Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=3&sort=other_investigators
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=other_investigators", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=other_investigators", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=4&sort=other_investigators", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=2&sort=other_investigators" }, "data": [ { "type": "Grant", "id": "14961", "attributes": { "award_id": "5R01NS129836-02", "title": "Mechanisms and Functions of Cortical Activity to Restore Behavior", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Neurological Disorders and Stroke (NINDS)" ], "program_reference_codes": [], "program_officials": [ { "id": 22923, "first_name": "Janet", "last_name": "He", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-07-01", "end_date": "2028-06-30", "award_amount": 487477, "principal_investigator": { "id": 27578, "first_name": "Diany Paola", "last_name": "Calderon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 825, "ror": "", "name": "WEILL MEDICAL COLL OF CORNELL UNIV", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Monitoring the transition to wakefulness is critical during restoration to consciousness after brain injury, anesthesia, and in those COVID-19 survivors that have altered consciousness. However, we have an imprecise understanding of neural dynamics linked to behavioral changes as subjects awaken. Our previous work discovered that stimulating the anterior nucleus gigantocellularis (aNGC) promotes arousal from a coma-like state. We proposed recruiting multiple arousal pathways through aNGC as an avenue to triggering widespread activation resulting in wakefulness. Notably, aNGC activation increased frontal-motor cortical activity and restored full mobility through modulation of an aNGC-to-frontal-motor-cortex pathway despite high anesthetic concentration exposure. We also showed that animals emerging from diverse coma-like states share a common dynamic process of cortical and motor arousal that can be consistently sequenced from deep to high arousal levels. We identified five cortical periods that tracked restored motor behavior in a hypoglycemic coma and a range of anesthetics, whether inhaled or injected, alongside conventional righting reflex assays. Based on these findings, we postulate that restoring waking is a common progressive process in which cortical patterns contain metrics of consciousness that distinguish reflexive from purposeful movements. We hypothesize that cortical measurements that link neural responsiveness to defined behaviors are an applicable method that can extend the analysis of the recovery of consciousness beyond monitoring reflexive movements. Our proposal deepens our understanding of the contribution of cortical neural subtypes, the neuronal pathways underlying aNGC-induced changes in frontal-motor cortical activity, and the temporal dynamics that distinguish reflexive from the initiation of voluntary behaviors in our rodent-low arousal models. In addition, we will establish the cortical patterns that unpack these behavioral transitions. Since pathological states of unconsciousness are vastly heterogeneous, having a clear understanding of ordinary recovery serves to better appreciate the variability imposed by the injury to cortical activity and behavior. Thus, we will identify how damaged neural circuits affect established cortical activity pathways and dynamics that underlie behavior recovery. The proposed studies are thus significant because they will establish the mechanistic correspondence, examining activation of neural pathways and their dynamics linked to habitual and intentional behaviors that reveal novel, medically relevant biomarkers that promote a robust inference of arousal states during emergence from anesthesia and after brain injury.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15102", "attributes": { "award_id": "2337089", "title": "Collaborative Research: Point-of-Care Additive Manufacturing for Health: Cultivating and Assessing Engineering Students' Technical Knowledge and Professional Skills", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "IUSE" ], "program_reference_codes": [], "program_officials": [ { "id": 2088, "first_name": "Jennifer", "last_name": "Ellis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-15", "end_date": null, "award_amount": 59688, "principal_investigator": { "id": 31649, "first_name": "Ebtesam", "last_name": "Islam", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 782, "ror": "", "name": "Texas Tech University Health Science Center", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This project serves the national interest by preparing a qualified engineering workforce with important technical and professional skills for the health-based point-of-care (POC) additive manufacturing (AM) industry. Health-based POC-AM is a non-traditional form of manufacturing referring to the just-in-time creation of anatomical models, surgical instruments, prosthetics, scaffolds, etc., based on medical imaging data and need at the place of patient care. The growth of POC-AM requires the collaboration of medical, engineering, and social science professionals in that engineers must be trained to be socially adept and communicative about additive manufacturing specifically for healthcare applications. Despite the exponential growth in POC-AM market value and scholarly activities, the needed education and training components are underdeveloped, especially for undergraduate students in public engineering schools. This IUSE Engaged Student Learning