Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1405&sort=-principal_investigator
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Topology is an area of mathematics with applications to many fields, from the knotting of proteins and DNA to the structure of our universe. This project concerns the topology of three- and four-dimensional spaces, or manifolds, as well as the knotted objects they contain. To classify manifolds, one studies relationships between them; one such relationship is that of a branched cover. The PI will develop combinatorial and computational tools to study branched covers of three- and four-dimensional manifolds. Using these tools to make cutting-edge problems in the field accessible, the PI will design subprojects for undergraduate and post-baccalaureate students with a wide variety of mathematical backgrounds and professional goals. The educational component of this project expands and supports the PI’s current work in programs increasing access for women and underrepresented groups in the mathematical sciences, and addresses COVID impacts on the mathematical pipeline by providing support for students whose studies were disrupted. Crucial to this is the PI’s continued leadership role in the Center for Women in Mathematics Post-Baccalaureate Program at Smith College, as well as organization of conferences for undergraduate student researchers. These activities broaden participation in the field by preparing students for graduate study in the mathematical sciences. The project explores the geography and botany problems for branched covers of three- and four-manifolds, particularly when equipped with additional geometric structure. The geography problem asks which manifolds arise as branched covers of a given manifold, subject to constraints on the degree of the cover or complexity of the branching set; the botany problem asks for a classification of branched covering maps between a given pair of manifolds. A key strategy is the development of combinatorial and diagrammatic methods for computing invariants of knots and surfaces derived from branched covers of three- and four-manifolds, respectively. Applications will include resolution of open problems in several active areas of the field, including trisections of four-manifolds, knot concordance, and contact topology.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": "488", "attributes": { "award_id": "2149964", "title": "PlantSynBio/TR-Tech-PGR: Targeted Integration of User-Defined DNA in Plants", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [], "program_officials": [ { "id": 985, "first_name": "Diane Jofuku", "last_name": "Okamuro", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-01", "end_date": "2025-02-28", "award_amount": 2300000, "principal_investigator": { "id": 988, "first_name": "R Keith", "last_name": "Slotkin", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 261, "ror": "https://ror.org/000cyem11", "name": "Donald Danforth Plant Science Center", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 986, "first_name": "Xuemin", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 987, "first_name": "Veena", "last_name": "Veena", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 261, "ror": "https://ror.org/000cyem11", "name": "Donald Danforth Plant Science Center", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true }, "abstract": "The placement of new DNA into plant genomes is both inefficient and imprecise. This inefficiency hampers all approaches to develop new and enhance existing agricultural traits in crops. New technology such as CRISPR/Cas9 nucleases act like molecular “scissors” to cut DNA at specific locations in the genome, improving the precision of plant genome engineering. However, after cutting the DNA, the precise addition of new DNA has remained a grand challenge. This project identifies the missing counterpart to the molecular “scissors”--the molecular “glue” needed to precisely add DNA at sites cleaved by Cas9. Project aims include developing the precise addition of DNA into crop genomes, as well as test how this new genome engineering technology can be used to quickly make a single gene or entire plant stress-responsive. This project will also test key rules governing this system of targeted DNA addition, such as the size and type of the DNA that can be introduced. This enabling technology has the potential to transform the engineering of plant genomes for both research and industry. Lastly, this project will excite middle school students in STEM careers and educate undergraduate students that have not yet had other research experience opportunities.The overarching goal of this award is the production of a usable and accessible toolkit of technology that enables future plant synthetic biology approaches to introduce new abilities into key agricultural plants. First, this project aims to target the integration of new DNA into the genome of the discovery plant Arabidopsis thaliana and crop plants maize and soybean. Second, this project aims to explore the types and limitations of the synthetic cargo DNA that can be delivered to specific regions of the genome. These include the delivery of engineered expression cassettes, inserting epitope tags in-frame with protein-coding genes, and adding new enhancer elements upstream of gene coding regions. Third, this project aims to demonstrate how this system of targeted DNA integration can be used to rewire the transcriptional response and engineer the plant phenotype to overcome environmental stress on either a single gene level or for the entire genome. Lastly, this project aims to adapt the “Anyone Can Be A Scientist” outreach program to the COVID and post-COVID eras and develop a course-based undergraduate research experience based on one of the scientific aims to provide students with their first hands-on authentic biological research experience. All project outcomes including genetic and molecular resources will be made available upon request and through long-term repositories.