Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1384&sort=-id
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-id", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=-id", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1385&sort=-id", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1383&sort=-id" }, "data": [ { "type": "Grant", "id": "417", "attributes": { "award_id": "2148216", "title": "Collaborative Research: Patterns, Context, and Secondary Impacts of State Policy Responses to the Pandemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [], "program_officials": [ { "id": 790, "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": "2022-07-01", "end_date": "2024-06-30", "award_amount": 131829, "principal_investigator": { "id": 791, "first_name": "Frederick J", "last_name": "Boehmke", "orcid": "https://orcid.org/0000-0003-3309-0885", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['https://dataverse.harvard.edu/dataverse/sprc19/']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 220, "ror": "https://ror.org/036jqmy94", "name": "University of Iowa", "address": "", "city": "", "state": "IA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 220, "ror": "https://ror.org/036jqmy94", "name": "University of Iowa", "address": "", "city": "", "state": "IA", "zip": "", "country": "United States", "approved": true }, "abstract": "Understanding the role of policy developments in the response to the COVID-19 pandemic is vital for researchers, lawmakers, and the public to improve preparedness for future public health emergencies. This opportunity to learn from an ongoing pandemic and inform choices in the future requires extensive and timely data gathering. U.S. state governments have taken tens of thousands of public policy actions—executive orders, regulations, and laws in response to COVID-19. This project advances our understanding of the causes and consequences of states' policy responses to the pandemic. The PIs collect data on state governments' decisions to mitigate COVID-19's impact, analyze the adoption and diffusion of pandemic-related policies, track state officials' online discussion of the pandemic, and study health impacts of policies implemented in response to it. This project provides insights into how states manage a global health crisis, the ways that online communication and policy debate involving public officials factor into the contemporary management of a public health emergency, and the public health impacts of rapid innovations in state health policies. It provides comprehensive data sources on state policy activities as well as online communication by state officials. These data are helpful to others who seek to understand the causes and consequences of states' pandemic policies. The project also involves hiring and training a diverse, multidisciplinary team of graduate and undergraduate research assistants as well as a post-doc. The project leaders will provide extensive training, experience, and mentorship to these early career researchers.State policy responses to the COVID-19 pandemic have occurred rapidly, with information and choices updated daily. This timeframe provides an opportunity to study the rapid spread of policy responses—two orders of magnitude more frequent than the typical yearly timescale covered in policy diffusion research. The data collected in this study cover hundreds of pandemic-related policies and tens of thousands of policy actions by states, all unfolding over a three-year time period. The decision environment in which governments select their responses to COVID-19 involves several key components that have been found to be important in the study of the spread of public policy: information, imitation, competition, and coercion. Thus, the COVID-19 pandemic represents a rare opportunity to study the spread of public policy with voluminous contemporary information about the factors that influence states' decisions. Specifically, this project uses the recent timeline and high salience of COVID-19 policy to understand the relationship between online communication involving policymakers and official policymaking activity. Because most elected state officials are active online, particularly on Twitter, policy responses to COVID-19 present the opportunity to understand the relationship between online communication by officials and public policymaking. Lastly, to illustrate the utility of the data collected, this project uses a difference-in-differences approach to study the COVID-19 policies’ secondary impacts on health. Given that COVID-19 policy responses have affected every facet of public and private life, this project includes a focused study of the impacts of pandemic policy responses on maternal and infant health in the American states.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": "416", "attributes": { "award_id": "2148215", "title": "Collaborative Research: Patterns, Context, and Secondary Impacts of State Policy Responses to the Pandemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [], "program_officials": [ { "id": 787, "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": "2022-07-01", "end_date": "2024-06-30", "award_amount": 366181, "principal_investigator": { "id": 789, "first_name": "Bruce A", "last_name": "Desmarais", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 219, "ror": "", "name": "Pennsylvania State Univ University Park", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 788, "first_name": "Johabed Georgin Olvera", "last_name": "Esquivel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 219, "ror": "", "name": "Pennsylvania State Univ University