University Of Pennsylvania

NIH Research Projects · FY 2026 · 2026-02

Molecular Genetic Analysis of the Role of Senescence in Liver Disease Abstract Senescent cells play a critical role in liver injury and regeneration; however, human hepatocellular carcinoma can overcome replicative senescence through lengthening of telomere repeats in cancerous cells. While elimination of senescent cells has been shown to be beneficial in mouse models of hepatic fibrosis, the cell type responsible for this effect has not been identified. Likewise, in contrast to the situation in humans, tumor development in mice is not dependent on re- activation of telomerase function due to the extremely long telomere repeat arrays present in common laboratory mice. To address these limitations, we have developed two innovative mouse models. The first, termed the “SenKiller” mouse, enables cell-type specific elimination of senescent cells from the fibrotic, injured liver. The second, termed “Telomouse”, is the first laboratory mouse with generationally stable human length telomeres. Using these models, we will address critical knowledge gaps regarding the role of senescence in liver disease. In specific aim 1, we will employ the SenKiller mouse to specifically target senescent hepatocytes, cholangiocytes, stellate cells and Kupffer cells for elimination in two models of hepatic fibrosis, in order to determine which senescent cell type is most relevant for the beneficial effects of senolysis. We will also determine the molecular consequences eliminating senescent cells on the neighboring liver cells to gain a true mechanistic understanding of this process. In specific Aim 2, we will employ Telomice to determine the mechanism by which human-length telomeres limit liver repopulation, as well as examine the effects of telomere attrition on tumor initiation. Through the use of these two innovative mouse models, this project will contribute significantly to our knowlege of the impact of senescence on liver biology by: (1) revealing the senescent cell type(s) critical to liver injury, which will provide potential therapeutic targeting pathways, and (2) using the first human-telomere-length mouse model to fundamentally understand how replicative senescence affects the liver during regeneration and hepatic tumor initiation.

NIH Research Projects · FY 2026 · 2026-02

Abstract Frontotemporal dementia (FTD) is a heterogeneous neurodegenerative disorder characterized by progressive frontal and/or temporal lobe atrophy. In approximately 50% of cases, FTD is associated with TAR DNA-binding protein 43 (TDP-43) pathology, including nuclear depletion and cytoplasmic aggregation, which can lead to the generation of cryptic exons (CEs). Emerging evidence suggests that these cryptic peptides may be recognized by the immune system, contributing to disease pathology. CD8+ T cell infiltration has been observed in FTD patient brains, and C9orf72-related ALS/FTD mouse models show CD8+ T cell accumulation alongside microglial activation. However, the antigen targets, functional phenotypes, and temporal dynamics of these T cells remain poorly characterized. Our preliminary data, using cryptic peptides validated by mass spectrometry, show that CD8+ T cells from ALS and IBM patients recognize these cryptic peptides, undergo clonal expansion, and exhibit activation markers. Furthermore, CD8+ T cells transduced with cryptic-specific TCRs induce cytotoxicity toward astrocytes with TDP-43 knockdown, demonstrating the potential for cryptic peptides to be naturally processed and presented in neuronal cells. Given the shared pathology of cryptic exon production across ALS, IBM, and FTD, we hypothesize that clonally expanded CD8+ T cells in C9-FTD patients recognize cryptic peptides, with greater expansion in symptomatic compared to pre-symptomatic patients. In collaboration with Dr. Corey McMillan at the Penn FTD Center, we will test this hypothesis and establish a link between CD8+ T cells and FTD progress. This study will provide crucial first insights into the possible role of antigen-specific CD8+ T cells play in FTD pathology, paving the way for potential immunotherapeutic strategies targeting cryptic epitope-specific T cells.

NIH Research Projects · FY 2026 · 2026-01

What Interventions to Reduce Hospital Nurse Burnout Are Most Effective? Nurse burnout is a threat to healthcare safety, and to nurse and patient outcomes. Burnout among nurses has been a long-standing concern only accelerated by the COVID-19 pandemic. Burnout is a syndrome caused by chronic workplace stress and characterized by feelings of emotional exhaustion, cynicism towards one’s work, and decreased professional efficacy. Pre-pandemic, about 30% of nurses were burned out. Today, nearly half of 4.7 million nurses are experiencing burnout. This unsustainable high level of burnout has dire consequences for nurses and patients alike. Nurse burnout is associated with higher odds of patient mortality, failure to rescue, and prolonged length of stay, as well as nurse job dissatisfaction and turnover. We propose to integrate two approaches to addressing burnout: investigation of organizational characteristics as determinants of burnout, notably conducted by the proposed research team in recent decades, and health system administrators’ current implementation of interventions to reduce nurse burnout. Our preliminary studies reveal that organizational and individual interventions are being implemented nationwide and that nurses prefer organizational ones. It is unknown how preferred and implemented interventions relate to hospitals’ performance on nurse burnout, individual nurse burnout, and reducing burnout over time. Crucially, whether these interventions’ effectiveness depends on the work environment is unknown. Integration of these two approaches will yield a representation of reality across a large, geographically diverse hospital sample to inform whether certain intervention combinations are most effective and in what organizational contexts. The proposed aims address the Notice of Special Interest NOT-NR-23-012, “Addressing Organizational Factors to Prevent or Mitigate Nurse Burnout,” which invites “research studies to develop and evaluate novel organizational interventions to prevent and mitigate nurse burnout,” by identifying the currently preferred and implemented interventions, their work environment contexts, and their relation to nurse burnout, dissatisfaction, and intent to leave and hospital performance on nurse burnout. We propose to conduct a cross-sectional and longitudinal observational study utilizing 2024 and 2026 hospital nurse survey data from 31,942 nurses in 1,278 hospitals (in 2024) in 10 U.S. states to determine how preferred and implemented interventions relate to hospitals’ performance on nurse burnout, individual nurse burnout, and reducing burnout over time. The potential impact of the proposed study would be high because it would provide actionable results to optimize burnout intervention choices and contexts to mitigate pervasive nurse burnout.

