the drug design process. The primary objective is to create de novo amplifier molecules that enhance the body’s endogenous pain control mechanisms, delivering effective pain relief without the addiction risks associated with opioids. This will be achieved by developing a novel AI framework that integrates domain-specific scientific knowledge and grammar-based molecular encodings with machine learning techniques to generate viable candidate molecules. Additionally, the project will establish a transparent machine learning model capable of extracting hidden scientific rules and constraints, leading to interpretable and reliable AI-generated solutions. The key research activities involve creating a generative AI system using a conditional variational autoencoder with grammar-based and physics-informed constraints, designing a new transparent machine learning model using reinforcement learning to extract unknown constraints, and applying these models to design amplifier molecules for the body’s natural pain suppression system. This process includes generating potential molecules, validating them in vitro, and iteratively refining both the learned constraints and generated outputs to enhance the probability of discovering viable drug candidates. The project promises significant contributions to AI and pharmacology by developing an interpretable framework for drug design, supporting advancements in other scientific fields, and fostering multidisciplinary education in STEM fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Neutrinos are unique messengers, carrying information about the universe's most energetic astrophysical phenomena. Over the past decade, the IceCube Neutrino Observatory at the South Pole has made key discoveries by detecting high-energy neutrinos and identifying two active galaxies as neutrino sources. However, sub-TeV neutrinos (10–1000 GeV) remain a largely unexplored frontier with the potential to significantly expand our observation of the universe. This project leverages advanced artificial intelligence (AI) techniques to overcome computational challenges and improve the reconstruction of sub-TeV neutrinos using IceCube’s DeepCore subdetector. These advancements will enable detailed studies of astrophysical sources such as NGC 1068 at lower energy scales and pave the way for real-time public alerts of sub-TeV neutrinos, fostering coordinated follow-up observations across the electromagnetic spectrum. In addition to advancing neutrino astrophysics, the AI methodologies developed here will benefit a wide range of fields with similar challenges, including weather forecasting, neuroscience, smart cities, and precision farming, by enhancing the analysis of distributed sensor data. By integrating education initiatives, outreach programs, and undergraduate participation, this project promotes access to advanced research, supports STEM, and inspires the next generation of scientists and engineers. This project addresses the computational barriers to sub-TeV neutrino reconstruction through four interconnected research tasks. First, it designs a novel AI architecture capable of managing spatial and temporal irregularities in IceCube’s sensor data while embedding physics invariance for improved accuracy. Second, the project enhances computational efficiency by leveraging physics-informed inductive biases, enabling real-time processing of millions of neutrino events with low latency. Third, robust training methodologies will be implemented to address systematic uncertainties. Finally, the project investigates approximate symmetry modeling to allow AI models to adapt to practical deviations from the ideal physical model without compromising performance. These innovations will significantly improve the angular resolution and sensitivity of DeepCore to sub-TeV neutrinos, paving the way for transformative discoveries about astrophysical sources such as Seyfert galaxies as well as objects in the Milky Way. The tools and methodologies developed are designed to be broadly applicable, enabling breakthroughs in other scientific domains that rely on the analysis of complex spatiotemporal data. 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

Mechanical metamaterials (MMs), with their unique structural configurations rather than their chemical compositions, offer a wide range of unprecedented mechanical properties, from enhanced toughness and energy absorption to advanced vibration damping and soundproofing capabilities. Recent advancements in additive manufacturing have enabled the creation of these intricate geometries, leading to materials that excel in diverse applications, including safety gear, aerospace, noise-canceling technologies, and impact-resistant devices. Designing MMs today involves a complex, time-consuming iterative process where experts intuitively define designs, validate them with physics-based simulations, and refine them through trial and error. Recent advances in generative AI and machine learning offer the potential to disrupt this design cycle by automating and accelerating the process. This research proposes the development of a computational pipeline that integrates AI-driven optimization techniques with advanced simulations to streamline MM design. Using a graph-based representation of MM structures, the system effectively reduces the design space of general 3D MMs from hundreds of thousands of dimensions to a compact space with 10–100 dimensions. The system will incorporate advanced optimization techniques that learn efficient design solutions and enable the transfer of insights across different material properties, for example, from stiffness to shear strength or impact resistance. Furthermore, the approach facilitates a more comprehensive understanding of material performance, enabling designs that balance multiple desired properties. Tested primarily on MMs, the approach will also be applicable to a broad range of scientific and engineering problems requiring the design of complex, high-performance materials. If successful, this project will establish a new class of AI-driven design tools that enable faster, more flexible, and sustainable material development. The project will also provide open-source tools, encouraging further research and collaboration in the field. The educational goal of this project is to create tools that inspire K–12 students to consider STEM pathways and to disseminate new curricula adapted to interdisciplinary research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.