Park", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Understanding the role of policy developments in the response to the COVID-19 pandemic is vital for researchers, lawmakers, and the public to improve preparedness for future public health emergencies. This opportunity to learn from an ongoing pandemic and inform choices in the future requires extensive and timely data gathering. U.S. state governments have taken tens of thousands of public policy actions—executive orders, regulations, and laws in response to COVID-19. This project advances our understanding of the causes and consequences of states' policy responses to the pandemic. The PIs collect data on state governments' decisions to mitigate COVID-19's impact, analyze the adoption and diffusion of pandemic-related policies, track state officials' online discussion of the pandemic, and study health impacts of policies implemented in response to it. This project provides insights into how states manage a global health crisis, the ways that online communication and policy debate involving public officials factor into the contemporary management of a public health emergency, and the public health impacts of rapid innovations in state health policies. It provides comprehensive data sources on state policy activities as well as online communication by state officials. These data are helpful to others who seek to understand the causes and consequences of states' pandemic policies. The project also involves hiring and training a diverse, multidisciplinary team of graduate and undergraduate research assistants as well as a post-doc. The project leaders will provide extensive training, experience, and mentorship to these early career researchers.State policy responses to the COVID-19 pandemic have occurred rapidly, with information and choices updated daily. This timeframe provides an opportunity to study the rapid spread of policy responses—two orders of magnitude more frequent than the typical yearly timescale covered in policy diffusion research. The data collected in this study cover hundreds of pandemic-related policies and tens of thousands of policy actions by states, all unfolding over a three-year time period. The decision environment in which governments select their responses to COVID-19 involves several key components that have been found to be important in the study of the spread of public policy: information, imitation, competition, and coercion. Thus, the COVID-19 pandemic represents a rare opportunity to study the spread of public policy with voluminous contemporary information about the factors that influence states' decisions. Specifically, this project uses the recent timeline and high salience of COVID-19 policy to understand the relationship between online communication involving policymakers and official policymaking activity. Because most elected state officials are active online, particularly on Twitter, policy responses to COVID-19 present the opportunity to understand the relationship between online communication by officials and public policymaking. Lastly, to illustrate the utility of the data collected, this project uses a difference-in-differences approach to study the COVID-19 policies’ secondary impacts on health. Given that COVID-19 policy responses have affected every facet of public and private life, this project includes a focused study of the impacts of pandemic policy responses on maternal and infant health in the American states.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": "415", "attributes": { "award_id": "2148217", "title": "Collaborative Research: Patterns, Context, and Secondary Impacts of State Policy Responses to the Pandemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [], "program_officials": [ { "id": 785, "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": "2022-07-01", "end_date": "2024-06-30", "award_amount": 76027, "principal_investigator": { "id": 786, "first_name": "Jeffrey J", "last_name": "Harden", "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": [], "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": "Understanding the role of policy developments in the response to the COVID-19 pandemic is vital for researchers, lawmakers, and the public to improve preparedness for future public health emergencies. This opportunity to learn from an ongoing pandemic and inform choices in the future requires extensive and timely data gathering. U.S. state governments have taken tens of thousands of public policy actions—executive orders, regulations, and laws in response to COVID-19. This project advances our understanding of the causes and consequences of states' policy responses to the pandemic. The PIs collect data on state governments' decisions to mitigate COVID-19's impact, analyze the adoption and diffusion of pandemic-related policies, track state officials' online discussion of the pandemic, and study health impacts of policies implemented in response to it. This project provides insights into how states manage a global health crisis, the ways that online communication and policy debate involving public officials factor into the contemporary management of a public health emergency, and the public health impacts of rapid innovations in state health policies. It provides comprehensive data sources on state policy activities as well as online communication by state officials. These data are helpful to others who seek to understand the causes and consequences of states' pandemic policies. The project also involves hiring and training a diverse, multidisciplinary team of graduate and undergraduate research assistants as well as a post-doc. The