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": "487", "attributes": { "award_id": "2143866", "title": "CAREER: Complexity From Simplicity: Multi-scale Computational Deciphering of the Viral Life Cycle", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [], "program_officials": [ { "id": 983, "first_name": "Bianca", "last_name": "Garner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-01-01", "end_date": "2026-12-31", "award_amount": 290084, "principal_investigator": { "id": 984, "first_name": "Elsje", "last_name": "Pienaar", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 252, "ror": "", "name": "Purdue University", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 252, "ror": "", "name": "Purdue University", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117- 2).Viruses like Ebola and SARS-CoV-2 spread and cause disease through the combined action of multiple viral proteins working together. Limiting viral reproduction in patients will require a holistic view of how all of these proteins work together in one infected cell and across many cells in our bodies. This project will use a combination of experimental data and computer simulations to understand and predict the complex interactions that drive Ebola virus infection. Such an understanding will allow the identification of any weak points in this protein network that can be targeted with new drugs. The project will also develop new research tools to advance the use of computer simulations to accelerate viral research. The educational objectives of the project will complement the research objectives by training the next generation of scientists (from high school to graduate student level) to readily combine computational and experimental research methods. Together, the research and educational objectives will enable a comprehensive understanding of Ebola virus biology, integrated computational/experimental research tools, and a scientific workforce that can take advantage of computational technology to advance public health.The overall objective of this proposal is to identify interconnected subcellular and inter-cellular mechanisms that drive viral replication and spread within a host, using Ebola virus as a model system. Mechanistic computational models are powerful tools that generate virtual versions of real biological systems, to enable analysis of complex systems-level dynamics. In this project, mechanistic computational models, closely integrated with experimental data, will be used to identify key mechanisms in Ebola virus reproduction. New multi-dimensional analyses will be developed to elucidate coupled mechanisms, and computational predictions will be tested experimentally. Research objectives will quantify: 1) the impact of individual protein dynamics on viral production at the subcellular level using systems of ordinary differential equations; 2) the spatio-temporal impact of inter-cellular processes on cell-to-cell viral spread and proliferation using agent-based models; and 3) the combined impact of subcellular and inter-cellular mechanisms on viral replication across scales using multi-scale simulations. These simulations will be calibrated to and validated against experimental data from the Ebola virus minigenome system that allows careful isolation of individual viral proteins and steps in the viral life-cycle (e.g. transcription and assembly). The research objectives will support educational objectives at the intersection of biology and computation. The educational objectives will: integrate quantitative methods into existing biology curricula in an accessible and sustainable way; and advance interdisciplinary training in undergraduate biomedical engineering students. These objectives will be accomplished through a multi-tiered educational approach that connects students and teachers within, and between, high-school, undergraduate and graduate levels. The project will develop: 1) quantitative learning modules for biology courses; 2) international interdisciplinary undergraduate courses; and 3) interdisciplinary research training for graduate students.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": "486", "attributes": { "award_id": "2143242", "title": "CAREER: New Statistical Approaches for Studying Evolutionary Processes: Inference, Attribution and Computation", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)" ], "program_reference_codes": [], "program_officials": [ { "id": 981, "first_name": "Yulia", "last_name": "Gel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-02-01", "end_date": "2027-01-31", "award_amount": 233818, "principal_investigator": { "id": 982, "first_name": "Julia", "last_name": "Palacios", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 266, "ror": "https://ror.org/00f54p054", "name": "Stanford University", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 266, "ror": "https://ror.org/00f54p054", "name": "Stanford University", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Statistical inference from a sample of molecular sequences such as DNA poses a series of fundamental challenges. These challenges include