NIH Research Projects · FY 2026 · 2026-01

This study evaluates multi-level interventions—ranging from state-level policy action to healthcare organizational strategy and frontline care delivery innovations—to effectively prevent nurse burnout and mitigate the severity of burnout among the roughly half of hospital-based nurses already burned-out. Study objectives will be accomplished by leveraging unique data from thousands of nurses in approximately 535 hospitals in multiple states (CA, FL, NJ, PA) across 4 time-points spanning 20 years. We will generate repeated samples of these hospitals at multiple time-points (already collected: 2006, 2016, 2024, to be collected 2026). Using a repeated cross-sectional design with changing organizational and policy influences overtime, we are uniquely positioned to evaluate potentially causal relationships of modifiable organizational factors and state-level policy interventions on nurse burnout. Each time-period of data includes repeated measures of nurse outcomes (e.g., burnout, job dissatisfaction, intent to leave employment), and hospital factors and models of care (e.g., staffing levels, work environment, Magnet). These cross-sections of data will be linked with contemporaneous American Hospital Association data for considering structural features of hospitals (e.g. teaching status). In combination, we will have 4 cross-sections of data from 535 hospitals (with fluctuating nurse populations), with changing organizational, policy, and other intervening influences (e.g. CA staffing policy relative to non-policy states, 2008 Great Recession, 2020 Covid-19 pandemic). Our quantitative analytic approach uses hierarchical models with time-varying covariates to capture the multilevel structure of the data, as well as difference-in-difference models with propensity score weighting for rigorous causal inferences of changes in organizational factors on changes in outcomes. Using data collected in 2026, we will empirically identify typologies of hospitals with respect to their proportions of nurses with high burnout and average tenure and conduct in-depth interviews with key nurse leaders (hospital nurse executives, nurse managers) in hospitals representative of each of the typologies to elucidate the facilitators and barriers to reducing hospital nurse burnout and turnover. This multi-modal study has novel potential for sustained impact since it will (1) evaluate the impact of modifiable organizational and policy changes on hospital nursing and models of care on nurse burnout; (2) leverage 20 years of repeated cross-sections of data to evaluate potentially causal mechanisms between modifiable hospital factors and external policy interventions on nurse burnout; (3) evaluate currently employed nurses and those who recently left employment to understand whether the reasons nurses say they would leave hospital employment are the same as the reasons they actually leave; (4) integrate quantitative findings with qualitative frontline hospital leadership perspectives to move from evidence to action. The cumulative evidence will inform targeted recommendations for policy and hospital interventions for reducing the unprecedented high rates of nurse burnout and low retention.

NIH Research Projects · FY 2026 · 2026-01

1 Background. This research project leverages humans as a model system to investigate how 2 specific genetic variants and aging jointly influence polyunsaturated fatty acid (PUFA) 3 metabolism in white adipose tissue (WAT). WAT plays a crucial role in maintaining energy 4 homeostasis, serving both as an energy storage reservoir and a regulator of lipolysis and 5 lipogenesis. WAT is also the primary repository for PUFAs, governing their systemic 6 bioavailability. Disruptions in WAT PUFA metabolism can lead to altered production of WAT- 7 derived eicosanoids, contributing to metabolic disorders and neurodegenerative diseases. 8 Aging significantly alters WAT function and is associated with increased production of pro- 9 inflammatory eicosanoids. Concurrently, genetic variants influence eicosanoid metabolism, as 10 demonstrated by recent genome-wide association studies identifying 41 genetic loci 11 associated with plasma eicosanoid levels, including PNPLA3. Preliminary data. Our 12 preliminary data show i) that PNPLA3-I148M carriers accumulate PUFA-rich triglycerides in 13 WAT and ii) a strong positive correlation between age and PUFA content in PNPLA3-I148M 14 carriers, suggesting a gene-by-age interaction. Knowledge Gap. Despite established 15 associations between WAT dysfunction and disease, the mechanisms by which genetic 16 variants and aging synergistically affect PUFA metabolism and WAT eicosanoid production 17 are poorly understood. Hypotheses: We hypothesize that a) the presence of PNPLA3-I148M 18 in adipocytes induces the release of arachidonic acid from adipocytes membrane, leading to 19 WAT eicosanoid production, and metabolic dysfunction; and b) there is a synergy between 20 genetic variants and aging, leading to altered WAT eicosanoid production and metabolic 21 dysfunction. Aim 1: Using human-derived iPSCs differentiated into white adipocyte-like cells, 22 we will elucidate the mechanistic role of PNPLA3-I148M in adipocyte function and PUFA- 23 derived eicosanoid production. Aim 2: We will analyze WAT from bariatric surgery patients, 24 focusing on previously identified eicosanoid-associated loci, to identify genetic variants that 25 synergistically interact with aging to influence WAT eicosanoid production. Significance. This 26 research will advance our understanding of how genetic variants and aging jointly influence 27 WAT dysfunction and eicosanoid production. Our findings could lead to precision medicine 28 approaches that tailor interventions (such as NSAIDs or omega-3 supplementation) based on 29 an individual's genetic profile and age, creating more effective strategies for mitigating age- 30 related metabolic and neurodegenerative diseases.

NIH Research Projects · FY 2025 · 2026-01

PROJECT SUMMARY Infertility is a common and world-wide health issue that can be devastating to affected couples. The rate of infertility is between 5% to 15% in both developed and under-developed nations. People in every socioeconomic status, both males and females, and every race and ethnicity are affected. There are several factors that play a role in reproductive success. One of the most important factors is epigenetic regulation of the genome in the germline, initially through a major reprogramming event where DNA methylation and histone modification erasure sets the baseline for subsequent sex-specific epigenetic mark establishment. While most of the genome undergoes passive demethylation via replication-coupled passive dilution, there are certain regions of the genome that require active demethylation via Ten-Eleven Translocation enzymes (TETs). In particular, research from our lab and others has shown that TET1 is responsible for active demethylation of a subset of imprinting control regions (ICRs) as well as at meiosis specific promoters, among other germline- specific differentially methylated regions. DNA methylation erasure at these loci is critical for proper development. Abnormal methylation patterning at these loci results in imprinting gene disorders such as Prader-Willi, Angelman, Beckwith-Wiedemann, and Silver Russell syndromes. In addition, hypermethylation at meiotic promoters can result in delayed meiotic entry, further compromising gamete integrity. While our lab and others have explored the effect of loss of TET1 function on PGCs, little is known about the effects this has on offspring. My preliminary data in combination with previously reported data from our lab and others show that when Tet1 mutant mice are bred with wildtype females, there is a subfertility phenotype occurring at ~E10.5- E12.5, we observed ~50% survival to adulthood. At the same developmental timepoints, mutant offspring from this cross have hypermethylated ICRs. Nevertheless, the mechanism underlying the fetal demise has not yet been elucidated. Additionally, our lab has developed Tet1 catalytic mutants, which have a subset of hypermethylation perturbations relative to Tet1 null mice, but which also have mid-gestation partial lethality. The goals and training plan outlined in this proposal will address this question, in Aim 1 I will determine the effects of loss of TET1 in the germline on early embryonic development in paternal Tet1 mutant offspring. I will examine DNA methylation and RNA expression at various timepoints after fertilization to determine (1) when and if DNA methylation profiles are corrected and (2) the gene expression changes that could be responsible for fetal demise. Aim 2 will characterize the heritable effects of loss of TET1 in the paternal germline on offspring’s germline and future reproductive success. Together these experiments will determine the consequences of abnormal DNA methylation reprogramming in the paternal germline.

