Case Western Reserve University

NIH Research Projects · FY 2025 · 2024-09

Deposition of amyloid plaques is a major pathological hallmark in brains of Alzheimer’s disease (AD). Excess accumulation of amyloid-β (Aβ), caused by increased Aβ production and impaired Aβ degradation, leads to progressive brain degeneration in AD patients; hence reducing Aβ accumulation should be highly beneficial to amyloid pathology in AD brain. N6-methyladenosine (m6A) methylation of RNA is the most prevalent, abundant and conserved internal modification in eukaryotic RNAs and it influences fundamental aspects of RNA metabolism including degradation, translation, splicing, and nuclear export. While RNA m6A dysregulation is implicated in the neurodegenerative diseases, the potential role of RNA m6A dysregulation in Aβ metabolism (production and degradation) in AD has never been investigated. Our group reported that neuronal METTL3-m6A reduction contributes to neurodegeneration in Alzheimer’s Disease (AD). Both methylation profiling and a sequence-based m6A modification site predictor identified multiple m6A sites in the mRNAs of APP, its secretases and Aβ-degrading enzymes. Our preliminary results demonstrated that METTL3 reduction led to significantly increased Aβ level likely through modulation of Aβ metabolism genes. Based on these studies, we hypothesized that METTL3-mediated m6A reduction modulates gene expressions in Aβ metabolism-related pathways and contributes to the amyloid pathology of AD. We will determine the effect of METTL3 deficiency-mediated m6A reduction on mRNA metabolisms by affecting the decay, translation, splicing and nuclear export in primary neurons. The successful completion of this study will provide novel mechanistic insights into Aβ accumulation and amyloid pathology in AD. Identifying a molecular target for Aβ metabolism will offer new venue for therapeutic intervention for AD.

NSF Awards · FY 2024 · 2024-09

Reinforcement Learning (RL) is a powerful Artificial Intelligence technique that enables machines to teach themselves optimal decision making by repeatedly learning from the consequences of their actions. RL has demonstrated impressive gains in building autonomous agents in a variety of scientific and engineering domains such as autonomous driving, robotics, and game playing to name a few. However, training these agents is extremely time consuming. Moreover, existing research on reducing the execution time of RL is inaccessible to RL application developers as they require expertise in designing careful compute orchestration among different types of hardware devices (such as Graphics Processing Units (GPU), Field Programmable Gate Arrays (FPGA)) available in modern data centers. To address this issue, the objective of this project is to develop an easy-to-use library that can enable automatic deployment of RL applications on a data center composed of GPU, FPGA, and AI accelerators. By abstracting away the complexities of deployment and orchestration of computations on data centers, the project will significantly increase the productivity of AI application developers leading to more robust AI agents with faster development cycles. Existing RL libraries only consider homogeneous platforms that are composed of a single type of hardware device. Thus, there is a demonstrated need from researchers in the Computer & Information Science & Engineering (CISE) communities, including AI system development, RL algorithm, and domain application user communities (e.g., scientific computing and cyber-physical systems) for a library that can enable seamless deployment of RL applications on emerging heterogeneous platforms (composed of multiple types of hardware devices) while achieving high performance (for example, reduced training time). To address this need, this project will leverage novel algorithmic, architectural, and memory optimizations across heterogeneous devices to create a performance portable library that will enable automatic system composition for high-throughput RL on heterogeneous cyber infrastructures. The library will build upon and harden the research artifacts developed in the recent NSF-funded work of the investigators on accelerating RL on CPU-FPGA platforms. It will be portable to various heterogeneous cyber infrastructures and support a wide range of RL hyperparameters, algorithms, and policy models. Furthermore, it will offer APIs to facilitate productive development and seamless integration with existing RL ecosystems. Additionally, the project will include the following community interactions and sustainability plans: 1. Interactions with key processor, GPU, and FPGA vendors (AMD, Intel and NVIDIA) to integrate the proposed library into their software development tools (AMD-Xilinx Vitis, Intel oneAPI and NVIDIA CUDA-X). 2. Collaborations with NSF Open Cloud Testbed, and NSF NCSA to integrate the library into their cyber infrastructures. 3. Making the library compatible with existing RL frameworks (e.g., RLlib) and RL simulation toolkits (e.g., Gymnasium). 4. Ensuring the availability of the library to a broader audience by collaborating with commercial cloud service providers such as Microsoft and Amazon. 5. Demonstrating end to end applications in various domains through collaborations with NSF AI Institutes including ACTION, AgAID, and ICICLE. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY The goal of this application is to obtain additional funds for the Midwest Digestive Disease Research Core Centers (DDRCC) Alliance Conference to be held during in December 2024. The Midwest DDRCC Alliance (MDA) is currently comprised of five NIDDK-funded DDRCCs located in the Midwest: Case Western Reserve University, Cincinnati Children's Hospital, Mayo Clinic, University of Chicago, and Washington University in St Louis. The Midwest DDRCC Alliance Conference (MDAC) was initiated in 2013 to promote the career development of postdoctoral and junior faculty members and to provide a forum for cross-fertilization of knowledge and research progress among the participating institutions. The conference’s scientific program is comprised of junior faculty research projects that seek to advance the understanding of biological systems influencing digestive health and disease. The requested funds will allow the conference to continue to provide opportunities for junior investigators' career development, mentoring, and networking and to familiarize them with core services and scientific and technology expertise available at the MDA partner sites. Finally, a major component of this application is to promote careers of underrepresented populations in the scientific research enterprise as they seek to become investigators in digestive and liver disease. We will feature successful several senior level investigators from diverse backgrounds to serve as role models and mentors and to expand our community of researchers interested in digestive and liver disease.

