Case Western Reserve University

NIH Research Projects · FY 2026 · 2025-03

Proteasome inhibitors are effective in a small subset of malignancies, the most important one is multiple myeloma, a cancer of plasma cells. The underlying reasons for the exquisite sensitivity of myeloma to proteasome inhibitors are associated with the activation of higher stress conditions that ultimately promote apoptosis. Similar to myeloma, acute myeloid leukemia (AML) develops in the bone marrow and disseminates between bone marrows until becomes detectable in the bloodstream. The bone marrow microenvironment supports AML survival and promotes resistance to therapy. We have shown that a common feature associated with proteasome inhibition in vitro and in vivo is the suppression of mTORC1 activity, a kinase that controls cell metabolism and activation thereof is common to multiple cancers, including AML and myeloma. We reasoned that mTORC1 suppression is a key factor in promoting survival to proteasome inhibition and contributes to development of resistance. We developed a small molecule that activates mTORC1 and synergizes with PIs to kill AML in vitro and in vivo. When the molecule is combined with a proteasome inhibitor, mitochondrial respiration is strongly suppressed, and the membrane potential of the mitochondria is gradually lost. When the mTORC1 activator is added to quiescent AML cells, cell cycle resumes, and the cells regain sensitivity to chemotherapy. We will study the underlying mechanisms of both phenomena, mitochondrial damage, and reversal of quiescence. We will map the mitochondrial respiratory lesion, identify the predisposition of the mitochondria to stress and establish a connection with drug resistance. We will determine the translation mechanisms that reverse quiescence and evaluate the importance to AML therapy using in vitro and in vivo models. This study aims to establish a mechanistic understanding of the role of mTOR hyperactivation as a chemosensitizer of AML. These pharmacodynamic features should unravel novel metabolic vulnerabilities in AML that can be addressed pharmacologically.

NSF Awards · FY 2025 · 2025-02

This project addresses the growing challenges in collaborative machine learning (CML) across industries like healthcare and finance, where participants jointly develop machine learning (ML) models while preserving data privacy. Current CML systems struggle with participant heterogeneity, varying network conditions, and diverse pricing models in multi-cloud environments. To tackle these challenges, we present ElasticCML, an integrated framework that introduces three key innovations: intelligent resource management, efficient distributed training strategies, and dynamic cloud resource orchestration. ElasticCML features adaptive mechanisms that automatically optimize resource allocation based on participants' contributions and capabilities. Its communication layer intelligently reduces data transfer overhead while preserving model quality. The framework also implements smart scheduling algorithms that minimize operational costs across multiple cloud platforms. These components work together to optimize resource utilization while maintaining model training performance under diverse infrastructure conditions. The framework continuously adapts to changing system dynamics and varying computational capabilities of participants, ensuring efficient and inclusive ML development. The project's broader impact lies in democratizing advanced ML capabilities, particularly in sensitive and resource-constrained domains. Through open-source contributions, educational programs, and workshops, ElasticCML aims to advance resource-efficient AI development while fostering innovation across academia, industry, and society. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

People who are paralyzed from the shoulders down rely on 24-hour care to complete basic daily activities. Restoring arm and hand function would greatly increase their independence. Assistive robotics and functional electrical stimulation can potentially restore arm and hand function, but each has significant drawbacks. The objective of this project is to develop a cooperative control strategy for functional elbow and wrist movements in people with high cervical spinal cord injuries using functional electrical stimulation and a robotic exoskeleton. The results of the project will help move functional electrical stimulation and upper limb robotics from laboratory assistive technologies to wearable devices used for everyday tasks by people with full-arm paralysis. The complementary strengths of functional electrical stimulation (FES) and assistive robotics can potentially enable people with high tetraplegia to independently feed and groom themselves. FES provides free power using a person’s own muscles but cannot sufficiently control all joints simultaneously due to permanent denervation of some muscles. Assistive robots can provide additional power and control, but can be rigid, bulky, and heavy. This project's objective is to develop a cooperative control strategy that demonstrates functional elbow and wrist movements in people with high tetraplegia using a hybrid FES+rigid support robot. By maximizing the utility of muscles activated by FES, the proposed hybrid strategy will reduce the need for robot power and size, paving the way for using FES with soft wearable robotics. This project will use a rigid robot as the testbed for developing cooperative control strategies, allowing for exploration of the entire design space for future development of soft wearable exosuits coupled with FES. FES+robot assisted muscle-induced torques will be measured in real time during elbow and wrist movements. This information will feed into a coordinated FES+robot control scheme that aims to decrease robot work while maintaining tracking accuracy. Performance and robustness of the control scheme will be benchmarked for varying robot capabilities during a self-feeding task. The outcome will be a model, mapping robot capabilities to task completion success, to be used to design future wearable hybrid FES-robotic systems for upper limb movement restoration. These advances will open up new research horizons in commanding and controlling hybrid neuroprostheses that could not otherwise be achieved. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

This project will develop a new technology to separate and detect specific molecules, particles and biomarkers in solution. Unlike conventional methods that rely on chemical affinity, this new method will use optical forces to separate and detect constituents in solution as the solution flows through a microfluidic device. The microfluidic device will have an engineered surface that can manipulate light and control its characteristics at the nanoscale to generate a force on molecules or particles and separate them in the flow. The device will enable precise, rapid, scalable, and cost-effective detection and separation methods for applications in healthcare and environmental monitoring. Broader impacts include advancing education through the development of interactive tools like the “HoloNano” holographic app, which will enhance public and student understanding of nanoscale photonic interactions. The project will design and fabricate a photonic metasurface integrated with a microfluidic device to generate optical forces capable of separating and sensing molecules, particles, and chiral enantiomers. Key objectives include developing a computational framework for predicting and optimizing optical forces and experimentally validating the metasurface-integrated microfluidic platform. This approach applies machine learning for inverse design and utilizes T-matrix methods for simulation. The anticipated outcomes include a scalable, efficient optomechanical device that offers higher throughput and specificity for molecular separation. The technology is expected to achieve low-cost and high-efficiency separations, advancing biophotonic devices for applications such as glioma biomarker sensing and environmental purification of heavy metals. The research also explores the fundamental properties of chiral-dependent optical forces, expanding knowledge in photonics and optomechanics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