NSF Awards · FY 2025 · 2025-12

The convergence of sensing and communication technologies is becoming increasingly critical in emerging multi-agent systems, such as robot swarms, autonomous vehicles and virtual reality. These systems require both precise spatial awareness for coordination and real-time data exchange for collaborative decision making. However, existing solutions face fundamental limitations. Optical tracking systems are expensive and sensitive to occlusion, while Radio Frequency-based systems struggle to achieve comparable accuracy. Moreover, existing wireless solutions cannot simultaneously deliver the high-precision spatial tracking and high-throughput data exchange needed for real-time coordination in dynamic environments. A unified solution that enables both high-precision tracking and efficient communication remains an open challenge. This project tackles the above problem. Developing such new wireless technology is strategically important for setting the US as the leader in 6G and its applications. The proposed research is interdisciplinary in nature and will have a significant impact on different aspects of the society such as education, smart environment, health, robotics, warehouse automation and emergency response. The goal of this project is to enable precise spatial awareness and real-time sensor data communication using millimeter-wave (mmWave) backscatter technology. To achieve this, it first designs and fabricates novel backscatter tags that overcomes environmental interference and achieves submillimeter position accuracy and sub-degree orientation tracking. Then, to enable communication, it incorporates ultra-low power designs that directly encode analog sensor data into backscatter signals and develops an add-on receiver module for radars that enables data streaming for digital sensors. Finally, the system will be integrated into multi-robot systems performing collaborative perception tasks like 3D object detection and mapping. By combining hardware innovations, advanced algorithms, and system integration, this project provides a robust and energy efficient platform that enables new possibilities in networked multi-agent applications where both precise spatial coordination and real-time data sharing are essential. 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 · 2025-10

Microbial energy and mass transfers have profound ecological and biogeochemical consequences, yet the controls directing microbial metabolic trade-offs remain poorly understood. The proposed project will study metabolic shifts in energy and mass transfers by microorganisms living through some of the earliest-evolving metabolisms known on Earth. By focusing efforts towards microbes driving primary production anaerobically with molecular hydrogen — through a metabolic process known as chemosynthesis — the project will test whether metabolic trade-offs can be predicted as a function of (i) growth temperature and/or (i) the amount of free energy associated with anaerobic respiration. State-of-the-art continuous cultivation techniques will be applied to compare changes in respiration rates, cell yields, and cell doubling times within and among chemosynthetic microbes under steady-state conditions. These experiments are key for integrating parameters describing microbial metabolic changes into mathematical and/or thermodynamic models of microbial growth and its biogeochemical consequence. Overall, this work will transform understanding of the underlying metabolic behavior of unicellular life at the base of the Universal Tree of Life and at the base of dark ecosystems. The objective of the proposed research is to determine whether microbial metabolic rate/yield trade-offs of hydrogenotrophic NO3--, Fe3+- and CO2-reducing microorganisms exhibit predictable adaptations to temperature and to the oxidation-reduction (redox) chemistry of catabolism during chemosynthesis. Specifically, we will test the hypotheses that (i) microbial metabolic rate-to-yield ratios will increase for all proposed microbial processes when at higher temperatures and that (ii) microbes relying on generally less exergonic catabolic redox reactions will systematically display higher anabolic efficiencies than those relying on more exergonic catabolic redox reactions. To test these hypotheses, the project will employ experimental microbiological and geochemical approaches to compare microbial respiration rates, cell (and total biomass) yields, and cell doubling times of different anaerobic chemosynthetic microorganisms during batch and continuous growth. The resulting metabolic and environmental data will demonstrate how microbial metabolic behaviors are shaped by their abiotic environments. 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 · 2025-10

What happens in the brain when we move our hand to touch a glass of water, listen to the sound of rustling leaves, or play a game of chess? In most of our experiences, perception and sensation are orchestrated through the activity of large-scale circuits of neurons distributed throughout the brain. While new advances in neural recording have expanded our ability to measure the activity of large populations of (hundreds or thousands of) neurons, parsing through neural recordings to "read out" intent or behavior is still an outstanding challenge. The goal of this CAREER proposal is to develop new machine learning methods for learning robust mappings between neural activity and complex behavior. With new approaches that can go from the brain to behavior, it will be possible to better understand neural computation, compare neural activity between individuals, and create dynamic models that capture the ever-changing nature of the brain. The project will be organized into three aims, each of which focuses on development of methods to tackle key challenges in building a mapping between the brain and behavior. In Aim 1, the project will develop new methods for learning representations from neural population activity, with a focus on building invariances through self-supervised and contrastive learning methods. In Aim 2, the project will focus on the problem of learning representations jointly across multiple neural recordings and using this technology to understand common factors and differences across individuals. In Aim 3, the project will develop approaches to extract dynamic latent factors that model the shift in representations over longer time scales and apply them to study the study of healthy aging and neurodegenerative disease. This project will develop machine learning frameworks and theory for learning robust representations from neural recordings and provide new ways to quantify changes in brain activity across individuals, over time, aging, or disease. 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 · 2025-10

Generative large language models (LLMs) and generative diffusion models (GDMs) have become known for generating data that can have an astounding resemblance to human-generated content. Yet, the content generated by these models can introduce serious risks in specific applications. These models are known to replicate biases of their training data, produce unsafe outputs, and generate content that is misleading, false, and reprehensible. This project tackles these challenges within the general framework of alignment. Large pretrained models for image and language generation are available in the public domain but they are generic. It is of interest to most users to retrain these models to adapt them to their specific goals and principles. Our success will make it possible to better incorporate, among others, fairness, safety, reliability, robustness, and truthfulness requirements. This is a necessary development for tools that will be deeply integrated into the social and economic fabrics of our country. Our technical approach builds on three properties of alignment problems in generative AI: (P1) Alignment problems are reinforcement (RL) problems in which the value function is known. This makes alignment easier than generic RL because most of the typical complications of general RL problems are related to the learning of the value function. (P2) Alignment problems in generative language models and generative diffusion processes share the same structure. The objective is to align a generative model to user requirements given a prior reference in the form of a pretrained generative model. (P3) Alignment problems are highly nonconvex in the parameter space of deep neutral networks or transformers. However, they are strongly concave in distribution space. Property (P1) shows that alignment in generative AI is simpler than often thought. Property (P2) motivates an integrated research program on constrained generative AI that encompasses its two extant versions. Property (P3) is the fundamental key to our proposed research. Optimization and statistical foundations of constrained generative AI will be developed by drawing connections between the (strongly convex) problem in distribution space and the (highly nonconvex) problem in parameter space. 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 · 2025-10

