Grant List
Represents Grant table in the DB
GET /v1/grants?sort=approved
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=approved", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=approved", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=2&sort=approved", "prev": null }, "data": [ { "type": "Grant", "id": "768", "attributes": { "award_id": "2050640", "title": "Planning Virtual Strategies to Prepare Science and Mathematics Teachers in Mississippi", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)" ], "program_reference_codes": [], "program_officials": [ { "id": 1805, "first_name": "Susan", "last_name": "Carson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-03-01", "end_date": "2023-02-28", "award_amount": 124992, "principal_investigator": { "id": 1809, "first_name": "Mitchell M", "last_name": "Shears", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 396, "ror": "https://ror.org/01ecnnp60", "name": "Jackson State University", "address": "", "city": "", "state": "MS", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1806, "first_name": "Abu O", "last_name": "Khan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1807, "first_name": "Alicia K", "last_name": "Jefferson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1808, "first_name": "Nadine", "last_name": "Gilbert", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 396, "ror": "https://ror.org/01ecnnp60", "name": "Jackson State University", "address": "", "city": "", "state": "MS", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national need for skilled secondary science and mathematics teachers in high-need school districts. To do so, the project seeks to lay the foundation for secondary-education certification programs adapted to the novel demands of pre- and post-COVID teaching/learning environments. Conceived initially as a response to the COVID-19 pandemic, the project aims to use technology and virtual approaches to deliver remote learning opportunities for future teachers. As such, the project will enable Jackson State University to explore the feasibility of a large-scale effort to increase use of evidence-based, distance-learning strategies in teacher education. Examples of strategies include virtual simulations, digital credentialing, and online social and emotional learning. The work will be situated in the urban setting of the Mississippi State capital. This project at Jackson State University includes partnerships with Hinds Community College and Jackson Public Schools, a high-need school district. The long-term goal of this collaborative effort is plan how to recruit, support, and graduate teachers who will help meet the shortage of science and mathematics teachers at high-need schools often staffed by rotating long- and short-term substitute teachers. The project builds on the conceptual framework of Jackson State’s College of Education and Human Development vision of the “responsive educator” who provides and embodies: 1) a Committed Response; 2) a Knowledgeable Response; 3) a Skillful Response; and 4) a Professional Response. Additionally, the project builds on the current infrastructure of the University’s Physics and Mathematics Education curriculum. The goals of this Capacity Building project are to: 1) develop evidence-based innovative models and strategies for recruiting, preparing, and supporting teachers; 2) create plans for collecting data to determine need, interest, and capacity for increasing STEM teacher development; and 3) establish the infrastructure for preparing a Track 1: Scholarship & Stipend proposal in the future. This Capacity Building project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the persistence, retention, and effectiveness of K-12 STEM teachers in high-need school districts.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": "3072", "attributes": { "award_id": "1934962", "title": "HDR TRIPODS: Collaborative Research: Foundations of Greater Data Science", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "HDR-Harnessing the Data Revolu" ], "program_reference_codes": [], "program_officials": [ { "id": 9528, "first_name": "Huixia", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2019-09-15", "end_date": "2022-08-31", "award_amount": 814165, "principal_investigator": { "id": 9534, "first_name": "Mujdat", "last_name": "Cetin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 464, "ror": "https://ror.org/022kthw22", "name": "University of Rochester", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 9529, "first_name": "Alex", "last_name": "Iosevich", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 9530, "first_name": "Daniel", "last_name": "Gildea", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 9531, "first_name": "Daniel", "last_name": "Stefankovic", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 9532, "first_name": "Tong Tong", "last_name": "Wu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 464, "ror": "https://ror.org/022kthw22", "name": "University of Rochester", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The University of Rochester and Cornell University jointly establish the Greater Data Science Cooperative Institute (GDSC). The GDSC is based on two founding tenets. The first is that enduring advances in data science require combining techniques and viewpoints across electrical engineering, mathematics, statistics, and