Abstract This Project aims to reduce mortality from Esophageal Adenocarcinoma (EAC) by pioneering two paradigm changing approaches. First, we will demonstrate the efficacy of non-endoscopic biomarker-based early detection of Barrett's esophagus (BE), in a population without symptoms of gastroesophageal reflux disease (GERD). Second, we will establish a novel molecular technology, dubbed “BAD”, for early detection and interception of BE progression toward EAC. BE is the precursor lesion of EAC, a cancer with 80% 5-year mortality. BE is currently detected only when individuals with GERD undergo endoscopic (EGD) screening. Once detected, BE patients undergo triennial surveillance EGD with random biopsies to catch progression to high-grade dysplasia (HGD), that can be eradicated endoscopically to prevent cancer. Weaknesses of the approach include: i) low acceptance of EGD screening among GERD patients; ii) the absence of any recommended screening among non-GERD patients, who fall completely outside of BE screening guidelines, but who account for 40% of EAC; frequent failure of random surveillance biopsies to detect early EAC, with many cancers arising in between surveillance exams and others already metastatic when detected. Our team developed a novel swallowable balloon-based device to enable targeted screening of the distal esophagus (the site of BE origin) in a simple 5- minute office procedure. We mated this device with a methylated DNA biomarker panel that sensitively detects BE. In the GERD population we showed this approach detects non-dysplastic BE (NDBE) with 90% sensitivity and 92% specificity. We now find that non-GERD patients with 3 or more BE risk factors (age, obesity, smoking, male sex, white race) have BE risk similar to GERD patients. Aim 1 of this proposal will conduct a human trial demonstrating that our non-endoscopic biomarker-based technology will enable BE detection in this non-GERD population, with positive predictive value greater than that of current guidelines for EGD screening of GERD patients. Second, our group applied methods of deep DNA sequencing and AI analysis, developed for detecting cancer DNA in liquid biopsies, to instead detect abnormal DNA from nascent clones of progressed BE captured in esophageal brushings that comprehensively surveille the full BE disease segment. We showed this method, “BAD”, detects as “Very-BAD” 97% of EAC and 68% of HGD. We also showed Very-BAD identifies a 7% subset of non-dysplastic BE, that on retrospective review showed high risk of early progression to HGD or EAC. Aim 2 of this proposal will: i) implement improvements to the BAD methodology aimed at increasing sensitivity for HGD to 85%, while preserving specificity; ii) prospectively demonstrate that Very-BAD NDBE defines a population with high 3-year risk of BE progression to HGD; iii) implement BAD as a method to enable frequent non-endoscopic BE surveillance by adapting BAD to work with samples from our non-endoscopic balloon device. Last, molecular studies will determine the basis of false positive and false negative BE calls in non-GERD subjects, and will also visualize and molecularly interrogate the early progressed BE cells that are detected as Very-BAD.

NIH Research Projects · FY 2026 · 2025-01

Project Abstract This project aims to determine whether the precursor HIV-1 protease (PR) plays an important role in the development of drug resistance to protease inhibitors (PIs). The precursor PR dimer is far less susceptible to inhibition by PIs than the mature PR dimer, leading to the hypothesis that drug resistance mutations may evolve through the relative resistance of the precursor PR. Using innovative techniques ‘locking’ PR in the precursor configuration, we have unexpectedly found that viruses can mature, fuse with target cells, and replicate without formation of mature PR, providing a clear path through which drug resistance can evolve. Aim 1: Assess how mutations within PR or the p6*-PR cleavage site influence dimerization and activity of the precursor and mature PR enzymes. Aim 2: Assess whether PI resistance that develops without canonical mutations in PR alters dimerization and precursor or mature PR function. Aim 3: Perform in vitro viral propagation experiments to better understand how the precursor PR contributes to drug resistance.

NSF Awards · FY 2025 · 2025-01

The Ham radio Science Citizen Investigation (HamSCI) network is a Distributed Array of Small Instruments (DASI) designed for the study of space weather impacts. The Personal Space Weather Station (PSWS) platform was previously developed through DASI Track 1 program in 2019. PSWS stations have one or more instruments, each capable of sensing a different aspect of the geospace environment. Several stations were deployed as proof-of-concept including those by amateur Ham radio operators and today, the PSWS network consists of over thirty-five stations located primarily in the continental US, but some also in Canada, Alaska, and Europe. It is used to study the ionospheric impacts of solar flares, solar eclipses, geomagnetic storms, traveling ionospheric disturbances, and other small-scale ionospheric variability. This project will provide the backbone for the HamSCI PSWS network to enable a range of scientific investigations by deploying thirty standardized stations capable of observing high frequency (HF) Doppler shifts, HF amateur radio transmissions, Very Low Frequency transmissions and natural radio emissions, and the geomagnetic field. Ten fully automated, Global Positioning System (GPS) disciplined amateur radio transmitters will be also deployed to serve as a new source of GPS-stabilized HF beacon signals. Once deployed, this enhanced network will enable researchers to investigate both local and continental space weather effects, including those caused by traveling ionospheric disturbances, solar flares, and geomagnetic storms. The network has been developed as a collaboration between the professional scientific and amateur radio communities. It thus provides a unique opportunity for participation by and outreach to over 730,000 licensed US amateurs and about 3 million worldwide. This work will improve synergies between professional scientific and amateur radio communities, develop open technologies and observation networks that can be used in conjunction with existing geospace infrastructure, and develop materials that can be used in formal and informal educational institutions to teach space and radio science. HamSCI has a large online presence and following within the amateur radio community. This translates to the potential for extensive public relations and large outreach. This project will also support several undergraduate students’ participation and include major participation of both a Minority Serving Institution and an emerging (non-R1) academic institution. This project is funded by the Geospace Facilities program with co-funding from the Aeronomy program in the Division of Atmospheric and Geospace Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