This is a project jointly funded by the National Science Foundation’s Directorate for Geosciences (NSF/GEO) and the National Environment Research Council (NERC) of the United Kingdom (UK) via the NSF/GEO-NERC Lead Agency Agreement. This Agreement allows a single joint US/UK proposal to be submitted and peer-reviewed by the Agency whose investigator has the largest proportion of the budget. Upon successful joint determination of an award recommendation, each Agency funds the proportion of the budget that supports scientists at institutions in their respective countries. It is important to understand how melting ice on the surface of ice sheets, affects the movement of ice sheets. Melting ice at the surface of ice sheets form ponds of meltwater. In order to have an impact on the movement of the Greenland Ice Sheet, the meltwater must reach and lubricate the bottom of the ice sheet. For example, lakes on the surface of the ice sheet can drain through cracks and reach the bottom of the ice sheet within a few hours. To understand the formation of these cracks and the cause of draining lakes on the Greenland Ice Sheet, we plan to use deep learning, an artificial intelligence algorithm, to find the locations of cracks and draining lakes in satellite imagery. Based on this new dataset, we will use mathematical models to understand the formation of new cracks and their impact on the movement of the ice sheet. Our approach contains an exciting mix of observations and mathematical models. The ability to use artificial intelligence to detect cracks and draining lakes offers opportunities to drive new understandings at the ice-sheet scale. Broader Impacts: This project will support (1) a US-UK collaboration; (2) students and junior scientists; (3) the development of open-source artificial intelligence codes for the Arctic sciences community; (4) the production of a comprehensive and freely available database of the Greenland Ice Sheet cracks and draining lakes; and (5) a community-led mentoring program called COMPACT (COmmunity-led Mentoring Program for Advancing Cryosphere Trainees), which will facilitate multi-mentor networks within the US and UK cryospheric communities for doctoral students. Meltwater that forms on the surface of the ice sheet can seep through moulins and fractures that connect the surface to the bed, lubricating the bottom of the ice sheet and influencing its dynamics. Surface-to-bed meltwater pathways are prevalent across the Greenland Ice Sheet. However, we currently lack the continent-wide maps of moulins, crevasses, and draining lakes needed to understand the formation of surface-to-bed meltwater pathways. Utilizing deep learning techniques for automated detection and mapping of ice sheet surface features can greatly enhance the glaciology community's capacity to analyze high-resolution satellite imagery, leading to new discoveries. By harnessing deep neural networks, this project aims to generate continent-wide databases of surface features that can be used to mechanistically model the ice sheet conditions that create new surface-to-bed pathways and their impact on ice-sheet dynamics. The ability to scale up feature detection to the ice sheet scale can enrich both remote sensing and modeling communities. This project will foster a US-UK collaboration involving junior principal investigators, postdoctoral researchers, and graduate students. The project aims to develop open-source deep learning code, remote-sensing algorithms, and subglacial hydrology model code for the broader glaciological community. The resulting database of Greenland Ice Sheet surface-to-bed pathway locations and supraglacial lake drainage dates and locations will be made open source. The principal investigators will also collaborate to establish a community-led mentoring program within the US and UK cryospheric community to promote the retention of doctoral students and junior faculty/scientists within the polar science community. The societal benefit of this research will be a better understanding of ice sheet processes and an improved ability to predict ice sheet change. Understanding the evolving hydrology of the Greenland Ice Sheet remains an important topic given the unknown, but potentially significant, role that meltwater drainage via hydro-fracture may play in the ice-sheet’s dynamic response to an expanding ablation area. 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 · 2025-10

Predicting future sea level relies on improved modeling of how the climate forces the ice sheet to change. This is particularly challenging at the ice-ocean boundary where there are multiple processes occurring simultaneously. At present, no single equation adequately describes the changing ice-ocean boundary, which poses problems for coupling ice sheet models to climate models. This project will improve understanding of how the variables that influence the ice-ocean boundary may change over time and space using machine learning to search for relationships amidst available data. This technique will allow the research team to categorize glacier terminus behavior and identify the relevant parameters forcing change for a particular glacier around the Greenland Ice Sheet. Results of the machine learning exercise will be used to develop an equation to represent ice-ocean interactions in an ice sheet model which will be used to determine future changes to the ice sheet forced by the ocean into the future. Models of future ice sheet change yield reliable forecasts of sea level rise only when all the critical processes controlling ice sheet evolution are appropriately accounted for. However, many physical processes are currently poorly understood. One such process is ablation (iceberg calving and submarine melt) at the terminus of outlet glaciers, which has been shown to be the dominant control on mass change at particular glaciers. The goal of this project is to improve model forecasts of sea-level from Greenland by using machine learning analyses of glaciological observations to inform physics-based modeling of outlet glaciers, with a focus on the ice-ocean boundary. Machine learning tools will be used to determine what controls changes in terminus position over a range of time scales for all glaciers in Greenland over a period of pronounced historical change (the satellite era). Analysis of the model performance will enable the research team to determine the dominant controls on terminus position for individual and groups of glaciers and to test how well the model performs as new glaciological and environmental data become available. The machine learning model of terminus positions will be used to improve projections of outlet glacier mass change using a physically-based numerical ice flow model. The team will examine how robust model prediction is on various time-scales as more and more data become available over the course of this project. The project will result in refined projections of dynamic loss from the Greenland Ice Sheet, which is important for policy makers needing to make critical infrastructure and resource decisions globally. This goal is a central focus for research within NSF's Office of Polar Programs, NSF's Navigating the New Arctic, and other national (e.g., NASA, NOAA) and international priorities. The project integrates researchers across disciplines, genders, and career stages. Data products and methods produced through this project will be make publicly available and will be useful to the broader scientific community. This project is co-funded by a collaboration between the Directorate for Geosciences and Office of Advanced Cyberinfrastructure to support AI/ML and open science activities in the geosciences. 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 · 2025-10

This program provides a ten-week summer research experience targeted at undergraduate students seeking in-depth and hands-on research in labs devoted to a wide range of topics connected with the theme of the Interconnected Mind. The site’s goal is both to support the participating students in developing their careers and to work towards a better future for cognitive science. The experience is designed to provide students with broader knowledge of the field, important technical and professional skills, and connections to faculty and labs at the forefront of research. The ultimate objective is to help prepare the next generation of US scientists for continued research in important areas of national interest related to the nature of human intelligence and behavior (e.g., mapping the mind, understanding consciousness, understanding the bases of cooperation, developing industries in artificial intelligence and neurotechnology, and translating basic science to help people with cognitive and neurological diseases). The program begins with an introductory workshop, followed by direct involvement in a research project in an established lab, and closes with a series of student project presentations. Throughout the program, the site will also hold journal club meetings, faculty research seminars, and professional development workshops. In their lab projects, student research will focus on different aspects of how the human mind works, integrating insights from multiple disciplinary approaches, such as neurobiology, psychology, linguistics, computer science, artificial intelligence, and machine learning. This site thus provides an integrated research, learning, and mentoring opportunity that introduces students to both the breadth (via programmed activities) and depth (via individual projects) of the field. The site seeks to help students see themselves as active contributors to a larger endeavor of understanding the nature of human minds and behavior. Students will get involved in conducting actual research on scientific questions supported by mentors drawn from a pool of faculty; and they will have the opportunity to explore interdisciplinary perspectives in cognitive science in a collaborative and supportive environment. Programming is designed according to evidence-based teaching and learning principles, and the research experience involves extensive scaffolding through mentorship, support, professional development and skills training. The site thereby aims to ensure that students have a positive experience that increases both their interest in and ability to continue in research careers and compete for admission to graduate programs. 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 · 2025-10