NIH Research Projects · FY 2026 · 2024-09

Medically tailored groceries (MTG) generally involve fresh and shelf-stable unprocessed grocery items to be prepared at home, selected by a nutritional professional based on a treatment plan and are typically picked up at a clinic, market, or pantry. This clinic-based market or pantry model (CB-MTG) is increasingly being adopted by health care systems in their effort to address food insecurity in their patient population, including University Hospitals of Cleveland (UH) and MetroHealth Medical Center (Metro), two of the three largest health systems serving Northeast Ohio. Often offered to patients with food-related chronic conditions, CB-MTG have shown to improve medication adherence, increase fruits and vegetable consumption and decrease HbA1c in people with diabetes. However, less evidence is available on the impact of CB-MTG with food insecure pregnant patients, where food insecurity has been strongly associated with prematurity and other negative birth outcomes. While promising, the CB-MTG approach requires transportation, having the tools and equipment to prepare meals at home and some basic food preparation skills, all potential barriers for low-income pregnant patients, especially younger parents-to-be or those already with children. The Greater Cleveland Food Bank and partners, seeking to address these barriers, recently developed a home delivered version of MTG (HD-MTG), offered to low-income pregnant patients across the county, with promising results. We seek to integrate these approaches into patient care for food insecure, pregnant women and test the effectiveness of these two approaches, alongside an additional intervention arm that adds supplemental nutrition and culinary education and support to the home-delivered approach (HD-MTG PLUS). These three approaches will be offered (via randomization) to 360 pregnant patients (120 per arm) screened for food insecurity from within UH and Metro’s largest urban obstetric practices, each with direct EHR referral systems to their “food as medicine” clinics/markets for those screened food insecure. Data are collected at baseline, near/at delivery and 6 months post-delivery. This study seeks to understand the unique contribution of each approach, as well as implementation and intervention uptake barriers, with the goal of building the evidence base of MTG interventions and making recommendations to providers and health systems seeking to address food insecurity and nutrient deficiencies during pregnancy.

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY Complex human traits and outcomes have substantial genetic and environmental contributions that, when identified, lead to a better understanding of underlying biological process and are potential targets of prevention, intervention, and treatment strategies. Advances in genotyping and sequencing technologies over the last 20 years has enabled a golden age of genomic discovery, resulting in the identification of thousands of genome- wide genotype-phenotype associations. Comparatively, the identification of modifiers of these genetic associations has been slow. And most interrogations of modifiers of genetic associations have been limited to select non-genetic variables such as sex and age or exposures such as smoking. Relatively few have explored the impact of social determinants of health despite their independent associations with health disparities. Regardless of the modifier under study, their identification requires large, well-phenotyped and diverse cohorts with exposure and genome-wide data available for powerful statistical analysis. Compounding the challenge is defining the phenotype or outcome of interest generalizable to populations but still useful in specific clinic settings. The relatively recent availability of UK Biobank and All of Us, among other diverse datasets linked to phenotypes, exposures, and genome-wide data, makes the earnest search for environmental modifiers of genetic associations possible. Here we propose to use APOL1 as a model and possible framework for the methodical search for environmental “second hits” that distinguish truly at-risk participants and eventually patients from others. APOL1, associated strongly with negative kidney outcomes that disproportionately affect African Americans, is an ideal model given that its renal risk variants are 1) common among African-descent and admixed groups and 2) found exclusively on African-specific haplotypes. We aim to access NEPTUNE, UK Biobank, and All of Us to quantify the evidence that APOL1 genetic risk for rare (focal segmental glomerulosclerosis or FSGS) and common negative kidney outcomes (albuminuria, kidney-disease related eGFR, and composite chronic kidney disease) is modified using observed heterogeneity in its effect sizes across populations (African Americans, British Blacks, and Hispanics). We further aim to interrogate potential modifying effects of heavy metal exposure, a quantitative marker association with social determinant of health. The Specific Aims proposed here, when accomplished, will provide additional data toward at-risk profiles for APOL1 renal risk variant carriers useful for clinical risk prediction models as well as targets for prevention efforts and/or development of novel therapeutics. The accomplished Aims will also provide a framework for the systematic interrogation of “second hits” relevant for human outcomes with known genetic associations.

NIH Research Projects · FY 2025 · 2024-09

Ligand activation of Eph receptors plays a decisive role in cell migration during blood vessel formation and neuronal axon guidance. Eph receptors can also signal independently of ligands, whereby the migration- and adhesion-repulsive signaling shifts to a pro-migratory stimulus that contributes to metastasis and drug resistance in various cancers. Recent studies have substantiated the role of Eph receptors in the dysfunction of the blood brain barrier (BBB) during ischemic stroke, invasion of pathogenic organisms and the early stages of Alzheimer’s Disease (AD) and other neurodegenerative diseases. Yet, the molecular mechanisms behind Eph receptor function remain poorly understood. Eph signaling depends on a variety of inter-molecular and intra-molecular Eph-Eph interactions which involve portions of the protein structure (domains) but also the cellular membrane. The overall project seeks to identify key residues in sets of interactions, which are not yet well characterized, but are likely to be key to the different functional states of the overall protein interactions. Ephs are unique within the superfamily of transmembrane receptor tyrosine kinases due to their C-terminal 5- helix folded domain, part of the SAM (sterile alpha motif) adaptor protein family. We and others’ have reported a novel role of the SAM domain to auto-inhibit EphA2 kinase activity. In preliminary experiments for this proposal, we discovered that mutations in the SAM domain functionally mimic its complete deletion and can abolish EphA2 autoinhibition. However, the molecular details of how the SAM domain inhibits the kinase domain are still missing and will be investigated in Aim 1. EphA2 is cleaved at the cell surface by Membrane- type I matrix metalloproteinase and γ-secretase, key proteins for AD. However, the structure and function of these intracellular and extracellular-transmembrane receptor cleavage products are poorly understood. We will characterize a protein construct encompassing the intracellular region (ICR), which consists of the JM region, the Kinase Domain (KD) and the SAM domain and another with the two membrane- proximal extracellular FibroNectin III domains (FN1&2), the transmembrane (TM), the juxtamembrane (JM) region. In aim 2, we will use these EphA2 fragments to investigate the interaction between Eph domains and with the membrane. This knowledge is crucial for understanding the hierarchical organization of these regulatory interactions. The studies will be extended to the EphA1, EphA4 and EphB2 receptors, delineating how specific differences in domain-domain contacts relate to different levels of kinase activity between the different Ephs. In addition, the proximity of the Eph receptor Fibronectin domains to the membrane is noteworthy: Our preliminary data for Aim 3 support the interaction between these domains and Aβ, a key peptide in AD. The structural insights we pursue are essential for the development of diagnostic and therapeutic agents targeting neuronal and vascular diseases, including the breakdown of the blood brain barrier in neurodegenerative diseases.

