Cornell University

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY: My research proposal addresses the critical health issue of insufficient human milk production. Only a quarter of mothers in the U.S. meet recommended breastfeeding targets, resulting in significant costs and infant mortality. Low milk production is a prevalent reason for early cessation of breastfeeding, yet the physiological factors contributing to this problem remain poorly understood, and as a result diagnosis markers and treatments are limited. This proposal utilizes my molecular biology expertise, in conjunction with clinical studies, to illuminate the physiological and transcriptional aspects of human milk production regulation. In my preliminary work, I generated genomic datasets, including bulk RNA-seq and single cell (sc) RNA-seq, from human milk samples obtained from a cohort of individuals with low, normal, and high milk production and found several differentially expressed genes, including a yet to be characterized gene called C6orf15. In this proposal, I will characterize the function of C6orf15 using cell line and mouse models to understand how it is involved in increasing milk production. Using samples collected from a cohort of breastfeeding mothers I will explore the factors influencing infant weight gain by conducting a comprehensive analysis, including a 24-hour test weighing and milk composition. In addition, I will use transcriptomic profiling of mammary gland to identify additional markers and better understand the molecular changes under low milk production. In my independent phase of this award, I will characterize the efficiency of commonly used breast pumping protocols to increase milk supply. A randomized controlled trial will determine the optimal frequency of pumping required to enhance milk production and will also determine if lower frequency of pumping increases compliance of mothers to the protocol and leads to a better breastfeeding experience and longer breastfeeding duration. Finally, I will identify molecular and cellular mechanisms that lead to the observed increased milk production through bulk RNA-seq on samples collected before and after intervention and will be analyzing milk composition (inflammation markers, milk maturation, and macronutrient) and hormonal regulation. This work will uncover the changes in the mammary gland transcriptome and milk composition under different pumping protocols. In summary, my application combines clinical and molecular studies to shed light on the factors influencing human milk production. The results will improve the diagnosis and treatment of insufficient milk production, addressing a significant women's health issue with far-reaching implications for infant and maternal well-being.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Osteoarthritis is a leading cause of disability in the US affecting almost 30 million people at an annual cost of $128 billion. Initiation of osteoarthritis is tied to genetic predisposition, chronic overload, or acute injury due to joint trauma. The development of osteoarthritis after acute injury is particularly prevalent with more than 50% of injury developing full blown symptomatic osteoarthritis 10-20 years after injury. Despite the importance of this topic and decades of research, the local mechanical events that occur in cartilage during tissue injury and how they affect chondrocyte phenotypes and ultimately cell fate are still poorly understood. Importantly, developing an understanding of the mechanotransduction response in cartilage tissue is confounded by the heterogeneity of cellular responses arising from spatially complex strain fields induced during impact and the heterogeneity of cartilage tissue itself. These factors indicate that to understand the coordination of mechanotransduction throughout the tissue it will be critical to develop a framework capable of simultaneously analyzing the real time response of multiple signaling pathways for thousands of cells distributed in locations throughout the tissue. Recently, we developed a novel SpatioTemporal Response Analysis IN Situ (STRAINS) tool that combines in situ real time measurements of chondrocyte behavior with big data machine learning analysis techniques to provide a spatiotemporal map of cellular behavior throughout a tissue explant. In this proposal we take advantage of this newly developed microscopy and image analysis techniques to determine the location dependent distribution of cell phenotypes and how they change after an impact. Our aim is to use these techniques to probe the peracute response of chondrocytes to impact trauma to more fully understand the processes that occur during the very early disease process, and, more specifically, the effects that manipulating Ca2+ signaling or protecting MT bioenergetics have on cell fate after joint injury. Such studies have the potential to identify a window of opportunity for intervening in the disease process of post traumatic osteoarthritis, when disease modification is still possible. The specific aims of the proposal are to: 1) Determine whether local peak strain magnitude affects the distribution of cellular phenotypes similarly in the superficial and middle zones. 2) Determine how altering known calcium dependent mechanotransduction pathways alters distribution of phenotypes after impact. 3) Determine how altering mitochondria related cellular responses affects the distribution of phenotypes after impact. The proposed work will develop an understanding of the mechanisms that govern cell fate after traumatic injury. Identifying the specific cellular behaviors that accompany hyperphysiologic loading will provide new targets for future therapies in post-traumatic osteoarthritis. Consistent with an R21 mechanism, this work focuses on developing the model and experimental techniques on healthy tissue. Future work will expand these methods to study diseased tissue ex-vivo and in-vivo using intravital imaging in animal models.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Psychedelics are increasingly studied as a potential treatment for mental illnesses. For example, recent clinical trials showed positive outcomes after psilocybin-assisted psychotherapy for patients with major depressive disorder. The results are notable because of the durability of the beneficial effects after one or two dosing sessions, in which significant reduction of symptoms was detectable for at least several weeks and potentially up to several months. Although the neurobiology responsible for the potential therapeutic effect remains unclear, it has been suggested that neural plasticity in cortical pyramidal cells may be a contributing factor. However, pyramidal cells are embedded in cortical microcircuits and interact closely with GABAergic interneurons. The extent to which major interneuron subtypes may contribute to the psilocybin-induced changes in neural plasticity and behavior is unknown. The goal of this proposal is to determine the effects of psilocybin on the three major subtypes of GABAergic interneurons in the mouse medial frontal cortex. In Aim 1, we will use cell type-specific electrophysiology to measure how each cell type respond in spiking activity to the administration of psilocybin. In Aim 2, we will determine whether serotonin receptors that are expressed in the interneurons may be mediating psilocybin’s plasticity-promoting actions. In Aim 3 we will test if manipulating interneuron activity may alter psilocybin’s effect on stress-related behaviors. These experiments are designed to provide insights into how the excitatory-inhibitory microcircuit may shape the action of psilocybin in the neocortex and to identify cell types that are essential for the effects of psilocybin on neural plasticity and behavior.