This project advances national health and promotes science and technology development by providing algorithms, software, and systems that can train machine learning models on electronic health records (EHRs) for accurate and early prediction of Alzheimer’s Disease and Related Dementias (ADRD). ADRD is a severe neurodegenerative disorder that effects over 5,000,000 people over the age of 65 that is characterized by progressive memory, cognitive impairment and personality changes, which can further evolve to dementia and death. Early prediction of ADRD is crucial for timely intervention and improved patient outcomes. Recent studies have shown that personal risk factors such as education, employment, and lifestyle or family history significantly influence ADRD onset and progression. However, these factors are not recorded in a structured format within the existing EHRs. In contrast, personal risk factors are often embedded within the free text of clinical notes or discharge summaries that are not easily searchable, computable, or standardized. This creates a major technical barrier for their integration into the ADRD prediction models. To address this, this project develops a computational platform using novel machine learning and natural language processing to automatically extract personal risk factors from EHR clinical narratives and leverage them for accurate and early prediction of ADRD. This research significantly improves ADRD prediction accuracy and timeliness, with potential generalizations to other neurological disorders. By exploring the interaction between personal and clinical factors in disease development, this project pushes the boundaries of current knowledge in machine learning and ADRD research, potentially transforming approaches to early detection and management of complex neurological disorders. To achieve the goal of developing personal risk factor enhanced machine learning models for early ADRD prediction, this project develops four thrusts of novel approaches, each addressing key methodological challenges. First, the project develops a domain knowledge guided large language model to extract risk factors from EHR clinical narratives, which can adeptly cope with the complexities inherent in real world EHR clinical narratives, such as noise and incomplete data entries. Second, the project develops an interpretable method using neural additive models that automatically identifies the individual risk factor’s contribution to the early ADRD prediction. Building upon this interpretable result, in the third thrust, the project develops a survival-based ADRD prognosis model that can be used to estimate the likelihood of ADRD development at any given point in the future, capturing the dynamics of risk trajectory. This approach can enhance clinical decision-making by identifying high-risk individuals who may benefit from more intensive care or early intervention. Fourth, this project constructs a personalized knowledge graph that integrates personal and other clinical risk factors into a unified format for capturing the overall health status for everyone at risk of developing ADRD. Moreover, this project develops adaptive machine learning algorithms that can dynamically update this knowledge graph to incorporate the evolving risk factors. Together, these approaches converge to address the fundamental limitations of existing ADRD risk prediction models, such as inability to handle complex and unstructured data, insufficient interpretability, and high computational overhead. 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 · 2025-10

Government agencies, healthcare organizations, and research institutions often need to combine information from different databases to solve important problems like tracking disease outbreaks, improving public services, or conducting medical research. However, these organizations currently cannot share their data because it contains sensitive personal information protected by privacy laws and regulations. This creates a significant barrier to research and policy development that could otherwise improve public health, enhance government services, and advance scientific discovery. This project addresses this challenge by developing a secure system that allows organizations to answer important questions using combined datasets without actually sharing the sensitive information itself. This work serves the national interest by enabling evidence-based policymaking through secure data collaboration, advancing public health research while protecting individual privacy, strengthening government efficiency through improved data-driven decision making, and supporting American leadership in privacy-preserving data science technologies. This project develops the Trusted Integration Data Exchange System, a platform that enables multiple organizations to perform complex data analysis on jointly held sensitive datasets while maintaining compliance with data sharing policies and federal, state, and local regulations. The research activities include developing formal languages that capture regulations, dataset structures, and study objectives, which are then processed using novel cryptographic database operators. The project introduces two new models of multiparty computation: helper-assisted two-party computation using confidential computing for integrity to serve as an additional party that simplifies privacy-preserving operators for data integration, and a novel paradigm for performing secure multiparty computations when regulations prevent data sharing even in encrypted form. The Trusted Integration Data Exchange System will include an automated compliance checker that confirms whether an integration study is legally viable. Additionally, the project will develop user-friendly tools including development environments and debugging utilities to help data scientists and engineers write policies and queries, diagnose potential platform issues, and debug study results. The platform will be evaluated across multiple domains including healthcare, education, and government services, with comprehensive training provided to students, scientists, and engineers in privacy-preserving data analysis 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 2025 · 2025-10

Epilepsy care primarily involves periodic visits to a neurology clinic for assessment and refinement of medications. A key gap in care is the inability to robustly monitor seizure activity outside of the clinic. Currently available wearable devices include wrist-based seizure detection devices that cannot record brain activity and suffer from poor sensitivity. At-home brain recordings with bulky full-scalp electroencephalography (EEG) caps are not practical for chronic use. To meet these challenges, this project is developing a wireless ear-based wearable, or “earable,” for monitoring neural activity using EEG electrodes behind each ear. The familiar form factor, similar to commercial earphones, will be more compatible with use during activities of daily living and can reduce the stigma that often accompanies wearable medical devices. The earable will feature a state-of-the-art custom integrated circuit with programmable artificial intelligence (AI) to infer seizure activity from the ear EEG signals. It will provide low-power wireless integration with a mobile app to alert the user and caregivers when seizure events are occurring or likely to occur. Access to remote seizure monitoring could improve refinement of medication while decreasing health care costs by requiring fewer in-person visits to the neurology clinic. A promising recent approach to epilepsy monitoring is to use ear-based wearables due to their access to EEG signals and unobtrusive form factor. It has been demonstrated that a limited behind-the-ear EEG device can have the same seizure detection sensitivity and false detection rate as a full EEG cap. However, robust real-time seizure monitoring with earables has yet to be achieved. Engineering challenges hamper practical, everyday use. Key issues include miniaturization, power efficiency, and real-time edge AI capability to detect critical features in noisy ear EEG signals. This project aims to: (1) collect an unprecedented dataset in treatment-refractory epilepsy patients comprising ear-EEG and intracranial EEG, the latter being the gold standard for seizure localization, (2) develop a novel two-stage edge deep learning model for ear-EEG-based seizure detection, achieving high sensitivity and low false positives by leveraging transfer learning, data augmentation, and knowledge distillation; and (3) design a programmable AI chip featuring dynamic quantization, mixed-precision computation, specialized processing element arrays, and optimized data flow scheduling, enabling ultra-low power inference with minimal latency. The resulting dataset, algorithm, and hardware can individually advance current knowledge in their respective domains. Together, they could establish a path toward improved remote care and quality-of-life for individuals with epilepsy. 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 · 2025-10