complex modeling of the sample's ancestry and past evolutionary history, large and noisy data. The ongoing large-scale increase of genetic data has led to a situation in which current methods are not applicable to the amount of data available and researchers are forced to down-sample available data or to infer parameters from insufficient summary statistics. This research project will address the need for optimally designed coalescent modeling for inference from modern molecular data. The coalescent is a probability model on genealogies, that is, the trees which represent the ancestry of the sample. Coalescent models are used for inferring parameters of scientific relevance such as effective population size, migration patterns and selection. The research goals of this project are to expand the class of coalescent models and to design novel efficient statistical algorithms, allowing us to address many practical problems that advance science. Furthermore, the outcomes of the projects will foster the development of new statistical theory and tractable methods that contribute to biological solutions. This project also outlines an active plan for a broad range of educational and outreach activities that will broaden participation in statistical sciences and will enhance more inclusive atmosphere in science. The undergraduate and graduate students involved into the project will be offered a unique opportunity for interdisciplinary hands-on research training at the interface of statistical sciences and biology, allowing them to contribute to progress in evolutionary biology, molecular biology, population genetics, phylogenetics, cancer genomics, probabilistic modeling, statistical inference, and related fields. The PI will actively participate in multiple outreach activities such as the Stanford undergraduate summer research program, which will allow for recruiting more diverse pool of future data scientists and for fostering more inclusive climate in science. The research findings of the project will serve as foundation for new program in statistical genetics and will be integrated into undergraduate and graduate courses. Concretely, this project will expand the class of coalescent models and provide a suite of new algorithmic and statistical approaches by exploiting a metric notion of genealogies, lumpability of Markov chains and divide-and-conquer strategies. The specific aims include (1) develop coalescent models to incorporate various sampling schemes and biological processes such as dynamic population structures, recombination and strong selection; (2) develop a metric framework for coalescent theory and applications; (3) develop scalable strategies for Bayesian inference of evolutionary parameters and (4) implement, validate and analyze molecular sequences of infectious disease such as SARS-CoV-2, ancient and modern human DNA samples and cancer single cell variation. Furthermore, the project will actively contribute to broadening participation in statistical sciences at multiple fronts, from team-based interdisciplinary research training to community outreach.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": "485", "attributes": { "award_id": "2146260", "title": "CAREER: Quantifying Adaptation and Recombination in Pathogen Populations", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)" ], "program_reference_codes": [], "program_officials": [ { "id": 979, "first_name": "Krastan", "last_name": "Blagoev", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-05-15", "end_date": "2027-04-30", "award_amount": 313676, "principal_investigator": { "id": 980, "first_name": "Daniel B", "last_name": "Weissman", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 265, "ror": "https://ror.org/03czfpz43", "name": "Emory University", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 265, "ror": "https://ror.org/03czfpz43", "name": "Emory University", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).To usefully anticipate the evolution of pathogenic viruses and bacteria, quantitative theories that can connect it to the genetic sequencing data are needed. In this project, the PI will use mathematical models, computer simulations, and analysis of genetic data to determine the quantitative rules of pathogen evolution. The PI will focus on three main research objectives. First, the PI will understand how adaptations driven by large-effect mutations, such as is typical for immune escape or antibiotic resistance, interfere with pathogens’ ability to simultaneously adapt in other ways, including via small-effect mutations and combinations of mutations. Second, the PI will find how interactions among mutations could have led to the sudden emergence of the Alpha, Beta, and Gamma SARS-CoV-2 Variants of Concern. In addition whether they likely evolved as the virus was passed from person to person, or within individual immuno-compromised people over the course of long-term infections will be studied. Third, the PI will find quantitative rules for how often bacteria within a species exchange genes with each other, using models together with a database of tens of thousands of genomes from the skin bacterium Staphylococcus aureus, including many methicillin-resistant (MRSA) samples. Quantitative theories for pathogen evolution will have substantial benefits for human health. The specific work in the proposed project will help us predict how much we need to limit the spread of the SARS-CoV-2 pandemic to avoid the potential emergence of Alpha-like adaptations on the Delta background, or even more complex adaptations. It