theoretical computer science. The investigators' goal is to forge a consensus perspective on data science that transcends any individual field. The second is that data-science research must be grounded in an application domain. This helps to ensure that assumptions about the availability and quality of data are realistic, and it allows methodological results to be tested experimentally as well as theoretically. As such, the GDSC aims to consider applications in medicine and healthcare, an important application domain and one for which advances in data science can have a direct, positive impact on society. The GDSC aims to tackle foundational questions that are motivated by problems in healthcare, obtain solutions that fuse domain expertise with application-agnostic methodologies, and ultimately yield scientific advances that impact the way healthcare is provided. The GDSC aims to leverage the physical proximity of the two institutions, and the unique strengths in each of the core disciplines above and in medicine.\n\nThe GDSC's cross-disciplinary research directions include: (i) Topological Data Analysis. The challenges that high-dimensional, incomplete, and noisy data present are great, but in many applications, exploiting the topological nature of the problem is possible. GDSC aims to develop new fundamental methods and theory to rigorously explore the promise of this unique approach. (ii) Data Representation. Data compression, embeddings, and dimension reduction play a fundamental role in data science. Inspired by new core challenges in biomedical imaging, genomics, and neural-spike training data, GDSC aims to develop novel source models and distortion measures, and ultimately seek a unifying theoretical framework across domains and disciplines. (iii) Network & Graph Learning. Many of the fundamental challenges in applying data science to non-homogeneous populations are best explored through a network or graph structure. GDSC aims to develop new techniques for parameter-dependent eigenvalue problems in spectral community detection, density-estimation methods on networks, and a theoretical framework for time-varying graphical models to study dynamic variable relations in time-evolving networks. (iv) Decisions, Control & Dynamic Learning. Sequential decisions are high-stakes in medicine. GDSC aims to utilize systems and control-engineering methods to improve health and disease management and develop new foundational theories and methods for label-efficient active learning and dynamic treatment regimes. (v) Diverse & Complex Modalities. Big data is complex data, and major new innovations are needed. GDSC aims to develop theoretical frameworks for inference under computational and privacy constraints and for high-dimensional data without parametric model assumptions. Text, image, and audio data present further challenges. To address such challenges, GDSC aims to explore transition systems for graph parsing of natural language and new fusion approaches for fully multimodal analysis. \n\nThis project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.\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": "2048", "attributes": { "award_id": "2029292", "title": "RAPID: Collaborative Research: Relationships, social distancing, social media and the spread of COVID-19", "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": [ "025Z", "065Z", "096Z", "7434", "7914" ], "program_officials": [ { "id": 5498, "first_name": "Jan", "last_name": "Leighley", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-15", "end_date": "2021-04-30", "award_amount": 117340, "principal_investigator": { "id": 5499, "first_name": "Katherine", "last_name": "Ognyanova", "orcid": "https://orcid.org/0000-0003-3038-7077", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['https://covidstates.org/']", "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 218, "ror": "", "name": "Rutgers University New Brunswick", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "This project seeks to improve the national response to the COVID-19 pandemic by launching large-scale data collection through a rolling national survey linked to individual social media data. We generate information useful to policymakers and local authorities and offer near-real-time state-by-state disease tracking. Our data allow officials to understand where the virus is currently spreading, facilitating improved allocation of resources. We also evaluate the networked nature of the disease, tracking its flow based on the reported social relationships of the survey participants and their social distancing behaviors. The project captures how well the information and communication needs of Americans are met during this crisis, observes patterns of citizen compliance with government recommendations, stay-at-home orders, and enforced lockdowns, and assesses their impact on suppressing the spread of the virus among diverse populations.The project has two core objectives: (1) producing information that will be immediately useful in improving the national response to COVID-19; and (2) using COVID-19 data to understand how people adapt to and make sense of a national crisis that has important and immediate ramifications for their daily lives. We rely on a large-scale, rolling national survey that is conducted on a daily basis, with approximately 3000 respondents per day. We also link the survey data to the social media behavior of respondents. The large sample sizes collected daily offers near-real-time state-by-state disease tracking, as well as the ability to observe key differences in responses to policies across demographic groups. The design will capture how people use technology to work, get informed, and stay connected, and respondents’ financial difficulties, employment experiences, and parenting and educational challenges in response to the pandemic.