NIH Research Projects · FY 2025 · 2024-09

Multi-modality evaluation of high-risk coronary atherosclerotic plaque Summary Non-invasive, quantitative assessment of coronary atherosclerosis will lead to improved, personalized patient management for the leading cause of death in the US — cardiovascular disease. Detecting high-risk lesions at the earliest stages of coronary artery disease would facilitate timely medical interventions to hinder the pro- gression of coronary atherosclerosis and prevent catastrophic complications. Intravascular imaging modalities such as intravascular optical coherence tomography (IVOCT) have been used to identify the presence and characteristics of coronary atherosclerosis. IVOCT, with its high resolution and contrast, is recognized as the best method for identifying local high-risk lesions (e.g., thin cap fibroatheroma). However, intravascular imag- ing is invasive, limiting its applicability, especially for patients early in the disease process. Coronary computed tomography angiography (CCTA) is the only non-invasive imaging modality allowing the assessment of luminal stenosis as well as plaque morphology. We will develop new AI methods for CCTA evaluations of high-risk coronary atherosclerotic plaque by comparing them to concurrently-acquired, high-resolution/contrast IVOCT, deemed the best method to assess high-risk plaque. To enable a new, non-invasive evaluation of atheroscle- rosis, we will register CCTA images to concurrently-acquired IVOCT images and determine image features in CCTA that associate with and predict high-risk plaques as seen in IVOCT. In addition to this concurrent deter- mination of high-risk plaque in CCTA, we will take what we learn and apply it to the long-term prediction of ma- jor adverse cardiovascular events (MACE). Specifically, we will: 1) Create highly automated methods for as- sessing high-risk plaques seen microscopically in IVOCT and develop CCTA feature candidates suggestive of high-risk plaque; 2) Use novel IVOCT to CCTA registration to associate segmental CCTA features to IVOCT- defined high-risk plaque features and to create a CCTA classification model for high-risk plaque; and 3) Apply the most promising segmental CCTA features to predict long-term adverse outcomes in CCTA data. With suc- cess, our research will lead to decision support software for the prediction of MACE, facilitating revasculariza- tion strategies, therapeutic decisions, and furthering precision medicine approaches. The project team will build on expertise in cardiology, machine learning, biostatistics, and advanced image analysis of IVOCT and CCTA. In this project, I will build upon my experience in machine/deep learning analysis of intravascular images to include new training in image analytics of CCTA data, biostatistics, and bioinformatics of metabolomics and genomics, providing me with a foundation for a future career in cardiovascular disease. For example, I will be well situated to integrate CCTA image analytics and genomics to further my understanding of cardiovascular disease. Importantly, I will explore issues in cardiovascular disease and expand my network to include premi- ere cardiologists who can help me understand new issues and formulate hypotheses for future studies.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT Traumatic brain injury (TBI) is the greatest environmental risk factor for Alzheimer’s disease (AD). Although both neurological conditions share many of the same pathological hallmarks including neurodegeneration, the underlying mechanisms that establish this relationship between TBI and AD are not well understood. Acute TBI transitions into chronic TBI, which is characterized by progressive neurodegeneration. The pathophysiologic events associated with the transition of acute TBI into a chronic neurodegenerative condition can last for months, years, or even a lifetime and are also thought to be associated with the pathogenesis of AD after TBI. Investigating the mechanistic link between acute TBI, chronic TBI, and AD is important as there are currently no therapeutic strategies to protect patients. My proposed interdisciplinary project aims to address this knowledge gap by assessing the interplay between TBI, hematopoietic stem cell (HSC) dysfunction, chronic neurodegeneration, and AD. Specifically, I am testing the novel hypothesis that acute TBI induces changes to HSCs that in turn play a role in impairing the immune system and propagating chronic neurodegeneration after TBI, and that these TBI-induced alterations are related to the increased risk of AD after TBI. HSCs generate the entire spectrum of blood and immune cells, and my preliminary studies thus far indicate that TBI induces major changes to HSCs, including impaired HSC function, disrupted blood cell counts, and elevated inflammatory markers. This study employs a clinically relevant model of TBI, cytometric analyses, and immunohistochemical approaches to generate a deeper understanding of hematopoietic dysfunction in TBI and AD and the implications of this dysfunction on the immune system and chronic neurodegeneration. Furthermore, this proposal is tailored for a physician-scientist in training as it investigates the underlying mechanisms by which acute TBI transitions into a chronic neurodegenerative condition, with implications for novel therapeutic targets for reducing neurodegeneration after TBI and protecting TBI patients from developing AD.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Despite the advances in cancer treatment, surgery remains the cornerstone, and more than 80% of cancer patients have a surgical procedure at some point in their cancer evolution. In oncology surgery, intraoperative pathology provides surgical guidance and identification of tumor margins, allowing confirmation of complete tumor resection before oncology surgeons close the surgical wound and helping patients avoid a second tumor resection surgery. Most localized tumors with negative margin resection show improved patient outcomes and a lower chance of tumor recurrence. However, the intraoperative frozen section technique suffers from a series of limitations: tissue loss, compromised quality due to freezing artifacts, suboptimal cutting of fatty specimens, and inability to diagnose bony lesions. In our preliminary results, we have developed the 3D contour scanning ultraviolet photoacoustic microscopy (UV- PAM) to acquire histology-like images of thick bone specimens, which addresses the long-standing challenge of intraoperative bone histology. The rapid photoacoustic histology images of bone specimens well match the conventional histology images stained by hematoxylin and eosin (H&E), allowing pathologists to identify the cancerous features following existing pattern recognition parameters readily. Although these results showed the feasibility of intraoperative photoacoustic histology of bone specimens, the system has a relatively slow imaging speed fundamentally limited by the low laser repetition rate of UV lasers and applies only to only bone specimens. This research proposal aims to develop a high-throughput photoacoustic histology platform for pathologists and surgeons to diagnose intraoperatively and remotely with an imaging speed at least 100 times faster than any published reflection-mode UV-PAM systems. Specific Aim 1: Develop a structured illumination UV-PAM for ultrafast histology imaging of slide-free specimens. Aim 1.1. We will develop an ultrafast reflection mode UV-PAM using multifocal illumination with a single element transducer. Aim 1.2. We will design and fabricate DOEs for structured illumination UV-PAM with an extended depth of focus for slide-free specimens with irregular surfaces to allow high-throughput imaging of slide-free specimens in clinical settings. Specific Aim 2: Implement neural networks for virtual staining of photoacoustic histology and real-time intraoperative diagnosis. Aim 2.1. We will implement neural networks and unsupervised deep learning techniques to virtually stain photoacoustic images in various tissue types. The utilization of virtual stained PAM images for intraoperative diagnostic will be evaluated by pathologists in clinical practices. Aim 2.2. We will develop and train a deep learning neural network to classify tumor types and stages in different tissues using photoacoustic histology to build a computer-aided platform for real-time intraoperative diagnosis.