NSF Awards · FY 2024 · 2024-09

Computational models describing the relationship between climate and streamflow are a tool that scientists and engineers use to improve our understanding of the natural world and in for the water cycle, it helps in important disaster forecasting such as floods and storms, and water management to ensure reliable water supply and assessment. However, the influence of human actions on the water cycle, also known as the hydrologic cycle, remains a major source of uncertainty in streamflow estimates. This project will use modeling to address this uncertainty by focusing on dams, which control about 75% of annual runoff in the continental United States. By analyzing how the representation of human actions affects the reliability of hydrologic models, the project will help scientists and engineers keep pace with human-induced changes in the water cycle. Moreover, the outcomes of this project will enhance infrastructure for research by creating an observational dataset describing dam operations across the United State. This dataset will expand the tools available to the hydrologic modeling community. To broadly disseminate findings, datasets, and models, the project will complement traditional dissemination avenues with two scientific workshops. Finally, the project will provide an opportunity for training Postdoctoral researchers and students. The representation of dam operations is a major source of structural uncertainty that curbs our ability to study hydrologic processes and fluxes in regulated basins. The project will build on the convergence of four research domains, namely remote sensing, large-scale hydrology, catchment hydrology, and water resources systems analysis, by bringing together new models and data such as observed and remotely sensed reservoir storage at the CONUS scale. By combining elements from these domains, the project will develop a computational framework demonstrated for 18 large river basins with one in each HUC2, with a total of about 200 dams. Through this framework, the project will pursue four outcomes by (1) characterizing the structural uncertainty associated with the choice of dam operation models, (2) explaining how this uncertainty propagates to the parameterization of large-scale hydrologic models, (3) quantifying the impact of this uncertainty on the simulation of hydrologic processes, and (4) generalizing practical modelling guidelines. To test the generality and scale of the approach, the investigators will apply their approach to two major river basins with the US - the Colorado and Columbia River basins. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

An award is made to provide support for strategic planning for the Drosophila Species Stock Center (DSSC) at Cornell University. Flies in the genus Drosophila have been important models to the study of genetics, development, neurobiology, ecology, and evolution for over 100 years. Studies on this simple fruit fly have helped society better understand the complex genetic bases of development and stimulated advances in medicine, human health, and our understanding of biological diversity. The DSSC is a national repository for a diverse collection of over 1400 living stocks from approximately 250 species of Drosophila and related genera. The DSSC has maintained these cultures since the 1940s and distributes to researchers working in a variety of biological fields to facilitate research aimed at studying the underlying principles and mechanisms of life. This award will support development of a strategic plan to sustain and facilitate comparative research on Drosophila species, in the face of rising staff and supply costs, in ways that best serves the research community. DSSC staff provides technical expertise in the areas of husbandry, natural history, systematics, evolution, and ecology of Drosophila. Whole genome sequences now exist for all ~250 species currently maintained in living culture, and sequencing on all ~1400 geographic strains is underway. This aspect of the collection further adds to its value and utility as a resource for comparative research into the correlation between phenotypic change, genome evolution, and species divergence. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project focuses on the intricate, often overlooked, and unavoidable imperfections within composite electrodes used in all-solid-state batteries. Understanding this “microstructure” is crucial for advancing battery technology with improved safety and energy density. By examining how these features behave during synthesis and electrochemical cycling, the research aims to uncover their impact on lithium diffusion and structural integrity. This study is significant because it fills a critical knowledge gap and advances solid-state battery technology. Beyond the scientific advancements, the project has broad societal impacts by fostering sustainable energy technologies and promoting diversity and inclusivity in STEM fields. By leveraging the diverse demographics of Cornell University and Arizona State University, the project will engage underrepresented communities in materials science through outreach initiatives targeting K-12 students and inter-institutional collaborations. These efforts aim to inspire a passion for STEM, cultivate a diverse future workforce, and enhance the interdisciplinary and inclusive nature of scientific research, ultimately contributing to national health, prosperity, and welfare. The project investigates the microstructure, including grain boundaries, secondary phases, and defects, within composite electrodes composed of solid-state electrolytes and cathode active materials. The research aims to quantify defect formation mechanisms and monitor operando microstructural evolution, and to elucidate to what extent these changes impact the mechanical and electrochemical properties of the electrodes. By combining tailored synthesis, advanced electrochemical characterization, real-time operando x-ray techniques, including single-particle diffraction and coherent imaging, and rigorous modeling, this study promises to unravel the profound influence of microstructural defects on ionic transport, mechanical resilience, and fracture toughness, paving the way for the development of high-performance solid-state batteries. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This IRES project addresses the significant socio-environmental changes affecting dryland systems in Kenya, including arid, semiarid, and dry sub-humid areas, through the formation of a student-scout partnership to understand and promote sustainable and fair transitions in these socio-environmental systems. Selected undergraduate students undergo extensive preparation, including learning Swahili and studying dryland system sustainability and research methods. Each cohort of U.S. undergraduates spend eight weeks in Kenya conducting field research, using various methods such as GPS-tracking livestock, plant surveys, wildlife censuses, household surveys, and interviews. This research contributes to important findings in dryland sustainability while training a diverse group of future STEM professionals. This project investigates the sustainability of dryland systems by focusing on the essential role of mobility in connecting social and environmental components. It aims to understand pastoralists’ livelihood strategies and resource access and examines how to balance agricultural intensification with mobile herding. Methods include tracking livestock movements and assessing rangeland vegetation dynamics, which provide insights into rangeland governance that support dryland system resilience. The project is interdisciplinary, aligning with NSF’s goals to encourage convergence research and leverage data to understand system transitions and tipping points. Collaboration involves Cornell, Michigan, and Princeton, with mentorship and local support from long-term collaborators in Kenya. This project advances the scientific understanding of dryland sustainability and contributes significantly to training a new generation of U.S. researchers capable of addressing global sustainability challenges. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This new REU Site: Summer Research Program at SRC JUMP2.0 SUPREME is hosted by Cornell University and MIT. Workforce development for the semiconductor industry is paramount to meet the demands in semiconductor industry. Essential skillsets for the new workforce are device fabrication, process, and packaging and cleanroom experiences. This site will host 10 REU students each year (five at Cornell and five at MIT), who will participate in semiconductor research in the cutting-edge cleanroom facilities. Students will align their individual research projects with the latest semiconductor research for future microelectronics applications. They will experience extensive cleanroom experiences using world-class cleanroom facilities in academic settings. The mentoring experience will be tailored to each student. Participants will also interact with semiconductor industry liaisons, engage in professional development via workshops and talks on ethics in research, effective presentation and writing, intellectual properties and entrepreneurship, career paths, and become part of the network of Semiconductor Research Corporation (SRC) research communities. REU participants will gain valuable skills in cleanroom and semiconductor technologies, exposure to latest microelectronics research, and networking opportunities with semiconductor industry partners. The new REU site focuses on building a semiconductor industry-ready workforce and provide a launching pad for REU students to further pursue higher education degrees in STEM fields. REU students will conduct research at the MIT-Cornell led SUPREME center. SUPREME (Superior Energy-efficient Materials and Devices), a Semiconductor Research Corporation (SRC) funded JUMP (Joint University Microelectronics Program) 2.0 center. The REU participants will conduct research aligned with the 2021 Decadal Plan for Semiconductors. Four semiconductor research areas are featured, including new digital and analog devices, novel memory and applications, interconnects and materials discovery, and advanced metrology and processing. Recruitment efforts will focus on students from institutions where research opportunities are limited. Technologically, the REU student research projects are aligned with the goals of the semiconductor industry for the next generation of electronics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Cells in the human body are exposed to a broad range of mechanical forces. Cells respond to such mechanical stimuli with the expression of specific mechanoresponsive genes, enabling the cells to adapt to their physical environment. This ‘mechanotransduction’ process is particularly important in tissues subjected to large and highly variable mechanical stresses, such as skeletal muscle, cardiac muscle, and skin, where impaired mechanotransduction can lead to muscular dystrophy, heart disease, and other pathologies. Research on mechanotransduction mechanisms has typically focused on proteins at the cell surface and in the cytoskeleton, along with the signaling pathways activated by these proteins. Recent studies and our preliminary data using advanced techniques to detect rapid changes in gene expression, however, found that mechanical stimulation induces expression of mechanoresponsive genes faster than the time needed for cytoplasmic signaling cascades to reach the nuclear interior, suggesting the existence of novel, yet to be determined mechanotransduction mechanisms. The overall objective of this proposal is to identify the mechanism responsible for this ‘ultra-rapid’ induction of mechanoresponsive genes and to determine the functional consequences of impaired nuclear mechanotransduction. Given the importance of mechanotransduction in muscle development, maintenance, and disease, the proposed research will focus on skeletal muscle cells. Nonetheless, insights gained from this research are expected to be also broadly applicable to many other cell types. The central hypothesis of this proposal is that the nucleus is not just a receiver of cytoplasmic mechanotransduction signals, but actively participates in transducing mechanical forces in changes in gene expression. Supporting this idea, deletion or mutation of nuclear envelope proteins that physically connect the nucleus to the cytoskeleton, such as lamins and components of the Linker of Nucleoskeleton and Cytoskeleton (LINC) complex, lead to impaired activation of mechanoresponsive genes and cause various muscle diseases. Nonetheless, how these proteins, and the nucleus in general, participate in cellular mechanotransduction and interface with established mechanotransduction pathways remains unresolved. The specific aims of the proposed work are to (1) determine the molecular mechanisms for the ultra-rapid mechanically induced gene expression and (2) define the role of nucleo-cytoskeletal force transmission, the LINC complex, and lamins in the mechanotransduction process in muscle cells. The long-term goal is to understand the fundamental mechanisms by which cells sense and respond to their physical environment, and to determine the effect of disease-causing mutations on this process. Gaining better mechanistic insights into how mechanical stimulation activates mechanoresponsive genes in skeletal muscle is critical to the development of new targeted therapeutic approaches for diseases such as muscular dystrophy caused by perturbed cellular mechanotransduction.