Level 2 project will bridge this talent gap by creating an undergraduate engineering course that is broadly accessible and will be able to define, cultivate, and assess students' technical and professional skills needed by the booming POC-AM industry. This project features a project-based learning plan to develop students' theoretical and hands-on skills to create a broad range of medical objects from non-patient-specific personal protection equipment and anatomical models to patient-specific prosthetics, tissues, and implants. This project will strongly emphasize the development of students' reflective communication skills, both written and verbal, with colleagues in both engineering and in healthcare. The project will also design a protocol for assessing and developing those communication skills using objective and subjective metrics.<br/><br/>Thus, the goal of this project is to remove barriers between POC-AM research and education while interconnecting key concepts in multiple related sub-disciplines through teaching this unique skillset to undergraduate students at two large public universities. The innovative course that focuses on students' technical and communication skills development will train holistic and well-rounded engineering students who can solve complex problems that require a broad integration of technical knowledge and communication skills. The combination of cutting-edge learning about POC-AM and a targeted and efficient communication skills development targeted to the needs of the post-COVID student population makes this project highly effective for undergraduate education. The developed instructional and assessment materials will be publicly available as this project can be a model for other similar upper division engineering courses, especially in an emerging and practical field. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. This project is jointly funded by the Established Program to Stimulate Competitive Research. This project is jointly funded by IUSE and the Established Program to Stimulate Competitive Research (EPSCoR).<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15155", "attributes": { "award_id": "2441449", "title": "CRII: III: Pursuing Interpretability in Utilitarian Online Learning Models", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Info Integration & Informatics" ], "program_reference_codes": [], "program_officials": [ { "id": 27554, "first_name": "Raj", "last_name": "Acharya", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-08-15", "end_date": null, "award_amount": 175000, "principal_investigator": { "id": 27713, "first_name": "Yi", "last_name": "He", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 414, "ror": "", "name": "College of William and Mary", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "In today's world, the real-time generation of enormous amounts of data has become commonplace, spanning domains such as e-commerce, social media, environmental science, urban disaster and pandemic monitoring, and many others. Such streaming data necessitate data mining (DM) models that can analyze them in time as they emerge, derive actionable insights, and make adjustments on the fly. For instance, predicting crowd movement due to public events (such as concerts, games, parades, and protests) based on data streaming from social media and city sensors can aid in reducing the traffic by steering clear of overcrowded areas. However, as DM models become more prevalent in practice, interpretability has emerged as a vital issue. User comprehension and trust in DM model outputs are critical for their acceptance in daily routines and workflows. Nonetheless, existing research on data streams has focused mainly on model accuracy, producing models that are too complex for human interpretation. This gap between DM researchers and practitioners calls for new research that optimizes model accuracy and interpretability simultaneously. This project aims to bridge the gap by developing novel online algorithms that are transparent to human users and can provide a complete explanation of the logic behind each prediction, earning the trust of human operators and increasing legal defensibility when used to support decision-making in crucial domains such as healthcare, economy, security, and social goods.<br/><br/>The overarching goal of this project is to advance interpretability research of online DM models through three research objectives: (1) understanding the dynamism of varying feature spaces and its impact on model structure; (2) quantifying model prediction uncertainty in the absence of adequate supervision labels; and (3) indexing and elucidating model inference paths. To achieve these objectives, the project will focus on four research thrusts. The first thrust will develop novel algorithms that capture and model the variation patterns of feature spaces through an expository feature correlation graph, allowing for joint learning of graphs and predictive models. The second thrust will focus on developing unsupervised methods to quantify the uncertainty of model predictions and identify geometric manifolds underlying data streams with memory-efficient structures. The third thrust will devise new systems to index, track, and illustrate the complete generation process of online predictions. The fourth thrust will establish evaluation metrics and protocols to standardize interpretability measurement in streaming data contexts. The project aims to contribute to interpretable data mining and machine learning research, which will help bridge the gap between data scientists and domain-specific forecasting experts. The educational component of the project will involve mentoring and educating researchers interested in pursuing DM careers in academia or industry, with a particular focus on underrepresented, financially disadvantaged, or disabled undergraduate students in computer science research. The project will also pioneer new classes at the forefront of data mining research and organize workshops at city libraries to engage with the broader public.