Head and neck squamous cell carcinomas (HNSCCs) are a diverse collection of cancers originating from distinct anatomical sites within the head and neck region including the oral cavity. Human papilloma virus (HPV), tobacco, and alcohol use are etiologic factors HNSCC pathogenesis. Current first-line modalities for HNSCC have limited efficacy in HPV- patients and such approaches are associated with high morbidity and quality of life issues highlighting an unmet clinical need. Cyclin D1 dysregulation through gene amplification, mutation, or protein stabilization is observed frequently in human cancers including HNSCC implying that the cyclin D1/CDK4 kinase provides cells with a proliferative advantage needed for cancer development. Fbxo4, a component of the E3 ligase, SCFFbxo4 that regulates ubiquitin-dependent degradation of cyclin D1, loss occurs in head and neck, esophageal, and uterine cancers among others as demonstrated by us. We have demonstrated that Fbxo4 knockout mice are susceptible to CDK4/6-dependent tumors and that such tumors are addicted to glutamine. Glutamine-dependence is a feature of many tumor cell types due to glutamine being the major anaplerotic carbon source for rapidly dividing cells. Glutamine deprivation can trigger reduced growth and apoptosis in tumor cells offering a potential point of therapeutic intervention. In the context of Fbxo4 loss, glutamine addiction is partially due to cyclin D1 overexpression, thereby suggesting additional SCFFbxo4 substrates that contribute to Fbxo4- dependent tumor suppression and metabolic regulation. Given the emergence of inhibitors of glutamine biosynthetic pathways, our observation provides a potential novel perspective in HNSCC therapy. For this vision, it is essential that we elucidate how the SCFFbxo4 E3 ligase coordinates metabolic reprogramming during HNSCC tumorigenesis. Through a combination of proteomic and genomic approaches we identified additional substrates. Among these is LKB1 (Serine/Threonine Kinase 11, STK11), an established regulator of cell stress and metabolism and depending upon cell context harbors either tumor suppressive or tumor promoting activities. We hypothesize that SCFFbxo4 is a master regulator of HNSCC, coordinating tumor cell proliferation and metabolic reprogramming via cyclin D1 and LKB1, thereby linking Fbxo4 to novel effectors in HNSCC pathogenesis. Loss of Fbxo4 drives a cyclin D1/CDK4-dependent increase in cell proliferation that requires metabolic reprogramming and activation of cellular adaptive pathways. This hypothesis is tested in the following Aims: Aim 1: Determine the ability of p53R172H to cooperate with D1T286A and drive HNSCC in vivo; Aim 2: Determine the mechanisms of Fbxo4-dependent regulation of LKB1 and its impact on HNSCC; Aim 3: Determine the therapeutic potential of targeting glutamine-metabolism in HNSCC with dysregulated Fbxo4-cyclin D/CDK4.

NIH Research Projects · FY 2026 · 2024-12

Commercial tobacco use is high among population groups with socioeconomic disadvantage, who continue to face substantial barriers to accessing and using evidence-based cessation services. Engaging with freely accessible tobacco quitlines can significantly increase smoking cessation rates, yet there are socioeconomic disparities in quitline awareness, which impacts subsequent use. Ask-Advise-Connect (AAC), a streamlined Screening, Brief Intervention, and Referral to Treatment (SBIRT) model and health system-based approach to increase uptake of quitline counseling, increases cessation treatment access and improves cessation rates. However, 25-30% of smoking adults, many of whom are low-income, do not visit a primary care provider on an annual basis, and thereby miss the critical opportunity to receive advice and resources to quit. This R01 application seeks to enhance the reach and effectiveness of the tobacco quitline for socioeconomically disadvantaged people who smoke, by building on our prior work leveraging food pantries as a promising community-based setting for tobacco cessation outreach. We will adapt, implement, and examine the sustainability of an adapted AAC model across food pantry settings via the study aims. Aim 1 is to adapt AAC for a community-based setting using participatory co-design, involving stakeholder engagement. Aim 2 is to implement the Community-Adapted Ask-Advise-Connect (CA-AAC) in food pantries to examine the reach and effectiveness. We will conduct a two-arm pragmatic cluster randomized clinical trial (RCT) across 18 pair-matched food pantries, hypothesizing that CA-AAC will have higher reach and higher impact (measured as reach x effectiveness) for quitline engagement than an information-only comparison arm. Aim 3 is to identify multilevel contextual factors that impact the provision of tobacco cessation treatment in food pantries. To inform implementation and sustainability, we will use RE-AIM and PRISM frameworks to examine factors that impact the reach, effectiveness, implementation, and maintenance of CA-AAC in food pantries across contextual domains (multi-level perspectives, multi-level partner characteristics, external environment, and implementation and sustainability infrastructure). This proposal addresses a significant health disparity issue related to the high prevalence of tobacco use among socioeconomically disadvantaged groups. This work aligns with priorities of the NIH in shifting to solution-oriented approaches in health disparities research, by implementing evidence-based interventions in a new setting. By adapting AAC for relevance across a range of food pantry settings and their varying models, this proposal represents a critical opportunity to test and refine implementation strategies that can flexibly and sustainably improve access and uptake of cessation services to a health disparity group.

NIH Research Projects · FY 2026 · 2024-12

Radiomics-based risk prediction of heart failure using CT calcium score exam Project Summary Heart failure (HF) is a major cause of morbidity and mortality in the United States. There is a compelling need for personalized pre-emptive HF risk prediction to facilitate precise preventive strategies to alleviate the population burden. However, currently, there are no widely validated models for HF risk prediction, which hinders the timely identification of a pre-emptive initiation of therapies in at-risk patients. Non-contrast low-dose CT scans for coronary artery calcium scoring (CT calcium scoring, CTCS), already widely utilized for risk assessment in atherosclerotic cardiovascular disease, offer an opportunity to identify key pathophysiologic pathways causally or consequentially linked with HF risk. The central premise of this transformative proposal is that a reliable and reproducible CTCS-based HF-specific clinico-radiomic risk model (CTCSHF) will enable the precision identification of patients at risk for HF. We will leverage the University Hospitals CLARIFY program, the world’s largest free CTCS program (>100k unique participants with ~15K increase per year and > 3000 HF events) and our Houston Methodist HeartScan CTCS program (> 50k unique participants with ~10K increase per year and ~2k HF events) in addition to 2 external prospective NIH-funded cohorts (CARDIA and CRIC), that together will provide a robust opportunity for model derivation and validation. Together with institutional expertise in computational image analysis and cardiovascular imaging, we will develop and validate a comprehensive machine learning based analysis of CTCS to identify key image-based biomarkers (radiomics) corresponding to 5 pathophysiologic domains linked with HF risk. They include cardiac remodeling (size/shape), atherosclerosis (coronary/vascular calcification), hemodynamics (aorta/pulmonary artery, size/valvular calcium), visceral adiposity (liver and epicardial adipose tissue), and sarcopenia (skeletal muscle, bone density), combined with clinical factors and demographics, to predict future HF events. In Aim 1, we will develop and validate an automated radiomic extraction tool from CTCS. In Aim 2, we will develop an HF risk prediction model (CTCSHF) incorporating CTCS-derived radiomics and clinical risk factors in >160,000 participants from 4 large well-charactered prospective cohorts with > 5000 incident HF events. In Aim 3, we will explore CTCSHF model fairness across socio-racial groups and investigate the utility of fairness-aware clinico-radiomic HF risk models (i.e., in subgroups of race and socioeconomic status) in improving accuracy. Improved characterization of HF risk will advance knowledge of cardiometabolic disease phenotypes and support clinical therapeutic decision-making and patient counseling for improved adherence.