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": "1280", "attributes": { "award_id": "2032016", "title": "RAPID: Interaction of the Coronavirus M protein with a Myosin V motor protein", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7465", "7914" ], "program_officials": [ { "id": 3294, "first_name": "Matt", "last_name": "Buechner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-01", "end_date": "2021-07-31", "award_amount": 200000, "principal_investigator": { "id": 3295, "first_name": "James", "last_name": "Goldenring", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 456, "ror": "https://ror.org/05dq2gs74", "name": "Vanderbilt University Medical Center", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 456, "ror": "https://ror.org/05dq2gs74", "name": "Vanderbilt University Medical Center", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 coronavirus represents one of the most acute and dangerous threat to global health in a century. The present COVID-19 outbreak is caused by the SARS-CoV-2 virus and is exacerbated by our present inability to inhibit the assembly of virus inside cells, which affects the spread of coronavirus within human patients. SARS-Cov-2 is a member of a family of related coronaviruses that includes SARS, MERS, and Mouse Hepatitis Virus (MHV). These are RNA viruses wrapped in a lipid membrane, which has a number of different proteins embedded in it. In general however, relatively little is known about how viral proteins are packaged together inside human cells to assemble coronavirus particles. This lack of knowledge impedes the development of strategies that could target the assembly of virus particles inside human cells. However, one membrane protein encoded by the virus, designated the M protein because of its membrane association, serves as the starting seed for the assembly of all the other coronavirus proteins. It has recently been discovered that the tail of the MHV version of the M protein can interact with a motor protein used for trafficking inside human cells. Because the sequence of the M-protein tails in this class of coronaviruses is very conserved, this project seeks to determine if this interaction is generalizable among SARS-CoV-2, and other related coronaviruses. If this interaction is present in SARS-CoV-2, then this project will identify the parts of the M protein that interact with the myosin motor. This would allow future research to evaluate drugs that can interrupt this interaction, which may alter the ability of the SARS-CoV-2 and other coronaviruses to assemble and spread. As a Broader Impact, the Project will provide critically needed information for fight the Covid-19 pandemic. Relatively little is known about the processes required for the assembly of coronavirus virions in human cells. The family of beta-coronaviruses, including SARS-CoV-2, SARS, MERS and MHV all utilize a similar compendium of proteins, including a membrane glycoprotein protein, termed the M protein. M protein serves as the nidus for assembly of other coronavirus proteins into the mature virus particle. This project seeks to expand on the recent finding that the M-protein of MHV interacts with a specific 26 amino acid alternatively spliced exon of Myosin Vb (MYO5B), termed exon D. This observation is the first to identify a key intracellular trafficking protein that interacts with the M protein. This project seeks to identify whether this interaction is preserved in the M proteins of other beta-coronavirus family members, including the SARS-CoV-2 virus. The Project will also determine the molecular basis of the interaction between M protein and Exon D of MYO5B. The Project will first utilize yeast 2-hybrid assays to evaluate the binding of the internal tails of SARS-CoV-2 (COVID-19), SARS and MERS and other related coronaviruses. Second, random mutagenesis of the coronavirus tails will be utilized to determine the common amino acid motifs used for interaction with MYO5B Exon D. Third, wild type and mutant coronavirus proteins will be expressed in HeLa cells, and mutations that block interactions with MYO5B on M protein will be evaluated for their effects on trafficking through the endoplasmic reticulum, the Golgi apparatus and to the plasma membrane. If verified with the SARS-CoV-2 M protein, the site of interaction with MYO5b Exon D could be utilized as a target for disruption of the assembly of coronavirus virions. This RAPID Project is expected to provide insights for the future development of molecular strategies to disrupt virion assembly in host cells, which could lead to new therapeutics for the treatment of Covid-19.