will also help us predict which bacterial strains are likely to exchange genes, including those responsible for virulence and antibiotic resistance. The proposed middle-school science club will help encourage public interest in science, diversify the pool of future scientists, and train undergraduate and graduate students in scientific outreach and teaching The PI will also start a science enrichment program at a local middle school for refugee girls, leading hands-on lessons that will show that evolution follows regular patterns like all other natural phenomena.In this project the PI will study the dynamics of adaptation and develop tools to be able to predict it from a set of measurements, primarily genetic sequencing. In the proposed work, the PI will find new ways to use sequencing data to infer potential genetic interactions, modes of evolution, and patterns of gene transfer. He will also find how much other adaptation one should infer to have been deferred by interference when rapid adaptation is observed in response to strong selection. These theories will be applicable to all microbial life, not only pathogens, and some aspects will be useful for understanding eukaryotic adaptation.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": "484", "attributes": { "award_id": "2145236", "title": "CAREER: Advancing Combinatorial Optimization Accelerataors with Compute in Memory Design Approach", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 977, "first_name": "Sankar", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-01-15", "end_date": "2026-12-31", "award_amount": 103513, "principal_investigator": { "id": 978, "first_name": "Jaydeep P", "last_name": "Kulkarni", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Combinatorial optimization problems find many real-world social and industrial data intensive compute applications. Examples include optimization of mRNA sequences for COVID-19 vaccines, semiconductor supply-chains, and financial index tracking, to name a few. Such optimization problems are computationally intensive, and a brute-force search method for finding the optimum solution becomes untenable as the problem size increases. An efficient way to solve an optimization problem is to let nature perform the exhaustive search in the physical world by mapping the problem onto an Ising model. The Ising model describes spin dynamics in a ferromagnet, wherein spins naturally orient to achieve the lowest energy state, representing the optimal solution to a given optimization problem. Performing such Ising computations using conventional methods requires numerous compute iterations. This results in frequent off-chip memory accesses and incur significant energy overheads. The goal of this project is to advance the development of energy-efficient as well as cost-efficient combinatorial optimization hardware accelerators to be integrated in modern integrated circuits for solving critical optimization problems as mentioned above. The research results from this project will be disseminated to the students in the form of course design case-studies. Reciprocally, some of the course projects will be aligned with Ising accelerator designs enabling tight research-teaching integration. The project also aims to engage with underrepresented and minority students in the form of undergraduate and graduate student mentoring and research experiences. This project proposes a unique analog compute-within-memory design approach performing the Ising computations by reconfiguring existing memory array circuitry. In contrast to prior near-memory, digital-arithmetic computing approaches, this compute-in-memory approach performs Ising Hamiltonian computations in the analog domain within a memory array with minimal circuit changes. It maps Hamiltonian computations on to available memory wordline and bitline circuitry, which has remained a key technical challenge so far. In addition, this project will investigate the ways to seamlessly map large Ising models across multiple memory banks, thereby scaling up the Ising spin count significantly. The project aims to demonstrate compute-in-memory Ising accelerator silicon prototypes, perform design-space exploration, and quantify the benefits over prior approaches. Furthermore, the project will explore the high-density memory needs for future complex combinatorial-optimization accelerators utilizing large-scale Ising models. This project will systematically investigate device-technology circuit co-design aspects of emerging monolithically integrated 3D memory technologies. This can potentially leapfrog the benefits of compute-in-memory based Ising accelerators for solving extreme-scale optimization problems. The tightly-integrated research, education, and outreach plan aims to establish a close industry relationship, integrate this research with a graduate course, deliver online courses, expand K-12 outreach, and train students in the area of memory devices, circuit designs, and combinatorial-optimization algorithms in service of furthering the creation of the STEM workforce.