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15051", "attributes": { "award_id": "5K23HL168212-02", "title": "Individualizing Steroid Use in Pneumonia", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Heart Lung and Blood Institute (NHLBI)" ], "program_reference_codes": [], "program_officials": [ { "id": 22653, "first_name": "EMMANUEL FRANCK", "last_name": "Mongodin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-06-01", "end_date": "2028-05-31", "award_amount": 169020, "principal_investigator": { "id": 27399, "first_name": "Yewande", "last_name": "Odeyemi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1426, "ror": "", "name": "MAYO CLINIC ROCHESTER", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true }, "abstract": "Pneumonia is the leading infectious cause of death worldwide. While inflammation is a hallmark pathologic feature of pneumonia, the use of corticosteroids to blunt the profound inflammatory response remains undefined. Decades of studies in heterogeneous populations failed to show a consistent benefit, leading to conflicting guidelines on the use of corticosteroids. Although corticosteroids have been shown to be effective in COVID-19 pneumonia, the use of corticosteroids in other infectious pneumonia remains unclear with high variability in clinical practice. This variability in practice presents an opportunity to see how corticosteroid administration in pneumonia can be optimized and tailored to patient specific characteristics. The objective of this study is to adapt advanced statistical and machine learning methods to already available robust observational data from the electronic health record to identify predictors of clinical deterioration and to develop an individualized treatment rule for steroid use in patients with community acquired pneumonia. This will be accomplished through three specific aims: 1) To develop and validate a machine learning prediction tool for in-hospital disease progression, 2) To develop and test an individualized treatment rule (ITR) for steroid use, and 3) To conduct a single center feasibility clinical trial comparing ITR and biomarker guided corticosteroid use and dosing to usual care in patients with community acquired pneumonia. We hypothesize that: 1) a combination of demographics, physiological parameters, clinical and laboratory data will be accurate in predicting risk of in-hospital disease progression and identifying steroid-responsive patients in whom benefit from adjunct corticosteroid treatment outweighs potential harm, and 2) ITR and biomarker-guided corticosteroid use, and dosing will be feasible. This career development award will provide important preliminary data for future larger clinical trials focused on optimizing corticosteroid use in pneumonia while training a junior investigator in the essential skills needed to become an independent researcher.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "14946", "attributes": { "award_id": "5R01HL169760-02", "title": "Bispecific immunotherapeutic delivery system for lung diseases", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Heart Lung and Blood Institute (NHLBI)" ], "program_reference_codes": [], "program_officials": [ { "id": 22589, "first_name": "CHRISTIAN RENE", "last_name": "Gomez", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-08-01", "end_date": "2027-05-31", "award_amount": 905828, "principal_investigator": { "id": 28090, "first_name": "Jan Eugeniusz", "last_name": "Schnitzer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2076, "ror": "", "name": "PROTEOGENOMICS RESEARCH INSTIT/SYS/ MED", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Modern medicine has created precision drugs blocking a single therapeutic target like TGF-β with high affinity and specificity. Yet treating lung diseases remains challenging in part because lung microvascular endothelium represents a key restrictive barrier to effective drug delivery. Current systemic therapeutics rely solely on convection and diffusion to extravasate passively into the tissue interstitium where disease targets and cells can readily be reached and directly treated. The goal of this research proposal is to design, develop and test a novel drug delivery system for immunotherapeutics that overcomes this key barrier by targeting caveolae to facilitate active and specific transcytosis into lungs after intravenous injection. The ideal is to deliver the entire therapeutic dose inside the lung tissue with all other tissues minimally exposed. We attempt to approach this ideal by achieving robust transendothelial pumping precisely into lung tissue to comprehensively block the therapeutic target TGF-β, which regulates inflammation and remodeling in diseased tissues. Because TGF-β also exerts various homeostatic effects in many organs, caution is necessary when systemic targeting of its function is attempted. Precision lung targeting proposed here will maximize efficacy and therapeutic indices by minimizing dosages, eliminating toxicities, and reducing cost of treatment. To that end, we have genetically engineered the first “dual precision” immunotherapeutics, namely