NIH Research Projects · FY 2026 · 2024-12

Project Summary_Abstract Age-related clonal hematopoiesis (CH), characterized by the enhanced fitness of hematopoietic stem/progenitor cells (HSPCs) carrying somatic mutations, is associated with increased risks of developing hematological malignancies such as myelodysplastic syndrome or acute myeloid leukemia (AML), as well as solid tumors, cardiovascular disease, chronic obstructive pulmonary disease, and severe symptoms during certain microbial infections. While CH is often linked to unfavorable outcomes, it also corelates with beneficial conditions, including a reduced risk of Alzheimer disease, an increase in the graft-versus-leukemia effect, and preserved T-cell immunity. The central question arises: Can we sustain the heightened self-renewal of HSPCs associated with CH while preventing its progression? Heat shock transcription factor 1 (HSF1) plays a pivotal role in cellular stress responses, metabolism, aging, and cancer. Our recent research, involving genetic knockout and pharmacological degradation of HSF1, has highlighted its critical role in maintaining AML stem cells while being nonessential in normal HSPCs. Utilizing newly generated DNMT3a/HSF1 and TET2/HSF1 double knockout mouse models, we observed that the deletion of HSF1 along with Tet2 or Dnmt3a sustains or enhances HSPC engraftment and maintains balanced lineage commitment. Notably, these double knockout mice exhibit a reduced incidence of myeloid or lymphoid neoplasms compared to counterparts with sole TET2 or DNMT3a deleted. Gene set enrichment analysis revealed an upregulation in mitochondrial oxidative phosphorylation (OXPHOS) and/or glycolysis pathways in DNMT3aHSF1 or TET2/HSF1 doubly deleted HSPCs compared to those with sole DNMT3a or TET2 deletions. Additionally, we found that HSF1 protein expression is higher in bone marrow immature cells compared to mature cells, upregulated in TET2 and DNMT3a deleted bone marrow cells, and decreased during aging, suggesting a potential protective role in maintaining HSPC function in CH. The observed ability of HSF1 deletion to sustain or enhance HSPC self- renewal, maintain balanced lineage commitment, and prevent CH progression underscores its appeal as a target. Based on these preliminary findings, we hypothesize that modulating HSF1 may hold the key to transforming detrimental CH into a state favoring normal hematopoiesis while simultaneously preventing CH progression. We aim to address the following two fundamental questions: 1) how DNMT3aHSF1 or TET2HSF1 doubly deleted HSPC function is maintained or enhanced with upregulated mitochondrial function, considering the consensus that adult HSPC homeostasis relies on glycolysis; and 2) how HSF1 deletion prevents CH progression and the impact of HSF1 nuclear degrader in CH. These findings have the potential to significantly impact the development of targeted interventions, preserving normal hematopoiesis and impeding CH progression.

NIH Research Projects · FY 2026 · 2024-12

In the general population, suicide is the second-leading cause of death for ages 10 to 24. Child Welfare (CW) and Juvenile Justice (JJ) involved youth have approximately 3 times greater risk for suicide ideation, attempts, and completions (i.e., self-injurious thoughts and behaviors – SITB) than non-systems-involved youth. Sexual and gender minority (SGM) (i.e. lesbian, gay, bisexual, queer and/or transgender) in the general population have 2-4 times the risk of SITB compared to their heterosexual, cisgender peers. Notably, SGMY are disproportionately overrepresented in CW and JJ, with estimates ranging from 16-32% compared to 2-8% in the general population. In sum, the risk of suicidality for SGMY who are involved with public systems is markedly compounded; however, the unique needs of this population have been largely ignored. Few CW or JJ jurisdictions identifying SGMY identities or providing SGM- affirming care. The proposed quasi-experimental clinical trial study will implement and evaluate the feasibility, acceptability, and initial impact of Peer Support Specialist (PSS) services for public system-involved SGMY at risk of suicide. The goal of this study is to evaluate the feasibility, acceptability, and initial impact of a multi-level intervention, Youth Empowerment & Safety (YES), comprised of two coordinated components: 1) system-level improved identification and referral (I/R) of SGMY at-risk for suicide, and 2) introduction of a SGM- affirming PSS to enhance engagement and support with behavioral health treatment and other support services. The rationale for the YES intervention is that we will improve outcomes for SGMY because our SGM-affirming PSS will strategically address modifiable risk factors (e.g. client-perceived SGM-based provider stigma, mistrust, and internalized queer-phobia) and protective factors (e.g. peer support, SGM-community connection, self-affirming beliefs, and hope) for engagement and SITB among SGMY. To do this, we will first develop and implement standard operating procedures (SOPs) and processes for YES tailored to each setting (JJ & CBMH). This aim will include a full set of protocols and a training and treatment manual (Aim 1). We will then evaluate the feasibility, acceptability, and preliminary impact of YES on system- level targets (Aim 2) and youth-level targets (Aim 3). The proposed research is significant because embedding PSS in the service continuum where SGMY are overrepresented and under- or un-identified is a critical systems innovation. YES has the potential to be a low resource/high impact intervention that could significantly improve service engagement and equity for systems-involved SGMY. This research is in response to the NIMH call for innovative systems-based mental health interventions (PAR 121-083) and it is aligned with the NIH-Wide Minority Health and Health Disparities Strategic Plan 2021-2025. The results of this study will provide information about if and how affirming identification, referral and connection with an affirming PSS is feasible, acceptable and impact SGMY identified at risk of suicide. This information will be used to inform decisions about the need for further research on SGM- affirming interventions designed to improve the impact of mental health services for this high risk and overlooked population, with implications for other populations and service settings.