NIH Research Projects · FY 2025 · 2024-09

SCH: Transfer Regression to Enable Cross-Domain Cardiovascular Event Prediction This project proposes fundamental novelties (e.g., transfer volume regression) in computer science, data science, and biomedical engineering to address the critical health challenge of lacking a standardized clinical risk prediction for major cardiovascular events (CVe)—defined as stroke, heart attack, heart failure (HF), and death. Our proposal includes a pioneering transfer regression learning method, combined with several novel machine learning and data science techniques, to develop the first smart, standardized, fairness-aware, and user-friendly CVe prediction model. This model leverages commonly used, low-cost (sometimes free), safe, and quick screening programs (featuring low radiation and no need for contrast agents), aiming to significantly enhance clinical outcomes. The efficacy and robustness of this model are to be validated using four large and diverse datasets, exemplifying a significant advancement in Smart Health and Biomedical Research in the era of Artificial Intelligence and Advanced Data Science (SCH). RELEVANCE (See instructions): We will develop and validate transfer volume regression to enable quantitative clinical risk predictions for major cardiovascular events (defined as stroke, heart attack, heart failure, and death in this proposal) to guide preventive therapy.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Severe traumatic brain injury (sTBI) can lead to permanent motor and cognitive deficits and often result in exclusion from participation in society. Recovery of function through neurotherapy can continue years after the injury. However, it is a slow process in a sterile environment and difficult to integrate into one’s life. For decades, the concept of enriched environments, that is, scenarios that combine cognitive, social and physical stimuli, have shown neuroplastic benefits in animal models. Recent calls have been made to translate environmental enrichment to humans. Our long-term goal is to combine the principles of enriched environments and vocational reintegration for an immersive therapy for adults with sTBI. This project continues our work on the therapeutic café, a multimodal platform which combines therapies and first-phase vocational training. In this proposed work, we will recruit chronic sTBI survivors to participate in a randomized controlled pilot experiment that compares immersive therapy in the café to conventional physical and occupational therapy. The participants in the immersive group will participate in a fully functioning kiosk located at the MetroHealth Rehabilitation Institute cafeteria over a six-week training program under the supervision of a therapist. The kiosk is equipped with a harness to protect from falls and is stocked with hot and cold refreshments. Participants will interact with customers, collect payment, restock items and clean the area, all with guidance from the therapist. The conventional therapy group will participate in physical and occupational therapy for an equivalent duration as the immersive group. Our goal is to compare immersive to conventional therapy to obtain preliminary data for a larger clinical trial. In Aim 1, we compare the differences in two therapy strategies based on clinical measures of gait, balance, upper limb movement and function, and cognition. In Aim 2, we examine the differences in physical activity between groups by quantifying limb and joint motion with wearable sensors. We expect to find that for the same amount of motion, immersive therapy results in greater improvements in clinical measures because it engages speech, cognition, and physical function in a real-world manner. This work is important because it uses a randomized controlled trial to evaluate an innovative motivating, real-world, vocational therapy option for people with the most severe level of TBI. This is innovative as it is a completely new platform for combining therapeutic gains and community participation in this population. The work also helps define how to quantify the amount of therapy activities and illustrates how it can be used to understand the relationship between physical activity and clinical outcomes.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY The most pressing challenge in oncology is the need for accurate biomarker-driven prognostic/predictive risk- stratification to identify patients who are unlikely to benefit from standard of care (SOC) chemotherapy early in their treatment, as they might be better candidates for alternative therapies (e.g., genome-targeted agents, immunotherapy). Unfortunately, only ~31% of eligible cancer patients achieve partial/complete response to cytotoxic chemotherapy. For instance, over 40% of GB patients will inevitably recur within 6-8 months after chemotherapy, suggesting that they could have been better candidates for newer experimental therapies. A significant challenge in management of these patients is thus, segregating GB patients based on their outcomes/response to treatment. Similarly, the aggressive chemoradiation protocol for rectal cancers results in up to 70% of patients achieving 3-year disease-free survival. However, reliably determining which rectal cancer patients will not benefit from this protocol could allow for targeted adjuvant therapy to ensure optimal outcomes. Considering a “micro” to “macro” view of the tumor, comprehensive clinical evaluation for cancer involves acquiring multi-scale data, including radiology (e.g., CT, MRI) which provides macroscopic morphology and structural tumor details, histology images containing rich phenotypic information at cellular level, molecular data (e.g., genome sequencing, gene expression, epigenomics, also known as multi-omics) which captures the underlying biological processes, and the clinical data (e.g., age, sex). Ability to comprehensively combine disparate sources of information through computational approaches could enable discovery of new prognostic and predictive markers to reliably assess risks associated with response of chemotherapy and clinical outcomes. The F99 phase of this proposal continues my dissertation research on developing deep leaning (DL) multimodal models (mmSurvNet) to build prognostic markers for clinical outcomes, by combining MRI and digital pathology, in rectal and GB tumors. My research for the F99 phase is driven by the hypothesis that DL models, using co- registered pathology and radiology images that capture spatially co-localized tumor biology, can yield robust and reliable prognostic integrated-markers to predict clinical outcomes. Towards this, I will construct multimodal survival (mmSurvNet) models employing DL architectures that maximize spatial information across pathology and radiology. The attention maps for mmSurvNet will allow for establishing biological relevance, by spatially correlating radiology images with corresponding pathology which will contain annotations of known prognostic tissue characteristics. My proposed K00 phase will involve building predictive DL models (mmPredictNet) through incorporation of genomic, clinical, longitudinal data together with radiology and pathology images to build integrated markers predictive of response to chemotherapy, while also accounting for population health disparities. The current and future goals of my research are to develop comprehensive and reliable computational tools for clinically impactful treatment decision support in oncology.