Optical phase gives rise to the most fundamental and defining features of light. Light waves interfere constructively or destructively with each other, depending on their relative phase relation. Based on interferometry, precise detections of optical phase have enabled transformative discoveries and technologies including the observation of gravitational waves, biomedical pathology diagnosis, coherent optical communications, imaging, and computer vision. Hence, leveraging the advanced photonic technology to build highly efficient phase detection systems on-chip has been at the forefront of integrated optical informatics. However, current integrated phase detection architectures require the integration of interferometers that must be accurately calibrated and flexibly programmed to offset the undesired fabrication imperfections. Such interferometer hardware takes up the majority of the footprint budget and hinders the scalability of the detectable information space on-chip. In this project, a novel multimode phase detection scheme for silicon photonics will be investigated. Utilizing the mode degree of freedom in silicon photonic multimode waveguides, the on-chip photonic hardware planning will be minimal, without a deliberate hardware design of an interferometer. Rather, the phase detection will be carried out through numerical optimization and digital signal processing. The advantages of this novel detection scheme in terms of device footprint and detection speed will be identified. The proposed multimode phase detection scheme categorically eliminates the stringent hardware requirements in the interferometer-based phase detections, defies current integrated phase detection architecture by liberating immense space budget on the photonic chip, and has the potential to revolutionize the next generation of optical communication, imaging and beyond. This research will also be a medium to boost increased experimental learning in graduate and undergraduate students by involving them in meaningful research activities related to integrated photonics, coupled with software algorithms for digital signal processing in optical imaging and microscopy. A variety of outreach programs will be conducted to attract talented students to STEM and increase the participation from K-12 students. Detecting optical phase on-chip can empower disruptive technologies in optical signal processing and metrology, bearing far-reaching significance in optical communications, sensing, and imaging. Most integrated phase detection platforms to date perform on-chip interferometry using integrated interferometers. However, such interferometers 1) inevitably inherit variations from the nanofabrication processes and require tunability from either electro-optic or thermal phase shifters, adding fabrication cost and complexity to the photonic hardware, and 2) occupy a significant amount of space, where most of the on-chip footprint budget is yielded to wiring of the optical and electronic interconnects, leaving limited space for an aperture to receive the impinging optical signal, and the detection scalability is in turn compromised. In this project, a software-hardware synergy will be built to initiate a novel software-augmented phase detection scheme in a multimode silicon photonic platform, without the need for deliberately-designed interferometer hardware. As a result, large spatial superposition of optical states with arbitrary amplitude and phase combinations can be detected on-chip with minimal hardware design. The multimode waveguide, with its mode degree of freedom, inherently houses a higher order of information channels in a single waveguide, reducing the footprint consumption. Notably, any deviation in the photonic hardware due to fabrication error will be accounted for during a calibration process and effectively neutralized by digital signal processing, posing no challenge to the detection accuracy. This novel multimode detection scheme releases the footprint throttle in the photonic hardware by digital processing in the software, and carries great technological potential in advanced detection tasks involving high-dimensional, multiple degrees-of-freedom optical signals, directly on an integrated silicon photonic chip. 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 · 2025-09

Project Summary/Abstract The aorta is central to cardiovascular health, with its structure and function significantly influencing disease risk and organ damage. Aging leads to structural changes such as elastin degradation, fiber rupture, collagen deposition, and calcification, resulting in adverse hemodynamic effects, including increased cardiac workload and microvascular damage. While aortic stiffening has been extensively studied, the effects of age-related changes in aortic geometry on cardiovascular health are less understood. Advances in artificial intelligence, particularly convolutional neural networks, have enabled automated quantification of aortic structures, uncovering prognostic phenotypes and genetic loci associated with aortic structure. These initial findings suggest that aortic structure has significant predictive value for incident cardiovascular events, and that aortic structure is influenced by genetic factors; however, time-varying remodeling patterns of aortic geometry, and their relationship towards cardiovascular disease and genetics are unknown. Generative multimodal modeling offers a solution by learning the underlying data distribution and creating task-agnostic, low-dimensional representations that can be leveraged for multiple downstream tasks. This research will address key gaps in our understanding of aortic structure by developing generative and multimodal AI methods to simulate age- and disease-associated remodeling and creating an organ-specific and genetically-informed foundation models. Aim 1 seeks to develop a novel generative method to simulate three-dimensional aortic remodeling patterns. Using imaging, clinical, and genetic data from the UK Biobank, a novel variational autoencoder (VAE) will be designed to integrate imaging and clinical-genetic features. Aortic remodeling patterns will be generated by varying clinical input variables and analyzing point-cloud shape variations. Through these simulations, novel phenotypes will be identified based on 3D mesh variations. Novel phenotypes will be validated using time-to-event analyses. Aim 2 focuses on constructing a foundation model to improve diagnostic and prognostic tools for aortic and cardiovascular diseases. Using multimodal contrastive learning, this model will integrate over 1,000,000 CT scans, paired radiology reports, and genomic data from the Penn Medicine Biobank to align imaging features with organ-specific measurements and genetic information. Segmentation and phenotype extraction protocols will be employed to compute organ-specific features. Genetic information will be incorporated using 100+ polygenic scores. The foundation model will support advanced classification tasks, predicting incident cardiovascular events with improved precision and generalizability compared to task-specific models. Together, these efforts will enhance understanding of aortic remodeling processes, inform mechanisms underlying disease progression, and provide innovative tools for cardiovascular risk assessment and intervention.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY The long-term goal of this research is to change the prognosis of relapsed or refractory (r/r) T-cell Non-Hodgkin Lymphoma (T-NHL) using next-generation chimeric antigen receptor T-cell (CART) immunotherapy. CART therapy has led to remarkable clinical outcomes in B-cell malignancies, but the needle has not moved for most other cancers. Relapsed or refractory T-NHL are considered mostly incurable with available therapies and present a particular challenge for antigen-specific T-cell immunotherapy since normal and malignant T cells largely express the same surface antigens. Our group developed a novel approach to circumvent CART fratricide by deleting the pan T-cell antigen CD5 in T cells that are transduced to express an anti-CD5 CAR. Of note, our work demonstrated that the deletion of CD5 also enhances T-cell effector function by enhancing CAR signaling. The present project seeks to test this discovery in a first-in-human clinical trial to evaluate the feasibility, toxicity, and efficacy of CD5 deleted anti-CD5 chimeric antigen receptor CART for T-NHL. The central hypothesis of this grant is that CD5 deleted anti-CD5 CAR T cell immunotherapy is safe and can lead to potent and long-term clinical responses against T-cell lymphomas. Moreover, this research will explore whether the CD5 KO untransduced (CAR-negative) T cells from the CD5 KO CART5 product can provide short-term T-cell immunity in case of CART5 toxicity against the normal T cells that express CD5. The feasibility of this proposal is underpinned by our preliminary results and our Institution’s extensive expertise in translating CART therapies. A dedicated study team was formed with experts with complementary expertise, including the proposal’s principal investigator (Dr. Barta, clinical lead), co-PI (Dr. Ruella, correlative lead), biostatistician (Dr. Hwang), clinical research staff, and laboratory personnel to ensure the timely success of this proposal. Financial support has been secured from viTToria biotherapeutics, Inc, who serves trial sponsor. The central hypothesis will be tested by pursuing two specific aims. In Aim#1, the phase I clinical trial will be performed to evaluate feasibility, toxicity, and efficacy of autologous CD5-deleted anti-CD5 CAR T-cells for r/r non-leukemic T-NHL. The trial will utilize an Bayesian optimal interval design to establish the recommended phase II dose. In Aim#2, two working hypotheses will be tested using clinical samples obtained from Aim#1: i. CD5-deleted CART5 can potently expand and infiltrate tumors; and ii. CD5-deleted untransduced cells presented in the product can persist in the blood, limiting T-cell aplasia. Several laboratory techniques will be used for the correlative studies, including will flow cytometry, single-cell transcriptomics, cytokine profile and digital spatial profiling to study the T-cell dynamics and function in the blood and tumors. The proposed research is highly relevant to public health because patients with r/r T- NHL have no available cures with standard non-cellular therapies. This study will significantly impact the field of cancer immunotherapies by developing a novel potent anti-T-cell lymphoma immunotherapy and such address an unmet need in a rare disease.