project leaders will provide extensive training, experience, and mentorship to these early career researchers.State policy responses to the COVID-19 pandemic have occurred rapidly, with information and choices updated daily. This timeframe provides an opportunity to study the rapid spread of policy responses—two orders of magnitude more frequent than the typical yearly timescale covered in policy diffusion research. The data collected in this study cover hundreds of pandemic-related policies and tens of thousands of policy actions by states, all unfolding over a three-year time period. The decision environment in which governments select their responses to COVID-19 involves several key components that have been found to be important in the study of the spread of public policy: information, imitation, competition, and coercion. Thus, the COVID-19 pandemic represents a rare opportunity to study the spread of public policy with voluminous contemporary information about the factors that influence states' decisions. Specifically, this project uses the recent timeline and high salience of COVID-19 policy to understand the relationship between online communication involving policymakers and official policymaking activity. Because most elected state officials are active online, particularly on Twitter, policy responses to COVID-19 present the opportunity to understand the relationship between online communication by officials and public policymaking. Lastly, to illustrate the utility of the data collected, this project uses a difference-in-differences approach to study the COVID-19 policies’ secondary impacts on health. Given that COVID-19 policy responses have affected every facet of public and private life, this project includes a focused study of the impacts of pandemic policy responses on maternal and infant health in the American states.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": "414", "attributes": { "award_id": "2138052", "title": "Multiscale Modeling of Coronavirus Virions in the Respiratory System", "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": 783, "first_name": "Sylvio", "last_name": "May", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-07-01", "end_date": "2026-06-30", "award_amount": 260184, "principal_investigator": { "id": 784, "first_name": "Alexander V", "last_name": "Neimark", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 218, "ror": "", "name": "Rutgers University New Brunswick", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 218, "ror": "", "name": "Rutgers University New Brunswick", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "NONTECHNICAL SUMMARY One of the ways COVID-19 spreads is by breathing in small liquid droplets that contain the coronavirus SARS-CoV-2. In order to cause an infection, the virus must first pass from the respiratory tract through a layer of molecular thickness, the lung surfactant film, into the lung. The passage through the lung surfactant film is a process that is not well understood because the coronavirus and lung surfactant film continuously affect and remodel each other. The lung surfactant film is a complex and fragile nanoscale biomaterial that consists mostly of surfactant molecules. Certain types of surfactant molecules are capable of destabilizing and breaking up the coronavirus. This capability may be utilized by the medical administration of exogenous surfactants in surfactant therapy, which is currently being tested in clinical trials as a COVID-19 treatment approach. This award supports computational research and educational activities to study in detail and on a molecular level how SARS-CoV-2 and its variants adhere to the lung surfactant film, how the virus particle is capable of passing through the film, and how surfactant molecules affect this process. The computational research method, known as molecular dynamics, is applied with the goal to learn how pathophysiological behavior can emerge from the material properties of lung surfactant layer and associated coronavirus particles. The materials-centric approach places this research at the interface between condensed matter physics and biology. Among the broader impacts is the potential to inform the search for novel therapeutic pathways that inhibit coronavirus activity by using exogenous surfactants. Educational and mentoring aspects of this project include training graduate and undergraduate students from diverse backgrounds and developing teaching modules on the modeling of complex biological nanoscale systems. The developed simulation codes will be made available for the scientific community through curated data repositories. TECHNICAL SUMMARY COVID-19 is transmitted by inhaling airborne coronavirus particles, SARS-CoV-2, which penetrate the respiratory system and cause severe acute respiratory syndrome (SARS) that can lead to lung failure. SARS-CoV-2 virions are spheroidal nanoparticles, with a lipid bilayer envelope of about 85 nanometer diameter decorated by a “crown” of 20 nanometer long spike protein protrusions. Whereas our knowledge of the biochemical structure and functions of SARS-CoV-2 is quickly growing, the interfacial properties of the virions, as nanoparticles interacting with the respiratory system environment, have not been addressed and are poorly understood. Bridging this knowledge gap is important for informing clinical studies on surfactant therapies to treat SARS by administering exogenous surfactants. This project aims at using multiscale molecular dynamics simulations to explore the fundamental mechanisms of interactions of SARS-CoV-2 virions with lung surfactant films and their fate