NSF Awards · FY 2024 · 2024-09

The plastic deformation of metals and alloys is controlled by the motion of imperfections in the material, called dislocations. At low deformation rates, dislocations overcome barriers through thermal fluctuations. At high strain rates, on the other hand, they interact with lattice vibrations (phonons) as they move at high speeds. Understanding the transition from thermal activation to dislocation-phonon interaction is critical for designing alloys that can withstand extreme conditions, such as impact loading. However, experimental approaches to study the high deformation rate regime are typically resource-intensive and low throughput. This award supports research in developing a novel, small-scale, high-throughput approach to study the characteristics of dislocation-phonon interactions in metals and alloys at extremely high deformation rates. The approach will also be used to systematically study the effect of alloying elements and grain size. The research will enable the design of new alloys that can suppress catastrophic failures at high deformation rates, with the potential to benefit the defense, automotive, and aerospace industries. A range of training, education, outreach, and dissemination activities will be carried out to promote diversity, equity, and inclusion among K-12 students and undergraduate students from underrepresented groups. The team will create a lab module and utilize it extensively for local outreach efforts. Additionally, education and workforce development initiatives will inform students about exciting new opportunities in the field of mechanics of metallic materials. As the strain rate increases, the dominant deformation mechanism in metals and alloys shifts from the thermally activated motion of dislocations to significant dislocation-phonon drag. The overall goal of this project is to systematically understand the dislocation-phonon drag regime in metallic materials across a wide range of strain rates. The mechanistic differences in similar deformation geometries from laser-induced microprojectile impact testing and spherical nanoindentation will be leveraged to isolate and study the characteristics of the dislocation-phonon drag regime. A physically based constitutive framework will be developed that, when coupled with the experimental measurements of microprojectile impact and nanoindentation, can precisely quantify the dislocation-phonon drag regime. The integrated experimental-computational framework will be used to study the effect of alloying elements, their concentration, and grain size on the characteristics of the dislocation-phonon drag regime. The understanding provided by this work can open new alloy design guidelines and accelerate the development of structural alloys for applications involving high strain rates. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Moving people to 'better' places underpins the rationale for over 42 billion dollars in federal spending on flagship programs in affordable housing and social policy. Yet remarkably little is known about how much and where integration actually occurs. This project deploys a national, large-scale, and mobility-based test of the major theories of how integration happens using cell phone location data. Case studies of metropolitan areas identify additional characteristics of places that correspond with more integration. Findings provide new evidence on the effects of housing policies, such as the social outcomes of vouchers and zoning incentives for mobility, as well as context-specific mechanisms and enabling conditions that may yield insights and inform place-based policies. The publicly available data products from this research enable scientific inquiries on mobilities across multiple spatial and temporal scales and set the stage for a wider range of work on related issues and their equity impacts. This project makes a novel intellectual contribution to questions of 'people versus place' and the study of human mobility data in the geospatial and social sciences. The research tests the tension between theories on the power of proximity and opportunity for individual outcomes, which drive most affordable housing policies in the US, and those of case studies that show persistent patterns of experienced segregation and exclusion inside mixed-income areas. Geospatial, statistical, and qualitative methods examine segregation and access to opportunity to incorporate a wider range of experienced contexts through human mobility data in a granular and spatiotemporally contingent manner. Findings nuance localized heterogeneity and persistent 'microsegregation' dynamics of high relevance to social and housing policy and contribute new knowledge to current prominent debates on the role of socioeconomic and racial integration in promoting opportunity in affordable housing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This award continues to support operations of the Center for High Energy X-ray Science (CHEXS), a national facility providing access to unique, world-leading experimental capabilities for the US and inter-national research community. CHEXS consists of four core research efforts, each attached to specific synchrotron beamlines at the Cornell High Energy Synchrotron Source (CHESS). These research pro-grams align with the goals of the three NSF directorates (BIO, ENG, and MPS) which have supported CHEXS since 2019. Research at all beamlines is fundamentally interdisciplinary, and proposals from any field of science are eligible for beamtime, subject only to external peer review bases on the NSF review criteria (scientific merit and broader impacts). CHEXS also supports efforts in X-ray technology R&D, education of the next generation of X-ray experts, and development and integration of advanced computation and data science for synchrotrons. CHEXS has an overarching mission to broaden participation in synchrotron research, and to recruit and train a diverse and growing user community. Located on the central campus of Cornell University, CHEXS is uniquely able to train the next generation of synchrotron scientists. CHEXS supports post-docs and Ph.D. students, hosts hands-on scientific workshops to train new users in X-ray methods, provides summer training and mentorship programs targeting undergraduates from underrepresented groups in STEM fields, and provides high quality in-formational materials to the general public. Specific research areas targeted at the Center for High Energy X-ray Science (CHEXS) are: (1) time-resolved studies of manufacturing processes of structural metals; (2) structural studies of biomole-cules in extreme environments to elucidate the rules of life; (3) high-throughput characterization of quantum materials to uncover intertwined quantum correlations; and (4) spectroscopic studies of va-lence electronic states in functional materials and inside operating devices. The CHEXS takes advantage of the high beam energy and large bunch charges available from the Cornell Energy Storage Ring (CESR) to offer exciting opportunities for nanosecond-scale measurements of dynamics inside heavy materials. CHEXS beamlines are harnessing the opportunity presented by rapidly growing data collec-tion rates, incorporating new analysis methods, data analytics and machine learning. Development of frontier research at CHEXS is supported by an X-ray Technology R&D program on next generation, cost-effective, high-performance undulator sources, high-heat-load crystal optics, faster x-ray detec-tors, and beamline automation. The specific X-ray beamlines include: Forming and Shaping Technology (FAST) Beamline supports sub-millisecond time-resolved studies of manufacturing processes such as laser welding and rapid quenching. Extreme Biology (XBio) Beamlines studies the building blocks of life at the molecular level, under extreme conditions such as high pressure, strict anoxic conditions, dissolved gasses, extremes of heat and cold, and harsh chemical environments. XBio combines both crystallography and small angle scattering to determine the atomic structure, shape, folding and oligomeric state of molecules. Q-Mapping for Quantum Materials (QM2) Beamline provides high-throughput characterization of quantum materials in reciprocal space (also known as “Q-space”) to uncover intertwined quantum correlations of spins, charges, and orbitals, from high to low temperatures and spanning entire phase diagrams. Photon-in, Photon-out X-ray Spectroscopy (PIPOXS) Beamline enables spectroscopic studies of valence electronic states in functional materials using hard x-rays, allowing access to opaque materials or sample environments. The beamline supports in situ and operando studies of man-made catalysts and enzymes with applications to fuel-cells, batteries, and electronic excitations in quantum materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Tendinopathies are common injuries that typically result from accumulation of sub-rupture fatigue damage. We have developed an in vivo model of sub-rupture fatigue damage accumulation using the rat patellar tendon to investigate the onset and pathogenesis of tendinopathy. We found that just one bout of fatigue loading results in collagen matrix damage and a 20% stiffness loss that is not recovered out to at least 10-weeks. We also found that running exercise leads to repair when initiated 2-weeks after onset of sub-rupture fatigue injury but promotes further degeneration when initiated 1-day after onset of injury, uniquely positioning us to determine the underlying mechanisms necessary to develop therapeutics to transform everyday activity into a reparative stimulus. We identified glycosaminoglycans (GAGs), specifically hyaluronan (HA) and dermatan sulfate (DS), to be increased prior to initiation of therapeutic but not degenerative exercise. Postmortem depletion of GAGs showed that their increase after sub-rupture fatigue injury reduces matrix shear strain and increases dynamic modulus which are properties that are associated with modulation of proliferation, apoptosis, and αSMA differentiation. Investigation of the repair response from therapeutic exercise identified an increase in population of αSMA+ cells and integrin α5 (α5+/tenocytes and α5+/αSMA+ cells); an integrin that enhances the capacity of cells to withstand loads thereby preventing cell death. Our inhibition of αSMA+ cells in therapeutic exercise increased the area of high severity matrix damage. Blocking integrin α5 altered the morphology of αSMA and tenocytes and decreased damage area, further enhancing the therapeutic effect of exercise. We will test the hypothesis that (1) the increase in GAGs after onset of fatigue injury modulates the stressful mechanical environment of cells in damaged tendons resulting from subsequent loading, leading to an increase in population of αSMA+ cells and integrin α5 (Aim 1); (2) the increase in αSMA+ cells will largely decrease the area of high matrix damage and that αSMA+ cells mediate tissue repair (Aim 2); and (3) that integrin α5 protects cells form apoptosis in response to higher loading but promotes a catabolic response from the surviving α5+/tenocytes while enhancing the functionality of α5+/αSMA+ cells (Aim 3). We will deplete HA and DS in vivo prior to initiation of therapeutic exercise to determine their role in transforming loading into a reparative stimulus (Aim 1). Pharmaceuticals will be used to inhibit the population αSMA+ cells (using Simvastatin in Aim 2) and to block integrin α5 (using ATN- 161 in Aim 3) to interrogate their role in promoting repair of fatigue damaged tendons. scRNAseq will be used to compare the cell populations that are associated with repair versus degeneration of fatigue damaged tendons and determine the effect of inhibition of αSMA+ cells and blocking of integrin α5 on these cell populations. The proposed studies will inform diagnostics to guide management of tendon injuries by identifying the biological environment that is associated with subsequent repair from continued use; and will inform therapeutics by determining key biological drivers of repair that can potentially be employed independently of exercise.