NIH Research Projects · FY 2025 · 2025-09

Robust, evidence-based treatments for Opioid use disorder (OUD) exist but substantial gaps remain in utilization and access, especially among marginalized populations. Emergency Departments (EDs), as a locus for 24/7 treatment access is an evidence- based strategy for initiation of buprenorphine with variable adoption. This study aims to evaluate pragmatic strategies to enhance the ED initiation of buprenorphine for OUD, leveraging locally adapted protocols across two health systems, Penn and Jefferson. These strategies include enhanced patient identification through triage screening, clinician supported buprenorphine initiation, specially trained peer navigation and low barrier follow up options via Penn's CareConnect virtual telehealth and Jefferson's Bridge Program. An additional innovation is to assess patient choice in medication strategies by also offering methadone initiation with next day follow up at Opioid Treatment Program (OTP) partners. Through collaboration with stakeholders, including those with OUD lived experience, we will develop a comprehensive toolkit to increase MOUD initiation in EDs, addressing barriers such as lack of clinician experience and follow-up options. We will assess prescribing rates, patient and provider engagement, and MOUD treatment within 30 days of ED discharge. Sustainability will be evaluated by measuring ongoing costs of the programs. Based on our prior experiences leading interventions to enhance MOUD prescribing at our own institutions and across other research networks, we propose organizing a learning and research collaborative as part of the coordinating center role of the Opioid Prevention, Treatment and Research Network (OPTRN) that can be a forum for sharing and vetting strategies to enhance rates of buprenorphine and methadone initiation. The learning collaborative will leverage the experience of collaborating sites within the network that demonstrate higher rates of buprenorphine initiation as well as visiting “content experts” who can contribute to focused webinars to address implementation and uptake challenges and to support the sites in evaluating outcomes across the network. We will also develop a network website as a communication and sharing platform to enhance shared knowledge and approaches especially important in a community of emergency clinicians with highly variable clinical schedules. Our findings will inform the development of scalable, sustainable interventions to reduce overdose-related mortality.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Cardiovascular disease (CVD) is the leading cause of death in men with prostate cancer (PCa), the most common male cancer in the US. With substantial advancements in PCa care, men with non-metastatic PCa have high 5-year cancer survival rates exceeding 99%. However, in this older male population, non-PCa specific comorbidities are widely prevalent. CVD results in a 1.5-fold greater risk of mortality than PCa itself, accounting for 23% of all deaths. The increased risk of CVD in PCa is due to the high burden of cardiovascular risk factors, prevalent CVD, and exposure to androgen deprivation therapy (ADT). The consequences of these co-morbidities and toxicities in the aging male are that the oncologic benefits of ADT may be outweighed by the substantial CVD risk. To enhance the overall health of men with PCa, we need to effectively understand and manage CVD risk. However, current CVD risk stratification tools, including the widely used ACC/AHA Pooled Cohort Equation (PCE) and most current AHA PREVENT, perform poorly in cancer patients as they do not consider critical determinants of CV risk in cancer, such as ADT, the social determinants of health, or the competing risks of PCa- and non-CVD related mortality. Our long-term goal is to improve upon the overall health outcomes of men with PCa through risk-based CVD management, i.e. tailoring the intensity of CVD prevention and management to the individual patient risk. The overall objective of this R01 proposal is to develop accurate CVD absolute risk prediction models in men with PCa and determine their clinical impact. Our rationale is that current clinical CVD risk prediction tools, developed in non-cancer populations, are fundamentally limited, as they do not incorporate cancer-specific variables, cardiotoxic cancer treatment, or emerging predictors of growing importance, such as the social determinants of health (SDOH). They also do not account for competing risks. The following Specific Aims are proposed: Aim 1: Develop a well-phenotyped, population-based cohort study by augmenting, harmonizing, and validating EMR data from a large, diverse health system (Penn) with SEER-Medicare and medication data (Part D). Aim 2: Derive and externally validate PCa-specific, accurate CVD absolute risk prediction models that incorporate individual patient characteristics, SDOH, clinical and cancer treatment information. Aim 3: Evaluate the impact of model implementation on patient health outcomes using plasmode simulation studies. The expected outcome is a PCa-specific, practical CVD absolute risk prediction tool that can readily be implemented into clinical care. Moreover, our overall strategy in building a robust cohort study to enable accurate absolute risk modeling is highly generalizable to other cancers. This work will have an important positive impact, as it will develop a key risk assessment tool that will advance the overall health of men with PCa and establish a paradigm-shifting, highly rigorous scientific approach widely adaptable to all cancer populations.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY: Hypoxic ischemic encephalopathy (HIE) is a developmental brain injury caused by transient reductions in oxygen supply to the term fetus. HIE is a significant cause of neurodevelopmental disorders like autism and epilepsy, suggesting that prenatal hypoxia exposure can cause lifelong neurological dysfunction. The underlying molecular mechanism(s) are not well understood, but an emerging hypothesis suggests that disruptions to epigenetics during prenatal hypoxia may be causal to neurodevelopmental phenotypes. Notably, hypoxia’s effect on cellular metabolism influences pools of metabolite precursors for epigenetic marks, highlighting the importance of investigating the metabolic-epigenetic axis of perinatal hypoxic brain injury. A paradigm of mild prenatal hypoxia demonstrated the upregulation of all three branched chain amino acids (BCAAs), leucine, isoleucine, and valine, in fetal mouse brain. Additionally, our analysis revealed that expression of branched chain amino acid transaminase 1 (Bcat1), the enzyme that catalyzes the catabolism of the BCAAs, is also significantly upregulated in almost all neuronal types by prenatal hypoxia. BCAT1 transaminates the BCAAs, in that it catalyzes the reversible transfer of the amino group from the BCAAs to alpha-ketoglutarate (a-KG) to form glutamate and the branched chain ketoacids (BCKAs). BCAA supplementation has recently emerged as a therapeutic for traumatic brain injury, and thus understanding hypoxia’s influence on BCAA metabolism could reveal a critical role for BCAT1 in neonatal hypoxic brain injury. Additionally, BCAT1 regulates the concentrations of glutamate, the overproduction of which can induce neuronal injury during prolonged periods of hypoxia, and a-KG, which is an essential cofactor for chromatin modifying enzymes. Moreover, accumulating evidence suggests BCAT1 may play a role in reducing oxidative stress in multiple cell types, making it an appealing potential neuroprotective target for studies of neonatal hypoxic brain injury. I hypothesize that BCAT1 plays a neuroprotective role during hypoxia by modulating histone methylation and reactive oxygen species (ROS) in neurons. In this proposed set of aims, I will address this hypothesis utilizing neurons differentiated from human induced pluripotent stem cells (hiPSCs) and primary mouse neurons to assess BCAT1’s impact on BCAA metabolism, epigenetics and oxidative stress during hypoxia. In Aim 1, I will use stable isotope tracing and steady state amino acid measurement by liquid chromatography-mass spectrometry (LC-MS) to define hypoxia’s effect on neuronal BCAA metabolism. Additionally, I will use Cleavage Under Targets and Tagmentation (CUT&Tag) and bulk mRNA-sequencing (RNA-seq) to profile BCAT1’s impact on histone methylation and gene expression during hypoxia. In Aim 2, I will test potential mechanisms of neuroprotection by measuring ROS, cell viability, and neuronal morphology in BCAT1-/- neurons exposed to hypoxia. Characterizing hypoxia’s influence on BCAT1 regulation of BCAA metabolism, epigenetics, and oxidative stress is crucial to evaluating the potential for BCAA supplementation or restriction as a novel therapeutic.