NSF Awards · FY 2024 · 2024-09

This research aims to develop a groundbreaking tool to help policymakers predict the economic and societal impacts of investments made by the National Science Foundation (NSF) in regional technology- and science-based ecosystems. These investments are intended to stimulate regional innovation and drive broad economic benefits. Current models lack the ability to accurately forecast the impact of such investments. We hope to advance the state of the art in two ways: 1) Better prediction of the impacts of investments in regional technology and science-based ecosystems, particularly for novel and disruptive technologies; and 2)Improved analysis of societal impacts of these investments. By combining historical case studies on successful and unsuccessful regions, causal matched-pair analysis of actual vs counterfactual investment locations, and advanced data analysis techniques such as machine learning, this project seeks to create a reliable tool that can guide policymakers in designing effective, impactful funding strategies. The ultimate goal is to support the creation of thriving innovation clusters that enhance economic growth and societal well-being, especially in underserved communities. The proposed research will develop a predictive tool using a three-pronged approach. The first prong involves qualitative case studies of regions that have experienced significant economic transformations, helping to identify key attributes and mechanisms driving success. The second prong integrates these qualitative insights with quantitative analysis, using historical data and econometric methods to identify causal effects and predictors of successful innovation investments. The third prong focuses on creating a proof-of-concept forecasting tool, leveraging machine learning and systems dynamics to model complex interactions and predict outcomes. This tool will be validated with out-of-sample testing and compared against international benchmarks to ensure robustness. The project’s innovative approach aims to revolutionize how the impacts of NSF investments are forecasted, offering a powerful resource for policymakers to optimize funding decisions and foster inclusive economic development. Our outcome measures will include both indicators of innovation (such as patenting and productivity) and of shared prosperity (such as median wages and health status). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT Living longer does not necessarily mean living better. As the global demographic shifts towards increased lifespan, so does the incidence and prevalence of aging-related diseases. Most common among them, Alzheimer's disease (AD) has no effective treatments for either prevention or recovery. This is primarily due to insufficient understanding of its pathophysiology. Increasing evidence connects AD to the primary cilium, a historically overlooked organelle that serves as the neuron's antenna. My analysis of differentially expressed genes (DEGs) in postmortem human AD brains has revealed a high prevalence of DEGs intricately linked to primary cilia. Notably, the gene ADCY3, which encodes for primary cilia-specific adenylate cyclase 3 (AC3), is significantly downregulated in human AD brains. Primary cilia are dependent on AC3, and loss of functional AC3 is associated with cognitive decline and reduced brain mass. One hallmark of AD is aberrantly processed amyloid precursor protein (APP), and it is notable that APP normally localizes near AC3 in the primary cilia. My preliminary studies further show that AC3 becomes insoluble at early stages of AD and localizes with amyloid beta (Ab) peptides outside of the neuron. This suggests a role for APP in normal maintenance of cilia structure, and the potential for interaction between AC3 and various Ab fragments derived from APP that could promote abnormal extracellular amyloid aggregation. My goals are (1) to characterize primary cilia pathology in AD patients and a preclinical mouse AD model, (2) to establish whether the accumulation APP within primary cilia can deteriorate cilial length, and (3) to test whether AC3-Ab interaction promotes accumulation of aberrant amyloid aggregates in AD. To this end, I propose three Specific Aims that combine in vivo and in vitro approaches. In Aim 1, I will characterize AD-related impairments to primary cilia structure using immunohistochemistry and electron microscopy in the brains of both human and mouse AD. In Aim 2, I will engineer cells in which the putative cilia-localization sequences within APP are abolished to determine whether the presence of APP within cilia is required for normal cilia structure. In Aim 3, I will define how AC3 impacts aberrant Ab fragment accumulation by utilizing an in vitro model of Ab aggregation into fibrils through coincubation with AC3 and the various Ab peptides found in normal and AD brain. Through these aims, I will establish the role that deterioration of primary cilia plays in AD. By providing key insights into the aggregation properties and kinetics of AC3 in amyloid plaque formation, I will also clarify how AC3 interacts with Ab peptides. My work could identify new therapeutic targets for patients with AD. I will additionally receive rigorous training in biostatistics, aggregation kinetics, and ultrastructural analysis of primary cilia and amyloid fragments. This will help prepare me for my desired career in translational aging research.