bispecific antibodies in quad format with one arm pair mediating precise binding/delivery to and penetration of lung tissue via caveolae pumping and the other pair constituting the precision therapeutic modality that blocks TGF-β effector function. Active transendothelial delivery improved precision lung targeting by 100-fold over standard passive transport. Delivering most of the injected dose into lungs within 1 hour enhanced therapeutic potency by >1000-fold in a rat pneumonitis model. Now our goal is to expand this promising preliminary work and further improve and rigorously test this drug delivery system to treat key lung diseases at distinct stages ranging from early acute inflammation to chronic and progressive fibrosis. We will optimize lung targeting of our dual precision immunotherapeutics and study their specific lung delivery, penetration, accumulation, localization, and therapeutic impact in rats using multiple imaging techniques (SPECT-CT, IVM, EM, and IHC). Therapeutic effects will be assessed in a rat bleomycin model that reproduces pathological hallmarks of many fatal human diseases including ALI, ARDS, COVID, pneumonias, and fibrosis. Our specific aims are: 1) to engineer and evaluate distinct caveolae-targeted antibody constructs for precision active delivery into normal lung tissue, 2) to quantify targeting and optimize delivery of bispecific immunotherapeutics in lung disease, 3) to test efficacy of bispecific immunotherapeutics to ameliorate lung disease and block TGF-β pathways. This work sets a foundation for caveolae-targeted therapies and could begin a paradigm shift from passive to active drug delivery for many diseases.", "keywords": [ "Acute", "Acute Respiratory Distress Syndrome", "Address", "Affect", "Affinity", "Animals", "Antibodies", "Antiinflammatory Effect", "Automobile Driving", "Binding", "Biodistribution", "Biological Markers", "Biological Products", "Bispecific Antibodies", "Bleomycin", "Blood Vessels", "COVID-19", "Cause of Death", "Caveolae", "Cells", "Cessation of life", "Chimeric Proteins", "Chronic", "Clinical", "Convection", "Cytokine Signaling", "Data", "Diffusion", "Disease", "Dose", "Drug Delivery Systems", "Drug Targeting", "Endothelial Cells", "Endothelium", "Engineering", "Exhibits", "Extravasation", "Fibrosis", "Foundations", "Genetic Engineering", "Goals", "Histopathology", "Hour", "Image", "Imaging Techniques", "Immunotherapeutic agent", "In Vitro", "Inflammation", "Inflammatory", "Intravenous", "Lung", "Lung Diseases", "Magic", "Mediating", "Modality", "Modeling", "Modern Medicine", "Organ", "Pathologic", "Pathology", "Pathway interactions", "Penetration", "Pharmaceutical Preparations", "Phosphorylation", "Physiology", "Pneumonia", "Pre-Clinical Model", "Precision therapeutics", "Preclinical Testing", "Property", "Pulmonary Inflammation", "Pulmonary Pathology", "Pump", "Rattus", "Research Proposals", "Rodent", "Signal Transduction", "Site", "Specificity", "Structure of parenchyma of lung", "System", "Testing", "Therapeutic", "Therapeutic Effect", "Therapeutic Index", "Time", "Tissues", "Toxic effect", "Transforming Growth Factor beta", "Treatment Cost", "Treatment Efficacy", "Vascular Endothelial Cell", "Vascular Endothelium", "Work", "Workplace", "arm", "comparative", "design", "dosage", "drug testing", "efficacy testing", "expectation", "human disease", "improved", "intravenous injection", "novel", "novel therapeutics", "passive transport", "pneumonitis and fibrosis", "precision drugs", "prevent", "prophylactic", "protein expression", "prototype", "research clinical testing", "response", "single photon emission computed tomography", "targeted treatment", "therapeutic target", "therapy outcome", "transcytosis", "uptake" ], "approved": true } }, { "type": "Grant", "id": "15123", "attributes": { "award_id": "2437982", "title": "EAGER: Collaborative Research: Fostering Collective Rationality Among Self-Interested Agents to Improve Design and Efficiency of Mixed Autonomy Networks and Infrastructure Systems", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "CIS-Civil Infrastructure Syst" ], "program_reference_codes": [], "program_officials": [ { "id": 2042, "first_name": "Siqian", "last_name": "Shen", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 169, "ror": "", "name": "Regents of the University of Michigan - Ann Arbor", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true } ] } ], "start_date": "2024-09-01", "end_date": null, "award_amount": 154063, "principal_investigator": { "id": 31681, "first_name": "Jia", "last_name": "Li", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 306, "ror": "https://ror.org/05dk0ce17", "name": "Washington State University", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "This EArly-Concept Grants for Exploratory Research (EAGER) project will investigate the emergence, mechanisms, and applications of collective rationality (CR) among self-interested agents in the design of mixed autonomy networks and infrastructure systems. In many natural and engineering systems, various collective phenomena, such as spontaneous cooperation, spatial segregation, and behavior evolution and formation of social norms, can emerge at system level when the decisions and maneuvers of self-interested agents interlace with each other. Strategic agent behaviors play a key role in this process. This observation suggests that one may obtain a system with desired properties by carefully designing behaviors of its agents. The research will explore this idea and put forward the concept of “collective rationality” of mixed traffic towards with the intent of explaining