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": "2816", "attributes": { "award_id": "1925596", "title": "CC* Compute: Accelerating Computational Research for Engineering and Science (ACRES) at Clarkson University, A Campus Cluster Proposal", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Campus Cyberinfrastructure" ], "program_reference_codes": [], "program_officials": [ { "id": 8385, "first_name": "Kevin", "last_name": "Thompson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2019-07-01", "end_date": "2021-06-30", "award_amount": 396950, "principal_investigator": { "id": 8387, "first_name": "Joshua", "last_name": "Fiske", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 597, "ror": "https://ror.org/03rwgpn18", "name": "Clarkson University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 8386, "first_name": "Brian", "last_name": "Helenbrook", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 597, "ror": "https://ror.org/03rwgpn18", "name": "Clarkson University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Clarkson University is building a computational cluster (ACRES: Accelerating Computation Research for Engineering and Science) to support data and computationally intensive projects aligned with Clarkson's four interdisciplinary research themes: Data Analytics, Healthy World Solutions, Advanced Materials Development, and Next Generation Healthcare. ACRES facilitates the conduct of high-impact, collaborative research that requires access to high-performance computing (HPC) resources, enables research currently not practical/feasible, and also supports student-learning opportunities through credit-bearing courses, undergraduate research, and an existing NSF REU site focusing on HPC. As a campus resource, ACRES is made available to any faculty member or student at the University according to queueing policies implemented to ensure fair-access. And, ACRES supports Clarkson's increased focus on computational research and a cluster hire of computationally active faculty. \n\nThe ACRES compute cluster replaces an existing, five-year-old high-performance compute cluster whose computational capacity provided 1.05M core-h/yr. Research need for computational capacity has grown to an identified total of 8.5M core-h/yr. ACRES is sized to meet current demands and modest near-term growth with unused computational capacity being shared via the Open Science Grid (OSG) to benefit the broader scientific community. This new computational resource provides 9.8M core-h/year through 1120 cores, high-speed Infiniband interconnect, four NVIDIA Tesla V100 GPUs, and 40 TB of scratch storage.\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": "1024", "attributes": { "award_id": "2136142", "title": "S2I2: Impl: The Molecular Sciences Software Institute", "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": 2505, "first_name": "Richard", "last_name": "Dawes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-01", "end_date": "2026-07-31", "award_amount": 3825190, "principal_investigator": { "id": 2510, "first_name": "Thomas D", "last_name": "Crawford", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 2506, "first_name": "Shantenu", "last_name": "Jha", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 2507, "first_name": "Teresa L", "last_name": "Head-Gordon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 2508, "first_name": "Theresa L", "last_name": "Windus", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 2509, "first_name": "Dominika", "last_name": "Zgid", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "The Molecular Sciences Software Institute (MolSSI) is supported by a joint award from the Divisions of Chemistry (CHE) and Molecular and Cellular Biosciences (MCB) and the Office of Advanced Cyberinfrastructure (OAC). Since its launch in 2016, the MolSSI has served as a nexus for the broad computational molecular sciences community by providing software expertise, community engagement and leadership, and education and training. Through a broad array of software infrastructure projects, teaching workshops, and community outreach, the MolSSI catalyzes the scientific advances needed to solve emerging scientific computing Grand Challenges. In its next phase, supported by this award, the MolSSI will capitalize on this success by continuing and extending its efforts for an even broader impact on our community's ability to address key scientific areas, including developing new energy technologies, fighting COVID-19 and future pandemics, developing climate solutions, exploring quantum information sciences, artificial intelligence and machine learning, and developing a more diverse and resilient workforce.Through the MolSSI's Software Scientists – a team of software engineering experts, drawn from the molecular sciences, computer science, and applied mathematics – the Institute will promote improved interoperability of community codes, easier deployment on heterogenous computing architectures, and greater parallel scalability of existing and emerging theoretical models. The MolSSI will help train the next generation of computational molecular scientists in modern software engineering tools and best practices through its Education Initiative that annually reaches thousands of students worldwide and its Software Fellowship program, which has already benefitted nearly 100 graduate students and postdoctoral fellows across the U.S. The MolSSI's Software Workshop program will bring the community together to identify and address the highest priority challenges and the MolSSI's Discovery, Outreach, and Sustainability programs will provide a key mechanism for community buy-in, provide insight into the needs of the molecular sciences community, and facilitate interactions among its members. The MolSSI's ultimate goal is to enable new science and broader impacts by building a community of molecular scientists prepared to provide solutions to problems impacting national health, social, environmental, and economic challenges.