NIH Research Projects · FY 2025 · 2024-09

Project Summary/Abstract The proposed work will develop a first-of-its-kind, transportable informatics tool, the Population Cancer Assessment and Surveillance Engine (Pop-CASE), that integrates patient-level cancer registry data for a population with granular community-level data in a standardized data model, while providing a user-friendly query tool to facilitate quick searching by researchers and community outreach professionals. Broadening access to these types of data for researchers and other stakeholders will advance the pace and breadth of cancer disparities research and will catalyze action to reduce disparities. Building on the work of an existing prototype and with ongoing input from a nationwide Steering Committee, we aim to 1) build the Pop-CASE data base and computational layer using an extended set of community and health system data sources and embedding additional calculation capabilities, 2) build a user-friendly interface with export capabilities, and 3) develop an Implementation kit featuring a software container for creating location-specific instances of Pop-CASE and an implementation guide. These aims will be accomplished by first creating a relational data base in PostgreSQL with tables derived from North American Association of Central Cancer Registry (NAACCR) standard record formats; U.S. Census American Community Survey data; Health Professional Shortage Area (HPSA) data; small area estimates for Behavioral Risk Factor Surveillance System (BRFSS) cancer screening, risk behavior, and comorbidity burden measures; National Provider Index (NPI) and Food and Drug Administration (FDA) provider and facility location information; historical “redlining” data reflecting discriminatory lending practices; and other sources as guided by our Steering Committee. A computational layer with logic for age adjustment, distance calculations, and calculation of common indices of disparity or disadvantage will be coded in Python. A user interface built in a Javascript-based framework will enable specification of complex queries with output at various user-selected geographic levels and options for report export—all with embedded capabilities for tiered access to small number counts and patient-level data sets. A modern container framework will be used to develop a “PopCASE-in-a-box” software container allowing other cancer centers or cancer registries with access to patient-level registry data to create a secure, location-specific instance of Pop-CASE. In the final project year, the University of Southern California (USC) project team members will use the PopCASE container to implement a PopCASE instance based on Los Angeles County cancer registry data and known as “LA-CASE.” PopCASE will provide a new tool for cancer control researchers, with place-contextualized cancer data at a fine geographic scale. It can also provide the Community Outreach and Engagement (COE) professionals and cancer center leaders working to curb cancer disparities at the local level with an unprecedentedly powerful catchment area data tool.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT HIV has a complex expression pattern of RNA with three different classes of transcripts: unspliced, singly spliced, and multiply spliced. The unspliced transcripts encode the Gag and Pol proteins, and the singly spliced transcripts encode the Env, Vif, and Vpu proteins. The multiply spliced transcripts encode the Tat, Rev, and Nef proteins. The multiply spliced transcripts are translated early on following infection due to their more efficient export from the nucleus. The unspliced and singly spliced transcripts, on the other hand, are retained in the nucleus due to cis-acting elements including the Rev Response Element (RRE). However, the post- transcriptional processing of these three classes of transcripts beyond their differential splicing is an understudied area of HIV research. Past research has revealed that alongside the virally encoded Rev protein, multiple host factors are also necessary for the export of unspliced and singly spliced HIV transcripts. Among these are RNA binding proteins including proteins that can recognize the RNA modification N6-methyladenosine (m6A). The role of m6A modification of HIV transcripts has been studied in active infection of cell lines but has yielded differing results. Some reports suggest that m6A modification of HIV transcripts enhances HIV replication while others suggest that it inhibits replication. Current maps of m6A sites on HIV transcripts cannot discern how the different transcript classes are differentially modified. Additionally, how m6A mechanistically impacts post- transcriptional processing of HIV transcripts and their nuclear trafficking is unknown. Preliminary data suggests that the HIV RNA species are, in fact, differentially modified and the presence or absence of m6A at these sites affects RNA expression levels. Therefore, I hypothesize that the presence or absence of m6A on HIV transcripts impacts the interactions of HIV RNA with nuclear RNA binding proteins, and this, in turn, affects the nuclear trafficking and stability of the RNA. To test this hypothesis, I will use nanopore direct RNA sequencing, knockdowns of the m6A machinery proteins and m6A reader proteins in actively infected and latently infected primary T cells, and HITS-CLIP to address the following questions: (i) where does m6A modification occur on HIV transcripts from primary T cell models of HIV latency and HIV+ donor cells? (ii) how does perturbation of the m6A machinery proteins and m6A readers affect the nuclear localization and distribution of the different HIV RNA species during active infection and following latency reversal? Ultimately, the results of this project will provide a better understanding of the post-transcriptional processing of HIV RNA in primary T cells which is paramount to the design of new therapeutics for eradicating the latent HIV reservoir.

NIH Research Projects · FY 2025 · 2024-08

Permanent disabilities following axonal injuries result from the failure of injured axons to regenerate and rebuild functional connections. Controlling axon regeneration is highly relevant to the development of therapeutic strategies for nervous system repair. Most axons in the adult mammalian central nervous system (CNS) fail to regenerate after injury, whereas axons in the peripheral nervous system (PNS) can regenerate. Interestingly, serotonin (5-HT) neurons in the CNS have an atypical capacity for axonal regrowth after injury. However, the intrinsic molecular mechanisms underlying 5-HT axon regeneration are not understood. It seems likely that elucidating these mechanisms will help to explain why 5-HT neurons are exceptional in the ability to regenerate connectivity. Recently, we showed that two transcription factors, Lmx1b and Pet1, control a 5-HT axonal growth program during a development and an axonal maintenance program in adult 5-HT neurons. These findings led me to hypothesize that Lmx1b and Pet1 may also be required to control a distinct intrinsic axonal regeneration program in injured adult 5-HT neurons. Interestingly, my preliminary in vivo results showed that adult-stage Lmx1b and Pet1 deficiency inhibits serotonergic regeneration. Thus, in this proposal I will investigate the following broad question: Do Lmx1b and Pet1 control a distinct axonal regeneration transcriptional program? To address this question, I will (1) investigate adult-stage Lmx1b and Pet1 function in the regeneration of 5-HT axons and synapses, (2) investigate the transcriptomic and chromatin mechanisms underlying 5-HT axon regeneration, and (3) investigate Lmx1b and Pet1 function in the regulation of an adult- stage 5-HT regeneration program. My long-term goal is to understand the mechanisms that can enable axons regenerate after injury and use the insights to develop therapeutic strategies. 5-HT neurons provide a unique opportunity to investigate the intrinsic mechanisms that enable axonal regrowth after injury. As my primary expertise is in Biochemistry and Genetics, my proposal studies bring me significant changes from my previous expertise and enable me to break into a new field of study, and acquire new techniques, including bioinformatical analyses, neuroimaging techniques, modified genetic tools, and behavior analysis. My career goal is to appropriately combine these techniques and scientific insights acquired though this K01 to clarify mechanisms underlying 5-HT axon regeneration, building a scientific career as an established researcher.

NSF Awards · FY 2024 · 2024-08

This doctoral dissertation research project investigates how new educational initiatives introduced to reduce academic burdens impact teachers and the role that teachers play in successful implementation of educational policies. The investigators specifically examine what decisions and adaptations drive educational change and balance robust professional outcomes for teachers and strong learning outcomes for students. By examining teacher decision-making and adaptations as significant to student outcomes the researchers provide a novel way of approaching the science of educational policy. The project trains a graduate student in scientific data collection and analysis and yields systematic findings that could influence educational policies and provide pathways for enhanced collaborations between teachers and educational administrators. Research findings will be shared through scientific publications and with educational policymakers to help develop policies that enhance workplace conditions and decision-making of teachers and that also enhance student performance. In order to understand the intersections between teachers' adaptations to new educational policies and student outcomes the investigators use both qualitative and quantitative research methods. Data collection methods include participant observation, semi-structured interviews, focus groups, and surveys, with analysis involving thematic coding using NVivo14, statistical evaluation of survey responses, and cross-validation with official documents. This study contributes to the anthropology of education, and to the science of organizational and policy implementation. 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 · 2024-08