NIH Research Projects · FY 2026 · 2024-12

Project Summary EphA1 and EphA2 are two closely related members among the 14 Eph receptor tyrosine kinases; they critically regulate diverse physiological and pathological processes. While A2 has been extensively investigated, little is known about A1 structure, signaling and function. Using a time-resolved fluorescence spectroscopy known as Pulsed Interleaved Excitation-Fluorescence Cross-Correlation Spectroscopy (PIE-FCCS), we discovered a strong hetero-interaction between A1 and A2 in the plasma membrane of live cells. Furthermore, functional studies show that A2 dictates the function of A1 within the heterotypic complex. When present alone, A1 and A2 have distinct homotypic molecular organizations. Unliganded A2 alone self-assembles into homotypic multimers through three interfaces and undergoes rapid clustering upon ligand binding. Surprisingly, A1 instead forms dimers that are refractory to ligand-induced clustering, unveiling unexpected and exciting differences between the two related receptors. In previous reports, we established that A2 has dual functions, depending on its interaction with ligands. 1) Ligand-induced canonical tumor-suppressive signaling, characterized by the catalytic activation of the A2 tyrosine kinase, that suppresses Ras/ERK and PI3K/Akt pathways and inhibits cell migration and growth. 2) Ligand-independent non-canonical oncogenic signaling through serine 897 phosphorylation (pS897) that promotes cell migration and growth. Consistent with biophysical results from PIE-FCCS, cellular and biochemical studies show dramatic differences in A1 and A2 signaling. A1 by itself is unresponsive to ligand and does not mediate canonical signaling. A1 is phosphorylated on S906 (pS906), the residue corresponding to A2 S897, suggesting noncanonical signaling like pS897-A2. However, noncanonical signaling by A1 and A2 is differentially regulated by ligand. Importantly, we discovered that hetero-interaction with A2 transforms A1 function, whereby A1 acquires ligand responsiveness, becoming catalytically active in sync with A2. The A1/A2 hetero-interactions are physiologically and pathologically relevant as they are widely co-expressed in many epithelial tissues, including the liver. Genetic studies in mice revealed that A1 and A2 exert opposite roles in controlling tumor susceptibility: Deletion of A1 markedly suppressed hepatocarcinogenesis, whereas knockout of A2 strongly promoted it. The overarching goal of this proposal is to elucidate the molecular details of A1 homo- dimerization and A1-A2 hetero-interaction, and to delineate their roles in A1 and A2 signaling and functions. In Aim 1 we will determine the molecular basis of EphA1-EphA2 hetero-interaction. Aim 2 will characterize the interfaces that mediates EphA1 homo-dimerization. The importance of the EphA1-EphA2 hetero-interaction and EphA1 homo-dimerization in regulating cell signaling and function, including migration and invasion both in vitro and in vivo, will be investigated in Aim 3. Our findings will add novel knowledge to the understanding of EphA1 and EphA2 in human cancers including hepatocellular carcinoma and provide potential new therapeutic targets.

NSF Awards · FY 2024 · 2024-12

The broader impact/commercial potential of this I-Corps project is the exploration of sugar based microparticles for accelerating metabolism in immunotherapies. This proposed technology is a drug delivery vehicle that can transport the drug of interest to immune cells, for which the healthcare and oncology industry can make use of. The drug delivery vehicle is constituted from a metabolite, allowing for increased energy levels of cells. This would be important for showing how metabolic pathways can induce a cure. Access to such a drug delivery technology has the potential to cure many patients. The technology can generate products that can improve cancer immunotherapies in patients with solid tumors. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of microparticles that can accelerate metabolism of specific immune cells to develop cancer immunotherapies. This technology will help in developing advanced cellular therapies so that they can effectively fight cancer. The microparticles are made of small molecules that can insert themselves in specific metabolic pathway of immune cells and thus elevate their need to be dependent on outside sources of nutrition in the tumor microenvironment. This technology can transform the cellular therapy space and develop a highly translational product for cancer immunotherapy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-12

PROJECT SUMMARY Acquired hearing loss (AHL) is the third most common health condition affecting the aging population, behind heart disease and arthritis. There is no cure for AHL, and our knowledge of the underlying mechanisms of AHL is not sufficient to develop robust treatment strategies to mitigate it. Cochlear hair cells are rich in mitochondria and are known to be prone to irreversible damage and cell death in AHL. Predictably, evidence implicates that mitochondrial dysfunction is a lead cause of several forms of AHL. Like in many other cell types, mitochondria in the cochlear hair cells are responsible for vital cellular functions, including energy production, apoptosis, cell signaling, and Ca2+ storage. These processes are dependent on the ability of mitochondria to modulate Ca2+ levels. Particularly, Ca2+ uptake via mitochondrial calcium uniporter (MCU) is critical for rapidly buffering significant increases in intracellular Ca2+ loads. Ca2+ signaling and handling by mitochondria are essential, especially in cochlear hair cells. For instance, outer hair cells are prone to Ca2+ overload, particularly in the high-frequency basal cochlear turn. We hypothesize that acoustic overexposure leads to mitochondrial Ca2+ overload in cochlear hair cells, contributing to their vulnerability. In Aim 1, we will determine initial changes in function and morphology of cochlear hair cells following overstimulation and compare the results to ones obtained from hearing-impaired Micu1–/– mice, a model for mitochondrial Ca2+ overload. Micu1–/– mice are prone to Ca2+ overload and develop high-frequency hearing loss starting at 3 months of age. Using deep learning-based analysis (DLA), we will assess cochlear hair cell morphology and cellular structures. We will quantify and closely evaluate the morphology of mitochondria at the serial electron microscopy level using FIB SEM and 3D reconstruction. We will also measure distortion product otoacoustic emissions, endocochlear potential, synaptic ribbon counts, and spiral ganglion neuron (SGN) counts on histological sections (using DLA). Using similar methods, within Aim 2, we will determine whether preventing mitochondrial Ca2+ overload protects against acoustic overstimulation and rescues hearing deficit in mice. We will assess whether EMRE-deficient mice, which lack rapid mitochondrial calcium uptake, are less susceptible to cochlear insults, such as noise exposure and ototoxicity. Similarly, we will test Mcufl/fl; Gfi1Cre+ and Mcufl/fl; NeuroD1Cre+ mice, which lack MCU in hair cells and spiral ganglion neurons correspondingly. In addition, we will evaluate whether reducing EMRE expression in Micu1–/– mice, a model of mitochondrial Ca2+ overload, alleviates hearing loss by assessing Micu1–/–; Emre+/– double mutant mice. As it was reported, deleting one allele of Emre normalizes mitochondrial Ca2+ uptake in Micu1–/– mice. We hypothesize that Micu1–/–; Emre+/– mice will exhibit improved hearing preservation in comparison to Micu1–/– mice. Linking the damaging effects of acoustic overstimulation to mitochondrial Ca2+ homeostasis may provide a mechanistic understanding of the process and identify novel clinical strategies to ameliorate noise-induced hearing loss.