the formation of cooperation among self-interested driving agents in mixed autonomy transportation systems, to reduce travel cost, uncertainties, fuel emission, as well as to enhance equity among all road users. Broader applications include autonomous vehicle behavior design, emergency evacuation, and mitigation of pandemic spread. The research will be further disseminated through curriculum design, K-12 education, and collaboration with practitioners, local government, and industry partners. <br/><br/>This research project will explore and rigorously define the concept of collective rationality in mixed traffic and its application in designing strategic behaviors of autonomous driving agents in mixed autonomy environments. The core research hypothesis is that collective rationality can emerge in broad scenarios even if the involved agents are self-interested. Game theory and reinforcement learning will be leveraged to verify this hypothesis theoretically and computationally. To establish theoretical models of collective rationality in mixed traffic, two classes of models with different levels of agent behavior details will be developed, respectively focusing on the one-shot interaction of n-class driving agents, and dynamic inter- and intra-class interactions and an analytical Fokker-Planck approximation to the corresponding evolution dynamics. To develop frameworks for collective rationality-informed autonomous vehicle behavior design, researchers will consider two autonomous vehicle behavior design frameworks using reinforcement learning, which incorporate collective rationality in reward design and employ a bi-level pricing structure to equitably fine-tune the benefit of cooperation among agents. The research team will also expand and explore the CR concept for other application contexts, such disaster evacuations.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15096", "attributes": { "award_id": "2403380", "title": "Collaborative Research: SHF: Medium: SCIOPT: Toward Certifiable Compression-Aware SciML Systems", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Software & Hardware Foundation" ], "program_reference_codes": [], "program_officials": [ { "id": 2785, "first_name": "Almadena", "last_name": "Chtchelkanova", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 272992, "principal_investigator": { "id": 31636, "first_name": "Martin", "last_name": "Burtscher", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 204, "ror": "", "name": "Texas State University - San Marcos", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The future of science-enabled discoveries critically relies on the speed of high-performance simulations conducted at large scales and high resolutions. Unfortunately, lacking such performance and scale, current approaches cannot keep up with the backlog of problems in areas of paramount societal consequence, such as climate science and the spread of pandemics. A principal reason for these shortfalls is the rising cost of moving huge amounts of simulation data between supercomputer memories and processors – a cost that increasingly dwarfs the time spent in actual computations. Thus, developing techniques to reduce the volume of data exchanged without sacrificing accuracy is key to future progress in computation-enabled research. Such data reduction is even more important in the emerging area of Scientific Machine Learning (SciML), where simulations are assisted by artificial intelligence (AI) based surrogate models, an area where the data exchange needs are often much higher. The investigators’ expertise in scientific machine learning, data compression, compilers, and program correctness will be central in our collaboration to help SciOPT achieve its goal of fast and reliable AI-assisted scientific simulations. The impact of this project will be to establish new technologies that reduce data volume without sacrificing accuracy in both high-performance computing and the emerging area of SciML. These technologies, in turn, translate directly into societal benefits such as improved healthcare and safer environments. The project will broaden participation in this area through undergraduate research plans that reach out to students from groups underrepresented in computing.<br/><br/>This research project, entitled SciOPT, will principally rely on data compression to reduce the amount of data moved: simulation data will be compressed before transmission and decoded upon reception before applying computations. The investigators will also pursue the potentially even more impactful approach of compressing the data and applying computations directly on the compressed data. SciOPT will evaluate both of these approaches in the context of challenging SciML applications that are currently bottlenecked by data exchanges. To ensure higher degrees of automation and productivity, SciOPT will develop efficient compiler-based methods to manage compressed data layout and locality. Moreover, it will automatically generate high-speed compression algorithms that are tailored to the data. To ensure the veracity of the computational results produced by these compressed-data simulations, SciOPT will include rigorous correctness-checking methods at multiple stages to guard the overall simulation workflows.