NIH Research Projects · FY 2025 · 2024-09

This project will credential two novel types of broadly applicable mouse models, which we will demonstrate in the lung. First, we will express genes that drive disease susceptibility on one side of the lungs only, a model we call a Unilateral Mosaic, creating disease-prone and disease resistant (internal control) lung tissue in a single animal. The lungs serve as a proof-of-concept organ, but this Unilateral Mosaic model applies to other asymmetric or paired organ systems such as the liver, and also to the limbs. The second type of engineered mouse will harbor an allele that installs permanent genetic marks within niche or neighbor cells, for in vivo cellular contact tracing in mice. Using a secreted and cell-permeant Tat-Cre protein, cells will be targeted for recombination in vitro using co-culture and media transfer experiments, and in vivo using transplantation of engineered Tat-Cre expressing cells into loxP-activated fluorescent reporter mice. There is an urgent need for mouse models that reduce animal to animal variability, and increase the in vivo resolving power for tracking cellular behavior. The models we present begin to address these challenges, and will appeal to a wide variety of investigators that employ mice to study complex human diseases. The proposed genetic tools are designed to address numerous categories of biological problems in living animals, but are not intrinsically confined to a specific disease state or organ. These studies employ the well-established Cre-LoxP system for targeted in vivo gene expression, applicable to the numerous floxed alleles in existence. This work will broadly impact the available applications for tissue- or lineage-specific expression of genes in mice, enabling investigators to track cells or induce gene expression in neighboring or transacting cells that contact driver cells of a given lineage. Specific Aim 1. Generate a Unilateral Mosaic model of disease susceptibility. a. Test whether human Ace2 excision can restrict SARS-CoV2 infection to one side of the lung. b. Determine whether excision of Cox2/Ptgs2 unilaterally in the lungs can compartmentalize metastatic disease after intravenous injection of metastatic mouse mammary cancer cells. Specific Aim 2. Test and deploy a Tat-Cre allele for in vivo lineage contact tracing We anticipate this project will yield rapid results, as we already have the ASE-Cre Unilateral Mosaic driver mice in house and have exciting preliminary data to reveal the target organs for its asymmetric expression. We have performed many SARS-CoV-2 viral challenge studies in human Ace2 knockin/knockout mice with a currently approved and active animal BSL-3 program in the lab. In addition to our experience in mouse mammary tumor models and lung developmental and cancer biology in mice, we have established collaborations to ensure the success of syngeneic mammary tumor transplant studies. For Aim 2, we already have cloned and present preliminary data to demonstrate the utility of the new Tat-Cre construct. In summary, we have the required experience and existing preliminary data and materials to bring the proposed studies to a successful completion.

NIH Research Projects · FY 2024 · 2024-09

Fungal infections are an increasing human health risk, and the development of fungal resistance to the limited repertoire of available drugs is exacerbating this threat. Five species within the genus Candida predominantly cause disease manifestations in humans, especially in immunocompromised individuals. Indeed, candidiasis is the most common opportunistic oral infection affecting people with AIDS. Earlier work by our group and others demonstrated changes in oral fungal composition and mutational changes in antimicrobial resistance (AMR) genes throughout the course of HIV infection and treatment, suggesting a uniqueness to the oral environment. Although there are several known molecular mechanisms that confer antifungal resistance, recent studies suggest the high likelihood that there are many novel mechanisms yet to be identified. Our goals are to employ evolutionary genomic approaches to gain both basic and applied insights on the oral environment that will inform diagnostics and ultimately treatment. Our central hypothesis is that fungi will evolve over time in the oral environment, and this R03 is designed to demonstrate both support for this hypothesis and feasibility of the novel methods we are developing. Our proposed work leverages an earlier case-control study (“Crosstalk”) by one of our team members and includes a set of 50 HIV-positive and HAART naïve individuals, and 76 HIV-negative controls, over a 5-year study period. Deidentified and cryopreserved saliva, caries swabs, fungal cultures, and bacterial cultures from these patients are immediately available, along with associated clinical and demographic data. In Aim 1 we will use direct long-read sequencing on patient saliva and caries swabs to determine the fungal mycobiome at unprecedented resolution. This profiling will come from direct sequence analysis of the full-length Candida rRNA operon from patient samples. In Aim 2 we will determine the fungal resistome of the same samples over the course of treatment. We will perform longitudinal profiling by hybridization-capture with biotinylated probes to target known fungal AMR and biofilm-associated genes. These genes will then be analyzed for SNPs over time and for signatures of positive selection pressure. We will next propose an R01 with a goal of identifying new fungal genetic AMR determinants using pan-genomic and molecular adaptation analyses on fungal cultures from Crosstalk and more recent isolates. This takes advantage of the recent discovery that fungi, like bacteria, have a core and an accessory genome; we plan to correlate the genes from the accessory genome, and SNPs across the genome, with AMR phenotype data, concentrating on Candida species common to the HIV oral environment. We expect that this project will identify putative novel Candida antifungal resistance mechanisms and provide much needed information on Candida evolution in the oral environment.

NSF Awards · FY 2024 · 2024-09

Modern Data Science, with its emphasis on "Big Data", presents a challenge for more traditional mathematical disciplines like Optimization. The mathematical techniques to be developed as part of this project aim to transform the design and analysis of algorithms across Optimization, bridging from current scholarship to vital computation in Data Science, robust control engineering and beyond. Graduate students will be central to this research, working in collaboration with the principal investigator on methodology and computation as well as preparing journal articles and conference presentations. This research will be incorporated into graduate coursework, made broadly accessible to the scientific community through targeted expository articles, involve collaboration across diverse fields, and be disseminated in international lectures to audiences across science and engineering. This project involves a multi-pronged approach to the fresh challenges posed by Big Data in contemporary optimization, relying heavily on the underlying problems' rich inherent mathematical structure. First, relying on the semi-algebraic nature of all computer-representable objectives, the project will extend classical Lojasiewicz-type rescaling techniques to design and analyze the complexity of first-order and active-set optimization algorithms in hyperbolic and other non-Euclidean and infinite-dimensional settings. Such nonlinear spaces model diverse data, ranging from mass distributions separated by Wasserstein earth-mover distances, to phylogenetic trees in Computational Biology. Secondly, the principal invesigator will analyze condition measures of nonsmoothness and nonconvexity in Lipschitz optimization, and their impact on popular computational heuristics: such heuristics apparently converge nearly linearly and yet currently lack rigorous complexity guarantees. To this end, the project pursues a shift from point- to path-based analysis, and to distributional derivatives. Thirdly, informed by these condition measures, the project envisages reliable new algorithms, fast and intuitive enough to satisfy practitioners in Machine Learning, High-Dimensional Statistics, and Imaging Science, and also (at more moderate scale) in Systems Control and beyond. The project thus pursues a transformation of the modern continuous optimization toolkit, with potential impact across the computational 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.

