Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1392&sort=-keywords
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-keywords", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1442&sort=-keywords", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1393&sort=-keywords", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1391&sort=-keywords" }, "data": [ { "type": "Grant", "id": "9551", "attributes": { "award_id": "2207688", "title": "NSF/MCB-BSF: Revealing the steps and modulators of coronavirus fusion using single-molecule tools", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Cellular Dynamics and Function" ], "program_reference_codes": [], "program_officials": [ { "id": 3441, "first_name": "Richard", "last_name": "Cyr", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-07-01", "end_date": "2025-06-30", "award_amount": 900000, "principal_investigator": { "id": 3886, "first_name": "Susan", "last_name": "Daniel", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 279, "ror": "https://ror.org/05bnh6r87", "name": "Cornell University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 3884, "first_name": "Nicholas L", "last_name": "Abbott", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3885, "first_name": "Gary R", "last_name": "Whittaker", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 279, "ror": "https://ror.org/05bnh6r87", "name": "Cornell University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] } ], "awardee_organization": { "id": 279, "ror": "https://ror.org/05bnh6r87", "name": "Cornell University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Infection by many viruses commences with a membrane fusion event between the viral envelope and host cell membrane, which leads to the transfer of the viral genome into the host cell. Typically, this process is mediated by viral fusion proteins. In this project, the coronavirus spike protein is examined. Within spike, the fusion peptide initiates membrane fusion when it inserts into the host membrane. To study this process, single molecular tools and techniques will be developed and used to study the interaction between the fusion peptide with membrane surfaces that mimic different kinds of host cells. The goal is to understand the relationship between fusion peptide sequence and target membranes using complementary techniques that enable examination of this process across scales, from the fusion peptide to the whole virus. With a better understanding of the science behind the chemical features that modulate this critical interaction, new predictions for virus adaptation to new hosts can be made and exploited to block the process. The Broader Impacts of this project include the intrinsic merit of the work as useful information will be gained that can be leveraged for the design of novel antiviral drugs, and to identify chemical rules that inform predictions of host susceptibility to viral entry. Given that major fusion players are highly conserved across the CoV family, these studies will be directly applicable to all CoVs, including those yet to emerge. In addition, training opportunities for graduate students will be provided, along with outreach activities for high school students. \n\nFor coronavirus, entry into a host cell is mediated by a single glycoprotein protruding from its membrane envelope, called spike (S). A key determinant of the ability of coronavirus to spread is the interaction of S with its target host membrane. Within S, the region that directly interacts with the membrane is called the fusion peptide, FP. It is the physico-chemical interactions of the FP with the host membrane that anchors it, consequently enabling the necessary deformations of the membrane that leads to membrane fusion and the delivery of the viral genome into the cell. Thus, understanding chemical coupling of the FP with the host cell at the nanoscale will facilitate the development of strategies to limit those interactions to stop infection, but also to enable predicting the characteristics of emerging strains and the susceptible hosts. The objective of this project is to identify and measure the specific intermolecular interactions responsible for insertion of FP into membranes. Single molecule tools and techniques will be developed, expanded, and used to understand the fundamental chemical code between the host membrane and FP that modulate interactions that control the fusion process. The intellectual merit of this project is discovering the relationship between the host membrane chemistry and amino acid sequence of the FP that drive the molecular scale interactions between virus and host. This project is co-funded by the Cellular Dynamics and Function together with the Molecular Biophyics programs, both in the Division for Molecular and Cellular Biosciences.\n\nThis 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": "9552", "attributes": { "award_id": "2227523", "title": "Symposium on Combinatorial Search - 2017", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Robust Intelligence" ], "program_reference_codes": [], "program_officials": [ { "id": 6997, "first_name": "James", "last_name": "Donlon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-02-15", "end_date": "2022-04-30", "award_amount": 10000, "principal_investigator": { "id": 25269, "first_name": "Nathan", "last_name": "Sturtevant", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This grant supports student travel for select students and post-doctoral researchers to participate in the Symposium on Combinatorial Search (SoCS-2017) June 16-17 in Pittsburgh, PA. SoCS is an annual event that brings together researchers in heuristic search and combinatorial optimization drawing from diverse areas of artificial intelligence, planning, robotics, constraint programming, operations research, bioinformatics, and computer games. This consortium is oriented on research and career development for students who have identified their PhD topics and are just embarking on that independent research. \n\nSponsoring student travel to SoCS fosters a community of researchers from otherwise diverse areas of computer science to both advance the state of the art in heuristic search and/or combinatorial optimization, and also use these tools in their research. Students participating in this symposium are also more likely to take full advantage of the top-tier conference in the International Conference on Automated Planning and Scheduling (ICAPS) 2017 taking place immediately prior to this symposium, and co-located with it. The entire event provides an opportunity for students to engage in discussion with scientists from around the world and to explore new research directions and topics.