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "1536", "attributes": { "award_id": "2029774", "title": "RAPID: Comparative genomics of SARS-CoV-2 susceptibility and immune defense in mammals", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7914", "9179" ], "program_officials": [ { "id": 4007, "first_name": "Joanna", "last_name": "Shisler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-15", "end_date": "2021-04-30", "award_amount": 199767, "principal_investigator": { "id": 4009, "first_name": "Elinor K", "last_name": "Karlsson", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 613, "ror": "https://ror.org/0464eyp60", "name": "University of Massachusetts Medical School", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 4008, "first_name": "Diane P", "last_name": "Genereux", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 613, "ror": "https://ror.org/0464eyp60", "name": "University of Massachusetts Medical School", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The goal of this project is to compare genomes of hundreds of mammal species, finding differences in DNA that distinguish species resistant to SARS-CoV-2 from those that are very susceptible. This information is needed to understand how the current SARS-CoV-2 virus spread to humans and to identify potential host animals (e.g., pet, livestock, and pest species) that may be susceptible to SARS-CoV-2 in the USA. SARS-CoV-2, the cause of the COVID-19 pandemic, can infect diverse species of animals. There is a variation in susceptibility to and severity of disease between species. This variation suggests that some species have genetic differences that dictate susceptibility to COVID-19. This work will identify how coronaviruses adapt to new host species, information that will help predict and control future coronavirus outbreaks. Funding will support training a graduate student in research, thereby training the next generation of the bioeconomy workforce. This project will investigate how the host genome shapes host-pathogen interactions, and how coronaviruses like SARS-CoV-2 evolve to exploit new hosts. The researchers will compare existing genomic data for hundreds of mammals using three complementary approaches: (1) Measure structural and sequence homology in two host proteins, ACE2 and TMPRSS2, necessary for infection in humans; (2) Analyze existing RNA-seq datasets to (a) identify species with co-expression of ACE2 and TMPRSS2, and potentially other proteases implicated in infection, in the same tissue, and (b) search for incidental coronaviral sequence data from diverse mammalian species; (3) Test for variants in evolutionarily conserved elements that are correlated with species susceptibility, using forward genomics. With these analyses, the researchers will identify species with potential as reservoirs for SARS-CoV-2 viral spillback into humans, and those that are promising systems for investigating SARS-CoV-2 evolution, host defenses, and host-pathogen interactions. This RAPID award is made by the Physiological and Structural Systems Cluster in the BIO Division of Integrative Organismal Systems, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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": "2304", "attributes": { "award_id": "2025954", "title": "LTER: Coastal Oligotrophic Ecosystem Research", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "LONG TERM ECOLOGICAL RESEARCH" ], "program_reference_codes": [], "program_officials": [ { "id": 6330, "first_name": "Paco", "last_name": "Moore", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-03-01", "end_date": "2025-02-28", "award_amount": 4750800, "principal_investigator": { "id": 6335, "first_name": "John", "last_name": "Kominoski", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 207, "ror": "https://ror.org/02gz6gg07", "name": "Florida International University", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 6331, "first_name": "James", "last_name": "Fourqurean", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 6332, "first_name": "Evelyn E", "last_name": "Gaiser", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 6333, "first_name": "Jennifer S", "last_name": "Rehage", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 6334, "first_name": "Kevin", "last_name": "Grove", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 207, "ror": "https://ror.org/02gz6gg07", "name": "Florida International University", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "Coastal ecosystems like the Florida Everglades provide many benefits and services to society including protection from storms, habitat and food for important fisheries, support of tourism and local economies, filtration of fresh water, and burial and storage of carbon that offsets greenhouse gas emissions. The Florida Coastal Everglades Long Term Ecological Research (FCE LTER) program addresses how and why coastal ecosystems and their services are changing. Like many