NIH Research Projects · FY 2026 · 2024-12

The ultimate goal of this project is to develop innovative anticancer nanovaccines utilizing multifunctional ionizable lipid nanoparticles for the curative treatment of pancreatic ductal adenocarcinoma (PDAC). PDAC is recognized for its aggressive nature and notably poor long- term survival rates, presenting a pressing clinical need for effective curative therapies. While cancer immunotherapy has demonstrated the potential for disease-free outcomes, the efficacy of current clinical immunotherapies for PDAC is limited by the immunosuppressive tumor microenvironment. Our central hypothesis is that innovative nanosized therapeutic cancer vaccines, specifically targeting multiple mutant oncogenes in pancreatic ductal adenocarcinoma (PDAC), could potentially overcome tumor heterogeneity, thereby achieving a high rate of disease-free survival. Additionally, we believe that the synergistic combination of these nanovaccines with inhibitors targeting a potent immune checkpoint protein could markedly amplify antitumor immune responses and enhance overall therapeutic efficacy. To non- invasively assess the therapeutic impact of these nanovaccines, we will utilize cutting-edge magnetic resonance molecular imaging (MRMI) technology. This approach will not only facilitate the real-time evaluation of the nanovaccines' effectiveness but also guide the fine-tuning of both the nanovaccine formulation and the immunotherapy protocol. Towards this, we will 1) to design and develop multifunctional ionizable ELNP based peptide vaccine for effective immunotherapy of PDAC; 2) to determine therapeutic efficacy of the ELNP vaccines to treat PDAC in mouse models under MRMI guidance; 3) to explore the combination therapy of nanovaccine and VISTA blockade to improve the disease-free therapeutic outcomes. Multidisciplinary approaches will be applied to this project by a research team with complementary expertise in cancer biology, immunology, oncology, pathology, drug delivery, molecular imaging, and biomedical engineering from Case Western Reserve University and Cleveland Clinic. The successful completion of this project holds the promise of developing groundbreaking therapeutics tailored to meet the urgent clinical needs in the treatment of PDAC patients.

NSF Awards · FY 2024 · 2024-10

Effective and efficient machine tool maintenance plays a significant role in ensuring manufacturing productivity, product quality, operational safety, and profitability. With the advancement of process sensing, the Internet of Things, data analytics and cloud computing, more manufacturing plants are favoring predictive maintenance over traditional preventive maintenance. Predictive maintenance involves monitoring and predicting machine tool condition and performance. It avoids unnecessary maintenance and prevents catastrophic failure of machine tools, thereby saving operational costs and improving production reliability. However, there are barriers to fully implement predictive maintenance such as insufficient accuracy and reliability of machine tool anomaly or fault detection by existing techniques. This award supports fundamental research on designing a next-generation process sensing-machine learning architecture for capturing manufacturing process dynamics that reveals the underlying dependency of product quality on process settings and machine conditions. This research engages industry in assessing the performance and scalability of this novel machine tool health-monitoring technique at actual manufacturing plants, with the outcomes offering a competitive edge to the U.S. manufacturing sector in the global market. The research involves disciplines such as advanced manufacturing, sensor networks, machine learning, and computing. Knowledge gained is applied to developing manufacturing curricula to equip the next generation of engineers with new skills in manufacturing and data sciences. This project advances the fundamental understanding of complex manufacturing process dynamics for root cause analysis of process sensing variation and detection of machine anomaly occurrences, through discovering the process-observation causal relationships by an innovative machine learning technique. To improve the trustworthiness and computational efficiency of data-driven analysis and decision making in actual manufacturing plants, a next-generation process sensing-machine learning architecture is designed with capabilities in: 1) high-accuracy modeling and high-efficiency computation upon an optimal architecture without extensive manual tuning; 2) physically interpretable discovery of system input-output causal relationships and process dynamics through the integration of model training with manufacturing domain knowledge; 3) allowing for modeling from unbalanced data and incremental learning from evolving machine conditions without repeated model training for robust and scalable anomaly detection. A physically interpretable convolutional neural network with automated architecture search is developed to correlate process parameters, multivariate sensing data (e.g., force, vibration, power), and part quality specifications (e.g., strength, surface quality) that are acquired from turning and milling processes. The discovered relationships are then diagnosed by an incremental sparse classifier for machine fault detection and classification. The outcomes from this project establish a scientific foundation for the systemic realization of machine tool anomaly detection and predictive maintenance that is not achievable with existing techniques. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Insects rely on odor-guided flapping flight to mate and hunt for prey. They navigate by tracking odor trails in complex flow environments to detect and locate distant targets. During this odor-guided navigation, the flapping wings serve for force production and actively draw odor plumes towards the antennae via wing-induced flow. It is hypothesized that the “flapping” used by insects serves the same function as the “sniffing” in mammals for enhancing olfactory detection. Understanding how insects achieve the balance between aerodynamic performance and olfactory sensitivity is the stepping stone towards transforming this feat in engineering solutions for the navigation of miniature aerial vehicles in GPS-denied environments, with important applications for search in natural disasters, chemical leaking monitoring, and drug trafficking detection. To this end, the objective of this project is to establish a physics-driven understanding of the odor-tracking flapping flight in nature. The project also encompasses a variety of education and outreach activities to promote diversity in engineering and strengthen the future STEM workforce. The underlying fluid dynamic principles of olfactory searching in nature remain largely unknown. This project will test the hypothesis that the enhancement of the olfactory sensitivity during navigation can be achieved by regulating the odorant transport in unsteady wing-induced flow through modulating flapping locomotion. A combined high-fidelity computational simulation and theoretical treatment will be used to examine the unsteady flow generated by flapping wings and its associated odorant transport process. The application of a novel computational fluid dynamics-informed simultaneous localization and mapping will be used to explore the odor-tracking algorithms in flying insects. The research will reveal the overarching fluid dynamic mechanisms of odor-guided navigation in nature after the completion of three specific aims: 1) characterize the unsteady aerodynamics and odorant transport in odor-tracking flights; 2) determine the influences of wing-induced unsteady flow on the spatiotemporal distribution of odor plume structures; 3) elucidate the interactions between the unsteady flow and odorant transport during navigation. The findings will advance the development of design principles for bio-inspired flying robots with superior aerodynamic performance and olfactory sensitivity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Recent advances in areas such as automation, data science, and artificial intelligence, are creating new opportunities for advanced manufacturing. However, most machine learning-based solutions are developed in lab environments, which require extensive model tuning and expensive data labeling to be implemented in manufacturing plants. The technical barrier arises from the major discrepancies in the amount, distributions, veracity, and modality between lab and plant data. This Faculty Early Career Development (CAREER) award will investigate new machine learning methodology to make machine learning generalizable and deployable. If successful, the project will accelerate the deployment of artificial intelligence in manufacturing plants and lower the entrance barrier to Industry 4.0 for small and medium manufacturers. This project is also expected to contribute to the development of new manufacturing workforce by engaging middle/high school students and local industries. This project aims to develop a machine learning architecture with expandable modules to learn from massive unlabeled data streaming and adapt to dynamically changing manufacturing conditions in plants. The lab-to-plant transformation will be realized upon testing two scientific hypotheses: (1) a generic model for characterizing massive unlabeled data can effectively learn the similarities of plant data; (2) an established model can be fully adapted to unseen but related scenarios with limited tuning. A transformer architecture-based novel machine learning framework will be configured to simultaneously realize: i) task-agnostic self-supervised contrastive learning from massive plant data for multi-level data characterization; ii) normalizing flow for building one-to-one mapping between sensing data toward virtual sensing data generation in plants for improved quality prediction; and iii) prompt model turning for effectively and efficiently adapting models between different manufacturing conditions. If successful, this project will enable generalizable, deployment-ready machine learning solutions that will be readily scalable for a broad scope of manufacturing applications and help U.S. manufacturers adopt smart manufacturing technologies at an accelerated pace. This project is jointly funded by the Advanced Manufacturing 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 Awards · FY 2024 · 2024-10