ABSTRACT Obstructive sleep apnea (OSA) affects 26% of adults over age 30 and is associated with significant morbidity and mortality. At the time of presentation, African American have more severe OSA and have more associated complications compared to other groups. Continuous positive airway pressure (CPAP) is an effective treatment for OSA that improves sleepiness and quality of life in a dose response fashion. Therefore, it is recommended that patients use it whenever they sleep. However, CPAP use among African Americans is low. Many patients with OSA visit a health care provider at the urging of their bed partners who themselves may be experiencing decreased sleep and quality of life due to their partners’ OSA. We reasoned that bed partners may positively effect CPAP adherence. We conducted interviews of African American patients with OSA and their bed partners. They recommended a couple-oriented intervention to increase CPAP adherence. With their help we developed a couple-oriented behavioral therapy intervention. We now propose a full scale randomized controlled trial involving 220 African American patients with OSA of 2 large, urban healthcare systems and their bed partners. Patients randomized to the usual care arm will receive a CPAP machine and supplies with standardized OSA and CPAP education from a certified sleep technician. Patients and bed partners in the intervention arm will also receive a CPAP machine and supplies with standardized education regarding OSA and CPAP from a certified sleep technician. In addition, patients and partners will receive tailored text messages encouraging CPAP adherence. The couples will receive five 1-hour virtual cognitive behavioral couple therapy sessions with a behavioral sleep psychologist. Primary analyses will compare CPAP adherence at 1 year. Secondary analyses will compare CPAP adherence at 1, 3, and 6 months. We will also measure the duration and quality of sleep, functional status, and quality of life of patients and their bed partners. Novel features of the proposed project include a rigorous randomized controlled trial design, short- and long-term follow-up, and remotely monitoring of daily CPAP adherence data directly from the machines. The project has the potential not only to improve patient sleep and well-being but also to improve the sleep and well-being of their bed partners. Furthermore, it may serve as a model for future trials of couple-oriented therapy among patients with other sleep disorders.

NIH Research Projects · FY 2025 · 2024-08

Project Summary Our preliminary studies show that the APP E590D variant, identified in two AD patients’ brains, substantially induces more Aβ than APP-WT in vitro cell line and primary mouse cortical neurons. This APP E590D variant also significantly increases APP protein stability and APP protein's endocytic process more than APP-WT. Compared to APP-WT and many other APP mutants, APP E590D generates not only Aβ peptide but also another extracellular 15kda fragment. Therefore, we take advantage of the human neurons directly converted from primary fibroblasts to test the hypothesis that APP E590D promotes Aβ pathology while inducing abnormal endocytosis of APP protein, mitochondrial dysfunction, and impairment of synaptic integrity in human neurons. Unlike most other classified pathogenic APP missense variants located in the Aβ sequence or C terminus, the APP E590D variant site is six residues away from the β-cleavage site beyond the Aβ sequence at the N terminus APP protein, which results in a very conservative protein structure. Considering the patient carrying the APP E590D variant has not only excessive amyloid deposition but also shows other non-AD-associated neuropsychological dysfunctions at a very young age, we will (1) identify new C terminal cleavage site of APP beyond the β-secretase cleavage using a mass spectrometry approach; (2) generate Aβ sequence-humanized APP E590D knock-in mouse model of AD and test the pathogenic role of APP E590D in vivo. The successful conclusion of these studies will (1) determine the pathogenic role of APP E590D in human neurons and mice brains in vivo and uncover a new APP cleavage site with its unique fragment from APP E590D; (2) provide a new APP E590D-based knock-in animal model of AD for other investigators in the field.

NSF Awards · FY 2024 · 2024-08

Cilia are flexible hair-like appendages commonly used to create fluid motion in biological systems, facilitating swimming, feeding, reproduction, and other functional behaviors. Typical cilia are tens of microns long, but ctenophores (comb jellies) use cilia at much larger scales—around a millimeter in length. At small scales, ciliary flow is highly constrained by fluid viscosity. However, at larger scales, inertia becomes more important, leading to quantitative and qualitative differences in the velocities and forces produced by the cilia. These differences will be explored with a combination of laboratory experiments and computational simulations, using ctenophores as a model system for large-scale cilia. A better understanding of the fluid dynamics of cilia across scales will provide new tools to ask and answer questions related to biology, ecology, and the fundamental physics of how flexible structures create flow across scales. This knowledge may lead to new developments in engineering, including bioinspired devices, sensors, and robots. The project will also include the development of several educational components, including a new module on the viscous-inertial transition for high school physics students and outreach activities for young women interested in engineering. The overall goal of the project is to understand the physical principles that govern ciliary flows from low to intermediate Reynolds numbers. This study will explicitly examine the effects of substrate geometry and deformability on ciliary flows. A combined experimental-numerical approach will be used to investigate hydrodynamic interactions of multiple flexible propulsors at low-to-intermediate Reynolds numbers and develop useful scaling laws. The experimental approach will employ both planar and volumetric particle image velocimetry to visualize the flows generated by living ctenophores across a range of animal and propulsor sizes. The material properties of the ciliary substrate (mesoglea) will also be characterized during the investigation. These results will guide the development of a scalable computational fluid dynamics model, which will be used to investigate the larger parameter space of ciliary flow generation across scales. The project will focus on the effects of three key variables: (i) propulsor kinematics, including the degree of bending and spatiotemporal asymmetry; (ii) substrate geometry, from flat to curved; and (iii) substrate deformability, from rigid to highly deformable. This integrated approach will enable an in-depth investigation of how flexible structures generate flow across the viscous-inertial transition, and the development of broadly applicable scaling principles to guide future technology development. 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 · 2024-08