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": "483", "attributes": { "award_id": "2138539", "title": "ERI: Analyzing the Impact of Outdoor Water-Use Restrictions and the COVID-19 Pandemic on Water Consumption in Massachusetts", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 975, "first_name": "Yueyue", "last_name": "Fan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-01-15", "end_date": "2023-12-31", "award_amount": 114276, "principal_investigator": { "id": 976, "first_name": "Katherine", "last_name": "Schlef", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 264, "ror": "https://ror.org/007cnf143", "name": "Western New England University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 264, "ror": "https://ror.org/007cnf143", "name": "Western New England University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This Engineering Research Initiation (ERI) project will provide critical understanding of the impacts of outdoor water-use restrictions on water consumption in Massachusetts. Despite relatively abundant rainfall and regulations on water withdrawal, Massachusetts’ water supplies are stressed. Consequently, public water suppliers are often required to implement outdoor water-use restrictions by the regulatory authority. These restrictions are complex, non-uniform, and contentious, and the COVID-19 pandemic is yet another confounding factor. This project, the first of its kind to be implemented at a state-wide scale anywhere in the United States, will (a) provide new insights to facilitate integrated water resources management at the scale of major river basins, (b) provide comprehensive analysis to inform regulatory action, and (c) empower public water suppliers with improved understanding of the factors influencing restriction effectiveness. Furthermore, this project will identify the impact of the pandemic, thus providing knowledge about the resilience of Massachusetts’ water supply systems and revealing important lessons for ensuring water supply under future unforeseen conditions. Lastly, this project will provide meaningful research experiences for undergraduate and master students, including those in minority/underrepresented groups, resulting in valuable benefits such as increased independence and intrinsic motivation to learn.The project consists of developing a panel regression model, complemented by interviewing public supply water managers. The panel regression will be based on 11 years of monthly data across all Massachusetts’ public water suppliers, with water consumption as the dependent variable and drivers of consumption as the independent variables. The consumption drivers fall into three main categories: hydrometeorological conditions (e.g., precipitation, temperature, and drought status), consumer characteristics (e.g., income, household size, and political affiliation), and public water supplier management decisions (e.g., restriction severity and timing, promotion of water conservation, and pricing). Additional independent variables will be used to represent impervious or green area and to address the presence and severity of the pandemic. The interviews with water managers will be used to create storylines that guide the development and interpretation of the panel regression. The interview questions focus on demand and drought management and on how the pandemic has affected water consumption. The project will provide the first state-wide assessment of water-use restrictions to be performed anywhere in the United States, will generate insight on the effectiveness of both drought-based and permanent restrictions, and will identify the impacts of the pandemic on water consumption.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": "482", "attributes": { "award_id": "2153814", "title": "RAPID: Characterization of Aerosolized Droplet and Droplet Nuclei in Cough", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 971, "first_name": "Nora", "last_name": "Savage", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-01-15", "end_date": "2022-12-31", "award_amount": 199859, "principal_investigator": { "id": 974, "first_name": "Olusegun J", "last_name": "Ilegbusi", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 173, "ror": "", "name": "The University of Central Florida Board of Trustees", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 972, "first_name": "Jihua", "last_name": "Gou", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 973, "first_name": "Bari", "last_name": "Hoffman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 173, "ror": "", "name": "The University of Central Florida Board of Trustees", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "There is considerable interest in the behavior of cough-generated droplets in the environment due to evidence of host-to-host transmission of viruses through aerosolized droplets. Previous investigations have mostly focused on how such droplets interact with the external environment and not much has been explored on their behavior inside the body. Yet, the droplet behavior inside the airway largely determines their subsequent characteristics outside the body (such as size and dispersion), as well as their potential for retention inside the body to cause lung infection, pneumonia, aspiration, and mortality. It is also unknown whether people are at greater risk for pulmonary infection and pulmonary pneumonia if their cough is too weak to expel virus-laden droplets as may occur under some pre-existing conditions. The objective of this multidisciplinary research project is to combine computational modeling with experiments to fully understand the behavior of aerosolized cough droplets inside the human airway depending on the cough strength and assess the potential of virus-laden droplets to be retained in the airway or transmitted to the lungs. The research outcome will be clinically relevant to the development of technologies to minimize the spread of COVID-19, hospitalization, and death. The focus on droplet behavior inside the airway will be particularly relevant for elucidating