NSF Awards · FY 2024 · 2024-09

Accounts of extreme weather typically follow the logic of the "perfect storm", in which several factors come together to produce an event of exceptional severity. Following this logic the event occurred because all the factors aligned in just the right way, a rare occurrence that happened by chance. In that case we should not look for a single deterministic cause for the event but ask instead what factors contributed to it, in what ways, and to what extent did each factor increase the likelihood or severity of the event. This way of looking at contributions to an extreme event has been pioneered in "attribution" studies which quantify the extent to which climate change has increased the likelihood of warming-related extreme events. For instance one study provides evidence that the warming of the world over the past few decades has roughly tripled the odds of the extreme temperatures that occurred during the exceptionally deadly Russian heat wave of 2010. Work performed here applies the framework of extreme even attribution to events which evolve over the subseasonal timescale, meaning more than the one week timescale of typical frontal weather systems but less than a season. The focus is primarily on cold air outbreaks (CAOs), in which winds from the north cause extreme cold temperatures over the middle-latitude continents. Previous work suggests that CAOs are more likely when the polar vortex of the Northern Hemisphere stratosphere is weak, as is the case following sudden stratospheric warmings (SSWs). SSWs are in turn driven, at least in part, by the rapid amplification of upward-propagating planetary waves from the troposphere. Thus we can construct a "storyline" in which rapid amplification of planetary waves leads to an SSW which in turn leads to a CAO. The event attribution goal is then to determine how much the SSW increased the odds of the CAO and how much the planetary wave amplification increased the odds of the SSW. The project pursues these attribution questions using observations as well as simulations from models at varying levels of complexity, including a dry dynamical core model, a Linear Inverse Model (LIM), and the Community Earth System Model (CESM). A key challenge in the work is the rarity of the events of interest, which usually means that very long model simulations are needed to generate a statistically significant sample of extreme events. The project addresses this challenge using a rare-event sampling algorithm in which an ensemble of model simulations is steered toward an extreme event of interest by pruning ensemble members which are not evolving toward the extreme state and replacing them with new members spun off from ensemble members which are. The rare-event sampling algorithm dramatically reduces the amount of simulated time required to produce enough extreme events to draw reliable conclusions. The educational component of this CAREER proposal includes two activities, one of which is the development of a modular course intended to introduce statistical thinking to undergraduates. The course is motivated by the observation that most students are not familiar with the probabilistic conceptual framework that underpins much of geoscience. The course is intended for students pursuing majors in atmospheric science and statistics and follows a flipped classroom approach in which most of the class time is devoted to hands-on assignments addressed in small groups. The course uses real datasets to illustrate key aspects of probabilistic thinking without requiring high-level mathematics. Course materials are published online to facilitate their use by instructors worldwide. The second activity is a set of workshops that bring together users of weather and climate forecasts and undergraduate atmospheric science students at Cornell, carried out in collaboration with the Northeast Regional Climate Center (NRCC). Speakers are invited from regional organizations which represent a variety of sectors including renewable energy, emergency response, and water management. The speakers specifically address the use of weather forecasts and climate change projections in their decision making. Students are recruited from both Cornell and other colleges and universities in upstate New York, and research projects are developed in partnership with workshop speakers. The project has clear societal relevance given the hazards posed by severe weather. The work is of interest both for its specific results and for its development of event attribution and rare-event sampling methodologies that can be applied to subseasonal predictions of extreme events. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

New technologies promise to augment the interactions of group members with their environment and with each other, enhancing the learning and effectiveness of work teams, community groups, classrooms, and other collectives. Already, many communities communicate via "smart" technological environments that are dynamically adapted to support shared goals. This project examines how to optimize such technological augmentations for collective intelligence and goes a step further, by investigating how to give groups, themselves, agency over the optimization process. Giving a community control over its own socio-technical learning environment could potentially allow a greater degree of effectiveness and legitimacy, but it invites new sets of challenges: How does a collective develop and learn the rules that will best structure their interactions? What are the unintended effects of granting such freedom on collective outcomes? The investigators address these questions through the development of large-scale online experiments and computational models aimed at understanding collective learning and its relation to self-governance. Data collected and analyzed will provide insight into technological mechanisms that support small-scale democratic decision-making. This research will advance science at the intersections of sociology, cognitive science, and computer science and will help prepare the workforce to work optimally and cooperatively, in future technological environments. The research aims of this project focus on the parameters that drive group-directed learning in three areas important to groups: cooperative behavior, collective intelligence, and collective creativity. The experiments bring together hundreds of participants in computationally designed environments to understand how their interactions can be tuned to optimize group outcomes along these three dimensions. This work increases the access of scientists to complex, large-scale experimental designs and accelerate the pace of scientific research on human group behavior. Research and planning tools that empower communities to incrementally explore the rule spaces that govern their interactions will be shared with the research and other relevant communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

This project aims to revolutionize cardiovascular research and healthcare by developing an Al-augmented image-based hemodynamic modeling platform. The primary objective is to establish efficient and reliable data-enabled, patient-specific computational modeling capabilities to enhance the comprehensive understanding of cardiovascular physiology and pathophysiology. It addresses the gaps in existing image-based computational modeling frameworks that are labor-intensive, computationally expensive, and subject to large uncertainties. Specifically, the project will automate the transformation of medical images into precise 30 geometries for computational fluid dynamics (CFO) and fluid-structure interaction (FSI) simulations via deep learning, significantly reducing manual labor and computational costs. By integrating physics with mesh-based geometric deep learning through differentiable programming, this project aims to enable fast surrogate CFD/FSI simulations, epitomizing an innovative blend of machine learning with domain-specific knowledge and allowing for rapid predictions of functional information such as blood flow patterns, pressure and wall shear stresses. Furthermore, an ensemble Bayesian learning framework will be developed to propagate and quantify uncertainties in model predictions, thereby enhancing the reliability and trustworthiness of the results. Finally, the introduction of advanced visual analytics to interpret vast hemodynamic data ensembles underscores the project's commitment to accessibility and user-centric design. Collectively, these innovations aim to enhance the accuracy, efficiency, and reliability of patient-specific hemodynamic modeling, making them more accessible and actionable for healthcare professionals. The project's long-term goals include improving the understanding, diagnosis, and treatment of cardiovascular diseases, aligning with the National Heart, Lung, and Blood lnstitute's (NHLBI) mission to combat heart, lung, and blood diseases and extend the lives of those afflicted. By offering a novel approach to accurately and reliably model cardiovascular dynamics, the project holds the potential to significantly impact the field of cardiovascular research and healthcare, providing a basis for better clinical decisions and treatment strategies. RELEVANCE (See instructions): This research focuses on developing patient-specific models and diagnostic tools for cardiovascular diseases, aligning with the NHLBl's goal of improving health and extending life by furthering the knowledge and treatment of heart, lung, and blood conditions. By enhancing the accessibility and reliability of image-based computational modeling, the project will revolutionize cardiovascular healthcare, significantly advancing public health through improved prevention, diagnosis, and treatment.