in the respiratory system. Consideration of SARS-CoV-2 virions and the lung surfactant film as nanoscale multifunctional biomaterials that interact within the respiratory system environment represents the main methodological novelty of the project. The PI will (a) develop original coarse-grained computational models of SARS-CoV-2 virions and lung surfactant films, (b) establish in-silico the molecular mechanisms of the SARS-CoV-2 virion interfacial interactions with lung surfactant films and exogenous biosurfactants, and (c) explore the effects of these interactions on the stability of lung surfactant films and the fate of SARS-CoV-2 virions in the respiratory system. The project will produce multiscale computational models of interfacial processes involving SARS-CoV-2 variants to address the currently unresolved questions: (1) how sorption of lung surfactant lipids and proteins affects the envelope membrane and spike proteins, (2) how SARS-CoV-2 virions affect the integrity and stability of lung surfactant films, (3) if detergent activity and sorption of pulmonary and exogenous surfactants can induce lysis of the viral envelope, and (4) to what extent the difference in specifics of lung surfactant interactions with mutated SARS-CoV-2 virions may explain why some coronavirus variants cause more infections and spread faster than others. Special attention will be paid to the differences between SARS-CoV-2 variants with respect to adhesion of pulmonary and exogenous surfactants. Answers to these questions will advance fundamental understanding of SARS-CoV-2 virions and lung surfactant films as interacting nanoscale biomaterials and may have clinical implications for the selection of exogenous surfactants for prophylaxis and treatment of COVID-19. The PI expects the project will have an interdisciplinary transformative impact by advancing computational studies of pathophysiological behavior and fate of coronavirus virions in pulmonary environment, surfactant-induced inhibition of the viral activity, as well as adhesion and translocation of synthetic virion-type drug carrier nanoparticles through cell membranes and other physiological interfaces. The proposed research will support diversity, train graduate and undergraduate students, and produce modules on the modeling of complex biological nanoscale systems.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": "413", "attributes": { "award_id": "2210471", "title": "EAGER: MEMS Enabled Real Time Detection of Pathogens Viruses and Biomarkers", "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": 780, "first_name": "Krastan", "last_name": "Blagoev", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-01", "end_date": "2024-02-29", "award_amount": 299987, "principal_investigator": { "id": 782, "first_name": "Borislav", "last_name": "Ivanov", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 189, "ror": "https://ror.org/02vm5rt34", "name": "Vanderbilt University", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 781, "first_name": "G. Kane", "last_name": "Jennings", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 189, "ror": "https://ror.org/02vm5rt34", "name": "Vanderbilt University", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "Micro-cantilever-based tools like variable Atomic Force Microscopies have been widely and successfully used in the field of Nano Science and Technology for decades. Based on vast available experimental results and expertise in multiple domains, there is a significant potential to use cantilever-based systems for detection of SARS-CoV-2 viruses binding by responding to the added mass. This approach will enable real time monitoring and handling the COVID-19 pandemic and will enable the observation of the infection and the transmission of the host for first time. However, the exploitation of cantilever devices for pathogen detection faces multiple hurdles, in addition to technical challenges, as high selectivity to the targeted virus or biomarker is required. To become practical these instruments need a technology for functionalization of the cantilevers that will make them stable in air and liquids for hours. This will allow the sensors to work in real time, thus breaking the current pathogen diagnostics paradigm. In this project the PIs will develop such functionalization of existing micro-cantilevers. They will develop a method for small pitch immobilization of antibodies selectively on the surface of the active cantilevers. The proposed project explores function-driven design of materials and technology to address the problem of real time detection of active viruses like SARS-CoV-2. The integrated approach developed in this project is versatile and transferable in developing proteins/antibody coatings for many other applications. Moreover, the proposed project, with its integration of material synthesis, UV exposure/patterning, characterization, and performance evaluation in the targeted applications, will serve as an excellent educational platform for participating graduate students to experience the full range of challenges in the cross-linking domains of microelectronics, biochemistry and health.Specifically, these goals will be achieved through the combination of coating of fluorescent tagged antibody solutions in combination with maskless UV photoinduced patterning for immobilization that ensures selective binding of active viruses on cantilevers. The versatile approach of the PIs will include application of different benzophenone class of compound radicals generated by UV light and capable of reliably binding the targeted spike protein’s antibody at the molecular level. Specific goals of this project are the identification and testing of UV maskless technology for selective immobilization of spike protein antibodies on piezoresistive MEMS cantilevers as well as optimizing the parameters of the detection system in order to achieve short detection time and high sensitivity. To achieve noise-free operation, application-specific arrays of active and reference piezoresistive cantilevers will be used. To ensure detection of SARS-CoV-2 viruses in air/aerosol by affinity reactions, small pitch patterns will be selectively coated, exploiting maskless UV photoinduced protein immobilization of antibodies. Specifically, the PIs will focus on the selective functionalization of the optimized cantilevers in synergy with novel measuring methods for detecting the mass of ultra-small pathogens.