the behavior of new COVID-19 strains which have been found to generate higher viral loads in the airway compared to the original strain, making the new strains much more contagious to others. The research will enable determination of how long these new variants reside in the airway, which will aid in the development of technologies that mitigate their potential transmission outside the body. An important component of the research is also the education of the next generation of scientists and engineers, especially those from under-represented groups by providing them an opportunity to work on a challenging multidisciplinary problem of public health significance.Since the advent of the COVID-19 pandemic, most studies have understandably focused on the interaction of virus-laden cough droplets with the ambient environment. Yet, the behavior of droplets inside the airway largely determines their subsequent characteristics outside the body (such as size and transmission distance), as well as their potential for retention inside the body to cause lung infection, pneumonia, aspiration, and mortality. The role of cough strength in the retention of droplets laden with the new viral strains inside the airway with potential to cause serial environmental transmission has also not been fully explored. The objective of this multidisciplinary research project is to integrate Computational Fluid Dynamics with experiments to fully characterize the behavior of aerosolized droplets and nanoparticles relative to cough strength inside the human upper airway. The experiments for model calibration and validation will utilize a realistic three-dimensional-printed upper airway structure produced with a novel volumetric printing process. Cough will be simulated in the structure with fluorescein solution atomized to produce seed droplets. Droplet sizes will be quantified using a blue-light filter and digital image processing of endoscope images. The research will: (a) Quantify small droplet and nanoparticle interaction with the airway, in subjects with and without standard facemask; (b) Quantify droplet characteristics (size distribution, residence time, trajectories) within the airway under normal and disordered cough functions; (c) Quantify aspiration capacity and delayed transmission potential of droplets relative to cough strength; and (d) Validate the computational models using the experimental data. By establishing the fundamental features of droplet and nanoparticle interaction with cough flow and the airway, this project will deliver the strategies for characterization of complex nanoparticle behavior under cough flow in particular and transient explosive flow condition in general. The project outcome will be clinically relevant in the development of technologies to minimize the spread of COVID-19, hospitalization, and death. The focus on particle behavior inside the airway will be particularly relevant to exploring the behavior of new COVID-19 strains which have been found to generate higher viral loads in the nasal and oral cavities compared to the original strain, making the new strains much more contagious to others. The model developed will enable quantification of the residence times of these new variants and explore intervention technologies to mitigate their potential for transmission outside the body or aspiration pneumonia and lung infection. As the longer-term impact of post-COVID patients becomes better understood, the droplet behavior relative to cough strength will be an important risk marker as the micro aspirations that retain in the lung tissue can result in lung infection, pneumonia, or death. The sequence of symptoms and other comorbidities occurring in post-COVID patients amplify the significance of the aspiration event being investigated. This research will also assist the development of respiratory intervention technologies to improve deficits of cough function in patients with pre-existing conditions such as post-stroke individuals, sedentary elderly or those who have undergone cancer related treatment. The education objective of the research will focus on educating the next generation of scientists and engineers, especially those from under-represented groups by providing them an opportunity to work on a challenging multidisciplinary problem of public health significance. The research findings will be integrated directly in two undergraduate courses and two graduate courses taught by the PIs.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": "481", "attributes": { "award_id": "2209814", "title": "EAGER: An AI-driven Paradigm for Collective and Collaborative Community Resilience in the COVID-19 Era and Beyond", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 969, "first_name": "Wei", "last_name": "Ding", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-11-01", "end_date": "2023-07-31", "award_amount": 283438, "principal_investigator": { "id": 970, "first_name": "Yanfang", "last_name": "Ye", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 171, "ror": "https://ror.org/00mkhxb43", "name": "University of Notre Dame", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "The coronavirus disease (COVID-19) pandemic has exposed a critical set of vulnerabilities that have impacted community resilience in responding to escalating societal, economic, and behavioral issues. Unfortunately, there are no established solutions or proven models for us to depend on to tackle the complex challenges with significant uncertainties and unknowns. This project engages novel disciplinary perspectives to help