coastal ecosystems, the Florida Everglades has been threatened by diversion of fresh water to support urban and agricultural expansion. At the same time, sea-level rise has caused saltwater intrusion of coastal ecosystems which stresses freshwater species, causes elevation loss, and contaminates municipal water resources. However, restoration of seasonal pulses of fresh water may counteract these threats. Researchers in the FCE LTER are continuing long-term studies and experiments to understand how changes in freshwater supply, sea-level rise, and disturbances like tropical storms interact to influence ecosystems and their services. The science team is guided by a diversity and inclusion plan to attract diverse scientists at all career stages. The team includes resource managers – who use discoveries and knowledge from the FCE LTER to guide effective freshwater restoration – and an active community of academic and agency scientists, teachers and other educators, graduate, undergraduate, and high school students. The project has a robust education and outreach program that engages the research team with the general public to advance science discoveries and protection of coastal ecosystems.\n\nThe FCE LTER research program addresses how increased pulses of fresh and marine water will influence coastal ecosystem dynamics through: (i) continued long-term assessment of changes in biogeochemistry, primary production, organic matter, and trophic dynamics in ecosystems along freshwater-to-marine gradients with a focus on how these affect accumulation of carbon and related elevation change, (ii) meteorological studies that evaluate how the climate drivers of hydrologic presses and pulses are changing, (iii) social-ecological studies of how governance of freshwater restoration reflects the changing values of ecosystem services, and (iv) use of high-resolution remote sensing, coupled with models to forecast landscape-scale changes. A new experimental manipulation will determine drivers and mechanisms of resilience to saltwater intrusion. Data syntheses integrate month-to-annual and inter-annual data into models of water, nutrients, carbon, and species patterns and interactions throughout the Everglades landscape to compare how ecosystems with different productivities and carbon stores respond (maintain, increase, or decline) to short- (pulses) and long-term changes (presses) in hydrologic connectivity. Synthesis efforts will use data from national and international research networks aimed at understanding how chronic presses and increasing pulses determine ecosystem trajectories, addressing one of the most pressing challenges in contemporary ecology.\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": "1792", "attributes": { "award_id": "2028981", "title": "RAPID: Procedural Changes in State Courts During COVID-19", "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": [ "096Z", "7914", "9178" ], "program_officials": [ { "id": 4728, "first_name": "Naomi", "last_name": "Hall-Byers", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-04-15", "end_date": "2021-03-31", "award_amount": 34712, "principal_investigator": { "id": 4729, "first_name": "Alyx", "last_name": "Mark", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 677, "ror": "https://ror.org/05h7xva58", "name": "Wesleyan University", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 677, "ror": "https://ror.org/05h7xva58", "name": "Wesleyan University", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true }, "abstract": "State courts are rapidly changing their operating procedures in response to the COVID-19 pandemic. As courthouses close their doors to the public, judges, administrators, and staff are developing and implementing policies that are responsive to the needs of people who otherwise would rely on in-person court processes for remedies to their civil legal problems. The COVID-19 pandemic provides a unique opportunity to study the generation and consequences of massive change and innovation to court policies and procedures across the United States. This RAPID project will investigate the processes underlying the development of these major institutional changes as state courts respond to the challenges of remote operations during COVID-19, how the various changes are implemented, and the effect of these changes on access to justice for state court consumer populations.By taking advantage of the unique circumstance of forced innovation during COVID-19, the project will examine the procedural changes state courts craft in their move to remote operations. Employing a mixed-method approach, the research will catalogue the rapidly evolving COVID-19 responses in the states. The project will include surveys and interviews of court administrators, judges, and staff about their involvement in the changes and their attitudes about institutional design and implementation. Further, the project will analyze how these changes influence outcomes and processes on state court staff and consumers. By testing theories of institutional arrangements and design, findings from the project will provide an understanding of the external and internal forces that drive institutional change, how policies disperse and replicate across states, and the consequences of institutional design and changes thereto for access to justice during COVID-19.