NSF Awards · FY 2024 · 2024-09

This National Science Foundation Research Traineeship (NRT) award to Cornell University will develop an interdisciplinary graduate training and research program on AI for Sustainability Sciences and Engineering (AISSE), focusing on decarbonizing energy and agri-food systems. The project addresses the urgent need for innovative solutions to sustainability challenges, such as climate change, highlighted by the record-breaking global average temperature in July 2023. By integrating artificial intelligence (AI) advancements, the NRT seeks to cultivate a skilled workforce proficient in both AI and sustainability, responding to the increasing demand in these rapidly evolving fields. The project anticipates training 105 PhD students, including 25 NRT Fellows, 30 Project-Focused Fellows, and 50 NRT Travel Grant Awardees, from 16 academic departments across Cornell’s colleges of Engineering, Computing and Information Science, Agriculture, and Arts & Sciences. This interdisciplinary effort also partners with five minority-serving institutions and several Cornell research centers specializing in AI, sustainability, digital agriculture, and energy systems, aiming to equip trainees with the technical and professional skills to address scientific, societal, and workforce needs. The AISSE NRT program will develop advanced AI methodologies to create sustainable materials, decarbonize energy systems, enable climate-smart food production, and analyze the energy-food-climate nexus. These research efforts will tackle forefront scientific and societal challenges while advancing transformative AI methodologies. Key training elements of the NRT include a small-grant initiative, a rigorous practicum program, and an immersive bootcamp combining a crash course with a team-science workshop. The curriculum will feature harmonized courses, cross-field lab rotations, and joint mentorships to promote interdisciplinary research training. Additionally, the program emphasizes developing professional skills in scientific communication, team science, ethics, leadership, and entrepreneurship. Expected outcomes include creating new AI tools and methods, advancing sustainability science and engineering, and preparing graduates with interdisciplinary expertise and professional skills. By fostering interdisciplinary collaboration and innovative education, the AISSE NRT aims to produce a workforce capable of using AI to tackle sustainability and decarbonization challenges, ensuring long-term impact. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Trapped ions constitute a leading approach to quantum computing and simulation, owing to their high coherence, operation fidelities, and connectivity. While the complexity of classical electrical control required is comparable to other architectures, efficient modulation and delivery of laser light presents a major bottleneck for scaling beyond a few tens of qubits, especially considering the visible/UV wavelengths required for most species which preclude use of scalable optical modulators which have been developed primarily in the infrared. Passive visible/UV addressing photonics integrated within trap structures are being pursued by multiple efforts in the field to address the beam delivery/routing problem, but reliance on bulk optical modulators external to the trap device coupled to the quantum system via separate fiber inputs will limit the number of parallel modulated channels to a few tens. Our project aims to address this challenge by moving light modulation and routing into integrated photonics devices, enabling small footprints, and low power consumption to open a pathway to future co-integration with the waveguide-driven ion trap chips. This project will assess two complementary routes to integrated modulation at VIS and UV wavelengths, based on both acousto- and electro-optics. This collaborative program leverages expertise in photonic materials and devices, ultra-sensitive measurements using ions, as well as relevant fabrication facilities at Cornell (COR) and the Paul Scherrer Institute (PSI). The project explores a novel device architecture for integrated acousto-optic modulators at UV and some VIS wavelengths leveraging high-index photonic materials at short wavelengths integrated with surface-acoustic-wave transducers and guiding structures, which will be pursued in theory and experiment. In parallel, the project leverages mature foundry platforms to develop and validate high-extinction visible-wavelength electro-optic modulator arrays. The work will also generate critical data, e.g. on photo-refractive damage in lithium niobate, and limits to extinction ratios and stability in short-wavelength electro-optic devices. The project involves characterization and performance comparison of the two approaches, and on application of the modulators developed in experiments for trapped-ion quantum control, which serve as ultimate performance benchmark, at both COR and PSI. Together, this work carries relevance to a wide range of applications beyond trapped ion quantum computing, such as atomic clocks, Light Detection and Ranging (LIDAR), and the life sciences. This collaborative U.S.-Swiss project is supported by the U.S. National Science Foundation (NSF) and the Swiss National Science Foundation (SNSF), where NSF funds the U.S. investigator and SNSF funds the partners in Switzerland. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Toxic contaminants, such as heavy metals and organic compounds, pose serious health threats to sur- rounding communities. Bioremediation, which uses living organisms such as bacteria to degrade, detoxify, or remove pollutants, has emerged as an environmentally sustainable approach to cleaning up polluted sites. The Gram-negative bacterium Shewanella is capable of reducing metals like uranium and chromium to less-toxic forms as well as of degrading organic compounds through redox reaction as part of their anaerobic respiration. Leveraging the anaerobic respiration mechanism in Shewanella can thus revolutionize bioremediation and wastewater treatment technologies. The biological foundation of this anaerobic respiration is the extracellular electron transfer (EET) process, in which the bacterium exchanges electrons with extracellular electron accep- tors or donors by employing a cascade of proteins residing at different cellular compartments, in which CymA, an inner-membrane-anchored protein, acts as a central hub of EET pathways for relaying electrons across the inner-membrane, either toward outside the cell or into the cell. Little is known, however, on how the involved proteins, especially those at different cellular compartments, coordinate spatially and temporally in the cell to ensure efficient electron transfer. The long-term goal here is to understand how electroactive bacteria carry out EET to provide knowledge to better utilize or engineer such bacteria for bioremediation of contaminated sites. The objective here is to define how CymA coordinates spatially and temporally with its redox partners to mediate efficient EET across the cell envelope in live Shewanella oneidensis cells. Preliminary studies reveal that CymA changes its spatial distribution in the cell from a dispersed pattern into a punctate pattern when actively engaged in EET, which leads to our hypothesis that CymA and its redox partners dynamically cluster and colocalize spa- tiotemporally in the cell to ensure efficient electron transfer across the cell envelope. The research will test this hypothesis and use quantitative single-molecule/single-cell imaging approaches, together with specific protein tagging, genetic manipulations, single-cell (photo)electrochemical manipulation/measurements, and bulk bio- chemical assays. There are two aims: 1) Define the spatial pattern and temporal dynamics of CymA in the cell in relation to cell's EET activity. 2) Define the spatiotemporal pattern of periplasmic EET partners and their colo- calization with CymA in the cell. The research is significant because it will provide insights into the mechanism of EET across the cell envelope of Gram-negative bacteria and the molecular basis of anaerobic respiration in electroactive bacteria, and it will provide knowledge to potentially engineer S. oneidensis and other electroactive bacteria for bioremediation applications. The research is innovative because of the novel mechanism of spatio- temporal colocalization of EET proteins, as well as of the innovative combination of single-cell/molecule fluores- cence microscopy, electrochemical manipulation, and photoelectrochemical microscopy.