address the devastating effects caused by COVID-19, i.e., leveraging the extracted information of experiences, ideas and support from positive-energy communities who are successfully navigating threats that can be transformed and transferred into actionable information to assist vulnerable communities to cope, progress and move forward. More specifically, by advancing artificial intelligence (AI) innovations, the goal of this project is to design and develop an AI-driven paradigm for collective and collaborative community resilience in responses to a variety of crises and exposed vulnerabilities in the COVID-19 era and beyond. With additional validation, this research will provide foundation to assist the federal and state governments, corporations, societal leaders to develop and implement strategies that will guide local and regional communities, and the nation into a successful new normal future.This exploratory yet transformative high risk-high payoff work that involves radically different approaches will have three main research components. First, the research team will construct a novel attributed heterogeneous information network (AHIN) to comprehensively model the up-to-date multi-source pandemic related data for abstract representation. Second, to understand how users interact and how information are propagated within and cross-community in social media, the team will develop an innovative nonnegative matrix factorization regularized deep graph learning model for community detection in the AHIN by considering the heterogeneity of the network. Third, the team will propose an integrated adversarial disentangler to separate the distinct, informative factors of variations hidden in the milieu to learn post embeddings for emotion and topic analysis for community classification and framing, and thus to derive supportive and constructive information for community resilience improvement. The developed AI-driven paradigm in this project will provide in-depth insights and customized guidance that can help public health experts, social workers, law enforcement, economists, and policy makers in decision-making and also enable a conceptual framework for the development of resilient community engagement strategies in responses to a variety of crises created by COVID-19 and future natural or health-related disasters. The research will be beneficial to multidisciplinary areas, including data mining, machine learning, epidemiology, economics, social and behavioral sciences. The outcomes of this project will be made publicly accessible and broadly distributed. The project will integrate research with education through curriculum development, the participation of underrepresented groups, and student mentoring activities.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": "480", "attributes": { "award_id": "2211867", "title": "RAPID: Behavioral Drivers and Social Pathways in the Spread of the COVID-19 Omicron Variant", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [], "program_officials": [ { "id": 967, "first_name": "Jeffrey", "last_name": "Mantz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-01-01", "end_date": "2022-12-31", "award_amount": 20884, "principal_investigator": { "id": 968, "first_name": "Amy E", "last_name": "Stambach", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 263, "ror": "", "name": "University of Wisconsin-Madison", "address": "", "city": "", "state": "WI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 263, "ror": "", "name": "University of Wisconsin-Madison", "address": "", "city": "", "state": "WI", "zip": "", "country": "United States", "approved": true }, "abstract": "This project examines the behavioral drivers and real-time changes in behavior affecting the potential transmission of the Omicron variant off the SARS-COV-2 respiratory virus during critical moments of crowding, mingling, and interacting in close quarters. It aims specifically to understand the social complexities of ritual and travel that reduce or heighten the risk of COVID-19 infection. Data from the study contributes intellectually to high-priority biomedical research that seeks to advance public health interventions. This study will provide critical information for the development of public health programs and guidelines that address the risks communities face with the Omicron variant. The project also provides training for graduate and undergraduate students in methods of rigorous, scientific data collection and analysis.This study tests existing theoretical presumptions that people disregard state-mandated health regulations at moments when cultural, religious, and social rites of solidarity and obligations take priority. The emergence of the highly contagious Omicron variant during a peak travel season provides an opportunity to map transmission pathways in real time. This project triangulates ephemeral data regarding holiday travel and religious ceremonies with social network information and household demographics. Data from behavioral observations of religious ceremonies will yield information about social practices compared with state-mandated regulations of masking, vaccinating, and social distancing. Data regarding social networks will yield information about linked urban-rural migrant pathways for potential disease transmission. Data from interviews will record age, ethnicity, sex, socioeconomic status, marital status, family size, and will aid in mapping migratory patterns and social networks.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": 1405, "pages": 1424, "count": 14236 } } }