As the demand for computing professionals in the United States continues to grow, it is critical to develop programs that will increase the number of students who complete bachelor’s degrees in computational fields. Because the number of women and students from historically underrepresented groups in computing is low as compared to the general population, recruitment and retention of these students into computing majors will help to fill the growing workforce needs. Over the course of this five-year project, fifteen institutions of higher education in Ohio will join together to form the Ohio Pathways to Undergraduate Computing Success (OPUCS) project. This consortium of nine four-year and six two-year institutions will devise transfer pathways that will increase the number of students who complete their Bachelor of Science degrees in computer science with a focus on supporting women students. This project benefits society by preparing students from diverse backgrounds for high-paying positions, thus meeting the needs of employers and contributing to social mobility for graduates and their families. OPUCS will also provide and study the impact of innovative structural support to women computing students at the academic institutions involved in the consortium. The OPUCS project objectives are to (1) establish a statewide consortium of nine four-year independent institutions, six two-year community colleges, and 15 industrial partners; (2) develop clearly articulated curricular pathways for students from two-year institutions to computer science bachelor degree completion at a four-year institution; (3) increase the number of students who transfer from two-year to four-year institutions and to complete their bachelor degrees within two years of transfer; (4) increase the number of women transfer students studying computing; (5) increase the number of women who complete internships with our industrial partners; and (6) establish an Association for Computing Machinery’s Council on Women in Computing (ACM-W) chapter at 75% of the consortium institutions. To complete these objectives, the project team will (1) develop an agreed-upon curriculum for the first two years of a computer science program among all OPUCS institutions; (2) use that curriculum as the basis for development of formal articulation agreements; (3) provide specific, detailed, and ongoing training to academic advisors so that students have a planned pathway for degree completion in four years; (4) provide faculty development training to improve the quality of instruction across the consortium; (5) train admission counselors so they are prepared to talk with prospective students about opportunities in computer science; and (6) provide tailored recruitment materials based on proven strategies from the National Center for Women & Information Technology (NCWIT). Contributions to knowledge will come from investigation into two research questions: (1) To what extent does building a clear academic pathway and offering a familiar social/professional support network among the institutions involved in that pathway lead to an increase in the number of women studying and completing post-secondary computing degrees across Ohio? and (2) Which activities of the OPUCS consortium are perceived as most impactful by women pursuing computing degrees? This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Approximately 91% of the world population lives in environments that do not currently meet air quality standards. In the United States (U.S.), the Clean Air Act of 1970 has resulted in air pollution concentrations dropping below national standards, meaning that most communities in the U.S. have cleaner air. However, clean air is not realized across all communities, especially in communities of color, where air quality can differ significantly. Further, regulatory air quality sensors that are sparsely deployed may not accurately detect the quality of air that residents breathe in their communities. With the availability of low-cost sensors and advancement of low-cost single-board computers and microcontrollers, this research aims to provide residents with an ability to accurately understand their air quality through the deployment of an Internet of Things (IoT) air quality sensor. We will meet with residents that have been affected by both redlining and nearby pollution sources to better understand how air quality affects their daily lives and what air quality information is most beneficial to them. In addition, the team will closely collaborate with partner school(s) to create K-12 curriculum for students to learn how to create their own air quality sensor, deploy it at their school, and make the air quality readings publicly available. In this research, we will combine the availability of low-cost particulate matter sensors with the accessibility of IoT compatible single-board computers and microcontrollers to enable publicly available fine-grained air quality information. To provide real-time access to the data, a prototype mobile application for both iOS and Android, along with a web dashboard, will be developed. To address common challenges of both power and connectivity, we will partner with PCs for People to deploy the sensors and provide connectivity through their existing infrastructure. An enclosure will be developed that ensures proper airflow, has low interference with wireless communication, and is modular to allow other sensing capabilities in the future. We will compare the findings from a test deployment of the sensors with regulatory sensors readings and share the results with the community and local officials. To ensure the sustainability of the project and provide an opportunity for it to expand, we will create an open-source Computer Science and Engineering curriculum in partnership with a local middle school and we will pilot a tech camp at our university. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