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": "15679", "attributes": { "award_id": "1R01HL176493-01", "title": "Pathogenic Mechanism and Therapeutic Approaches for Exercise Intolerance in Post-Acute Sequelae of COVID-19", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Heart Lung and Blood Institute (NHLBI)" ], "program_reference_codes": [], "program_officials": [ { "id": 32514, "first_name": "EMMANUEL FRANCK", "last_name": "MONGODIN", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-04-01", "end_date": "2029-01-31", "award_amount": 633045, "principal_investigator": { "id": 32524, "first_name": "Michael G", "last_name": "Risbano", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32525, "first_name": "Lianghui", "last_name": "Zhang", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 848, "ror": "", "name": "UNIVERSITY OF PITTSBURGH AT PITTSBURGH", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Post-acute sequelae of COVID-19 (PASC) is an emerging public health priority with up to 18% prevalence. Noteably, almost 30% patients diagnosed with PASC experence exercise intolerance. This activity limitation continues to negatively impact our workforce, and poses a persistent socialeconimic burden on our society. Our Post-Covid Recovery Clinic, a RECOVERY Vital site, has evaluated exercise intolerant PASC for nearly 4 years. We recently discovered pathophysiologic endotypes that contribute to exercise intolerance in PASC via invasive cardiopulmonary exercise testing (iCPET). Yet, the molecular drivers for this population remain elusive. Four- years after the onset of the pandemic we are left without PASC-defining biomarkers, or targeted therapeutics. Thus, it is crucial to investigate the interconnected molecular and pathophysiologic links in exercise intolerant PASC, a task uniquely within our team’s expertise. Angiotensin-converting enzyme 2 (ACE2) is not just an entry receptor for SARS-CoV-2 but also an enzyme with a protective function through regulation of the renin- angiotensin system. Studies have shown that a high level of plasma ACE2 is associated with an increased risk of SARS-CoV-2-related mortality. Our preliminary data showed that the catalytic activity of increased plasma ACE2 was significantly impaired in the exercise intolerant PASC patients, and closely correlated with reduced exercise capacity as measured by peak oxygen consumption evaluated during iCPET. Furthermore, to study the pathogenic mechanism of exercise intolerance in PASC, we established a novel PASC mouse model. In this model, we observed the persistence of the SARS-CoV-2 RNAs in lung microvascular ECs, impaired ACE2 activity, chronic pulmonary inflammation, along with a significant reduction in exercise capacity. Thus, we hypothesize that dysfunctional ACE2 shed from pulmonary ECs is a major driver for exercise intolerance in PASC and an engineered solube ACE2 with enhanced ACE2 activity will improve exercise capacity of PASC. To test our hypotheses, we will investigate the predictive value of ACE2 activity as a clinical biomarker and assess its association with exercise capacity over 12 months in PASC patients in Aim 1. We will define an engineered soluble ACE2 with enhanced ACE2 activity as an innovative therapeutic intervention to improve exercise capacity and vascular function in the PASC mouse model in Aim 2. Furthermore, we will explore the mechanism of ACE2 dysfunction shed from the pulmonary vasculature in Aim 3. If successful, we will identify a diagnostic and therapeutic paradigm urgently needed for PASC patients experiencing exercise intolerance, and remediate the deficient response to this global public health threat.", "keywords": [ "2019-nCoV", "ACE2", "Acute Lung Injury", "Adult", "Affect", "Binding", "Biological Markers", "Blood Vessels", "COVID-19", "COVID-19 mortality", "COVID-19 patient", "Cardiopulmonary", "Cell surface", "Characteristics", "Chronic", "Circulation", "Clinic", "Clinical assessments", "Data", "Diagnosis", "Diagnostic", "Disease Progression", "Disintegrins", "Endothelial Cells", "Endothelium", "Engineering", "Enzymes", "Exercise", "Exercise Test", "Fatigue", "Functional disorder", "Health", "Impairment", "Inflammation", "Knock-in", "Knockout Mice", "Left", "Link", "Long COVID", "Lung", "Measures", "Medicine", "Metalloproteases", "Modeling", "Molecular", "Outpatients", "Oxygen Consumption", "Pathogenicity", "Pathology", "Patients", "Peptides", "Plasma", "Population", "Post-Acute Sequelae of SARS-CoV-2 Infection", "Predictive Value", "Prevalence", "Proteins", "Public Health", "Pulmonary Inflammation", "Questionnaires", "RNA", "Recovery", "Regulation", "Renin-Angiotensin System", "Risk", "SARS-CoV-2 infection", "Site", "Societies", "Symptoms", "Testing", "Therapeutic", "Therapeutic Intervention", "clinical biomarkers", "clinical infrastructure", "design", "dosage", "endothelial dysfunction", "exercise capacity", "exercise intolerance", "experience", "improved", "innovation", "knock-down", "lung microvascular endothelial cells", "mortality", "mouse model", "novel", "pandemic disease", "post SARS-CoV-2 infection", "post-COVID-19", "public health priorities", "receptor", "remediation", "research clinical testing", "response", "symptom cluster", "targeted treatment", "treatment optimization" ], "approved": true } } ], "meta": { "pagination": { "page": 1, "pages": 1392, "count": 13920 } } }