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": "412", "attributes": { "award_id": "2213457", "title": "RAPID: STEM faculty support to address impacts from COVID-19 on Tribal Colleges and Universities Program institutions", "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": 777, "first_name": "Regina", "last_name": "Sievert", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-01", "end_date": "2023-03-31", "award_amount": 199906, "principal_investigator": { "id": 779, "first_name": "Lisa J", "last_name": "Azure", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 778, "first_name": "Edwin W", "last_name": "Kitzes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 195, "ror": "https://ror.org/02kk3dq58", "name": "United Tribes Technical College", "address": "", "city": "", "state": "ND", "zip": "", "country": "United States", "approved": true }, "abstract": "A goal of the Tribal Colleges and Universities Program (TCUP) is to increase the science, technology, engineering and mathematics (STEM) instructional and research capacities of specific institutions of higher education that serve the Nation's indigenous students. Expanding the STEM curricular offerings at these institutions expands the opportunities of their students to pursue challenging, rewarding careers in STEM fields, provides for research studies in areas that may be culturally significant, and encourages a community and generational appreciation for science and mathematics education, and sustainability of capacity gains is significantly enhanced by retaining the talent of credentialed STEM faculty. This project aligns directly with that goal.The coronavirus pandemic of 2020-2021 caused major disruptions to institutions of higher education. However, for tribal colleges and universities, whose core operating funds are directly aligned with student enrollment, drops in enrollment equate to loss of funding. To mitigate against detrimental effects on STEM instructional capacity, this award will support the position of one full-time STEM faculty member, as well as other resources to maintain United Tribes Technical College’s STEM program as it recovers from the impact of 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": "411", "attributes": { "award_id": "2143898", "title": "CAREER: LUCO: A Noninvasive Miniaturized Blood Gas Sensor for Respiration Monitoring", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 775, "first_name": "Svetlana", "last_name": "Tatic-Lucic", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-05-01", "end_date": "2027-04-30", "award_amount": 382477, "principal_investigator": { "id": 776, "first_name": "Ulkuhan", "last_name": "Guler", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 217, "ror": "https://ror.org/05ejpqr48", "name": "Worcester Polytechnic Institute", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 217, "ror": "https://ror.org/05ejpqr48", "name": "Worcester Polytechnic Institute", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "Among the vital signs of human health, respiratory parameters are key indicators of the physiological status of the human body. The accurate diagnosis of respiratory diseases mandates a measure of blood gases. The determination of blood gases requires an arterial blood sample, an invasive and painful process. This procedure, however, provides only a discrete measurement of respiratory efficacy during a rapidly changing situation. Transcutaneous monitoring is a noninvasive method of continuously measuring oxygen and carbon dioxide diffused through the skin, and any changes they undergo correlate closely with changes in blood gases. The contemporary methodology for measuring transcutaneous oxygen and carbon dioxide requires a heated sensor (that may burn the skin and require frequent alteration of the sensing spot) and a costly non-portable, bulky, corded sensing unit. This project will address a critical unmet need for a cost-effective noninvasive miniaturized wearable device capable of sensing multiple blood gas parameters that provide a comprehensive picture of one’s respiratory status from a home setting. The continuous and remote tracking of vital respiratory parameters will provide relevant and accurate data that alert a caregiver and influence the course of treatment. As the proposed system enables massive longitudinal blood gas data collected in non-clinical settings, clinicians and researchers can remotely assess and measure pulmonary outcomes objectively, and clinicians can further improve the home care management of patients with a fragile respiratory status. The educational program complementing this award will support STEM engagement in schools, provide research opportunities for underrepresented groups - particularly women, train students in state-of-the-art circuits and systems, biomedical, and