<br/><br/>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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15020", "attributes": { "award_id": "5R01CA277738-02", "title": "A dyadic exercise approach to prevent declines in physical and mental health in couples during radiation treatment for cancer: a hybrid type I efficacy-implementation trial", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Cancer Institute (NCI)" ], "program_reference_codes": [], "program_officials": [ { "id": 27622, "first_name": "AMANDA MARIE", "last_name": "Acevedo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-07-01", "end_date": "2028-06-30", "award_amount": 525495, "principal_investigator": { "id": 27623, "first_name": "KERRI M", "last_name": "WINTERS-STONE", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 765, "ror": "https://ror.org/009avj582", "name": "Oregon Health & Science University", "address": "", "city": "", "state": "OR", "zip": "", "country": "United States", "approved": true }, "abstract": "Breast and prostate cancer are two of the most common and survivable cancers and most survivors of these cancers will be married when diagnosed. Compared to couples that aren't facing a chronic illness, both cancer survivors and their spouses suffer from poorer physical and mental health that leads higher morbidity and mortality. Since couples experience and navigate an illness together their health becomes intertwined, thus programs aimed at one member of the dyad ignore the interdependent nature of the couple. Exercise improves quality of life among cancer survivors; but, we were the first to adapt exercise to be a partnered activity that amplifies the dose of exercise and builds teamwork to improve dyadic outcomes (i.e., physical and mental health of patients and partners). We developed and piloted Exercising Together in prostate cancer survivors and their spouses long after his diagnosis. The pilot showed that six months of partnered exercise could improve physical and mental health in both partners as well as their intimate relationship. We believe the program would be most effective at mitigating the impact of newly diagnosed cancer and treatment on the physical and mental health of each partner if implemented much earlier in the point of care for patients. We have preliminary data on 10 couples who participated in an adapted version of Exercising Together during his radiation treatment for prostate cancer (6-8 weeks). The adapted program is much shorter (8 v. 24 wks.) than the original and is more focused on developing teamwork as a mechanism to amplify the benefits of exercise on dyadic outcomes. All couples completed the program and improved physical and mental health and their level of communication; however, we had no control group so we cannot be certain if the program is efficacious or not nor how long lasting the effects of the program might be. We now propose a Type I hybrid effectiveness-implementation trial of Exercising Together adapted for the radiation oncology setting. This design allows us to formally test the efficacy of a clinic- based version of Exercising Together using a randomized controlled design, a larger sample, a broader set of outcomes, and a follow-up period. We will also examine putative dyadic mechanisms to explain how our intervention improves dyadic health. At the same time, we will gather critical information from multiple stakeholders to inform future implementation approaches to integrate Exercising Together into the care plan for cancer patients. We propose a randomized controlled Phase II trial in 200 couples who will be randomly assigned to participate in an 8-week program of Exercising Together at the start of his/her radiation therapy or to a usual care control group that receives standard exercise guidance and receipt of a video of the couples program at the end of participation. Couples are tested at baseline, post-intervention (2 mos.), and 4- and 6-mos. follow up. Based on adaptations in other trials developed during COVID19, exercise training and assessments will be delivered through remote technology, which allows us to better diversity the sample and widen the scalability of the program.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "14999", "attributes": { "award_id": "5R21AI168482-02", "title": "Leveraging cytoplasmic transcription to develop self-amplifying DNA vaccines", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 6908, "first_name": "JENNIFER L.", "last_name": "Gordon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-07-07", "end_date": "2025-06-30", "award_amount": 192500, "principal_investigator": { "id": 27788, "first_name": "Jean M", "last_name": "Peccoud", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 323, "ror": "https://ror.org/03k1gpj17", "name": "Colorado State University", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "The unprecedented speed of COVID-19 vaccine development has demonstrated the value of vaccine platforms. In particular, mRNA vaccines proved surprisingly successful at eliciting a strong immune response while having a remarkable safety profile considering the novelty of this system. Just as important are the remarkable speed and scale of their production. Despite their spectacular success, mRNA vaccines suffer from major limitations. mRNA is an inherently unstable molecule. One consequence of this property is that mRNA vaccines need to be stored in freezers and their shelf-life is measured in hours after they have been thawed. These storage requirements are considered difficult to ensure even in countries with developed healthcare systems and are extremely problematic in many other parts of the world. The second limitation of mRNA vaccines is that their production is unlike any other biomanufacturing process. As a result, it is limited by a critical lack of infrastructure and expertise. The COVID-19 mRNA vaccines provided an incentive to imagine the