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "9553", "attributes": { "award_id": "2202361", "title": "RCN: Wastewater Surveillance for SARS-CoV-2 and Emerging Public Health Threats", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "EnvE-Environmental Engineering" ], "program_reference_codes": [], "program_officials": [ { "id": 1123, "first_name": "Mamadou", "last_name": "Diallo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-08-01", "end_date": "2025-07-31", "award_amount": 500000, "principal_investigator": { "id": 4094, "first_name": "Kyle", "last_name": "Bibby", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 171, "ror": "https://ror.org/00mkhxb43", "name": "University of Notre Dame", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 2179, "first_name": "Rolf U", "last_name": "Halden", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, { "id": 4575, "first_name": "Jeseth Delgado", "last_name": "Vela", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25270, "first_name": "Alexandria", "last_name": "Boehm", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "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 ongoing COVID-19 pandemic remains an urgent national and global threat to public health. Wastewater surveillance (WWS) has emerged as a versatile and complementary platform to existing clinical tools for monitoring and tracking the spread and transmission of COVID-19 in communities. The basic premise of WSS is that the detection and quantification of microbial and viral biomarkers extracted from samples of untreated wastewater can be used to monitor the onset, spread and community transmission of infectious pathogens such as the SARS-CoV-2 virus and its variants. The overarching goal of this Research Coordination Network (RCN) is to support the large community of practitioners and stakeholders that employ WWS as a public health monitoring tool. To advance this goal, the core investigators of this RCN and their national/international partners will integrate their expertise and leverage their resources to address critical knowledge gaps and needs in WSS including improved analytical and sampling methods and more effective collaboration, communication and information sharing protocols/tools among researchers, practitioners, and public health stakeholders. The successful completion of this project will benefit society through the development and dissemination of new tools and protocols to support the large community of practitioners that employ WWS to monitor SARS-CoV-2 and other pathogens, including those capable of causing future pandemics. Additional benefits to society will be achieved through workforce development including the mentoring of two post-doctoral research fellows at the University of Notre Dame and Howard University. \n\nEarly success in the use of wastewater surveillance (WSS) during the COVID-19 pandemic in the United States has led to the establishment of a National Wastewater Surveillance System. Although the use of WWS has provided critical data and valuable insight into the spread and transmission of SARS-CoV-2, much work remains to be done to enable quantitative estimates, predictions, and actionable public health recommendations from the analysis of the data collected during a community wastewater surveillance program. This NSF Research Coordination Network (RCN) will address these critical knowledge gaps. The RCN builds upon the accomplishments of a previous NSF EAGER award that established a network of WWS researchers and practitioners from academia, government agencies, non-governmental organizations, and industry. The new RCN will advance its research, education, and workforce development goals through the implementation of five synergistic activities including 1) Monthly seminar series, 2) Quarterly topical workshops, 3) Development of a community-led ‘Wiki’ on the state of the science of WWS, 4) Annual wastewater surveillance training bootcamp, and 5) Annual virtual RCN conference with researchers, practitioners, and public health decision-makers. Through this award, the RCN investigators and their partners will facilitate and promote collaboration and communication among researchers, practitioners, and public health policy and decision-makers to advance next generation WWS for pandemic preparedness and prevention.\n\nThis 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": "9554", "attributes": { "award_id": "2200299", "title": "PIPP Phase I: Next Generation Surveillance Incorporating Public Health, One Health, and Data Science to Detect Emerging Pathogens of Pandemic Potential", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "PIPP-Pandemic Prevention" ], "program_reference_codes": [], "program_officials": [ { "id": 1030, "first_name": "Mitra", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-08-01", "end_date": "2024-01-31", "award_amount": 999977, "principal_investigator": { "id": 25274, "first_name": "David", "last_name": "Ebert", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 10631, "first_name": "Jason R", "last_name": "Vogel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25271, "first_name": "Michael C", "last_name": "Wimberly", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25272, "first_name": "Thirumalai", "last_name": "Venkatesan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25273, "first_name": "Aaron