optics, and support the future engineering workforce.This project will create a first-of-its-kind wearable blood gas monitor for managing the home care of individuals. More specifically, the core scientific contributions will include 1) the creation of a novel miniaturized custom-designed wearable sensor that measures two modalities of blood gases; 2) identification of factors affecting sensor readings such as temperature and drift for the self-calibration of blood gas sensors; 3) the exploration of innovative electronic interfaces for a specialized analog front-end for the proposed unique sensor with heterogeneous decay time, including one with an ultra-fast response; 4) determination of the feasibility and usability of the system during real-life activities in home settings by capturing the dynamic respiratory physiological status of individuals with longitudinal data. Having the ability to sense two vital respiratory parameters (namely, transcutaneous partial pressures of oxygen and carbon dioxide) with one wearable device is unique and superior to the current practice of measuring only oxygen saturation, and fills an important gap in the miniaturization of the transcutaneous blood gas sensor for noninvasive wearable device applications. In addition, the longitudinal new data will enable new biomedical research opportunities to assess therapies and further investigate medical conditions in which oxygen and carbon dioxide play a critical role, including novel respiratory diseases that are not yet fully understood (e.g., COVID-19). Progress on enabling the affordable and scalable remote monitoring of oxygenation and ventilation at home is transformative for prospective medical and scientific research.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": "410", "attributes": { "award_id": "2136850", "title": "SBIR Phase II: Machine Learning for Rapid Automated Viral Infectivity Assays (COVID-19)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "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": "2022-04-01", "end_date": "2024-03-31", "award_amount": 999946, "principal_investigator": { "id": 774, "first_name": "Ilya G", "last_name": "Goldberg", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 216, "ror": "", "name": "ViQi LLC", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 216, "ror": "", "name": "ViQi LLC", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this Small Business Innovation Research (SBIR) Phase II project is to accelerate the development of antiviral drugs and vaccines for conditions such as COVID-19. The ability to accurately determine if cells are infected with virus is crucial for evaluating antiviral drugs and vaccine candidates. Currently, determining if a virus is infectious is done by infecting cells and waiting for them to die, which can take many days. The proposed technology uses artificial intelligence (AI) to analyze images of cells for signs of virus. This can be done within hours of infection instead of days, which can greatly accelerate the development of vaccines and antiviral drugs. In addition, it is simpler because the AI analysis is automated and does not need special probes or dyes to detect viruses.The proposed project will collect images of cells infected with various viruses imaged with automated microscopes. AIs will be trained to distinguish healthy and sick cells at various times after infection. The project will study many different viruses and the changes they induce in cells. The trained AIs will be used to process infectivity assays. The software will run on remote data centers and thus images will be uploaded for analysis and reporting. An AI can be trained using images from the common 96-well plate.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": "409", "attributes": { "award_id": "2140420", "title": "Collaborative Research: Understanding Stochastic Spatiotemporal Dynamics of Epidemic Spread to Improve Control Interventions - From COVID-19 to Future Pandemics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 771, "first_name": "Jordan", "last_name": "Berg", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-15", "end_date": "2025-02-28", "award_amount": 215001, "principal_investigator": { "id": 772, "first_name": "Manish", "last_name": "Kumar", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 215, "ror": "", "name": "University of Cincinnati Main Campus", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 215, "ror": "", "name": "University of Cincinnati Main Campus", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "This grant will support research that will contribute new scientific knowledge related to how uncertainties in both human behavior and transmission characteristics of a causative pathogen (such as the novel coronavirus in the case of COVID-19) influence the spread of an epidemic, and how the new knowledge thus obtained about epidemic spread can contribute to interventional public health policy measures to effectively mitigate and control an epidemic. The research will advance both the science of predicting epidemic spread as well as national prosperity by enhancing national preparedness for early and effective mitigation of potential future epidemic outbreaks. Mathematical and computational models that can accurately predict an epidemic spread across geographical regions over specified periods of time are critical precursors to developing effective interventions for mitigation such as social-distancing measures and vaccination campaigns (when vaccines become available). However, the limitations of existing predictive models, as evident during the COVID-19 