Project Summary/Abstract: Childhood Sjögren's disease (cSD) is a rare autoimmune disease that primarily affects the glands, leading to salivary and lacrimal gland inflammation that results in reduced function and dryness symptoms. Active inflammation of the salivary glands is one of the most common symptoms in children with cSD. Despite the profound impact on affected children and their families, there is no pediatric-specific clinical trials to inform therapeutic strategies in cSD. Although symptomatic episodes of inflammation to the glands are critical to evaluate drug therapies, how to determine whether a patient has experienced a flare is lacking. There are no widely accepted cSD outcome measures to tell us whether a treatment is effective, especially with regards to flares involving the glands. New medications have the potential to be safe and effective treatments for cSD but they must be evaluated in trials. We propose a novel trial design known as N-of-1 trials to evaluate cSD therapies. N-of-1 trials is a personalized treatment strategy based on the individual patient's response. The goal of this grant will be to successfully finalize a rigorous and impactful clinical N-of-1 trial in cSD. We will accomplish this through three aims. We will develop patient- and parent-driven outcome measures for the evaluation of glandular inflammation and its flares, and their impact on quality of life and mental health for cSD clinical trials. The overall approach for this aim is based off frameworks successfully used previously in pediatric rheumatology. To reach consensus on cSD flare criteria and to develop patient-important cSD outcome measures, we will review the literature, conduct interviews, perform online surveys, and plan a final group meeting, of stakeholders including patients, health professionals, and methodologists. We will prepare and finalize the details for N-of-1 trials in cSD using two therapies. To accomplish this aim our team will develop a study protocol with patient involvement on critical steps of the trials such as the final choice of medications or outcome measures. We will establish a collaborative network of pediatric rheumatology centers for participation in the N-of-1 trial that we will test as a model for current and future studies in cSD.

NIH Research Projects · FY 2024 · 2024-08

PROJECT SUMMARY We propose to purchase a new Iconeus One functional ultrasound (fUS) neuroimaging system from the company, Iconeus, to enhance the biomedical ultrasound and neuroimaging capabilities of the Imaging Research Core of Case Western Reserve University (CWRU). The fUS system offers unique capabilities in the neuroimaging space, which are currently unmet by any other instrument. More specifically, fUS fills a critical gap between functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and standard small animal optical imaging methods by providing a method of acquiring ultra-fast (1 Mhz), high-resolution (<50 micron spatial resolution) data, which is highly sensitive to changes in cerebral blood volume and vascular morphology. The main points of distinction of the fUS system from fMRI are 1) the ability to acquire at an unprecedented temporal and spatial resolution changes of brain vasculature and cerebral blood flow in awake, mobile mice and rats; 2) a mobile, plug-and-play platform that can be relocated easily, if need be, to the site of an experiment; and 3) substantially lower cost and greater accessibility compared to an fMRI scanner. Moreover, it does not require the high maintenance costs of an MRI. Acquisition of this system would add value and advance numerous ongoing projects in brain imaging, neural engineering, deep brain stimulation, neurodegenerative diseases, pain, and development of theranostics for neuro-oncology applications. The fUS system would be the first of its kind in the greater Cleveland area and would enable investigators from CWRU and the surrounding four clinical affiliates (the Cleveland Clinic Foundation, University Hospitals Cleveland Medical Center, Louis Stokes Cleveland Department of Veterans Affairs Medical Center, and The MetroHealth System) to carry out cutting edge research in these very important fields. This new preclinical fUS capability will also be available to all regional investigators. A highly interdisciplinary group of 16 Major Users from all these institutions will work on collaborative projects using the Iconeus One. Ultimately, our goal in acquiring this advanced neuroimaging technology is to better visualize, design, evaluate, and translate treatments for neurological disorders. An advisory committee of highly experienced ultrasound and neuroimaging faculty at CWRU and external institutions will oversee the organization and usage of the system and recommend policies to maintain system performance and maximize utilization. These advancements will drive our research forward and ultimately improve human health.

NIH Research Projects · FY 2024 · 2024-08

Project Summary We propose to purchase a new Mediso nanoScan preclinical SPECT-CT scanner at Case Western Reserve University (CWRU). This new SPECT-CT system will restore the preclinical SPECT-CT capability in Cleveland for the first time since the failure of our last system before the pandemic, and will efficiently synergize our extensive preclinical radionuclide imaging research programs to support 20 major users with research in a wide range of areas including development of novel theranostic agents, animal models of diseases, new therapeutic strategies, effective anti-viral therapy and exciting immunotherapies. The proposed system will not only reinstitute SPECT-CT scanning to our regional institutions, and also provide opportunities to scan larger animals (e.g., marmosets, ferrets, woodchucks, rabbits, etc.), and animal models with infectious diseases using a BSL-3 compatible sealed imaging chambers, in addition to the rodents. Overall, this system provides the capability to support a broad set basic science research throughout Northeast Ohio. The nanoScan system includes: 1) a 4-headed SPECT subsystem with 3/8” NaI crystal and fully digitized detectors; 2) multi-focus, multi-size, multi-pinhole apertures in addition to the parallel hole LEUHR collimators; 3) CT subsystem (80W/1000µA) with standard QC phantom set; 4) multiple software licenses allowing efficient SPECT-CT image analysis and visualization for multiple users at a time; and 5) an animal monitoring, respiratory gating, and temperature control to keep the animal stable during imaging. Additional options requested with this new SPECT-CT system include sealed, BSL-3 compatible imaging chambers to specifically provide support for multiple users. This new preclinical SPECT-CT capability will be available to all regional investigators and will be operated by the Imaging Research Core at CWRU which is an integral component of multiple research centers at CWRU including the Case Comprehensive Cancer Center (NCI P30CA043703), the Digestive Diseases Research Core Center (NIDDK P30 DK097948), the Clinical and Translational Science Collaborative (NCATS UL1 TR002548), the Cystic Fibrosis Research Center, and the Center for Modular Manufacturing of Structural Tissues. This ongoing support for the Imaging Research Core is in addition to the direct institutional support of $148,500 to partially offset the cost for the extended warranty. An advisory committee with highly experienced imaging and therapy faculty at CWRU and an external institution will oversee the organization and usage of the system and recommend policies to maintain system performance and maximize utilization.

NSF Awards · FY 2024 · 2024-08

The National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) is a highly competitive, federal fellowship program. GRFP helps ensure the vitality and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based master's and doctoral degrees in science, technology, engineering, and mathematics (STEM) and in STEM education. The GRFP provides three years of financial support for the graduate education of individuals who have demonstrated their potential for significant research achievements in STEM and STEM education. This award supports the NSF Graduate Fellows pursuing graduate education at this GRFP institution . 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.