NIH Research Projects · FY 2025 · 2025-09

Cancer is a leading cause of death worldwide, with over 19 million new cases and about 10 million deaths per year. If the current trend continues, the burden of cancer is expected to increase to 22 million new cases annually by 2030, with 81% of new cases and almost 88% of mortality occurring in low- and middle-income countries (LMICs). Recently the U.S. launched the reignited Cancer Moonshot, with goals to reduce the death rate from cancer by at least 50% over the next 25 years and improve the experience of people and families living with and surviving cancer, ultimately ending cancer as we know it today. While the immediate goals are domestic, the ambitions of the cancer moonshot extend beyond the borders of the USA, especially as the burden of cancer falls heavily to LMICs. International work as part of the Cancer Moonshot is focused on fairness and collaboration, with one major area being research collaborations that could benefit both the USA and LMIC. A critical group that is needed for global health collaborations is that of Medical Physicists. Medical physicists are health care professionals who have received advanced and specialized training in using the principles and procedures of physics in medicine. They play a vital role in ensuring the quality and safety of radiation therapy (used in the treatment of over 50 % of cancer patients), diagnostic imaging, nuclear medicine, and other medical applications that involve ionizing or non-ionizing radiation. Medical physicists also contribute to the development of new technologies, techniques, and protocols that can improve the diagnosis, treatment, and prevention of cancer. Recent needs assessment work by the International Council (IC) of the American Association of Physicists in Medicine (AAPM) has identified areas including in research and education that could avail win-win collaborations between the USA and LMIC in consonance with the goals of the Cancer Moonshot. To maximize the impact of such collaborations, there is major need to build research capacity of medical physicists in global health to address the growing global burden of cancer and disparities and build sustainable collaborations. To address this unmet need, the primary goal of this project is to increase AMPERE (Access for Medical Physicists to Education and Research Excellence) in global health. The program is designed to train the next generation of AAPM members and their LMIC compeers for win-win global health collaborations with resulting innovations and capacity building that will advance the goals of the Cancer Moonshot in the USA and globally.

NIH Research Projects · FY 2025 · 2025-09

(PLEASE KEEP IN WORD, DO NOT PDF) Kidney failure is the 8th leading cause of death in the United States. Kidney transplantation (KT) is the preferred and optimal form of kidney replacement therapy because it improves survival and quality of life and is less costly than dialysis. Nearly 100,000 individuals in the U.S. are currently awaiting KT, but due to a severe undersupply of available organs, waiting time for KT often exceeds five years, and 12 individuals die each day waiting for a KT. Prolonged waiting times leave most KT candidates at high risk for physical function impairment and frailty, as kidney failure accelerates declines in muscle mass, strength, and performance that are compounded by high rates of physical inactivity. Nearly one in five KT candidates is frail, and an even greater number have physical impairments, increasing their risk of poor pre- and post-KT outcomes. Given the prognostic relationship between pre-KT physical function and adverse outcomes, the American Society of Transplantation and Kidney Disease International Group Outcomes developed recommendations to increase KT candidates’ functional capacities prior to transplant surgery to mitigate complications and maximize patient and graft survival. Growing evidence from major abdominal surgery literature demonstrates prehabilitation (i.e. interventions to increase fitness, wellbeing, and physiological reserve capacity prior to surgery) is feasible, and can improve functional capacity, as well as improve pre- and post-operative outcomes. Prehabilitation represents an exciting intervention for KT candidates, but evidence of such interventions in this population is lacking. A home-based prehabilitation intervention guided by behavior change theory that leverages technology represents an acceptable and feasible option to improve physical activity and function and reduce frailty in KT candidates. We will test such an intervention among a high-risk KT candidate population in a randomized trial to determine its feasibility and acceptability (Aim 1), effects on physical activity and function and frailty (Aim 2), and explore for its early efficacy on pre- and post-KT outcomes. To address the study aims, we will include a community advisory board to refine the study protocol and trial processes and conduct a two-arm randomized controlled trial comparing an online prehabilitation program with physical activity monitored by a Fitbit vs. an attention control at 12 weeks and a 12-week post-intervention follow up period to explore for durability. The central hypothesis is that a home-based prehabilitation intervention will be feasible, increase physical activity and function, reduce frailty, and have beneficial effects on pre- and post-KT outcomes relative to control. Ultimately, this study will determine the feasibility and early effects of a theory-informed prehabilitation program which could translate into health improvements and cost savings among the high-risk population on the KT waitlist. The methodically robust intervention has the ambition to serve as an accessible, scalable, and sustainable program that will be recommended by clinicians and used by KT candidates as an element of their ongoing management to improve their health and optimize pre- and post-KT outcomes.

NIH Research Projects · FY 2025 · 2025-09

Project summary Mechanotransduction is the process by which cells convert external mechanical signals into biochemical signals that shape their phenotypic adaptations. In cardiomyocytes, short-term extracellular stiffening induces readily reversible phenotypic adaptations, while sustained exposure to extracellular stiffening induces persistent changes in cellular structure and chromatin architecture: a phenomenon referred to as 'mechanical memory' (MM). We recently reported that stabilization of microtubule (MT) architecture is required for both the formation and maintenance of MM in cardiomyocytes. In this proposal, I focus on the time-dependent nuclear responses to extracardiac stiffening, including changes in chromatin architecture, gene expression and DNA damage responses. My working hypothesis is that DNA damage is a central component of persistent responses to extracellular stiffening and disease-relevant mechanical stresses. In Aim 1, I will determine the molecular conduits and epigenetic regulators of MM using normal adult cardiomyocytes and a novel cell-culture system with bidirectionally tunable stiffness. After defining the temporal and magnitude thresholds for inducing MM, I will perform a whole genome ATAC-seq to determine the distinct epigenetic landscapes associated with transience vs. persistence of the stiffness-induced phenotype in cardiomyocytes (K99 phase). Building on this foundation, I will then determine the role of the DNA damage response (DDR) in determining the reversibility of nuclear responses to extracellular stiffening. These studies will define whether DDR elements may be targets for therapeutics to limit or reverse MM in CMs. In Aim 2 studies, I will translate these experiments to the tissue level using living myocardial slices (LMS) from normal rat and human hearts. I will determine whether pathomimetic increases in afterload evoke the same time-dependent MM responses, mechanotransduction cascades, and DNA damage signals observed in isolated cardiomyocyte (K99 phase). I will then examine whether interventions targeting MT dynamics and the DDR mechanism will attenuate afterload induced MM (R00 phase). Finally, in vivo studies will explore whether time-dependent MM dynamics, and mitigating strategies, are relevant to the myocardial dysfunction observed in the viable myocardium following a large myocardial infarction(R00 phase). Through the proposed work, I will significantly expand my expertise and facility with several powerful and versatile skills (RNA-seq, ATAC seq, the LMS model, and rodent experimentation) while broadening my ability to work with large datasets and manage a multifaceted research program. During this process, I will pursue interactions and scientific connections with centers and collaborators within and outside my institution that will contribute to the advancement and completion of this work and prepare me for success as an independent research scientist.