M", "last_name": "Wendelboe", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 157, "ror": "", "name": "University of Oklahoma Norman Campus", "address": "", "city": "", "state": "OK", "zip": "", "country": "United States", "approved": true }, "abstract": "Emerging pathogens such as SARS-CoV-2 cross over from animals to humans and can cause new and deadly diseases. They spread before they are identified, allowing significant infection before detection and response. The threat of new diseases presents a Grand Challenge: How can we routinely collect and analyze data to provide early detection that can help prevent the spread of new diseases and stop the next pandemic? Delayed response to COVID-19 underscores the need for new early detection methods, more effective data management and integration, monitoring of the human-animal interface to detect new and emerging pathogens, and more cooperation and information sharing between animal and public health officials. This Predictive Intelligence for Pandemic Prevention (PIPP) Phase I: Development Grants project will improve our ability to monitor and predict infectious disease threats using traditional and new data sources with novel computer algorithms to produce actionable information that will improve public-health responses to future pandemic threats. We will work with local and state public and animal health officials, practitioners, and community leaders to train them on the cutting-edge science while translating the results into solutions for metropolitan, rural and tribal nation communities. The outcome will be a comprehensive animal and public health surveillance, planning, and response roadmap that can be tailored to the unique needs of communities while enabling effective community response and management.\n\nThis project will leverage multiple streams of information to identify signals of emerging threats. Achieving this goal requires the development of new diagnostic tools that provide novel information sources and computational frameworks that automate the process of ingesting, harmonizing, and analyzing large, dynamic, and heterogeneous data streams. This work develops and evaluates the outcomes of a set of techniques to surveil and identify the presence of and behavioral responses to an illness and/or pathogen in animals, communities, and individuals prior to symptom onset. The project leverages science-based, human-guided Artificial Intelligence (AI)/Machine Learning (ML) methods to analyze and fuse data streams from surveillance and environmental data to track predictive indicators across scales. These novel methods build on successful applications: wastewater surveillance to detect pathogens, pharmaceuticals, and human-health biomarkers indicative of community presence of existing or emerging infectious diseases (EIDs), animal surveillance to detect many EIDs, environmental modeling for forecasting infectious diseases, and breathomics to identify patients with lung cancer, COVID-19, and tuberculosis. This approach is novel in that it harnesses and integrates multiple surveillance data streams in a layered and parallel approach ensuring accuracy and specificity and enabling effective integration of individual-to-community-wide sampling scales into surveillance systems. This project enables effective design and evaluation of response planning techniques. \n\nThis award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG) and Social, Behavioral and Economic Sciences (SBE).\n\nThis 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": "9555", "attributes": { "award_id": "2205876", "title": "Conference: Workshop: Microbial Diversity Molecules to Modeling Across Biological Scales; Summer in WoodsHole", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Ecosystem Science" ], "program_reference_codes": [], "program_officials": [ { "id": 4932, "first_name": "Matthew", "last_name": "Kane", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-08-01", "end_date": "2025-07-31", "award_amount": 331166, "principal_investigator": { "id": 25275, "first_name": "Linda", "last_name": "Hyman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1056, "ror": "https://ror.org/046dg4z72", "name": "Marine Biological Laboratory", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The impact of the microbial world on the planet has never been clearer. The COVID-19 pandemic caused by the Sars-CoV-2 virus, and microbial responses to ecosystem disturbance are examples of the significant impact that microbial diversity is having on science and society. With this in mind, never has the Microbial Diversity (MD) course at the Marine Biological Laboratory (MBL), established fifty years ago, been more relevant and of greater value. The course provides a unique, immersive experience for twenty students, including advanced graduate students, post-doctoral fellows, and junior faculty. Initially established to explore the microbial world through cultivation, as the field has expanded, so have the techniques incorporated into the curriculum. Today, a combination of training opportunities across disciplines is key to a successful research career in microbiology, thus, the experimental focus goes beyond just growing cells in the lab to include cutting-edge microscopy, genomics, population genetics, and biochemistry. This grant will support the course's new focus on genetic and cellular mechanisms of diversity as drivers of microbial ecology and evolution. The intellecual goal will be through testing compelling research questions at the scale of single cells, populations, or ecosystems that will address global challenges. The course has a track record in broadening partipation, is strongly committed to expanding opportunities for groups under-represented in the sciences, and engages a diverse and inclusive community of faculty and students. \n\nThe Microbial Diversity (MD) research-training program at the Marine Biological Laboratory (MBL) offers Early Career Researchers (ECRs) advanced training through an intensive 6-week, experience that has served a unique role in training diverse scientists in environmental microbiology for over 50 years. The overall objective is to train ECRs in modern experimental approaches in MD, to empower them to ask and test compelling microbiological research questions at the scale of single microbial cells, populations, or ecosystems. In recent years, the advancement of culture-independent (e.g., metagenomics) studies has ushered in a new era of data-driven research, highlighting unique new challenges for training in MD. The course integrates physiology, molecular cell biology, and evolutionary ecology at the microbial scale to understand and predict the role of microbial communities in ecosystem function. In the next three year funding period, using the wealth of prior course-collected metagenomic data, the scope of research will be expanded to highlight evolutionary and ecological processes that create and maintain microbial diversity at various biological scales. Focused training includes: (i) genome-enabled laboratory cultivation and pure culture isolation, emphasizing the functional analyses of understudied bacteria, archaea, microbial eukaryotes, and viruses (ii); in situ physiological and imaging approaches to quantify spatial and temporal microbial dynamics in local habitats; and (iii) single-cell and population-based approaches to quantify genetic and phenotypic heterogeneity and evolutionary mechanisms creating and maintaining diversity in microbial populations. The newly updated curriculum includes theoretical and practical training in microbiology, cell biology, and microbial ecology and research in genetic and environmental mechanisms that generate microbial diversity. To extend the experience of collaboration and research training of the MD course, trainees participate in: i) a Regional Outreach and Communication in STEM program, designed to support teams of student alumni at their home institutions, to increase science literacy and connections to the local community; ii) the MD Ambassadors group, recruiting a diversity of students for the course; and iii) hosting an Art-Science Exhibition, to inform and inspire local artists and public in the visual aesthetics of the microbial world.\n\nThis 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": "9556", "attributes": { "award_id": "2200138", "title": "PIPP Phase I: Develop and Evaluate Computational Frameworks to Predict and Prevent Future Coronavirus Pandemics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "PIPP-Pandemic Prevention" ], "program_reference_codes": [], "program_officials": [ { "id": 1030, "first_name": "Mitra", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-08-01", "end_date": "2024-01-31", "award_amount": 1000000, "principal_investigator": { "id": 8018, "first_name": "Hong", "last_name": "Qin", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 894, "ror": "", "name": "University of Tennessee Chattanooga", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 25276, "first_name": "John", "last_name": "Choy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25277, "first_name": "Liqun", "last_name": "Zhang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25278, "first_name": "Letu", "last_name": "Qingge", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25279, "first_name": "Ziwei", "last_name": "Ma", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 894, "ror": "", "name": "University of Tennessee Chattanooga", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "Once a novel coronavirus or a new variant is detected, how likely would the novel coronavirus or new variant transmit from person to person, and how sick could patients become? What kind of new coronaviruses could cause future pandemics? Knowing the answers to these questions can help nations make proper strategic decisions. The dilemma is how to predict the behavior and pathogenic severity of new viruses as early as possible. A team of researchers thinks they have found ways to answer these questions by developing new artificial intelligence software tools to predict the virus’s behaviors based on its genome sequence. This team of researchers recognizes the potential bias in machine learning applications and the need to increase diversity in the future artificial intelligence workforce. Leveraging their expertise in genomics, data science, artificial intelligence, genetics, infectious disease, chemical engineering, public health, and communication, this team of researchers will organize training workshops and activities providing culturally responsive teaching of artificial intelligence, data science training to teachers, and context-relevant coding experiences to high school students. The team will promote public trust in science and discernment of misinformation through community outreach. \n\nThis research team will prototype a deep learning model based on biological knowledge and hypotheses that can predict viral pathogenic fitness from genomic sequences to test the potential rules for viral pathogenicity. The team will explore several methods to correct the sampling bias in viral genomic surveillance in order to accurately estimate the fitness of a viral strain. The team will investigate the mutation and recombination profiles in all available bat coronavirus genomes from the Southeastern Asia and build a prototype geospatial model to predict the recombination probability for all available bat coronaviruses. Leveraging their expertise in genetics and macromolecular structure modeling, the team will test a few candidate genes in SARS-CoV-2 for potential pathogenic rules. Based on the outcomes of these pilot projects, the team will be able to estimate the pathogenic fitness of an emerging SARS-CoV-2 variant or another novel coronavirus. \n\nThis award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG) and Social, Behavioral and Economic Sciences (SBE).