next generation of nucleic acid vaccines that would be easier to manufacture at scale and distribute to healthcare systems throughout the world. This proposal hypothesizes that a DNA-based vaccine could enable the design and deployment of safe and effective vaccines that would be faster, easier, and cheaper to manufacture at scale. The production of clinical- grade DNA relies on biomanufacturing processes that are some of the simplest, fastest, and most inexpensive processes in the industry. However, DNA vaccines have failed to elicit a protective immune response so far because only a small fraction of the DNA molecules entering a cell are transported to the nucleus where they can be transcribed. In this R21, researchers will test the feasibility of developing a new generation of DNA vaccines by applying methods from synthetic biology to introduce genetic circuits allowing the expression of the antigen to take place in the cytoplasm. Self-amplifying DNA vaccines will include several genes of viral origins that will transcribe the antigenic sequences from plasmids located in the cell cytoplasm. In addition, these vectors will include additional enzymes to modify mRNA molecules to increase their stability and translation efficiency. By introducing several levels of amplification, the expression of the antigen is expected to be several orders of magnitude higher than what can be achieved with traditional DNA vaccines. The project will proceed through eight iterations of the design-build-test-learn cycle to rationally improve vaccine designs using gene expression data in cell culture. If successful, future studies will test the platform compatibility with a broad range of antigens, optimize the delivery of DNA-based vaccines, and analyze the safety and efficacy of candidate vaccines in animal studies.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15137", "attributes": { "award_id": "2428786", "title": "SupplyChainDCL: Enhancing Resilience, Optimizing Efficiency, and Mitigating Disruption Risks in Supply Chain Networks", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "OE Operations Engineering" ], "program_reference_codes": [], "program_officials": [ { "id": 2155, "first_name": "Georgia-Ann", "last_name": "Klutke", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-01", "end_date": null, "award_amount": 414909, "principal_investigator": { "id": 31700, "first_name": "Agostino", "last_name": "Capponi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 196, "ror": "https://ror.org/00hj8s172", "name": "Columbia University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "This award will enhance national welfare by providing a systematic framework to analyze the efficiency and resilience of demand and supply networks. These systems are challenging to analyze due to the complex interdependencies among firms in the network, which adjust their decision-making processes collectively and strategically in response to both idiosyncratic and systemic shocks. The project will develop novel tools and measures for assessing the impact of risk mitigation plans against supply and demand shocks in the network. The framework will elucidate the mechanisms by which supply shortages of specific goods and services during periods of distress can lead to price spikes and increase the fragility of the supply chain network. For instance, the project will help understand how a global semiconductor shortage can cause significant price surges in the U.S. market for second-hand cars, or how an unexpected surge in demand for hand sanitizers during the pandemic led to widespread supply shortages, impacting the industry and its related sectors. This award will also provide research opportunities for graduate students, equipping them with the tools, background, and expertise to advance research in this area.<br/><br/>The project will develop a dynamic decision-making framework to quantify the trade-offs between efficiency and resilience within supply chain networks and provide an empirical analysis of supply chain fragility. This analysis aims to assess how diversification strategies can mitigate risks associated with supply chain vulnerabilities. The research will leverage, extend, and specialize tools from dynamic games, risk management, optimization, and network theory to incorporate the incentives of firms facing information and technological constraints in establishing cost-effective demand-supply relationships and managing risks against supply and demand shocks. The framework will explicitly model both preventive actions taken by firms to hedge against potential future shocks and corrective actions implemented in response to significant disruptions. The project will lead to the development of game-theoretical algorithms for determining optimal firms' levels of investment in production capacity and for final good producers to enter into competitive risk-sharing agreements with intermediate good producers to meet unanticipated demand and hedge against production shocks. The resulting analysis will quantify the conditions under which market-based supply networks are inherently fragile, particularly when these networks prioritize routine operational efficiency over systemic robustness. Additionally, the project will explore whether public institutions can reduce inefficiencies and facilitate outcomes superior to those achieved through decentralized market operations by implementing data-driven control policies.<br/><br/>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.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 3, "pages": 1392, "count": 13920 } } }