NSF Awards · FY 2024 · 2024-09

This project will help us understand how galaxies, like our Milky Way, grow and change over time. Galaxies are filled with gas that forms stars and fuels black holes. When stars explode as supernovae, they send out large amounts of energy and material into space, creating galactic winds that can shape how galaxies develop. These winds are important because they can stop galaxies from becoming too massive and full of gas. However, there is a big difference between what leading computer models predict about these winds and what we actually observe. The investigator will use advanced computer simulations to study how these winds interact with the gas that surrounds galaxies, known as the circumgalactic medium (CGM). By combining these simulations with real observations, the project will give us new insights into how galaxies evolve. This work will involve students, helping train the next generation of scientists and improving our understanding of the universe. The investigator team will address the glaring discrepancy between current cosmological galaxy formation models and observations of galactic wind outflow rates by focusing on the interaction between galactic winds and the CGM. Using the state-of-the-art adaptive mesh refinement (AMR) magnetohydrodynamics code framework, athena++, the investigators will conduct high-resolution simulations that capture the self-consistent launching and dynamics of these galactic winds and their interactions with the CGM. These simulations will include key physical processes such as magnetic fields, thermal conduction, and cosmic rays, UV shielding, thermal conduction, and non-equilibrium ionization, assessing their individual and collective impact. Crucially, the use of AMR in these simulations will enable unparalleled spatial fidelity, from the interiors of galaxies to the expansive CGM. A robust forward modeling pipeline will be developed to produce multi-wavelength mock observations, enabling direct comparisons with observational data from instruments such as the James Webb Space Telescope (JWST). This collaborative team includes with experts in simulations, theory, and observations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The tropical tropopause layer (TTL) is the top layer of the tropical troposphere, between about 14km, usually the highest level of convective cloud outflow, and 18km, where the cold point tropopause marks the end of the troposphere and the beginning of the stratosphere above it. The TTL is sometimes called the "gateway to the stratosphere" because the bulk of the water vapor and ozone in the stratosphere travels through the TTL to get there. The gateway terminology is appropriate because TTL temperature regulates the amount of ozone and water vapor entering the stratosphere, with lower amounts entering when the TTL is colder. An intriguing aspect of the gateway is its connection to tropical sea surface temperature (SST): the TTL is colder, and its gateway narrower, when the tropical oceans are warmer. The relationship is well documented but not well understood, and two competing theories have been proposed. One is that the warming of tropical SSTs leads to deeper and more vigorous convective clouds, producing rising motions in the TTL which cool the layer by adiabatic expansion. The other is that what matters is the pattern of SST change, as SST contrasts induce differences in convection that generate large-scale waves which propagate upward into the TTL, where they induce rising motions and cooling as they dissipate. Work under this award seeks to reconcile the two theories using idealized modeling experiments in which specially designed patterns of SSTs are used to isolate one mechanism or the other. The simulations are performed with two hierarchies of models of different levels of complexity, one tailored to each theory. Models used for the convection theory are versions of the System for Atmospheric Modeling (SAM) while models used to represent the wave dissipation mechanism include a dry dynamical core model (similar to the Held-Suarez model), and the Model of an idealized Moist Atmosphere (MiMA). Results of the work are then used to examine the role of tropical SSTs in driving long-term TTL trends, one issue being the extent to which the different SST trend patterns found in model simulations and the observational record (see for instance AGS-2203543) matter for the TTL and hence the gateway. The work is of societal as well as scientific interest due to the global impacts of stratospheric ozone, which shields the earth's surface from harmful ultraviolet radiation, and water vapor, which is a strong greenhouse gas. In addition, the project supports an undergraduate student participating in the NSF-funded Cornell GEOPAths Geoscience Learning Ecosystem (CorGGLE) in summer 2025. CorGGLE is a summer bridge program in which students carry out research projects and are introduced to a broad range of science and science-related careers. The project also provides support and training to a postdoctoral researcher, thereby providing for the future workforce in this research 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 2024 · 2024-08

This project sheds light on the understudied phenomenon of gender bias in the cases of women on death row. The team’s preliminary analysis indicates that legal actors frequently invoke biased tropes in the criminal trials of women sentenced to death, emphasizing women’s perceived sexual deviance, deficient mothering, and emotional manipulation. This project expands these findings and trains computational linguistic tools to identify gender-biased language in trial transcripts on a large scale. Led by national experts in gender, the death penalty, linguistics, and information science, this project ascertains the extent to which prosecutors and defense attorneys invoke gender stereotypes in the cases of women who are on trial for their lives. While this research focuses on women sentenced to death, project outcomes contribute to ongoing research about how gender stereotypes and bias affect the experiences of women throughout the criminal justice system. This project uses innovative, interdisciplinary qualitative and computational methods to explore and identify gender-biased language deployed in women’s capital trials. First, a team of researchers qualitatively codes the dataset, which consists of the trial transcripts of women on death row. The qualitative coding is informed by Critical Discourse Analysis, a methodological model that helps reveal the ways that gender stereotypes and biased discourses are both produced and influenced by specific language use. Additionally, the project draws from various disciplinary perspectives to describe the multiple forms of bias experienced by women of color. After qualitative data coding for gender-biased language, the team uses an iterative process to train and apply computational tools from computational linguistics to automate detection of gender-biased discourse invoked by courtroom actors; the model-detected phrases are confirmed by human experts and compared qualitatively across cases. This project uses a range of technologies to compare such discourse between womens’ and mens’ court transcripts, from established methods such as topic models and word embeddings, to emerging pretrained large language models (e.g. BERT, T5, GPT-Neo). By employing a range of analytical tools, this project produces the first analysis of the extent to which gender-biased discourse permeates capital trials. 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.