\n\nThis 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": "9557", "attributes": { "award_id": "2230853", "title": "I-Corps: Commercialization of a real-time, palm-size, low-cost, and wireless SARS-COV-II detector", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "I-Corps" ], "program_reference_codes": [], "program_officials": [ { "id": 602, "first_name": "Ruth", "last_name": "Shuman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-08-01", "end_date": "2023-07-31", "award_amount": 50000, "principal_investigator": { "id": 25280, "first_name": "Zheng", "last_name": "Tian", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 586, "ror": "", "name": "University of Arkansas", "address": "", "city": "", "state": "AR", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this I-Corps project is the development of a wireless nano-biosensor for detection of live coronavirus particles and potentially other pathogens. Currently, a positive result from an enzyme-linked immunosorbent assay (ELISA)- or polymerase chain reaction (PCR)-based COVID test does not determine if the coronavirus particles are live (infectious) or dead (or neutralized or noninfectious). The proposed technology is designed to detect interactions of live coronavirus particles with target cells, antibodies, and drugs in real-time. Those with a negative result from the proposed technology have confirmation that they are free of live, infectious coronavirus particles and would not need to quarantine. This technology may provide a tool for effectively combatting the COVID-19 virus and future pandemics/epidemics as well as other infections.\n\nThis I-Corps project is based on the development of a nano-biosensing methodology for pathogen detection initially focused on SARS COV-II. The proposed technology integrates a bio-ceramic nanowire/graphene core/shell nanocomposite with a radio frequency identification (RFID) tag on a wireless communication chip surface. The hardware of the nano-biosensing system is composed of three parts: a radio frequency nano-sensor, a radio frequency reader, and a handheld device or smart phone. The proposed nano-biosensor is made by coating the biosafe nanocomposite on a disposable RFID tag to maximize the sensitivity. Further, nano-biosensors may be integrated to simultaneously detect samples in a multi-well plate, which is versatile and effective in the detection of virus particles, viral antigens, and antibodies, as well as in evaluating virus-neutralizing antibodies and vaccine efficacy.\n\nThis 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": "9558", "attributes": { "award_id": "2127436", "title": "SBIR Phase II: COVID-19 Rapid Sensing Using Structural DNA Biosensors", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase II" ], "program_reference_codes": [], "program_officials": [ { "id": 773, "first_name": "Erik", "last_name": "Pierstorff", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-08-01", "end_date": "2024-07-31", "award_amount": 998507, "principal_investigator": { "id": 25169, "first_name": "Xiaohu", "last_name": "Yao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 557, "ror": "", "name": "ATOM BIOWORKS INC", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 557, "ror": "", "name": "ATOM BIOWORKS INC", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be the development of a platform technology for creating rapid virus diagnostics that directly recognizes the virus surface protein pattern from a patient sample and generates accurate results within minutes. Current standards for high-fidelity viral pathogen diagnostics require complex instruments, technical expertise to run the instruments, and hours to produce and interpret results. The proposed platform creates a virus-specific biosensor that selectively binds to the target virus and produces visible results without time-consuming pre-processing or expensive instruments. Lower cost tests and faster sample-to-result turnovers could result in more effective control of disease spread.\n\nThis project seeks to develop a highly functional, sensitive, and specific diagnostic for the detection of coronavirus. This technology is based on the company’s Pattern-Recognition Enhanced Sensing and Therapeutics (PEST) concept. The solution is a first-in-class diagnostics that uses algorithmically-designed structural DNA to form a trap that may detect and selectively bind a signature pattern of the pathogen. This recognition and binding may generate visual signals without the need of DNA/RNA preprocessing or amplification associated with the current generation of molecular tests (polymerase chain reactions, PCRs). This project will involve building a preclinical prototype of PEST-enabled lateral flow based COVID-19 rapid diagnostics with a goal of providing results for each sample within 5 minutes. This fast test result will be followed by preclinical validation to determine the test’s specificity, limits of detection, and implement mechanism to improve the assay specificity and to avoid cross-reaction to other virus types.\n\nThis 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": "9559", "attributes": { "award_id": "2235625", "title": "RAPID: Visualizing Epidemical Uncertainty for Personal Risk Assessment", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "Decision, Risk & Mgmt Sci" ], "program_reference_codes": [], "program_officials": [ { "id": 652, "first_name": "Robert", "last_name": "O'Connor", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-01-01", "end_date": "2023-07-31", "award_amount": 191696, "principal_investigator": { "id": 5441, "first_name": "Enrico", "last_name": "Bertini", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 167, "ror": "https://ror.org/0190ak572", "name": "New York University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 184, "ror": "https://ror.org/04t5xt781", "name": "Northeastern University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "COVID-19 is one of the most deadly and fastest transmitting viruses in modern