In today’s rapidly advancing digital landscape, artificial intelligence (AI) is reshaping industries, particularly in finance and healthcare. These sectors benefit significantly from AI’s ability to drive innovation and provide personalized services. However, the ethical, societal, and privacy implications of AI’s growth are increasingly pressing. This project aims to address these concerns by developing a responsible AI framework that ensures ethical use, transparency, and fairness. The core issue tackled is the current centralized data infrastructure, which poses risks to privacy and perpetuates biases, concentrating power among a few dominant platforms. By transitioning to a decentralized data architecture, this project seeks to empower individuals and communities with greater control over their personal data, fostering an inclusive and equitable digital future. This effort aligns with NSF’s mission to promote the progress of science and advance national health, prosperity, and welfare by creating a robust socio-technical foundation for AI that prioritizes privacy, accountability, and societal well-being. The project focuses on planning and laying the groundwork for a decentralized data architecture that supports responsible AI development in finance and healthcare. During this phase, we will concentrate on three main components: 1) developing a decentralized data architecture, 2) creating responsible AI models trained on ethically sourced and curated data, and 3) designing an ecosystem that promotes fair value allocation and stakeholder participation. The technical approach includes assembling a multidisciplinary team to conduct preliminary research, develop a prototype decentralized data agent architecture, and engage in inclusive workshops to gather insights from diverse stakeholders. The project will implement federated learning techniques to ensure privacy-preserving data sharing and algorithmic fairness. Furthermore, it will investigate new business models and governance frameworks to promote equitable benefit distribution within AI-enabled digital service ecosystems. By leveraging innovative privacy-preserving technologies and engaging with a wide range of perspectives, this planning phase aims to set the stage for a more detailed implementation in subsequent phases, ensuring that the project progresses towards creating a responsible AI ecosystem that benefits all members of society. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The broader impact of this Partnerships for Innovation - Technology Translation (PFI-TT) project is in enhancing the reliability of manufacturing facilities and the efficiency of production on shop floors, particularly for small and medium-sized manufacturers (SMMs). By developing affordable and self-sustaining predictive maintenance (PM) solutions, the project aims to reduce unexpected machine downtimes and unnecessary maintenance costs. The technology integrates advanced machine learning (ML) tools with an edge-cloud computing infrastructure, enabling continuous and real-time monitoring of industrial equipment. This innovation is expected to bridge the gap between cutting-edge research and practical application, providing SMMs with the tools necessary to compete in a technology-driven market. The societal benefits include increased operational efficiency, cost savings, and the promotion of sustainable manufacturing practices. The commercial potential includes the adoption of solutions that could revolutionize maintenance strategies across diverse manufacturing sectors, leading to broader economic benefits. The project addresses the critical need for cost-effective and generalizable PM solutions in the manufacturing industry. The primary research objective is to develop a low-cost, self-sustaining edge device, to be equipped with ML-based data analytics and deployed in a streamlined edge-cloud computing infrastructure, for real-time equipment monitoring, diagnosis, and prognosis. The project will focus on three key innovations: (1) designing an edge device that integrates sensors with an energy harvesting module and microcontroller-deployable ML algorithms, facilitating self-powered, continuous, and prompt machine monitoring and diagnosis; (2) creating a generalizable ML-based diagnosis and prognosis tool that can continuously update itself using unlabeled data streams and be scalable to diverse manufacturing environments, and (3) establishing an integrated edge-cloud data processing and decision-making pipeline for efficient deployment of these tools on the shop floor. These developed hardware and software solutions will be tested in both laboratory and industrial settings, followed by pilot projects to validate the technology's efficacy and adaptability. The anticipated technical results include high detection accuracy, reduced maintenance costs, and improved machine uptime, ultimately advancing the state of PM in manufacturing. This project is jointly funded by Partnerships for Innovation (PFI) 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 Awards · FY 2024 · 2024-10

This workshop aims to address the persistent barriers to education and employment faced by individuals with physical disabilities. It will explore how research-intensive universities can leverage innovations in public interest technology (PIT), particularly biotechnology, to enhance the accessibility, affordability, and practical utility of technological innovations in educational and employment settings. The project's significance lies in its potential to bridge the gap between technological advancements and real-world applications for people with disabilities. By bringing together experts from diverse fields, this project will contribute to the ongoing research in disability studies, biotechnology, and public policy. This project will ask and answer questions that shape design, safety, availability, and deployment of biotechnology, as well as associated social, ethical, and legal concerns. It will support education and training of graduate students in addressing inequalities in education and work. This project will contribute to development of a diverse, globally competitive workforce. It will also foster partnerships among academics, industry, government, nonprofit, and other organizations. The outcomes of this workshop have the potential to impact various fields, from engineering to social sciences, and may lead to significant advances in how we approach accessibility and inclusion in education and employment. This project will organize and hold a workshop that examines how research-intensive universities can effectively use their expertise in PIT, specifically biotechnology development, to overcome barriers to education and employment for individuals with physical disabilities. The workshop will involve 25 invited experts (academics, professionals, and policy makers, including those with lived experience with mobility disabilities), fostering interdisciplinary dialogue and knowledge exchange. This project will explore the design, safety, availability, and deployment of biotechnology, as well as associated social, ethical, and legal concerns in leveraging innovative technology to enhance employment and educational opportunities. In preparation for this workshop, the project team will host a preliminary data chat with eight individuals who have physical disabilities and lived experiences in education or employment access. This experiential knowledge will inform the workshop structure, which will be organized around themes such as complex legal and regulatory landscapes, financial constraints, discrimination, practical implementation challenges, and information dissemination failures. Participants will discuss knowledge gaps, recent research, and innovations. Transcripts of the data chat and meetings will be disseminated via the Inter-university Consortium for Political and Social Research. Findings will be disseminated through academic presentations, publications, policy briefs, and professional newsletter articles. 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

ABSTRACT Down syndrome (DS) is the most common genetically defined cause of intellectual disability and a major cause of early-onset Alzheimer’s disease (AD)-type dementia. Recently, we concluded and published the results of a randomized phase II trial of 16-week treatment with the AD drug memantine (the follow-up memantine trial) in which we investigated the safety and efficacy of this drug on cognitive and adaptive test results in adolescents and young adults with DS. This study was performed between May 13, 2015 and July 22, 2020. In total, 185 participants with Down syndrome were assessed for eligibility and 160 (86%) were randomly assigned either memantine (n=81) or placebo (n=79), which makes it one of the largest clinical trials in the field of DS to date. Participants (aged 15–32 years) with either trisomy 21 or complete unbalanced translocation of chromosome 21 and in general good health were recruited from the community at one site in Cleveland, Ohio, and another in São Paulo, Brazil. Although the trial found no evidence of cognitive-enhancing effects of the standard dose of memantine treatment in the study participants, hypothesis-generating, exploratory analysis in that work pointed toward the need of memantine doses higher than those typically used in patients with AD to produce significant cognitive-enhancing effects in healthy adolescents and young adults with DS. In addition to information on drug effects, this study generated a comprehensive array of neuropsychological and clinical data in a large and diverse set of individuals with DS. The trial protocol also included (as exploratory parallel experiments) collection of evoked electroencephalographic (EEG) data to test auditory brainstem responses and mismatch negativity before and after memantine treatment and quantification of plasma biomarkers of AD at the final visit of the study. Here, we propose to perform analyses on the relationships between scores in the various neuropsychological measures and the EEG and AD biomarker assessments and to share the de- identified data with the community. Accordingly, this project has three specific aims: (1) Analyze the EEG and plasma AD biomarker data generated in the follow-up memantine trial and study their relationship with scores of neuropsychological assessments; (2) Build a comprehensive database of all clinical, neuropsychological, electrophysiological, and biomarker data from the follow-up memantine trial; and (3) Contribute the database to the NIH-funded INvestigation of Co-occurring conditions across the Lifespan to Understand Down syndromE (INCLUDE) Project through the INCLUDE Data Coordinating Center (DCC). Transferring all the data collected from the follow-up memantine trial will represent a significant contribution to Component 2 of the INCLUDE project, which involves the “Assembly of a large cohort of individuals with Down syndrome across the lifespan to perform deep phenotyping and study co-existing conditions.”