outbreak in the US, underscore the need for new knowledge in this area. This award supports fundamental research to develop novel predictive models of epidemic spread and also to validate model predictions against the extensive COVID-19 spread data only now available. This research involves multiple disciplines including the mathematical theory of partial differential equations, stochastic analysis, control theory, and epidemiology and the results will likely have broader significance in the study of rare-event dynamics in areas such as ecology, climate science and wildfire propagation. Moreover, this cross-disciplinary project, a collaborative effort involving multiple institutions, will broaden the participation of underrepresented groups in research and training, and also advance science and engineering education.The research will advance the fundamental knowledge of how uncertainties, both in human behavior and pathogen characteristics, influence spatiotemporal, stochastic epidemic dynamics and also yield a control-theoretic framework to analyze interventions for mitigation. Specifically, the project will: (1) develop novel predictive dynamic models based on partial differential equations, (2) uncover effects of the interaction between nonlinearity and uncertainty such as noise-induced bifurcations, (3) study infection spikes using a stochastic approach, (4) validate the models using COVID-19 data, (5) establish a control-theoretic framework to analyze mitigative interventions, using a combination of averaging methods from stochastic analysis and feedback control theory, (6) obtain improved characterization of epidemiologic parameters such as basic and effective reproduction numbers, and (7) identify principles and strategies that can inform interventional public health policy.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": "408", "attributes": { "award_id": "2140405", "title": "Collaborative Research: Understanding Stochastic Spatiotemporal Dynamics of Epidemic Spread to Improve Control Interventions - From COVID-19 to Future Pandemics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 769, "first_name": "Jordan", "last_name": "Berg", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-15", "end_date": "2025-02-28", "award_amount": 231185, "principal_investigator": { "id": 770, "first_name": "Subramanian", "last_name": "Ramakrishnan", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 214, "ror": "https://ror.org/021v3qy27", "name": "University of Dayton", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 214, "ror": "https://ror.org/021v3qy27", "name": "University of Dayton", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "This grant will support research that will contribute new scientific knowledge related to how uncertainties in both human behavior and transmission characteristics of a causative pathogen (such as the novel coronavirus in the case of COVID-19) influence the spread of an epidemic, and how the new knowledge thus obtained about epidemic spread can contribute to interventional public health policy measures to effectively mitigate and control an epidemic. The research will advance both the science of predicting epidemic spread as well as national prosperity by enhancing national preparedness for early and effective mitigation of potential future epidemic outbreaks. Mathematical and computational models that can accurately predict an epidemic spread across geographical regions over specified periods of time are critical precursors to developing effective interventions for mitigation such as social-distancing measures and vaccination campaigns (when vaccines become available). However, the limitations of existing predictive models, as evident during the COVID-19 outbreak in the US, underscore the need for new knowledge in this area. This award supports fundamental research to develop novel predictive models of epidemic spread and also to validate model predictions against the extensive COVID-19 spread data only now available. This research involves multiple disciplines including the mathematical theory of partial differential equations, stochastic analysis, control theory, and epidemiology and the results will likely have broader significance in the study of rare-event dynamics in areas such as ecology, climate science and wildfire propagation. Moreover, this cross-disciplinary project, a collaborative effort involving multiple institutions, will broaden the participation of underrepresented groups in research and training, and also advance science and engineering education.The research will advance the fundamental knowledge of how uncertainties, both in human behavior and pathogen characteristics, influence spatiotemporal, stochastic epidemic dynamics and also yield a control-theoretic framework to analyze interventions for mitigation. Specifically, the project will: (1) develop novel predictive dynamic models based on partial differential equations, (2) uncover effects of the interaction between nonlinearity and uncertainty such as noise-induced bifurcations, (3) study infection spikes using a stochastic approach, (4) validate the models using COVID-19 data, (5) establish a control-theoretic framework to analyze mitigative interventions, using a combination of averaging methods from stochastic analysis and feedback control theory, (6) obtain improved characterization of epidemiologic parameters such as basic and effective reproduction numbers, and (7) identify principles and strategies that can inform interventional public health policy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1384, "pages": 1392, "count": 13920 } } }