history. In response to this pandemic, news agencies, government organizations, citizen scientists, and many others have released hundreds of visualizations of pandemic forecast data. While providing people with accurate information is essential, it is unclear how the average person understands the widely distributed depictions of pandemic data. Prior research on uncertainty communication shows that even common visualizations can be confusing. One possible source of inappropriate responses to COVID-19 is the lack of knowledge about personal risk and the nature of pandemic uncertainty. The goal of this research is to test how people understand currently available COVID-19 data visualizations and create communication guidelines based on these findings. Further, the researchers develop an application to help people understand the factors that contribute to their risk. Users are able to interact with the application to learn about the impact of their actions on their risk. This research provides immediate solutions for teaching people about their personal risk associated with COVID-19 and how their actions influence the risks of others, which could improve the public's response and decrease fatalities. Additionally, this work supports decision making for future pandemics and any subsequent outbreaks of COVID-19 or other viruses.\n\nSpecifically, the research team empirically examines how people in high and low impact regions reason with pandemic uncertainty by testing the effects of currently available visualizations on personal risk judgments and behavior. By studying how changes in factors influence risk perceptions, the research can contribute to understanding how people conceptualize compound uncertainties from different sources (e.g., uncertainties associated with location, time, demographics and risk behaviors). The researcher then use this information to produce a visualization application that allows people to change the parameters of a simulation to see how the resulting changes affect their risk judgments. For example, users in one city are able to see the pandemic risk to individuals of their age in their zip code and then see how that risk would change if the infection rate increased or decreased. The aim is to promote intuitive understanding of the epidemiological uncertainty in the forecast through participants’ experimentation with the application. While in line with current recommendations for intrinsic uncertainty visualization, this work is the first of its kind to test the effect of user interaction to convey uncertainty through visualization.\n\nThis 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": "9560", "attributes": { "award_id": "2151349", "title": "SBIR Phase II: Facilitating Early Childhood Teacher and Family Engagement During COVID-19", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase II" ], "program_reference_codes": [], "program_officials": [ { "id": 614, "first_name": "Diane", "last_name": "Hickey", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-07-15", "end_date": "2024-06-30", "award_amount": 964915, "principal_investigator": { "id": 1770, "first_name": "Tammy", "last_name": "Kwan", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 388, "ror": "", "name": "Cognitive ToyBox", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 388, "ror": "", "name": "Cognitive ToyBox", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to ease the burden on early childhood teachers and ensure they have the information needed to support children’s learning. COVID-19 has upended the lives of millions of children and families and disproportionately affected low-income children. The incoming class of preschoolers have increasingly variable levels of development and skills than ever before, and educators need to assess their need and help them to catch up academically. The industry leading tools in early childhood assessment rely solely on observation-based assessment, in which teachers make judgments about a child’s performance based on observations made in the classroom. Observation-based measures have limitations, including time burden, teacher bias, and variability in scoring due to differences in teacher education and training. Observational assessment is especially challenging in the remote context, since teachers were not able to physically monitor children’s development. Even when children are back in the classroom, face masks may shield some of the more subtle cues around learning and development.\n\nThis Small Business Innovation Research (SBIR) Phase II project seeks to ensure an accurate assessment of child development, so that teachers can leverage the data for tailored instruction to help children catch up from the effects of the COVID-19 pandemic. Child assessments are typically observational or direct. This Phase II project collects both observational and direct data, providing an opportunity to compare the data and help teachers understand areas where there may be assessment error due to teacher bias or other factors. At the end of Phase II, it is anticipated that teachers will have access to a self-led teacher training and reliability certification, which may help to ensure a more consistent assessment experience in the classroom. Moreover, educational programs will have access to an assessment quality monitoring dashboard that seeks to highlight differences between observation and game-based assessment scores and provide a path towards reconciliation. Educational programs may be able to customize this technology to fit the needs of English language learners, who are often subject to assessor bias. Finally, teachers may be able to leverage the game-based assessments to understand both academic and “soft” skill development. Overall, the technology may\\provide teachers with a robust child assessment system that helps them to accurately and efficiently assess whole child development.\n\nThis 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": 1392, "pages": 1442, "count": 14420 } } }